1 //
2 // Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5
6 #include "UnidirectionalSequenceLstmTestImpl.hpp"
7
8 #include <armnn/utility/NumericCast.hpp>
9
10 #include <armnn/backends/TensorHandle.hpp>
11
12 #include <armnnTestUtils/TensorCopyUtils.hpp>
13 #include <armnnTestUtils/WorkloadTestUtils.hpp>
14
15 #include <ResolveType.hpp>
16
17 namespace {
18
19 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
20 LayerTestResult<T, 3>
UnidirectionalSequenceLstmTimeMajorSingleBatchTestImpl(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory,const std::vector<T> & input,const std::vector<T> & outputExpected,const armnn::TensorShape & inputShape,const armnn::TensorShape & outputExpectedShape,float qScale=1.0f,int32_t qOffset=0,armnn::DataType constantDataType=armnn::DataType::Float32)21 UnidirectionalSequenceLstmTimeMajorSingleBatchTestImpl(
22 armnn::IWorkloadFactory& workloadFactory,
23 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
24 const armnn::ITensorHandleFactory& tensorHandleFactory,
25 const std::vector<T>& input,
26 const std::vector<T>& outputExpected,
27 const armnn::TensorShape& inputShape,
28 const armnn::TensorShape& outputExpectedShape,
29 float qScale = 1.0f,
30 int32_t qOffset = 0,
31 armnn::DataType constantDataType = armnn::DataType::Float32)
32 {
33 IgnoreUnused(memoryManager);
34 unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[0]);
35 unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]);
36 unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]);
37 unsigned numUnits = outputSize;
38
39 armnn::TensorInfo inputTensorInfo({1, batchSize , inputSize}, ArmnnType, qScale, qOffset );
40 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset);
41 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset);
42 armnn::TensorInfo outputStateOutTensorInfo({ batchSize, 1, outputSize }, ArmnnType, qScale, qOffset);
43 armnn::TensorInfo cellStateOutTensorInfo({ batchSize, 1, outputSize }, ArmnnType, qScale, qOffset);
44 armnn::TensorInfo outputTensorInfo({1, batchSize, outputSize}, ArmnnType, qScale, qOffset);
45
46 std::vector<T> inputVector;
47 inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
48
49 std::vector<T> cellStateInVector(batchSize * numUnits, T());
50 std::vector<T> outputStateInVector(batchSize * outputSize, T());
51
52 std::vector<T> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
53 std::vector<T> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
54 std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
55
56 std::vector<T> outputVector;
57 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));
58
59 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
60 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
61 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
62 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
63 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
64
65 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
66 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
67 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
68 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
69 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
70
71 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
72 armnn::WorkloadInfo info;
73
74 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
75 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
76 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
77
78 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
79 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
80 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
81
82 armnn::TensorInfo tensorInfo4({numUnits}, constantDataType , qScale, qOffset);
83 armnn::TensorInfo tensorInfo8({numUnits, 2}, constantDataType, qScale, qOffset);
84 armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
85
86 std::vector<float> inputToInputWeights = {-0.45018822f, -0.02338299f, -0.0870589f,
87 -0.34550029f, 0.04266912f, -0.15680569f,
88 -0.34856534f, 0.43890524f};
89
90 std::vector<float> inputToForgetWeights = { 0.09701663f, 0.20334584f, -0.50592935f,
91 -0.31343272f, -0.40032279f, 0.44781327f,
92 0.01387155f, -0.35593212f};
93
94 std::vector<float> inputToCellWeights = { -0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f,
95 -0.20583314f, 0.44344562f, 0.22077113f,
96 -0.29909778f};
97
98 std::vector<float> inputToOutputWeights = { -0.25065863f, -0.28290087f, 0.04613829f,
99 0.40525138f, 0.44272184f, 0.03897077f,
100 -0.1556896f, 0.19487578f};
101
102 std::vector<float> recurrentToInputWeights = {-0.0063535f, -0.2042388f, 0.31454784f,
103 -0.35746509f, 0.28902304f, 0.08183324f,
104 -0.16555229f, 0.02286911f, -0.13566875f,
105 0.03034258f, 0.48091322f, -0.12528998f,
106 0.24077177f, -0.51332325f, -0.33502164f,
107 0.10629296f};
108
109 std::vector<float> recurrentToForgetWeights = { -0.48684245f, -0.06655136f, 0.42224967f,
110 0.2112639f, 0.27654213f, 0.20864892f,
111 -0.07646349f, 0.45877004f, 0.00141793f,
112 -0.14609534f, 0.36447752f, 0.09196436f,
113 0.28053468f, 0.01560611f, -0.20127171f,
114 -0.01140004f};
115
116 std::vector<float> recurrentToCellWeights = { -0.3407414f, 0.24443203f, -0.2078532f,
117 0.26320225f, 0.05695659f, -0.00123841f,
118 -0.4744786f, -0.35869038f, -0.06418842f,
119 -0.13502428f, -0.501764f, 0.22830659f,
120 -0.46367589f, 0.26016325f, -0.03894562f,
121 -0.16368064f};
122
123 std::vector<float> recurrentToOutputWeights = { 0.43385774f, -0.17194885f, 0.2718237f,
124 0.09215671f, 0.24107647f, -0.39835793f,
125 0.18212086f, 0.01301402f, 0.48572797f,
126 -0.50656658f, 0.20047462f, -0.20607421f,
127 -0.51818722f, -0.15390486f, 0.0468148f,
128 0.39922136f};
129
130 std::vector<float> cellToInputWeights = {0., 0., 0., 0.};
131
132 std::vector<float> inputGateBias = {0., 0., 0., 0.};
133
134 std::vector<float> forgetGateBias = {1., 1., 1., 1.};
135
136 std::vector<float> cellBias = {0., 0., 0., 0.};
137
138 std::vector<float> outputGateBias = {0., 0., 0., 0.};
139
140 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo8);
141 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo8);
142 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo8);
143 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo8);
144 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16);
145 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
146 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
147 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
148 armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo4);
149 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4);
150 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4);
151 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
152 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
153
154 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
155 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
156 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
157 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
158 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
159 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
160 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
161 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
162 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
163 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
164 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
165 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
166 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
167
168 data.m_InputToInputWeights = &inputToInputWeightsTensor;
169 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
170 data.m_InputToCellWeights = &inputToCellWeightsTensor;
171 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
172 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
173 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
174 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
175 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
176 data.m_InputGateBias = &inputGateBiasTensor;
177 data.m_ForgetGateBias = &forgetGateBiasTensor;
178 data.m_CellBias = &cellBiasTensor;
179 data.m_OutputGateBias = &outputGateBiasTensor;
180
181 // Flags to set test configuration
182 data.m_Parameters.m_ActivationFunc = 4;
183 data.m_Parameters.m_CifgEnabled = false;
184 data.m_Parameters.m_PeepholeEnabled = false;
185 data.m_Parameters.m_ProjectionEnabled = false;
186 data.m_Parameters.m_ClippingThresCell = 10;
187 data.m_Parameters.m_ClippingThresProj = 0;
188 data.m_Parameters.m_TimeMajor = true;
189
190 std::unique_ptr<armnn::IWorkload> workload
191 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
192 inputHandle->Allocate();
193 outputStateInHandle->Allocate();
194 cellStateInHandle->Allocate();
195
196 outputStateOutHandle->Allocate();
197 cellStateOutHandle->Allocate();
198 outputHandle->Allocate();
199
200 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
201 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
202 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
203
204 workload->Execute();
205
206 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
207 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
208 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
209
210 return LayerTestResult<T, 3>(actualOutput,
211 outputVector,
212 outputHandle->GetShape(),
213 outputTensorInfo.GetShape());
214 }
215
216 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
UnidirectionalSequenceLstmLayerFloat32TestImpl(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory,const std::vector<T> & input,const std::vector<T> & outputExpected,const armnn::TensorShape & inputShape,const armnn::TensorShape & outputExpectedShape,float qScale=1.0f,int32_t qOffset=0,armnn::DataType constantDataType=armnn::DataType::Float32)217 LayerTestResult<T, 3> UnidirectionalSequenceLstmLayerFloat32TestImpl(
218 armnn::IWorkloadFactory& workloadFactory,
219 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
220 const armnn::ITensorHandleFactory& tensorHandleFactory,
221 const std::vector<T>& input,
222 const std::vector<T>& outputExpected,
223 const armnn::TensorShape& inputShape,
224 const armnn::TensorShape& outputExpectedShape,
225 float qScale = 1.0f,
226 int32_t qOffset = 0,
227 armnn::DataType constantDataType = armnn::DataType::Float32) {
228 IgnoreUnused(memoryManager);
229 unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[0]);
230 unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[1]);
231 unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]);
232 unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]);
233 unsigned numUnits = outputSize;
234
235 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, ArmnnType, qScale, qOffset);
236 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
237 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
238 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
239 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
240 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
241
242 std::vector<T> inputVector;
243 inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));
244
245 std::vector<T> cellStateInVector(batchSize * numUnits, T());
246 std::vector<T> outputStateInVector(batchSize * outputSize, T());
247
248 std::vector<T> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
249 std::vector<T> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
250 std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
251
252 std::vector<T> outputVector;
253 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));
254
255 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
256 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
257 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
258 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
259 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
260
261 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
262 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
263 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
264 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
265 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
266
267 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
268 armnn::WorkloadInfo info;
269
270 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
271 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
