ArmNN
 20.05
LstmLayer.cpp
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1 //
2 // Copyright © 2017 Arm Ltd. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 #include "LstmLayer.hpp"
6 
7 #include "LayerCloneBase.hpp"
8 
9 #include <armnn/LstmParams.hpp>
10 #include <armnn/TypesUtils.hpp>
13 
14 namespace armnn
15 {
16 
17 LstmLayer::LstmLayer(const LstmDescriptor& param, const char* name)
18  : LayerWithParameters(3, 4, LayerType::Lstm, param, name)
19 {
20 }
21 
22 std::unique_ptr<IWorkload> LstmLayer::CreateWorkload(const IWorkloadFactory& factory) const
23 {
24  LstmQueueDescriptor 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  }
61  }
62 
63  // Layer normalisation parameters
65  {
67  {
69  }
73  }
74 
75  return factory.CreateLstm(descriptor, PrepInfoAndDesc(descriptor));
76 }
77 
79 {
80  auto layer = CloneBase<LstmLayer>(graph, m_Param, GetName());
81 
83  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_InputToForgetWeights)
84  : 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  {
124  layer->m_PeepholeParameters.m_CellToInputWeights = m_PeepholeParameters.m_CellToInputWeights ?
125  std::make_unique<ScopedCpuTensorHandle>(*m_PeepholeParameters.m_CellToInputWeights) : nullptr;
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  layer->m_LayerNormParameters.m_InputLayerNormWeights = m_LayerNormParameters.m_InputLayerNormWeights ?
136  std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_InputLayerNormWeights) : nullptr;
137  layer->m_LayerNormParameters.m_ForgetLayerNormWeights = m_LayerNormParameters.m_ForgetLayerNormWeights ?
138  std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_ForgetLayerNormWeights) : nullptr;
139  layer->m_LayerNormParameters.m_CellLayerNormWeights = m_LayerNormParameters.m_CellLayerNormWeights ?
140  std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_CellLayerNormWeights) : nullptr;
141  layer->m_LayerNormParameters.m_OutputLayerNormWeights = m_LayerNormParameters.m_OutputLayerNormWeights ?
142  std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_OutputLayerNormWeights) : nullptr;
143  }
144 
145  return std::move(layer);
146 }
147 
148 std::vector<TensorShape> LstmLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
149 {
150  ARMNN_ASSERT(inputShapes.size() == 3);
151 
152  // Get input values for validation
153  unsigned int batchSize = inputShapes[0][0];
154  unsigned int outputSize = inputShapes[1][1];
155  unsigned int numUnits = inputShapes[2][1];
156 
157  std::vector<TensorShape> outShapes;
158  outShapes.push_back(TensorShape({batchSize, numUnits * (m_Param.m_CifgEnabled ? 3 : 4)}));
159  outShapes.push_back(TensorShape({batchSize, outputSize}));
160  outShapes.push_back(TensorShape({batchSize, numUnits}));
161  outShapes.push_back(TensorShape({batchSize, outputSize}));
162 
163  return outShapes;
164 }
165 
167 {
169 
170  auto inferredShapes = InferOutputShapes( {
174  );
175 
176  ARMNN_ASSERT(inferredShapes.size() == 4);
177 
178  // Check if the weights are nullptr
180  "LstmLayer: m_BasicParameters.m_InputToForgetWeights should not be null.");
182  "LstmLayer: m_BasicParameters.m_InputToCellWeights should not be null.");
184  "LstmLayer: m_BasicParameters.m_InputToOutputWeights should not be null.");
186  "LstmLayer: m_BasicParameters.m_RecurrentToForgetWeights should not be null.");
188  "LstmLayer: m_BasicParameters.m_RecurrentToCellWeights should not be null.");
190  "LstmLayer: m_BasicParameters.m_RecurrentToOutputWeights should not be null.");
192  "LstmLayer: m_BasicParameters.m_ForgetGateBias should not be null.");
194  "LstmLayer: m_BasicParameters.m_CellBias should not be null.");
196  "LstmLayer: m_BasicParameters.m_OutputGateBias should not be null.");
197 
198  if (!m_Param.m_CifgEnabled)
199  {
201  "LstmLayer: m_CifgParameters.m_InputToInputWeights should not be null.");
203  "LstmLayer: m_CifgParameters.m_RecurrentToInputWeights should not be null.");
205  "LstmLayer: m_CifgParameters.m_InputGateBias should not be null.");
