ArmNN
 24.02
NeonLstmFloatWorkload.cpp
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1 //
2 // Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
6 #include "NeonLstmFloatWorkload.hpp"
7 #include "NeonWorkloadUtils.hpp"
8 
9 #include <aclCommon/ArmComputeTensorUtils.hpp>
10 #include <aclCommon/ArmComputeUtils.hpp>
11 
12 #include <armnn/utility/NumericCast.hpp>
13 
14 #include "neon/NeonTensorHandle.hpp"
15 
16 namespace armnn
17 {
18 using namespace armcomputetensorutils;
19 
20 NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor& descriptor, const WorkloadInfo& info)
21  : FloatWorkload<LstmQueueDescriptor>(descriptor, info)
22 {
23  // Report Profiling Details
24  ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonLstmFloatWorkload_Construct",
25  descriptor.m_Parameters,
26  info,
27  GetGuid());
28 
29  arm_compute::LSTMParams<arm_compute::ITensor> lstm_param;
30 
31  // Basic parameters
32  m_InputToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
33  BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo());
34 
35  m_InputToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();
36  BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo());
37 
38  m_InputToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
39  BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo());
40 
41  m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
42  BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo());
43 
44  m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();
45  BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo());
46 
47  m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
48  BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo());
49 
50  m_ForgetGateBiasTensor = std::make_unique<arm_compute::Tensor>();
51  BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo());
52 
53  m_CellBiasTensor = std::make_unique<arm_compute::Tensor>();
54  BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo());
55 
56  m_OutputGateBiasTensor = std::make_unique<arm_compute::Tensor>();
57  BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo());
58 
59  // for future reference: check the AndroidNN API for the logic here
60  if (!m_Data.m_Parameters.m_CifgEnabled)
61  {
62  m_InputToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
63  BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo());
64 
65  m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
66  BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo());
67 
68  m_CellToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
69  if (m_Data.m_CellToInputWeights != nullptr)
70  {
71  BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo());
72  }
73 
74  m_InputGateBiasTensor = std::make_unique<arm_compute::Tensor>();
75  BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo());
76 
77  lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),
78  m_RecurrentToInputWeightsTensor.get(),
79  m_Data.m_CellToInputWeights != nullptr ? m_CellToInputWeightsTensor.get() : nullptr,
80  m_InputGateBiasTensor.get());
81  }
82 
83  if (m_Data.m_Parameters.m_ProjectionEnabled)
84  {
85  m_ProjectionWeightsTensor = std::make_unique<arm_compute::Tensor>();
86  BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo());
87 
88  m_ProjectionBiasTensor = std::make_unique<arm_compute::Tensor>();
89  if (m_Data.m_ProjectionBias != nullptr)
90  {
91  BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo());
92  }
93 
94  lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),
95  m_Data.m_ProjectionBias != nullptr ? m_ProjectionBiasTensor.get() : nullptr);
96  }
97 
98  if (m_Data.m_Parameters.m_PeepholeEnabled)
99  {
100  m_CellToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
101  BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo());
102 
103  m_CellToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
104  BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo());
105 
106  lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());
107  }
108 
109  if (m_Data.m_Parameters.m_LayerNormEnabled)
110  {
111  m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
112  if (!m_Data.m_Parameters.m_CifgEnabled)
113  {
114  BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo());
115  }
116 
117  m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
118  BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo());
119 
120  m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
121  BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo());
122 
123  m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
124  BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo());
125 
126  lstm_param.set_layer_normalization_params(m_Data.m_Parameters.m_CifgEnabled ?