272 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
273
274 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
275 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
276 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
277
278 armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset);
279 armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);
280 armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
281
282 std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,
283 -0.117484632f, 0.3298470976f, -0.1179017122f,
284 0.214305695f, 0.42135173085f, 0.003878414626f,
285 -0.348303917f, -0.1881275477f, 0.0343011027f };
286
287 std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
288 -0.3810434485f, 0.268383264f, -0.009807467424f,
289 -0.3522925403f, -0.24275735512f, -0.28344226125f,
290 0.13512269116f, -0.4932442977f, -0.10039821991f };
291
292 std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
293 0.386399507f, -0.259465157985f, -0.16545993089f,
294 -0.4230232555f, 0.341664791103f, -0.18127849691f,
295 -0.2277662414f, -0.55275535589f, 0.34184026718f };
296
297 std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
298 0.53969591851f, 0.23393625035f, -0.27140527306f,
299 0.50009280443f, 0.07511717046f, 0.3998299249f,
300 -0.51717478049f, 0.1889653282f, -0.367323637f };
301
302 std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,
303 -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,
304 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,
305 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f };
306
307 std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
308 -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
309 -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
310 -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };
311
312 std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
313 -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
314 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
315 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };
316
317 std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
318 -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
319 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
320 -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };
321
322 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
323
324 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
325
326 std::vector<float> cellBias = { 0., 0., 0., 0. };
327
328 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
329
330 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12);
331 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
332 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
333 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
334 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16);
335 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
336 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
337 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
338 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4);
339 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4);
340 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
341 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
342
343 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
344 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
345 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
346 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
347 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
348 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
349 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
350 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
351 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
352 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
353 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
354 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
355
356 data.m_InputToInputWeights = &inputToInputWeightsTensor;
357 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
358 data.m_InputToCellWeights = &inputToCellWeightsTensor;
359 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
360 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
361 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
362 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
363 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
364 data.m_InputGateBias = &inputGateBiasTensor;
365 data.m_ForgetGateBias = &forgetGateBiasTensor;
366 data.m_CellBias = &cellBiasTensor;
367 data.m_OutputGateBias = &outputGateBiasTensor;
368
369 // Flags to set test configuration
370 data.m_Parameters.m_ClippingThresCell = 10;
371 data.m_Parameters.m_ClippingThresProj = 0;
372 data.m_Parameters.m_ActivationFunc = 4;
373 data.m_Parameters.m_CifgEnabled = false;
374 data.m_Parameters.m_PeepholeEnabled = false;
375 data.m_Parameters.m_ProjectionEnabled = false;
376 data.m_Parameters.m_TimeMajor = false;
377
378 std::unique_ptr<armnn::IWorkload> workload
379 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
380 inputHandle->Allocate();
381 outputStateInHandle->Allocate();
382 cellStateInHandle->Allocate();
383
384 outputStateOutHandle->Allocate();
385 cellStateOutHandle->Allocate();
386 outputHandle->Allocate();
387
388 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
389 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
390 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
391
392 workload->Execute();
393
394 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
395 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
396 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
397
398 return LayerTestResult<T, 3>(actualOutput,
399 outputVector,
400 outputHandle->GetShape(),
401 outputTensorInfo.GetShape());
402 }
403
404 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
405 LayerTestResult<T, 3>
UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory,const std::vector<T> & input,const std::vector<T> & outputExpected,const armnn::TensorShape & inputShape,const armnn::TensorShape & outputExpectedShape,float qScale=1.0f,int32_t qOffset=0,armnn::DataType constantDataType=armnn::DataType::Float32)406 UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl(
407 armnn::IWorkloadFactory& workloadFactory,
408 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
409 const armnn::ITensorHandleFactory& tensorHandleFactory,
410 const std::vector<T>& input,
411 const std::vector<T>& outputExpected,
412 const armnn::TensorShape& inputShape,
413 const armnn::TensorShape& outputExpectedShape,
414 float qScale = 1.0f,
415 int32_t qOffset = 0,
416 armnn::DataType constantDataType = armnn::DataType::Float32) {
417 IgnoreUnused(memoryManager);
418 unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[1]);
419 unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[0]);
420 unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]);
421 unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]);
422 unsigned numUnits = outputSize;
423
424 armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, ArmnnType, qScale, qOffset);
425 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
426 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
427 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
428 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
429 armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, ArmnnType, qScale, qOffset);
430
431 std::vector<T> inputVector;
432 inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));
433
434 std::vector<T> cellStateInVector(batchSize * numUnits, T());
435 std::vector<T> outputStateInVector(batchSize * outputSize, T());
436
437 std::vector<T> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
438 std::vector<T> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
439 std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
440
441 std::vector<T> outputVector;
442 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));
443
444 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
445 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
446 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
447 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
448 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
449
450 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
451 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
452 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
453 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
454 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
455
456 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
457 armnn::WorkloadInfo info;
458
459 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
460 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
461 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
462
463 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
464 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
465 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
466
467 armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset);
468 armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);
469 armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
470
471 std::vector<float> inputToInputWeights = { 0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f,
472 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f,
473 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f,
474 -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f };
475
476 std::vector<float> inputToForgetWeights = { -0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f,
477 -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f,
478 -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f,
479 -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f };
480
481 std::vector<float> inputToCellWeights = { -0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f,
482 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f,
483 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f,
484 -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f };
485
486 std::vector<float> inputToOutputWeights = { -0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f,
487 -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f,
488 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f,
489 -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f };
490
491 std::vector<float> recurrentToInputWeights = { 0.23788475990f, -0.24948765337f, 0.50044941902f, 0.14431896805f,
492 -0.115940228137f, -0.717082679f, -0.17208620906f, 0.17850610617f,
493 -0.16702319684f, -0.11384502053f, -0.309785276245f, -0.3316611672f,
494 0.52380162477f, -0.06839632987f, -0.391478359627f, -0.10756178963f };
495
496 std::vector<float> recurrentToForgetWeights = { 0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f,
497 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f,
498 -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f,
499 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f };
500
501 std::vector<float> recurrentToCellWeights = { 0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f,
502 -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f,
503 -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f,
504 -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f };
505
506 std::vector<float> recurrentToOutputWeights = { -0.079031050201f, 0.041414566286f, -0.583727357285f, 0.1025384515f,
507 -0.172372072937f, 0.09214124082f, 0.178184121827f, -0.2439443916f,
508 0.104485116899f, 0.2600405514f, 0.064414866268f, 0.24141204357f,
509 0.281875759363f, -0.14234502664f, 0.15126448862f, -0.24421440064f };
510
511 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
512
513 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
514
515 std::vector<float> cellBias = { 0., 0., 0., 0. };
516
517 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
518
519 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12);
520 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
521 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
522 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
523 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16);
524 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
525 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
526 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