206 
207  ConditionalThrowIfNotEqual<LayerValidationException>(
208  "LstmLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
210  inferredShapes[0]);
211  }
212  else
213  {
215  "LstmLayer: m_CifgParameters.m_InputToInputWeights should not have a value when CIFG is enabled.");
217  "LstmLayer: m_CifgParameters.m_RecurrentToInputWeights should not have a value when CIFG is enabled.");
219  "LstmLayer: m_CifgParameters.m_InputGateBias should not have a value when CIFG is enabled.");
220 
221  ConditionalThrowIfNotEqual<LayerValidationException>(
222  "LstmLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
224  inferredShapes[0]);
225  }
226 
228  {
230  "LstmLayer: m_ProjectionParameters.m_ProjectionWeights should not be null.");
231  }
232 
234  {
235  if (!m_Param.m_CifgEnabled)
236  {
238  "LstmLayer: m_PeepholeParameters.m_CellToInputWeights should not be null "
239  "when Peephole is enabled and CIFG is disabled.");
240  }
242  "LstmLayer: m_PeepholeParameters.m_CellToForgetWeights should not be null.");
244  "LstmLayer: m_PeepholeParameters.m_CellToOutputWeights should not be null.");
245  }
246 
247  ConditionalThrowIfNotEqual<LayerValidationException>(
248  "LstmLayer: TensorShape set on OutputSlot[1] does not match the inferred shape.",
250  inferredShapes[1]);
251  ConditionalThrowIfNotEqual<LayerValidationException>(
252  "LstmLayer: TensorShape set on OutputSlot[2] does not match the inferred shape.",
254  inferredShapes[2]);
255  ConditionalThrowIfNotEqual<LayerValidationException>(
256  "LstmLayer: TensorShape set on OutputSlot[3] does not match the inferred shape.",
258  inferredShapes[3]);
259 
261  {
263  {
265  "LstmLayer: m_LayerNormParameters.m_inputLayerNormWeights should not be null.");
266  }
268  "LstmLayer: m_LayerNormParameters.m_forgetLayerNormWeights should not be null.");
270  "LstmLayer: m_LayerNormParameters.m_cellLayerNormWeights should not be null.");
272  "LstmLayer: m_LayerNormParameters.m_outputLayerNormWeights 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 LstmLayer::Accept(ILayerVisitor& visitor) const
310 {
311  LstmInputParams inputParams;
312  ConstTensor inputToInputWeightsTensor;
314  {
315  ConstTensor inputToInputWeightsTensorCopy(m_CifgParameters.m_InputToInputWeights->GetTensorInfo(),
317  inputToInputWeightsTensor = inputToInputWeightsTensorCopy;
318  inputParams.m_InputToInputWeights = &inputToInputWeightsTensor;
319  }
320  ConstTensor inputToForgetWeightsTensor;
322  {
323  ConstTensor inputToForgetWeightsTensorCopy(m_BasicParameters.m_InputToForgetWeights->GetTensorInfo(),
325  inputToForgetWeightsTensor = inputToForgetWeightsTensorCopy;
326  inputParams.m_InputToForgetWeights = &inputToForgetWeightsTensor;
327  }
328  ConstTensor inputToCellWeightsTensor;
330  {
331  ConstTensor inputToCellWeightsTensorCopy(m_BasicParameters.m_InputToCellWeights->GetTensorInfo(),
333  inputToCellWeightsTensor = inputToCellWeightsTensorCopy;
334  inputParams.m_InputToCellWeights = &inputToCellWeightsTensor;
335  }
336  ConstTensor inputToOutputWeightsTensor;
338  {
339  ConstTensor inputToOutputWeightsTensorCopy(m_BasicParameters.m_InputToOutputWeights->GetTensorInfo(),
341  inputToOutputWeightsTensor = inputToOutputWeightsTensorCopy;
342  inputParams.m_InputToOutputWeights = &inputToOutputWeightsTensor;
343  }
344  ConstTensor recurrentToInputWeightsTensor;
346  {
347  ConstTensor recurrentToInputWeightsTensorCopy(
350  recurrentToInputWeightsTensor = recurrentToInputWeightsTensorCopy;
351  inputParams.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
352  }
353  ConstTensor recurrentToForgetWeightsTensor;
355  {
356  ConstTensor recurrentToForgetWeightsTensorCopy(
359  recurrentToForgetWeightsTensor = recurrentToForgetWeightsTensorCopy;
360  inputParams.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
361  }
362  ConstTensor recurrentToCellWeightsTensor;
364  {
365  ConstTensor recurrentToCellWeightsTensorCopy(