127  nullptr : m_InputLayerNormWeightsTensor.get(),
128  m_ForgetLayerNormWeightsTensor.get(),
129  m_CellLayerNormWeightsTensor.get(),
130  m_OutputLayerNormWeightsTensor.get());
131  }
132 
133  const arm_compute::ITensor& input = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
134  const arm_compute::ITensor& output_state_in = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
135  const arm_compute::ITensor& cell_state_in = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[2])->GetTensor();
136 
137  arm_compute::ITensor& output_state_out = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[1])->GetTensor();
138  arm_compute::ITensor& cell_state_out = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->GetTensor();
139  arm_compute::ITensor& output = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[3])->GetTensor();
140 
141  // Get the batch_size and the num_units from the cellStateIn dimensions
142  const TensorInfo& inputTensorInfo = info.m_InputTensorInfos[2];
143  const unsigned int batch_size = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[0]);
144  const unsigned int num_units = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[1]);
145 
146  m_ScratchBuffer = std::make_unique<arm_compute::Tensor>();
147  if (m_Data.m_Parameters.m_CifgEnabled)
148  {
149  // 2D tensor with dimensions [num_units * 3, batch_size] with CIFG
150  armnn::TensorInfo scratchBuffer1({ batch_size, num_units * 3 }, DataType::Float32);
151  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer1);
152  }
153  else
154  {
155  // scratch_buffer [num_units * 4, batch_size] without CIFG
156  armnn::TensorInfo scratchBuffer2({ batch_size, num_units * 4 }, DataType::Float32);
157  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer2);
158  }
159 
160  float cell_threshold = m_Data.m_Parameters.m_ClippingThresCell;
161  float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj;
162 
163  // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations
164  arm_compute::ActivationLayerInfo activationLayerInfo =
165  ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc);
166 
167  m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(),
168  m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(),
169  m_RecurrentToCellWeightsTensor.get(), m_RecurrentToOutputWeightsTensor.get(),
170  m_ForgetGateBiasTensor.get(), m_CellBiasTensor.get(), m_OutputGateBiasTensor.get(),
171  &output_state_in, &cell_state_in, m_ScratchBuffer.get(), &output_state_out,
172  &cell_state_out, &output, lstm_param, activationLayerInfo,
173  cell_threshold, projection_threshold);
174 
175  armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);
176 
177  InitializeArmComputeTensorData(*m_InputToForgetWeightsTensor,
178  m_Data.m_InputToForgetWeights);
179  InitializeArmComputeTensorData(*m_InputToCellWeightsTensor,
180  m_Data.m_InputToCellWeights);
181  InitializeArmComputeTensorData(*m_InputToOutputWeightsTensor,
182  m_Data.m_InputToOutputWeights);
183  InitializeArmComputeTensorData(*m_RecurrentToForgetWeightsTensor,
184  m_Data.m_RecurrentToForgetWeights);
185  InitializeArmComputeTensorData(*m_RecurrentToCellWeightsTensor,
186  m_Data.m_RecurrentToCellWeights);
187  InitializeArmComputeTensorData(*m_RecurrentToOutputWeightsTensor,
188  m_Data.m_RecurrentToOutputWeights);
189  InitializeArmComputeTensorData(*m_ForgetGateBiasTensor,
190  m_Data.m_ForgetGateBias);
191  InitializeArmComputeTensorData(*m_CellBiasTensor,
192  m_Data.m_CellBias);
193  InitializeArmComputeTensorData(*m_OutputGateBiasTensor,
194  m_Data.m_OutputGateBias);
195 
196  if (!m_Data.m_Parameters.m_CifgEnabled)
197  {
198  InitializeArmComputeTensorData(*m_InputToInputWeightsTensor,
199  m_Data.m_InputToInputWeights);
200  InitializeArmComputeTensorData(*m_RecurrentToInputWeightsTensor,
201  m_Data.m_RecurrentToInputWeights);
202  if (m_Data.m_CellToInputWeights != nullptr)
203  {
204  InitializeArmComputeTensorData(*m_CellToInputWeightsTensor,
205  m_Data.m_CellToInputWeights);
206  }
207  InitializeArmComputeTensorData(*m_InputGateBiasTensor,
208  m_Data.m_InputGateBias);
209  }
210 
211  if (m_Data.m_Parameters.m_ProjectionEnabled)
212  {
213  InitializeArmComputeTensorData(*m_ProjectionWeightsTensor,
214  m_Data.m_ProjectionWeights);
215  if (m_Data.m_ProjectionBias != nullptr)
216  {
217  InitializeArmComputeTensorData(*m_ProjectionBiasTensor,
218  m_Data.m_ProjectionBias);
219  }
220  }
221 
222  if (m_Data.m_Parameters.m_PeepholeEnabled)
223  {
224  InitializeArmComputeTensorData(*m_CellToForgetWeightsTensor,
225  m_Data.m_CellToForgetWeights);
226  InitializeArmComputeTensorData(*m_CellToOutputWeightsTensor,
227  m_Data.m_CellToOutputWeights);
228  }
229 
230  if (m_Data.m_Parameters.m_LayerNormEnabled)