527 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4);
528 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4);
529 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
530 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
531
532 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
533 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
534 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
535 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
536 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
537 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
538 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
539 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
540 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
541 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
542 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
543 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
544
545 data.m_InputToInputWeights = &inputToInputWeightsTensor;
546 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
547 data.m_InputToCellWeights = &inputToCellWeightsTensor;
548 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
549 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
550 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
551 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
552 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
553 data.m_InputGateBias = &inputGateBiasTensor;
554 data.m_ForgetGateBias = &forgetGateBiasTensor;
555 data.m_CellBias = &cellBiasTensor;
556 data.m_OutputGateBias = &outputGateBiasTensor;
557
558 // Flags to set test configuration
559 data.m_Parameters.m_ClippingThresCell = 10;
560 data.m_Parameters.m_ClippingThresProj = 0;
561 data.m_Parameters.m_ActivationFunc = 4;
562 data.m_Parameters.m_CifgEnabled = false;
563 data.m_Parameters.m_PeepholeEnabled = false;
564 data.m_Parameters.m_ProjectionEnabled = false;
565 data.m_Parameters.m_TimeMajor = true;
566
567 std::unique_ptr<armnn::IWorkload> workload
568 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
569 inputHandle->Allocate();
570 outputStateInHandle->Allocate();
571 cellStateInHandle->Allocate();
572
573 outputStateOutHandle->Allocate();
574 cellStateOutHandle->Allocate();
575 outputHandle->Allocate();
576
577 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
578 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
579 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
580
581 workload->Execute();
582
583 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
584 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
585 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
586
587 return LayerTestResult<T, 3>(actualOutput,
588 outputVector,
589 outputHandle->GetShape(),
590 outputTensorInfo.GetShape());
591 }
592
593 } // anonymous namespace
594
UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatchTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)595 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatchTest(
596 armnn::IWorkloadFactory& workloadFactory,
597 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
598 const armnn::ITensorHandleFactory& tensorHandleFactory)
599 {
600 armnn::TensorInfo inputDesc({1, 2, 2}, armnn::DataType::Float32);
601 std::vector<float> input = {2., 3., 3., 4.};
602
603 armnn::TensorInfo outputDesc({1, 2, 4}, armnn::DataType::Float32);
604 std::vector<float> expectedOutput =
605 {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f,
606 -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f};
607
608 return UnidirectionalSequenceLstmTimeMajorSingleBatchTestImpl<armnn::DataType::Float32>(
609 workloadFactory, memoryManager, tensorHandleFactory,
610 input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape());
611 }
612
UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatchTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)613 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatchTest(
614 armnn::IWorkloadFactory& workloadFactory,
615 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
616 const armnn::ITensorHandleFactory& tensorHandleFactory) {
617 armnn::TensorInfo inputInfo({3, 1, 3}, armnn::DataType::Float32);
618 std::vector<float> input = { 1., 2., 3., 4., 5., 4., 3., 2., 1. };
619
620 armnn::TensorInfo outputInfo({3, 1, 4}, armnn::DataType::Float32);
621 std::vector<float> expectedOutput = { -0.0714901f, -0.162117f, -0.175168f, -0.0232934f,
622 -0.0424661f, -0.231802f, -0.513374f, -0.00680323f,
623 -0.0668735f, 0.204078f, -0.42765f, -0.0312321f };
624 return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>(
625 workloadFactory, memoryManager, tensorHandleFactory,
626 input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
627 }
628
UnidirectionalSequenceLstmLayerFloat32Test(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)629 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32Test(
630 armnn::IWorkloadFactory& workloadFactory,
631 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
632 const armnn::ITensorHandleFactory& tensorHandleFactory) {
633 armnn::TensorInfo inputInfo({3, 2, 3}, armnn::DataType::Float32);
634 std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
635 3., 2., 1., 2., 3., 4.,
636 5., 4., 3., 2., 1., 2. };
637
638 armnn::TensorInfo outputInfo({3, 2, 4}, armnn::DataType::Float32);
639 std::vector<float> expectedOutput = { -0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f,
640 -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f,
641 -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f,
642 -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f,
643 -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f,
644 -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f };
645 return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>(
646 workloadFactory, memoryManager, tensorHandleFactory,
647 input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
648 }
649
UnidirectionalSequenceLstmLayerFloat32TimeMajorTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)650 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32TimeMajorTest(
651 armnn::IWorkloadFactory& workloadFactory,
652 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
653 const armnn::ITensorHandleFactory& tensorHandleFactory) {
654 armnn::TensorInfo inputInfo({2, 3, 3}, armnn::DataType::Float32);
655 std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
656 3., 2., 1., 2., 3., 4.,
657 5., 4., 3., 2., 1., 2. };
658
659 armnn::TensorInfo outputInfo({2, 3, 4}, armnn::DataType::Float32);
660 std::vector<float> expectedOutput = { 0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f,
661 0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f,
662 -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f,
663 0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f,
664 0.11171641f, 0.043119f , 0.0762981f , -0.01228541f,
665 0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f };
666 return UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl<armnn::DataType::Float32>(
667 workloadFactory, memoryManager, tensorHandleFactory,
668 input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
669 }
670
UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)671 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest(
672 armnn::IWorkloadFactory& workloadFactory,
673 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
674 const armnn::ITensorHandleFactory& tensorHandleFactory)
675 {
676 IgnoreUnused(memoryManager);
677 unsigned int batchSize = 2;
678 unsigned int timeSize = 3;
679 unsigned int outputSize = 5;
680 unsigned int inputSize = 4;
681 unsigned numUnits = 6;
682
683 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
684 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
685 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
686 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
687 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
688 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
689
690 const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
691 3., 2., 1., 2., 3., 4.,
692 5., 4., 3., 2., 1., 2.,
693 1., 2., 3., 4., 5., 4.};
694
695 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
696 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
697
698 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
699 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
700 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
701
702 const std::vector<float> expectedOutput = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f,
703 -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f,
704 -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f,
705 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f,
706 -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f,
707 -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f };
708
709 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
710 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
711 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
712 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
713 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
714
715 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
716 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
717 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
718 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
719 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
720
721 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
722 armnn::WorkloadInfo info;
723
724 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
725 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
726 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
727
728 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
729 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
730 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
731
732 armnn::TensorInfo tensorInfo5({outputSize}, armnn::DataType::Float32);
733 armnn::TensorInfo tensorInfo6({numUnits}, armnn::DataType::Float32);
734 armnn::TensorInfo tensorInfo6x4({numUnits, inputSize}, armnn::DataType::Float32);
735 armnn::TensorInfo tensorInfo6x5({numUnits, outputSize}, armnn::DataType::Float32);
736 armnn::TensorInfo tensorInfo5x6({outputSize, numUnits}, armnn::DataType::Float32);
737
738 std::vector<float> inputToInputWeights = { 0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f,
739 -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f,
740 -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f,
741 -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f,
742 -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f,
743 -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f };
744
745 std::vector<float> inputToForgetWeights = { -0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f,
746 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f,
747 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f,
748 -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f,
749 -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f,
750 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f};
751
752 std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,
753 -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,
754 -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,
755 -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,
756 -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,
757 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f };
758
759 std::vector<float> inputToOutputWeights = { -0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f,
760 -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f,
761 -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f,
762 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f,
763 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f,
764 -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f };
765
766 std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f,
767 0.10380666f, 0.053110216f, -0.06928846f };
768
769 std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f,
770 0.23027696f, 0.11098921f, 0.08989442f };
771
772 std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f,
773 0.033463873f, -0.1483596f, 0.029460307f };
774
775 std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f,
776 0.12648113f, 0.027195795f, 0.35373217f };
777
778 std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,