368  recurrentToCellWeightsTensor = recurrentToCellWeightsTensorCopy;
369  inputParams.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
370  }
371  ConstTensor recurrentToOutputWeightsTensor;
373  {
374  ConstTensor recurrentToOutputWeightsTensorCopy(
377  recurrentToOutputWeightsTensor = recurrentToOutputWeightsTensorCopy;
378  inputParams.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
379  }
380  ConstTensor cellToInputWeightsTensor;
382  {
383  ConstTensor cellToInputWeightsTensorCopy(m_PeepholeParameters.m_CellToInputWeights->GetTensorInfo(),
385  cellToInputWeightsTensor = cellToInputWeightsTensorCopy;
386  inputParams.m_CellToInputWeights = &cellToInputWeightsTensor;
387  }
388  ConstTensor cellToForgetWeightsTensor;
390  {
391  ConstTensor cellToForgetWeightsTensorCopy(m_PeepholeParameters.m_CellToForgetWeights->GetTensorInfo(),
393  cellToForgetWeightsTensor = cellToForgetWeightsTensorCopy;
394  inputParams.m_CellToForgetWeights = &cellToForgetWeightsTensor;
395  }
396  ConstTensor cellToOutputWeightsTensor;
398  {
399  ConstTensor cellToOutputWeightsTensorCopy(m_PeepholeParameters.m_CellToOutputWeights->GetTensorInfo(),
401  cellToOutputWeightsTensor = cellToOutputWeightsTensorCopy;
402  inputParams.m_CellToOutputWeights = &cellToOutputWeightsTensor;
403  }
404  ConstTensor inputGateBiasTensor;
405  if (m_CifgParameters.m_InputGateBias != nullptr)
406  {
407  ConstTensor inputGateBiasTensorCopy(m_CifgParameters.m_InputGateBias->GetTensorInfo(),
408  m_CifgParameters.m_InputGateBias->Map(true));
409  inputGateBiasTensor = inputGateBiasTensorCopy;
410  inputParams.m_InputGateBias = &inputGateBiasTensor;
411  }
412  ConstTensor forgetGateBiasTensor;
413  if (m_BasicParameters.m_ForgetGateBias != nullptr)
414  {
415  ConstTensor forgetGateBiasTensorCopy(m_BasicParameters.m_ForgetGateBias->GetTensorInfo(),
417  forgetGateBiasTensor = forgetGateBiasTensorCopy;
418  inputParams.m_ForgetGateBias = &forgetGateBiasTensor;
419  }
420  ConstTensor cellBiasTensor;
421  if (m_BasicParameters.m_CellBias != nullptr)
422  {
423  ConstTensor cellBiasTensorCopy(m_BasicParameters.m_CellBias->GetTensorInfo(),
424  m_BasicParameters.m_CellBias->Map(true));
425  cellBiasTensor = cellBiasTensorCopy;
426  inputParams.m_CellBias = &cellBiasTensor;
427  }
428  ConstTensor outputGateBias;
429  if (m_BasicParameters.m_OutputGateBias != nullptr)
430  {
431  ConstTensor outputGateBiasCopy(m_BasicParameters.m_OutputGateBias->GetTensorInfo(),
433  outputGateBias = outputGateBiasCopy;
434  inputParams.m_OutputGateBias = &outputGateBias;
435  }
436  ConstTensor projectionWeightsTensor;
438  {
439  ConstTensor projectionWeightsTensorCopy(m_ProjectionParameters.m_ProjectionWeights->GetTensorInfo(),
441  projectionWeightsTensor = projectionWeightsTensorCopy;
442  inputParams.m_ProjectionWeights = &projectionWeightsTensor;
443  }
444  ConstTensor projectionBiasTensor;
446  {
447  ConstTensor projectionBiasTensorCopy(m_ProjectionParameters.m_ProjectionBias->GetTensorInfo(),
449  projectionBiasTensor = projectionBiasTensorCopy;
450  inputParams.m_ProjectionBias = &projectionBiasTensor;
451  }
452  ConstTensor inputLayerNormTensor;
454  {
455  ConstTensor inputLayerNormTensorCopy(m_LayerNormParameters.m_InputLayerNormWeights->GetTensorInfo(),
457  inputLayerNormTensor = inputLayerNormTensorCopy;
458  inputParams.m_InputLayerNormWeights = &inputLayerNormTensor;
459  }
460  ConstTensor forgetLayerNormTensor;
462  {
463  ConstTensor forgetLayerNormTensorCopy(m_LayerNormParameters.m_ForgetLayerNormWeights->GetTensorInfo(),
465  forgetLayerNormTensor = forgetLayerNormTensorCopy;
466  inputParams.m_ForgetLayerNormWeights = &forgetLayerNormTensor;
467  }
468  ConstTensor cellLayerNormTensor;
470  {
471  ConstTensor cellLayerNormTensorCopy(m_LayerNormParameters.m_CellLayerNormWeights->GetTensorInfo(),
473  cellLayerNormTensor = cellLayerNormTensorCopy;
474  inputParams.m_CellLayerNormWeights = &cellLayerNormTensor;
475  }