231  {
232  if (!m_Data.m_Parameters.m_CifgEnabled)
233  {
234  InitializeArmComputeTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights);
235  }
236  InitializeArmComputeTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights);
237  InitializeArmComputeTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights);
238  InitializeArmComputeTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights);
239  }
240 
241  // Force Compute Library to perform the necessary copying and reshaping, after which
242  // delete all the input tensors that will no longer be needed
243  m_LstmLayer.prepare();
244  FreeUnusedTensors();
245 }
246 
247 void NeonLstmFloatWorkload::Execute() const
248 {
249  ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID("NeonLstmFloatWorkload_Execute");
250  m_LstmLayer.run();
251 }
252 
253 arm_compute::Status NeonLstmFloatWorkloadValidate(const TensorInfo& input,
254  const TensorInfo& outputStateIn,
255  const TensorInfo& cellStateIn,
256  const TensorInfo& scratchBuffer,
257  const TensorInfo& outputStateOut,
258  const TensorInfo& cellStateOut,
259  const TensorInfo& output,
260  const LstmDescriptor& descriptor,
261  const LstmInputParamsInfo& paramsInfo)
262 {
263  arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info;
264 
265  // The inputs and outputs
266  const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input);
267  const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn);
268  const arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn);
269  const arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer);
270  const arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut);
271  const arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut);
272  const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output);
273 
274  // Basic parameters
275  const arm_compute::TensorInfo aclInputToForgetWeightsInfo
276  = BuildArmComputeTensorInfo(paramsInfo.GetInputToForgetWeights());
277  const arm_compute::TensorInfo aclInputToCellWeightsInfo
278  = BuildArmComputeTensorInfo(paramsInfo.GetInputToCellWeights());
279  const arm_compute::TensorInfo aclInputToOutputWeightsInfo
280  = BuildArmComputeTensorInfo(paramsInfo.GetInputToOutputWeights());
281  const arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo
282  = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToForgetWeights());
283  const arm_compute::TensorInfo aclRecurrentToCellWeightsInfo
284  = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToCellWeights());
285  const arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo
286  = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToOutputWeights());
287  const arm_compute::TensorInfo aclForgetGateBiasInfo
288  = BuildArmComputeTensorInfo(paramsInfo.GetForgetGateBias());
289  const arm_compute::TensorInfo aclCellBiasInfo
290  = BuildArmComputeTensorInfo(paramsInfo.GetCellBias());
291  const arm_compute::TensorInfo aclOutputGateBiasInfo
292  = BuildArmComputeTensorInfo(paramsInfo.GetOutputGateBias());
293 
294  arm_compute::TensorInfo aclInputToInputWeightsInfo;
295  arm_compute::TensorInfo aclRecurrentToInputWeightsInfo;
296  arm_compute::TensorInfo aclCellToInputWeightsInfo;
297  arm_compute::TensorInfo aclInputGateBiasInfo;
298  arm_compute::TensorInfo aclProjectionWeightsInfo;
299  arm_compute::TensorInfo aclProjectionBiasInfo;
300  arm_compute::TensorInfo aclCellToForgetWeightsInfo;
301  arm_compute::TensorInfo aclCellToOutputWeightsInfo;
302 
303  arm_compute::TensorInfo aclInputLayerNormWeightsInfo;
304  arm_compute::TensorInfo aclForgetLayerNormWeightsInfo;
305  arm_compute::TensorInfo aclCellLayerNormWeightsInfo;
306  arm_compute::TensorInfo aclOutputLayerNormWeightsInfo;
307 
308 
309  if (!descriptor.m_CifgEnabled)
310  {
311  if (descriptor.m_PeepholeEnabled)
312  {
313  aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToInputWeights());
314  }
315  aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToInputWeights());
316  aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToInputWeights());
317  aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputGateBias());
318 
319  lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo, &aclRecurrentToInputWeightsInfo,
320  descriptor.m_PeepholeEnabled ? &aclCellToInputWeightsInfo : nullptr,
321  &aclInputGateBiasInfo);
322  }
323 
324  if (descriptor.m_ProjectionEnabled)
325  {
326  if (paramsInfo.m_ProjectionBias != nullptr)
327  {
328  aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionBias());
329  }
330  aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionWeights());
331 
332  lstm_params_info.set_projection_params(&aclProjectionWeightsInfo,
333  paramsInfo.m_ProjectionBias != nullptr ?