779 -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f,
780 -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f,
781 -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f,
782 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f,
783 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f,
784 -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f,
785 0.14283475f, -0.07390571f };
786
787 std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,
788 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f,
789 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f,
790 -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f,
791 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f,
792 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f,
793 -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f,
794 -0.019443132f, -0.030755889f };
795
796 std::vector<float> recurrentToForgetWeights = { -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f,
797 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f,
798 -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f,
799 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f,
800 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f,
801 -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f,
802 -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f,
803 0.061878487f, -0.04729229f };
804
805 std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f,
806 -0.045984812f,-0.01255415f, -0.0026479573f,
807 -0.08196161f, -0.054914974f, -0.0046604523f,
808 -0.029587349f, -0.044576716f, -0.07480124f,
809 -0.082868785f, 0.023254942f, 0.027502948f,
810 -0.0039728214f, -0.08683098f, -0.08116779f,
811 -0.014675607f, -0.037924774f, -0.023314456f,
812 -0.007401714f, -0.09255757f, 0.029460307f,
813 -0.08829125f, -0.005139627f, -0.08989442f,
814 -0.0555066f, 0.13596267f, 0.025062224f };
815
816 std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f,
817 0.018586371f, -0.037586458f, -0.15312155f };
818
819 std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f,
820 -0.012770197f, 0.041331276f, -0.072311886f };
821
822 std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f,
823 0.002913762f, 0.17764764f, -0.5495371f };
824
825 std::vector<float> projectionWeights = { -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,
826 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,
827 -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,
828 -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,
829 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,
830 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f };
831
832 std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
833
834 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo6x4);
835 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo6x4);
836 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo6x4);
837 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo6x4);
838 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo6x5);
839 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo6x5);
840 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo6x5);
841 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo6x5);
842 armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo6);
843 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo6);
844 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo6);
845 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo6);
846 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo6);
847 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo6);
848 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo6);
849 armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo5x6);
850 armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo5);
851
852 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
853 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
854 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
855 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
856 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
857 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
858 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
859 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
860 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
861 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
862 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
863 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
864 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
865 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
866 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
867 AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
868 AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
869
870 data.m_InputToInputWeights = &inputToInputWeightsTensor;
871 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
872 data.m_InputToCellWeights = &inputToCellWeightsTensor;
873 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
874 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
875 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
876 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
877 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
878 data.m_CellToInputWeights = &cellToInputWeightsTensor;
879 data.m_InputGateBias = &inputGateBiasTensor;
880 data.m_ForgetGateBias = &forgetGateBiasTensor;
881 data.m_CellBias = &cellBiasTensor;
882 data.m_OutputGateBias = &outputGateBiasTensor;
883 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
884 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
885 data.m_ProjectionWeights = &projectionWeightsTensor;
886 data.m_ProjectionBias = &projectionBiasTensor;
887
888 // Flags to set test configuration
889 data.m_Parameters.m_ActivationFunc = 4;
890 data.m_Parameters.m_CifgEnabled = false;
891 data.m_Parameters.m_PeepholeEnabled = true;
892 data.m_Parameters.m_ProjectionEnabled = true;
893 data.m_Parameters.m_LayerNormEnabled = false;
894 data.m_Parameters.m_TimeMajor = false;
895 data.m_Parameters.m_ClippingThresCell = 10.0f;
896
897
898 std::unique_ptr<armnn::IWorkload> workload
899 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
900 inputHandle->Allocate();
901 outputStateInHandle->Allocate();
902 cellStateInHandle->Allocate();
903
904 outputStateOutHandle->Allocate();
905 cellStateOutHandle->Allocate();
906 outputHandle->Allocate();
907
908 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
909 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
910 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
911
912 workload->Execute();
913
914 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
915 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
916 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
917
918 return LayerTestResult<float, 3>(actualOutput,
919 expectedOutput,
920 outputHandle->GetShape(),
921 outputTensorInfo.GetShape());
922 }
923
UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)924 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest(
925 armnn::IWorkloadFactory& workloadFactory,
926 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
927 const armnn::ITensorHandleFactory& tensorHandleFactory)
928 {
929 IgnoreUnused(memoryManager);
930 unsigned int batchSize = 3;
931 unsigned int timeSize = 2;
932 unsigned int outputSize = 4;
933 unsigned int inputSize = 3;
934 unsigned numUnits = 5;
935
936 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
937 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
938 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
939 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
940 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
941 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
942
943 const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
944 3., 2., 1., 2., 3., 4.,
945 5., 4., 3., 2., 1., 2. };
946
947 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
948 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
949
950 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
951 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
952 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
953
954 const std::vector<float> expectedOutput = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f,
955 0.11458f, 0.0407109f, 0.300327f, 0.174301f,
956 0.0864761f, 0.0362912f, 0.178635f, 0.115689f,
957 0.108008f, 0.0386623f, 0.273471f, 0.167115f,
958 0.0859545f, 0.0331481f, 0.186051f, 0.11888f,
959 0.106649f, 0.0276847f, 0.229863f, 0.166958f };
960
961 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
962 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
963 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
964 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
965 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
966
967 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
968 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
969 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
970 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
971 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
972
973 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
974 armnn::WorkloadInfo info;
975
976 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
977 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
978 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
979
980 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
981 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
982 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
983
984 armnn::TensorInfo tensorInfo4({outputSize}, armnn::DataType::Float32);
985 armnn::TensorInfo tensorInfo5({numUnits}, armnn::DataType::Float32);
986 armnn::TensorInfo tensorInfo5x3({numUnits, inputSize}, armnn::DataType::Float32);
987 armnn::TensorInfo tensorInfo5x4({numUnits, outputSize}, armnn::DataType::Float32);
988 armnn::TensorInfo tensorInfo4x5({outputSize, numUnits}, armnn::DataType::Float32);
989
990 std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,
991 -0.117484632f, 0.3298470976f, -0.1179017122f,
992 0.214305695f, 0.42135173085f, 0.003878414626f,
993 -0.348303917f, -0.1881275477f, 0.0343011027f,
994 -0.38837709614f, -0.05636804124f, 0.4259087456f};
995
996 std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
997 -0.3810434485f, 0.268383264f, -0.009807467424f,
998 -0.3522925403f, -0.24275735512f, -0.28344226125f,
999 0.13512269116f, -0.4932442977f, -0.10039821991f,
1000 0.2726137042f, 0.09216640889f, -0.06551410215f};
1001
1002 std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
1003 0.386399507f, -0.259465157985f, -0.16545993089f,
1004 -0.4230232555f, 0.341664791103f, -0.18127849691f,
1005 -0.2277662414f, -0.55275535589f, 0.34184026718f,
1006 0.3954237699f, -0.19407111404f, 0.30412107706f};
1007
1008 std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
1009 0.53969591851f, 0.23393625035f, -0.27140527306f,
1010 0.50009280443f, 0.07511717046f, 0.3998299249f,
1011 -0.51717478049f, 0.1889653282f, -0.367323637f,
1012 -0.12584099173f, -0.12319286912f, 0.2407919466f};
1013
1014 std::vector<float> inputGateBias{ 0.03f, 0.15f, 0.22f, 0.38f, 0.05f };
1015 std::vector<float> forgetGateBias{ 0.1f, -0.3f, -0.2f, 0.1f, 0.4f };
1016 std::vector<float> cellBias{ -0.05f, 0.72f, 0.25f, 0.08f, 0.1f };
1017 std::vector<float> outputGateBias{ 0.05f, -0.01f, 0.2f, 0.1f, -0.2f };
1018
1019 std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,
1020 -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,
1021 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,
1022 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f,
1023 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f };
1024
1025 std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
1026 -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
1027 -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
1028 -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f,
1029 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f };
1030
1031 std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
1032 -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
1033 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
1034 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f,
1035 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f };
1036
1037 std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
