1 //
2 // Copyright © 2017 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
NeonLstmFloatWorkload(const LstmQueueDescriptor & descriptor,const WorkloadInfo & info)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
Execute() const247 void NeonLstmFloatWorkload::Execute() const
248 {
249 ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonLstmFloatWorkload_Execute", GetGuid());
250 m_LstmLayer.run();
251 }
252
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)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
FreeUnusedTensors()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
ReplaceInputTensorHandle(ITensorHandle * tensorHandle,unsigned int slot)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
ReplaceOutputTensorHandle(ITensorHandle * tensorHandle,unsigned int slot)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
Reconfigure()450 void NeonLstmFloatWorkload::Reconfigure()
451 {
452 throw armnn::UnimplementedException("Reconfigure not implemented for this workload");
453 }
454
455 } //namespace armnn
456