334  &aclProjectionBiasInfo : nullptr);
335  }
336 
337  if (descriptor.m_PeepholeEnabled)
338  {
339  aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToForgetWeights());
340  aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToOutputWeights());
341 
342  lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo);
343  }
344 
345  if (descriptor.m_LayerNormEnabled)
346  {
347  if (!descriptor.m_CifgEnabled)
348  {
349  aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputLayerNormWeights());
350  }
351  aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetForgetLayerNormWeights());
352  aclCellLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellLayerNormWeights());
353  aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputLayerNormWeights());
354 
355  lstm_params_info.set_layer_normalization_params(descriptor.m_CifgEnabled ?
356  nullptr : &aclInputLayerNormWeightsInfo,
357  &aclForgetLayerNormWeightsInfo,
358  &aclCellLayerNormWeightsInfo,
359  &aclOutputLayerNormWeightsInfo);
360  }
361 
362  float cell_threshold = descriptor.m_ClippingThresCell;
363  float projection_threshold = descriptor.m_ClippingThresProj;
364 
365  // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations
366  arm_compute::ActivationLayerInfo activationLayerInfo =
367  ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc);
368 
369  return arm_compute::NELSTMLayer::validate(&aclInputInfo,
370  &aclInputToForgetWeightsInfo,
371  &aclInputToCellWeightsInfo,
372  &aclInputToOutputWeightsInfo,
373  &aclRecurrentToForgetWeightsInfo,
374  &aclRecurrentToCellWeightsInfo,
375  &aclRecurrentToOutputWeightsInfo,
376  &aclForgetGateBiasInfo,
377  &aclCellBiasInfo,
378  &aclOutputGateBiasInfo,
379  &aclOutputStateInInfo,
380  &aclCellStateInInfo,
381  &aclScratchBufferInfo,
382  &aclOutputStateOutInfo,
383  &aclCellStateOutInfo,
384  &aclOutputInfo,
385  lstm_params_info,
386  activationLayerInfo,
387  cell_threshold,
388  projection_threshold);
389 }
390 
391 void NeonLstmFloatWorkload::FreeUnusedTensors()
392 {
393  FreeTensorIfUnused(m_InputToInputWeightsTensor);
394  FreeTensorIfUnused(m_InputToForgetWeightsTensor);
395  FreeTensorIfUnused(m_InputToCellWeightsTensor);
396  FreeTensorIfUnused(m_InputToOutputWeightsTensor);
397  FreeTensorIfUnused(m_RecurrentToInputWeightsTensor);
398  FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor);
399  FreeTensorIfUnused(m_RecurrentToCellWeightsTensor);
400  FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor);
401  FreeTensorIfUnused(m_CellToInputWeightsTensor);
402  FreeTensorIfUnused(m_CellToForgetWeightsTensor);
403  FreeTensorIfUnused(m_CellToOutputWeightsTensor);
404  FreeTensorIfUnused(m_InputGateBiasTensor);
405  FreeTensorIfUnused(m_ForgetGateBiasTensor);
406  FreeTensorIfUnused(m_CellBiasTensor);
407  FreeTensorIfUnused(m_OutputGateBiasTensor);
408  FreeTensorIfUnused(m_ProjectionWeightsTensor);
409  FreeTensorIfUnused(m_ProjectionBiasTensor);
410  FreeTensorIfUnused(m_ScratchBuffer);
411  FreeTensorIfUnused(m_InputLayerNormWeightsTensor);
412  FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor);
413  FreeTensorIfUnused(m_CellLayerNormWeightsTensor);
414  FreeTensorIfUnused(m_OutputLayerNormWeightsTensor);
415 }
416 
417 void NeonLstmFloatWorkload::ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot)
418 {
419  ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
420  this->m_Data.m_Inputs[slot] = tensorHandle;
421  try
422  {
423  Reconfigure();
424  }
425  catch(armnn::UnimplementedException& e)
426  {
427  // Cannot reconfigure, revert the slot back and throw the exception.
428  this->m_Data.m_Inputs[slot] = backupHandle;
429  throw e;
430  }
431 }
432 
433 // Replace output tensor handle with the given TensorHandle
434 void NeonLstmFloatWorkload::ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot)
435 {
436  ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
437  this->m_Data.m_Inputs[slot] = tensorHandle;
438  try
439  {
440  Reconfigure();
441  }
442  catch(armnn::UnimplementedException& e)
443  {
444  // Cannot reconfigure, revert the slot back and throw the exception.