1038 -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
1039 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
1040 -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f,
1041 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f };
1042
1043 std::vector<float> cellToInputWeights { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f };
1044 std::vector<float> cellToForgetWeights { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f };
1045 std::vector<float> cellToOutputWeights { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f };
1046
1047 std::vector<float> projectionWeights{ -0.1f, 0.2f, 0.01f, -0.2f,
1048 0.1f, 0.5f, 0.3f, 0.08f,
1049 0.07f, 0.2f, -0.4f, 0.2f,
1050 0.5f, -0.4f, 0.3f, -0.2f,
1051 0.3f, 0.08f, -0.07f, 0.2f};
1052
1053 std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
1054
1055 std::vector<float> inputLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.8f };
1056 std::vector<float> forgetLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.2f };
1057 std::vector<float> cellLayerNormWeights{ 0.7f, 0.2f, 0.3f, 0.8f, 0.5f };
1058 std::vector<float> outputLayerNormWeights{ 0.6f, 0.2f, 0.2f, 0.5f, 0.1f };
1059
1060 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo5x3);
1061 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo5x3);
1062 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo5x3);
1063 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo5x3);
1064 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo5x4);
1065 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo5x4);
1066 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo5x4);
1067 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo5x4);
1068 armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo5);
1069 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo5);
1070 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo5);
1071 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo5);
1072 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo5);
1073 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo5);
1074 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo5);
1075 armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo4x5);
1076 armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo4);
1077
1078 armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfo5);
1079 armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfo5);
1080 armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfo5);
1081 armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfo5);
1082
1083 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
1084 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1085 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1086 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1087 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
1088 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1089 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1090 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1091 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
1092 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
1093 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1094 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1095 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1096 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
1097 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
1098 AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
1099 AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
1100
1101 AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data());
1102 AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
1103 AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
1104 AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());
1105
1106 data.m_InputToInputWeights = &inputToInputWeightsTensor;
1107 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1108 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1109 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1110 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
1111 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1112 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1113 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1114 data.m_CellToInputWeights = &cellToInputWeightsTensor;
1115 data.m_InputGateBias = &inputGateBiasTensor;
1116 data.m_ForgetGateBias = &forgetGateBiasTensor;
1117 data.m_CellBias = &cellBiasTensor;
1118 data.m_OutputGateBias = &outputGateBiasTensor;
1119 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
1120 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
1121 data.m_ProjectionWeights = &projectionWeightsTensor;
1122 data.m_ProjectionBias = &projectionBiasTensor;
1123
1124 data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;
1125 data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor;
1126 data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor;
1127 data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor;
1128
1129 // Flags to set test configuration
1130 data.m_Parameters.m_ActivationFunc = 4;
1131 data.m_Parameters.m_CifgEnabled = false;
1132 data.m_Parameters.m_PeepholeEnabled = true;
1133 data.m_Parameters.m_ProjectionEnabled = true;
1134 data.m_Parameters.m_LayerNormEnabled = true;
1135 data.m_Parameters.m_TimeMajor = false;
1136 data.m_Parameters.m_ClippingThresCell = 10.0f;
1137
1138 std::unique_ptr<armnn::IWorkload> workload
1139 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
1140 inputHandle->Allocate();
1141 outputStateInHandle->Allocate();
1142 cellStateInHandle->Allocate();
1143
1144 outputStateOutHandle->Allocate();
1145 cellStateOutHandle->Allocate();
1146 outputHandle->Allocate();
1147
1148 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1149 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1150 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1151
1152 workload->Execute();
1153
1154 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
1155 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
1156 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1157
1158 return LayerTestResult<float, 3>(actualOutput,
1159 expectedOutput,
1160 outputHandle->GetShape(),
1161 outputTensorInfo.GetShape());
1162 }
1163
UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)1164 LayerTestResult<float, 3> UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(
1165 armnn::IWorkloadFactory& workloadFactory,
1166 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1167 const armnn::ITensorHandleFactory& tensorHandleFactory)
1168 {
1169 IgnoreUnused(memoryManager);
1170 unsigned int batchSize = 3;
1171 unsigned int timeSize = 2;
1172 unsigned int inputSize = 3;
1173 unsigned int outputSize = 4;
1174 unsigned numUnits = outputSize;
1175
1176 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
1177 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
1178 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
1179 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1180 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1181 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1182
1183 std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
1184 3., 2., 1., 2., 3., 4.,
1185 5., 4., 3., 2., 1., 2. };
1186
1187 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1188 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1189
1190 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1191 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1192 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1193
1194 std::vector<float> outputVector = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f,
1195 -0.0300169f, -0.195717f, -0.528679f, -0.0818106f,
1196 -0.0332748f, 0.155429f, -0.353966f, -0.0801505f,
1197 -0.032312f, -0.0407911f, -0.435053f, -0.0932317f,
1198 -0.0108233f, 0.165584f, -0.640424f, -0.0447535f,
1199 -0.031675f, 0.125987f, -0.526695f, -0.110093f };
1200
1201 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1202 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1203 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1204 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1205 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1206
1207 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1208 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
1209 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1210 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
1211 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1212
1213 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1214 armnn::WorkloadInfo info;
1215
1216 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1217 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1218 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1219
1220 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1221 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1222 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1223
1224 armnn::TensorInfo tensorInfo4({numUnits}, armnn::DataType::Float32);
1225 armnn::TensorInfo tensorInfo12({numUnits, 3}, armnn::DataType::Float32);
1226 armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::DataType::Float32);
1227
1228 std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
1229 -0.3810434485f, 0.268383264f, -0.009807467424f,
1230 -0.3522925403f, -0.24275735512f, -0.28344226125f,
1231 0.13512269116f, -0.4932442977f, -0.10039821991f };
1232
1233 std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
1234 0.386399507f, -0.259465157985f, -0.16545993089f,
1235 -0.4230232555f, 0.341664791103f, -0.18127849691f,
1236 -0.2277662414f, -0.55275535589f, 0.34184026718f };
1237
1238 std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
1239 0.53969591851f, 0.23393625035f, -0.27140527306f,
1240 0.50009280443f, 0.07511717046f, 0.3998299249f,
1241 -0.51717478049f, 0.1889653282f, -0.367323637f };
1242
1243 std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
1244 -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
1245 -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
1246 -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };
1247
1248 std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
1249 -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
1250 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
1251 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };
1252
1253 std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
1254 -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
1255 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
1256 -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };
1257
1258 std::vector<float> cellToForgetWeights{ 0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f };
1259
1260 std::vector<float> cellToOutputWeights{ -0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f };
1261
1262 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
1263
1264 std::vector<float> cellBias = { 0., 0., 0., 0. };
1265
1266 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
1267
1268 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
1269 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
1270 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
1271 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
1272 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
1273 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
1274 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo4);
1275 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo4);
1276 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4);
1277 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
1278 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
1279
1280 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1281 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1282 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1283 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1284 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1285 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1286 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
1287 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
1288 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1289 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1290 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1291