476  ConstTensor outputLayerNormTensor;
478  {
479  ConstTensor outputLayerNormTensorCopy(m_LayerNormParameters.m_OutputLayerNormWeights->GetTensorInfo(),
481  outputLayerNormTensor = outputLayerNormTensorCopy;
482  inputParams.m_OutputLayerNormWeights = &outputLayerNormTensor;
483  }
484 
485 
486  visitor.VisitLstmLayer(this, GetParameters(), inputParams, GetName());
487 }
488 
489 } // namespace armnn
std::unique_ptr< ScopedCpuTensorHandle > m_ForgetGateBias
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:69
std::unique_ptr< ScopedCpuTensorHandle > m_InputToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units].
Definition: LstmLayer.hpp:61
std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units].
Definition: LstmLayer.hpp:65
bool m_ProjectionEnabled
Enable/disable the projection layer.
LstmBasicParameters m_BasicParameters
Definition: LstmLayer.hpp:81
const ConstTensor * m_ProjectionWeights
Definition: LstmParams.hpp:55
const ConstTensor * m_CellBias
Definition: LstmParams.hpp:53
LstmDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
const TensorShape & GetShape() const
Definition: Tensor.hpp:88
std::unique_ptr< ScopedCpuTensorHandle > m_InputToInputWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units].
Definition: LstmLayer.hpp:29
const ConstCpuTensorHandle * m_RecurrentToForgetWeights
const ConstCpuTensorHandle * m_CellToOutputWeights
const ConstTensor * m_CellToOutputWeights
Definition: LstmParams.hpp:50
const ConstCpuTensorHandle * m_InputToCellWeights
const ConstCpuTensorHandle * m_InputToOutputWeights
std::unique_ptr< ScopedCpuTensorHandle > m_CellLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:21
virtual void VisitLstmLayer(const IConnectableLayer *layer, const LstmDescriptor &descriptor, const LstmInputParams &params, const char *name=nullptr)=0
Function an Lstm layer should call back to when its Accept(ILayerVisitor&) function is invoked...
std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units].
Definition: LstmLayer.hpp:63
const ConstCpuTensorHandle * m_ProjectionBias
virtual std::unique_ptr< IWorkload > CreateLstm(const LstmQueueDescriptor &descriptor, const WorkloadInfo &info) const
const ConstTensor * m_CellToInputWeights
Definition: LstmParams.hpp:48
const ConstCpuTensorHandle * m_OutputGateBias
const ConstTensor * m_InputGateBias
Definition: LstmParams.hpp:51
const ConstCpuTensorHandle * m_CellToInputWeights
std::unique_ptr< ScopedCpuTensorHandle > m_OutputGateBias
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:73
const ConstTensor * m_RecurrentToCellWeights
Definition: LstmParams.hpp:46
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
Makes a workload for the LSTM type.
Definition: LstmLayer.cpp:22
const ConstCpuTensorHandle * m_CellLayerNormWeights
std::unique_ptr< ScopedCpuTensorHandle > m_InputLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:17
const ConstTensor * m_ForgetLayerNormWeights
Definition: LstmParams.hpp:58
const ConstTensor * m_CellToForgetWeights
Definition: LstmParams.hpp:49
Copyright (c) 2020 ARM Limited.
This layer represents a LSTM operation.
Definition: LstmLayer.hpp:77
const ConstCpuTensorHandle * m_CellToForgetWeights
const IOutputSlot * GetConnection() const override
Definition: Layer.hpp:199
const ConstCpuTensorHandle * m_CellBias
const ConstCpuTensorHandle * m_RecurrentToInputWeights
std::unique_ptr< ScopedCpuTensorHandle > m_CellBias
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:71
const ConstTensor * m_OutputGateBias
Definition: LstmParams.hpp:54
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
Definition: Layer.cpp:339
std::unique_ptr< ScopedCpuTensorHandle > m_CellToForgetWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.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
std::unique_ptr< ScopedCpuTensorHandle > m_OutputLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:23
const ConstCpuTensorHandle * m_InputLayerNormWeights
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of LstmLayer.