445  this->m_Data.m_Inputs[slot] = backupHandle;
446  throw e;
447  }
448 }
449 
450 void NeonLstmFloatWorkload::Reconfigure()
451 {
452  throw armnn::UnimplementedException("Reconfigure not implemented for this workload");
453 }
454 
455 } //namespace armnn
armnn::LstmInputParamsInfo::GetCellBias
const TensorInfo & GetCellBias() const
Definition: LstmParams.hpp:173
armnn::LstmInputParamsInfo::GetInputToCellWeights
const TensorInfo & GetInputToCellWeights() const
Definition: LstmParams.hpp:129
armnn::TensorInfo
Definition: Tensor.hpp:152
NeonTensorHandle.hpp
armnn::LstmInputParamsInfo::GetProjectionBias
const TensorInfo & GetProjectionBias() const
Definition: LstmParams.hpp:185
armnn::DataType::Float32
@ Float32
armnn::ITensorHandle
Definition: ITensorHandle.hpp:16
armnn::LstmInputParamsInfo::GetInputGateBias
const TensorInfo & GetInputGateBias() const
Definition: LstmParams.hpp:165
armnn::LstmInputParamsInfo::GetRecurrentToInputWeights
const TensorInfo & GetRecurrentToInputWeights() const
Definition: LstmParams.hpp:137
armnn::LstmInputParamsInfo::GetRecurrentToForgetWeights
const TensorInfo & GetRecurrentToForgetWeights() const
Definition: LstmParams.hpp:141
armnn::TypedWorkload
Definition: Workload.hpp:101
armnn::LstmInputParamsInfo::GetRecurrentToCellWeights
const TensorInfo & GetRecurrentToCellWeights() const
Definition: LstmParams.hpp:145
NumericCast.hpp
armnn::InitializeArmComputeTensorData
void InitializeArmComputeTensorData(arm_compute::Tensor &tensor, TensorInfo tensorInfo, const ITensorHandle *handle)
Definition: NeonWorkloadUtils.hpp:68
armnn::LstmInputParamsInfo::GetInputLayerNormWeights
const TensorInfo & GetInputLayerNormWeights() const
Definition: LstmParams.hpp:189
armnn::LstmDescriptor::m_PeepholeEnabled
bool m_PeepholeEnabled
Enable/disable peephole.
Definition: Descriptors.hpp:1148
armnn::LstmDescriptor::m_ClippingThresProj
float m_ClippingThresProj
Clipping threshold value for the projection.
Definition: Descriptors.hpp:1144
armnn::IAclTensorHandle
Definition: ArmComputeTensorHandle.hpp:16
armnn::NeonLstmFloatWorkloadValidate
arm_compute::Status NeonLstmFloatWorkloadValidate(const TensorInfo &input, const TensorInfo &outputStateIn, const TensorInfo &cellStateIn, const TensorInfo &scratchBuffer, const TensorInfo &outputStateOut, const TensorInfo &cellStateOut, const TensorInfo &output, const LstmDescriptor &descriptor, const LstmInputParamsInfo &paramsInfo)
Definition: NeonLstmFloatWorkload.cpp:253
armnn::QueueDescriptorWithParameters::m_Parameters
LayerDescriptor m_Parameters
Definition: WorkloadData.hpp:66
armnn::LstmInputParamsInfo::GetCellToInputWeights
const TensorInfo & GetCellToInputWeights() const
Definition: LstmParams.hpp:153
armnn::LstmInputParamsInfo::GetRecurrentToOutputWeights
const TensorInfo & GetRecurrentToOutputWeights() const
Definition: LstmParams.hpp:149
armnn::LstmInputParamsInfo::GetInputToInputWeights
const TensorInfo & GetInputToInputWeights() const
Definition: LstmParams.hpp:121
armnn::WorkloadInfo
Contains information about TensorInfos of a layer.
Definition: WorkloadInfo.hpp:16
armnn::LstmInputParamsInfo::GetForgetGateBias
const TensorInfo & GetForgetGateBias() const
Definition: LstmParams.hpp:169
armnn::ConvertLstmActivationFuncToAclLayerInfo
arm_compute::ActivationLayerInfo ConvertLstmActivationFuncToAclLayerInfo(uint32_t activationFunction)
Definition: ArmComputeUtils.hpp:118
armnn::LstmInputParamsInfo::GetCellToForgetWeights
const TensorInfo & GetCellToForgetWeights() const
Definition: LstmParams.hpp:157
armnn::NeonLstmFloatWorkload::ReplaceOutputTensorHandle
void ReplaceOutputTensorHandle(ITensorHandle *tensorHandle, unsigned int slot) override
Definition: NeonLstmFloatWorkload.cpp:434
ArmComputeUtils.hpp
armnn::BoostLogSeverityMapping::info
@ info
armnn::NeonLstmFloatWorkload::ReplaceInputTensorHandle
void ReplaceInputTensorHandle(ITensorHandle *tensorHandle, unsigned int slot) override
Definition: NeonLstmFloatWorkload.cpp:417
armnn::QueueDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition: WorkloadData.hpp:27
ARMNN_REPORT_PROFILING_WORKLOAD_DESC
#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)
Definition: Profiling.hpp:227
armnn::BaseWorkload::GetGuid
arm::pipe::ProfilingGuid GetGuid() const final
Definition: Workload.hpp:67
armnn::LstmDescriptor
An LstmDescriptor for the LstmLayer.