1292 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1293 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1294 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1295 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1296 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1297 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1298 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
1299 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
1300 data.m_ForgetGateBias = &forgetGateBiasTensor;
1301 data.m_CellBias = &cellBiasTensor;
1302 data.m_OutputGateBias = &outputGateBiasTensor;
1303
1304 // Flags to set test configuration
1305 data.m_Parameters.m_ClippingThresCell = 10;
1306 data.m_Parameters.m_ClippingThresProj = 0;
1307 data.m_Parameters.m_ActivationFunc = 4;
1308 data.m_Parameters.m_CifgEnabled = true;
1309 data.m_Parameters.m_PeepholeEnabled = true;
1310 data.m_Parameters.m_ProjectionEnabled = false;
1311 data.m_Parameters.m_TimeMajor = false;
1312
1313 std::unique_ptr<armnn::IWorkload> workload
1314 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
1315 inputHandle->Allocate();
1316 outputStateInHandle->Allocate();
1317 cellStateInHandle->Allocate();
1318
1319 outputStateOutHandle->Allocate();
1320 cellStateOutHandle->Allocate();
1321 outputHandle->Allocate();
1322
1323 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1324 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1325 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1326
1327 workload->Execute();
1328
1329 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
1330 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
1331 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1332
1333 return LayerTestResult<float, 3>(actualOutput,
1334 outputVector,
1335 outputHandle->GetShape(),
1336 outputTensorInfo.GetShape());
1337 }
1338
UnidirectionalSequenceLstmLayerInt8Test(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)1339 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8Test(
1340 armnn::IWorkloadFactory& workloadFactory,
1341 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1342 const armnn::ITensorHandleFactory& tensorHandleFactory)
1343 {
1344 IgnoreUnused(memoryManager);
1345 unsigned int batchSize = 3;
1346 unsigned int timeSize = 2;
1347 unsigned int inputSize = 3;
1348 unsigned int outputSize = 4;
1349 unsigned numUnits = outputSize;
1350
1351 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
1352 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
1353 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
1354 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1355 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1356 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1357
1358 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1359 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1360 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1361
1362 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1363 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1364
1365 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1366 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1367 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1368
1369 const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120569f, -0.0116868f,
1370 -0.0350714f, -0.0343202f, -0.047504f, -0.0569789f,
1371 -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f,
1372 -0.0294759f, -0.0129935f, -0.0444175f, -0.0444354f,
1373 -0.0280855f, 0.00545101f, -0.051422f, -0.0463838f,
1374 -0.0310702f, 0.00915739f, -0.0625207f, -0.0482648f };
1375
1376 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1377 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1378 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1379 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1380 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1381
1382 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1383 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
1384 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1385 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
1386 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1387
1388
1389 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1390 armnn::WorkloadInfo info;
1391
1392 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1393 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1394 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1395
1396 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1397 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1398 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1399
1400 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
1401 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1402 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1403
1404 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 };
1405 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1406 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1407 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1408
1409 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 };
1410 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1411 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1412 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1413
1414 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
1415 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
1416 std::vector<float> cellBias = { 0., 0., 0., 0. };
1417 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
1418
1419 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput);
1420 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
1421 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
1422 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
1423 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput);
1424 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
1425 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
1426 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
1427 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp);
1428 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
1429 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
1430 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
1431
1432 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
1433 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1434 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1435 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1436 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
1437 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1438 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1439 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1440 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
1441 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1442 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1443 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1444
1445 data.m_InputToInputWeights = &inputToInputWeightsTensor;
1446 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1447 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1448 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1449 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
1450 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1451 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1452 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1453 data.m_InputGateBias = &inputGateBiasTensor;
1454 data.m_ForgetGateBias = &forgetGateBiasTensor;
1455 data.m_CellBias = &cellBiasTensor;
1456 data.m_OutputGateBias = &outputGateBiasTensor;
1457
1458 // Flags to set test configuration
1459 data.m_Parameters.m_ClippingThresCell = 10;
1460 data.m_Parameters.m_ClippingThresProj = 0;
1461 data.m_Parameters.m_ActivationFunc = 4;
1462 data.m_Parameters.m_CifgEnabled = false;
1463 data.m_Parameters.m_PeepholeEnabled = false;
1464 data.m_Parameters.m_ProjectionEnabled = false;
1465 data.m_Parameters.m_TimeMajor = false;
1466
1467 std::unique_ptr<armnn::IWorkload> workload
1468 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
1469 inputHandle->Allocate();
1470 outputStateInHandle->Allocate();
1471 cellStateInHandle->Allocate();
1472
1473 outputStateOutHandle->Allocate();
1474 cellStateOutHandle->Allocate();
1475 outputHandle->Allocate();
1476
1477 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1478 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1479 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1480
1481 workload->Execute();
1482
1483 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
1484 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
1485 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1486
1487 return LayerTestResult<float, 3>(actualOutput,
1488 outputVector,
1489 outputHandle->GetShape(),
1490 outputTensorInfo.GetShape());
1491 }
1492
UnidirectionalSequenceLstmLayerInt8TimeMajorTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)1493 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8TimeMajorTest(
1494 armnn::IWorkloadFactory& workloadFactory,
1495 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1496 const armnn::ITensorHandleFactory& tensorHandleFactory)
1497 {
1498 IgnoreUnused(memoryManager);
1499 unsigned int batchSize = 3;
1500 unsigned int timeSize = 2;
1501 unsigned int inputSize = 3;
1502 unsigned int outputSize = 4;
1503 unsigned numUnits = outputSize;
1504
1505 armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, armnn::DataType::Float32);
1506 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
1507 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
1508 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1509 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1510 armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, armnn::DataType::Float32);
1511
1512 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1513 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1514 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1515
1516 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1517 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1518
1519 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1520 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1521 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1522
1523 const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120122f, -0.0116868f,
1524 -0.0261295f, -0.0188487f, -0.0345463f, -0.049733f,
1525 -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f,
1526 -0.0291863f, -0.0369402f, -0.0354071f, -0.0296529f,
1527 -0.0419539f, -0.00617731f, -0.0814796f, -0.0804005f,
1528 -0.0244737f, 0.0119905f, -0.0457527f, -0.0331862f };
1529 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1530 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1531 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1532 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1533 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1534
1535 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1536 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
1537 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1538 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
1539 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1540
1541
1542 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1543 armnn::WorkloadInfo info;
1544
1545 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1546 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1547 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1548
1549 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1550 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1551 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1552
1553 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
1554 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1555 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1556
1557 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 };
1558 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1559 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1560 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1561
1562 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 };
1563 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1564 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1565 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1566