Definition: LstmLayer.cpp:166
const ConstCpuTensorHandle * m_OutputLayerNormWeights
const ConstTensor * m_RecurrentToOutputWeights
Definition: LstmParams.hpp:47
LstmLayer * Clone(Graph &graph) const override
Creates a dynamically-allocated copy of this layer.
Definition: LstmLayer.cpp:78
An LstmDescriptor for the LstmLayer.
#define ARMNN_ASSERT_MSG(COND, MSG)
Definition: Assert.hpp:15
const ConstCpuTensorHandle * m_ForgetGateBias
const ConstTensor * m_ProjectionBias
Definition: LstmParams.hpp:56
std::unique_ptr< ScopedCpuTensorHandle > m_CellToInputWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:47
const ConstCpuTensorHandle * m_InputToInputWeights
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
Definition: Tensor.hpp:199
bool m_PeepholeEnabled
Enable/disable peephole.
#define ARMNN_ASSERT(COND)
Definition: Assert.hpp:14
LstmOptLayerNormParameters m_LayerNormParameters
Definition: LstmLayer.hpp:85
#define CHECK_LOCATION()
Definition: Exceptions.hpp:192
std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToInputWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units].
Definition: LstmLayer.hpp:31
Layer::ConstantTensors GetConstantTensorsByRef() override
Retrieve the handles to the constant values stored by the layer.
Definition: LstmLayer.cpp:276
std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units].
Definition: LstmLayer.hpp:67
LstmOptPeepholeParameters m_PeepholeParameters
Definition: LstmLayer.hpp:84
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
LstmOptProjectionParameters m_ProjectionParameters
Definition: LstmLayer.hpp:83
std::unique_ptr< ScopedCpuTensorHandle > m_ProjectionBias
A unique pointer to represent 1D weights tensor with dimensions [output_size].
Definition: LstmLayer.hpp:41
bool m_CifgEnabled
Enable/disable cifg (coupled input & forget gate).
const ConstCpuTensorHandle * m_RecurrentToOutputWeights
void Accept(ILayerVisitor &visitor) const override
Apply a visitor to this layer.
Definition: LstmLayer.cpp:309
std::unique_ptr< ScopedCpuTensorHandle > m_InputToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units].
Definition: LstmLayer.hpp:59
const ConstTensor * m_RecurrentToForgetWeights
Definition: LstmParams.hpp:45
const ConstCpuTensorHandle * m_ForgetLayerNormWeights
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_CellToOutputWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:51
LstmLayer(const LstmDescriptor &param, const char *name)
Constructor to create a LstmLayer.
Definition: LstmLayer.cpp:17
bool m_LayerNormEnabled
Enable/disable layer normalization.
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
Definition: Layer.hpp:312
std::unique_ptr< ScopedCpuTensorHandle > m_ProjectionWeights
A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units].
Definition: LstmLayer.hpp:39
virtual const TensorInfo & GetTensorInfo() const =0
const ConstCpuTensorHandle * m_RecurrentToCellWeights
std::unique_ptr< ScopedCpuTensorHandle > m_ForgetLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:19
LstmOptCifgParameters m_CifgParameters
Definition: LstmLayer.hpp:82
const char * GetName() const override
Returns the name of the layer.
Definition: Layer.hpp:305
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: LstmLayer.cpp:148
const ConstTensor * m_OutputLayerNormWeights
Definition: LstmParams.hpp:60
std::vector< std::reference_wrapper< std::unique_ptr< ScopedCpuTensorHandle > >> ConstantTensors
Definition: Layer.hpp:363
const TensorInfo & GetTensorInfo() const override
Definition: Layer.cpp:63
std::unique_ptr< ScopedCpuTensorHandle > m_InputToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units].
Definition: LstmLayer.hpp:57
std::unique_ptr< ScopedCpuTensorHandle > m_InputGateBias
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmLayer.hpp:33
const ConstCpuTensorHandle * m_ProjectionWeights
const ConstTensor * m_InputToForgetWeights
Definition: LstmParams.hpp:41
const ConstTensor * m_InputToInputWeights
Definition: LstmParams.hpp:40
const ConstCpuTensorHandle * m_InputToForgetWeights