Definition: Descriptors.hpp:1102
armnn::LstmQueueDescriptor
Definition: WorkloadData.hpp:400
armnn::Status
Status
Definition: Types.hpp:42
armnn::NeonLstmFloatWorkload::NeonLstmFloatWorkload
NeonLstmFloatWorkload(const LstmQueueDescriptor &descriptor, const WorkloadInfo &info)
Definition: NeonLstmFloatWorkload.cpp:20
armnn::LstmInputParamsInfo::GetInputToOutputWeights
const TensorInfo & GetInputToOutputWeights() const
Definition: LstmParams.hpp:133
armnn::LstmDescriptor::m_CifgEnabled
bool m_CifgEnabled
Enable/disable cifg (coupled input & forget gate).
Definition: Descriptors.hpp:1146
armnn::BaseWorkload::m_Data
QueueDescriptor m_Data
Definition: Workload.hpp:89
armnn::TensorInfo::GetShape
const TensorShape & GetShape() const
Definition: Tensor.hpp:193
armnn::LstmInputParamsInfo::GetOutputGateBias
const TensorInfo & GetOutputGateBias() const
Definition: LstmParams.hpp:177
armnn::LstmDescriptor::m_LayerNormEnabled
bool m_LayerNormEnabled
Enable/disable layer normalization.
Definition: Descriptors.hpp:1152
NeonLstmFloatWorkload.hpp
armnn::LstmInputParamsInfo::GetCellToOutputWeights
const TensorInfo & GetCellToOutputWeights() const
Definition: LstmParams.hpp:161
NeonWorkloadUtils.hpp
armnn::LstmInputParamsInfo::GetOutputLayerNormWeights
const TensorInfo & GetOutputLayerNormWeights() const
Definition: LstmParams.hpp:201
armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
armnn::LstmInputParamsInfo
Definition: LstmParams.hpp:63
ArmComputeTensorUtils.hpp
armnn::LstmDescriptor::m_ProjectionEnabled
bool m_ProjectionEnabled
Enable/disable the projection layer.
Definition: Descriptors.hpp:1150
armnn::LstmInputParamsInfo::GetProjectionWeights
const TensorInfo & GetProjectionWeights() const
Definition: LstmParams.hpp:181
armnn::UnimplementedException
Definition: Exceptions.hpp:98
armnn::LstmDescriptor::m_ActivationFunc
uint32_t m_ActivationFunc
The activation function to use.
Definition: Descriptors.hpp:1140
ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID
#define ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID(label)
Creates a profiling event that uses GetGuid() and GetName() from the calling class.
Definition: NeonWorkloadUtils.hpp:32
armnn::NeonLstmFloatWorkload::Execute
virtual void Execute() const override
Definition: NeonLstmFloatWorkload.cpp:247
armnn::LstmDescriptor::m_ClippingThresCell
float m_ClippingThresCell
Clipping threshold value for the cell state.
Definition: Descriptors.hpp:1142
armnn::LstmInputParamsInfo::m_ProjectionBias
const TensorInfo * m_ProjectionBias
Definition: LstmParams.hpp:105
armnn::LstmInputParamsInfo::GetForgetLayerNormWeights
const TensorInfo & GetForgetLayerNormWeights() const
Definition: LstmParams.hpp:193
armnn::LstmInputParamsInfo::GetCellLayerNormWeights
const TensorInfo & GetCellLayerNormWeights() const
Definition: LstmParams.hpp:197
armnn::QueueDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition: WorkloadData.hpp:26
armnn::LstmInputParamsInfo::GetInputToForgetWeights
const TensorInfo & GetInputToForgetWeights() const
Definition: LstmParams.hpp:125