1567
1568 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
1569 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
1570 std::vector<float> cellBias = { 0., 0., 0., 0. };
1571 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
1572
1573 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput);
1574 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
1575 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
1576 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
1577 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput);
1578 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
1579 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
1580 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
1581 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp);
1582 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
1583 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
1584 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
1585
1586 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
1587 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1588 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1589 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1590 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
1591 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1592 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1593 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1594 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
1595 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1596 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1597 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1598
1599 data.m_InputToInputWeights = &inputToInputWeightsTensor;
1600 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1601 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1602 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1603 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
1604 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1605 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1606 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1607 data.m_InputGateBias = &inputGateBiasTensor;
1608 data.m_ForgetGateBias = &forgetGateBiasTensor;
1609 data.m_CellBias = &cellBiasTensor;
1610 data.m_OutputGateBias = &outputGateBiasTensor;
1611
1612 // Flags to set test configuration
1613 data.m_Parameters.m_ClippingThresCell = 10;
1614 data.m_Parameters.m_ClippingThresProj = 0;
1615 data.m_Parameters.m_ActivationFunc = 4;
1616 data.m_Parameters.m_CifgEnabled = false;
1617 data.m_Parameters.m_PeepholeEnabled = false;
1618 data.m_Parameters.m_ProjectionEnabled = false;
1619 data.m_Parameters.m_TimeMajor = true;
1620
1621 std::unique_ptr<armnn::IWorkload> workload
1622 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
1623 inputHandle->Allocate();
1624 outputStateInHandle->Allocate();
1625 cellStateInHandle->Allocate();
1626
1627 outputStateOutHandle->Allocate();
1628 cellStateOutHandle->Allocate();
1629 outputHandle->Allocate();
1630
1631 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1632 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1633 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1634
1635 workload->Execute();
1636
1637 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
1638 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
1639 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1640
1641 return LayerTestResult<float, 3>(actualOutput,
1642 outputVector,
1643 outputHandle->GetShape(),
1644 outputTensorInfo.GetShape());
1645 }
1646
UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)1647 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionTest(
1648 armnn::IWorkloadFactory& workloadFactory,
1649 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1650 const armnn::ITensorHandleFactory& tensorHandleFactory)
1651 {
1652 IgnoreUnused(memoryManager);
1653 unsigned int batchSize = 3;
1654 unsigned int timeSize = 2;
1655 unsigned int outputSize = 4;
1656 unsigned int inputSize = 3;
1657 unsigned numUnits = 4;
1658
1659 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
1660 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
1661 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
1662 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1663 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1664 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1665
1666 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1667 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1668 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1669
1670 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1671 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1672
1673 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1674 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1675 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1676
1677 const std::vector<float> expectedOutput = { 0.612103f, 1.56788f, 0.31966f, 1.42956f,
1678 0.909718f, 3.07916f, -0.560586f, 3.8907f,
1679 0.753671f, 1.77485f, 0.365122f, 1.60077f,
1680 0.812644f, 2.79092f, -0.605396f, 3.61742f,
1681 0.791857f, 1.64353f, 0.316588f, 1.55192f,
1682 0.807265f, 2.47012f, -0.539598f, 3.25654f };
1683
1684 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1685 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1686 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1687 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1688 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1689
1690 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1691 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
1692 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1693 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
1694 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1695
1696 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1697 armnn::WorkloadInfo info;
1698
1699 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1700 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1701 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1702
1703 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1704 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1705 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1706
1707 armnn::TensorInfo tensorInfoOut({outputSize}, armnn::DataType::Float32);
1708 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
1709 armnn::TensorInfo tensorInfoNum({numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
1710 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1711 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1712 armnn::TensorInfo tensorInfoOutNum({outputSize, numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
1713
1714 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 };
1715 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1716 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1717 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1718
1719 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 };
1720 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1721 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1722 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1723
1724 std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f};
1725 std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.3032477f, 0.23027696f};
1726 std::vector<float> cellBias = { -0.124379363f, 0.55531194f, 0.23377132f, 0.033463873f };
1727 std::vector<float> outputGateBias = { 0.046159424f, -0.12809046f, 0.03563469f, 0.12648113f };
1728
1729 std::vector<int8_t> cellToInputWeights = { 5, 10, 25, 15 };
1730 std::vector<int8_t> cellToForgetWeights = { -5, 15, 25, 3 };
1731 std::vector<int8_t> cellToOutputWeights = { 10, -10, -5, 50 };
1732
1733 std::vector<int8_t> projectionWeights = { -25, 51, 3, -5, 25, 127, 77, 20, 18, 51, -10, 51, -25, 88, 77, -13 };
1734
1735 std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
1736
1737 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput);
1738 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
1739 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
1740 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
1741 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
1742 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput);
1743 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
1744 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
1745 armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfoNum);
1746 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp);
1747 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
1748 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
1749 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
1750 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNum);
1751 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNum);
1752 armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfoOutNum);
1753 armnn::ScopedTensorHandle projectionBiasTensor(tensorInfoOut);
1754
1755 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
1756 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1757 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1758 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1759 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
1760 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1761 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1762 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1763 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
1764 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
1765 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1766 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1767 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1768 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
1769 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
1770 AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
1771 AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
1772
1773 data.m_InputToInputWeights = &inputToInputWeightsTensor;
1774 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1775 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1776 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1777 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
1778 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1779 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1780 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1781 data.m_CellToInputWeights = &cellToInputWeightsTensor;
1782 data.m_InputGateBias = &inputGateBiasTensor;
1783 data.m_ForgetGateBias = &forgetGateBiasTensor;
1784 data.m_CellBias = &cellBiasTensor;
1785 data.m_OutputGateBias = &outputGateBiasTensor;
1786 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
1787 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
1788 data.m_ProjectionWeights = &projectionWeightsTensor;
1789 data.m_ProjectionBias = &projectionBiasTensor;
1790
1791 // Flags to set test configuration
1792 data.m_Parameters.m_ActivationFunc = 4;
1793 data.m_Parameters.m_CifgEnabled = false;
1794 data.m_Parameters.m_PeepholeEnabled = true;
1795 data.m_Parameters.m_ProjectionEnabled = true;
1796 data.m_Parameters.m_LayerNormEnabled = false;
1797 data.m_Parameters.m_TimeMajor = false;
1798 data.m_Parameters.m_ClippingThresCell = 10.0f;
1799
1800
1801 std::unique_ptr<armnn::IWorkload> workload
1802 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
1803 inputHandle->Allocate();
1804 outputStateInHandle->Allocate();
1805 cellStateInHandle->Allocate();
1806
1807 outputStateOutHandle->Allocate();
1808 cellStateOutHandle->Allocate();
1809 outputHandle->Allocate();
1810
1811 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1812 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1813 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1814
1815 workload->Execute();
1816
1817 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
1818 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
1819 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1820
1821 return LayerTestResult<float, 3>(actualOutput,
1822 expectedOutput,
1823 outputHandle->GetShape(),
1824 outputTensorInfo.GetShape());
1825 }
1826
UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)1827 LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest(
1828 armnn::IWorkloadFactory& workloadFactory,
1829 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1830 const armnn::ITensorHandleFactory& tensorHandleFactory)
1831 {
1832 IgnoreUnused(memoryManager);
1833 unsigned int batchSize = 3;
1834 unsigned int timeSize = 2;
1835 unsigned int outputSize = 4;
1836 unsigned int inputSize = 3;
1837 unsigned numUnits = 5;
1838
1839 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
1840 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
1841 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
1842 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1843 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1844 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1845
1846 const std::vector<float> inputVector = { 1., 8., 3., 4., 5., 4.,
1847 3., 2., 1., 2., 3., 4.,
1848 5., 4., 3., 2., 1., 2. };
1849
1850 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1851 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1852
1853 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1854 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1855 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1856
1857 const std::vector<float> expectedOutput = { 0.0471276f, 0.0168155f, 0.0789885f, 0.16550f,
1858 0.0643133f, -0.0400722f, 0.100593f, 0.197722f,
1859 0.0465562f, -0.0600682f, 0.0622087f, 0.115053f,
1860 0.056287f, -0.0566218f, 0.0856832f, 0.148484f,
1861 0.0457859f, -0.0588112f, 0.0623636f, 0.114333f,
1862 0.0509271f, -0.0754262f, 0.058600f, 0.0801288f };
1863
1864 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1865 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1866 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1867 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1868 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1869
1870 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1871 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
1872 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1873 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
1874 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1875
1876 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1877 armnn::WorkloadInfo info;
1878
1879 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1880 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1881 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1882
1883 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1884 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1885 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1886
1887 armnn::TensorInfo tensorInfoOut({outputSize}, armnn::DataType::Float32);
1888 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
1889 armnn::TensorInfo tensorInfoNum({numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
1890 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1891 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1892 armnn::TensorInfo tensorInfoOutNum({outputSize, numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
1893
1894 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3, 2, 2, -4 };
1895 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1, -3, -2, -4 };
1896 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3, 2, 5, -4 };
1897 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4, -4, -1, -1 };
1898
1899 std::vector<float> inputGateBias = { 0.03f, 0.15f, 0.22f, 0.38f, 0.05f };
1900 std::vector<float> forgetGateBias = { 0.1f, -0.3f, -0.2f, 0.1f, 0.4f };
1901 std::vector<float> cellBias = { -0.05f, 0.72f, 0.25f, 0.08f, 0.1f };
1902 std::vector<float> outputGateBias = { 0.05f, -0.01f, 0.2f, 0.1f, -0.2f };
1903
1904 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3,
1905 5, -1, 1, 3, -1, -1, -1, 4, 2, 3 };
1906
1907 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3,
1908 5, -1, 1, 3, -2, -1, -1, 2, 2, 1 };
1909
1910 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2,
1911 1, 2, 3, -2, 3, -3, -1, -5, 1, 3 };
1912
1913 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3,
1914 -4, -1, -1, -1, 2, -1, 5, 1, -3, -4 };
1915
1916 std::vector<int8_t> cellToInputWeights = { 5, 3, 8, -5, 2 };
1917 std::vector<int8_t> cellToForgetWeights = { -2, -7, 5, -3, 4 };
1918 std::vector<int8_t> cellToOutputWeights = { 9, -10 , -5, 5, 1 };
1919
1920 std::vector<int8_t> projectionWeights = { -1, 2, 1, -2, 1, 5, 3, 8, 7, 2,
1921 -4, 2, 5, -4, 3, -2, 3, 8, -7, 2 };
1922
1923 std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
1924
1925 std::vector<float> inputLayerNormWeights = { 0.1f, 0.2f, -0.3f, -0.1f, 0.5f };
1926 std::vector<float> forgetLayerNormWeights = { -0.1f, 0.2f, 0.3f, 0.5f, 0.2f };
1927 std::vector<float> cellLayerNormWeights = { 0.5f, 0.2f, 0.3f, 0.4f, -0.5f };
1928 std::vector<float> outputLayerNormWeights = { 0.6f, -0.2f, -0.2f, 0.5f, 0.1f };
1929
1930 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput);
1931 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
1932 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
1933 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
1934 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
1935 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput);
1936 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
1937 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
1938 armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfoNum);
1939 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp);
1940 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
1941 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
1942 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
1943 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNum);
1944 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNum);
1945 armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfoOutNum);
1946 armnn::ScopedTensorHandle projectionBiasTensor(tensorInfoOut);
1947
1948 armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfoNumFp);
1949 armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfoNumFp);
1950 armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfoNumFp);
1951 armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfoNumFp);
1952
1953 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
1954 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1955 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1956 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1957 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
1958 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1959 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1960 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1961 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
1962 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
1963 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1964 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1965 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1966 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
1967 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
1968 AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
1969 AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
1970
1971 AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data());
1972 AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
1973 AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
1974 AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());
1975
1976 data.m_InputToInputWeights = &inputToInputWeightsTensor;
1977 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1978 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1979 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1980 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
1981 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1982 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1983 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1984 data.m_CellToInputWeights = &cellToInputWeightsTensor;
1985 data.m_InputGateBias = &inputGateBiasTensor;
1986 data.m_ForgetGateBias = &forgetGateBiasTensor;
1987 data.m_CellBias = &cellBiasTensor;
1988 data.m_OutputGateBias = &outputGateBiasTensor;
1989 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
1990 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
1991 data.m_ProjectionWeights = &projectionWeightsTensor;
1992 data.m_ProjectionBias = &projectionBiasTensor;
1993
1994 data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;
1995 data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor;
1996 data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor;
1997 data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor;
1998
1999 // Flags to set test configuration
2000 data.m_Parameters.m_ActivationFunc = 4;
2001 data.m_Parameters.m_CifgEnabled = false;
2002 data.m_Parameters.m_PeepholeEnabled = true;
2003 data.m_Parameters.m_ProjectionEnabled = true;
2004 data.m_Parameters.m_LayerNormEnabled = true;
2005 data.m_Parameters.m_TimeMajor = false;
2006 data.m_Parameters.m_ClippingThresCell = 10.0f;
2007
2008 std::unique_ptr<armnn::IWorkload> workload
2009 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
2010 inputHandle->Allocate();
2011 outputStateInHandle->Allocate();
2012 cellStateInHandle->Allocate();
2013
2014 outputStateOutHandle->Allocate();
2015 cellStateOutHandle->Allocate();
2016 outputHandle->Allocate();
2017
2018 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
2019 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
2020 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
2021
2022 workload->Execute();
2023
2024 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
2025 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
2026 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
2027
2028 return LayerTestResult<float, 3>(actualOutput,
2029 expectedOutput,
2030 outputHandle->GetShape(),
2031 outputTensorInfo.GetShape());
2032 }
2033
UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest(armnn::IWorkloadFactory & workloadFactory,const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager,const armnn::ITensorHandleFactory & tensorHandleFactory)2034 LayerTestResult<float, 3> UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest(
2035 armnn::IWorkloadFactory& workloadFactory,
2036 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
2037 const armnn::ITensorHandleFactory& tensorHandleFactory)
2038 {
2039 IgnoreUnused(memoryManager);
2040 unsigned int batchSize = 3;
2041 unsigned int timeSize = 2;
2042 unsigned int inputSize = 3;
2043 unsigned int outputSize = 4;
2044 unsigned numUnits = outputSize;
2045
2046 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
2047 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
2048 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
2049 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
2050 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
2051 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
2052
2053 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
2054 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
2055 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
2056
2057 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
2058 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
2059
2060 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
2061 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
2062 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
2063
2064 const std::vector<float> outputVector = { -0.0072104f, -0.00991171f, -0.00650478f, -0.00713055f,
2065 -0.0191782f, -0.0161269f, -0.0233683f, -0.054299f,
2066 -0.00783725f, 0.00635271f, -0.0126718f, -0.022613f,
2067 -0.0161351f, -0.00775868f, -0.021054f, -0.0339778f,
2068 -0.0146392f, 0.00330261f, -0.0258733f, -0.0407797f,
2069 -0.0174297f, 0.0050105f, -0.0266275f, -0.0362564f };
2070
2071 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
2072 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
2073 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
2074 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
2075 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
2076
2077 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
2078 tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
2079 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
2080 tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
2081 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
2082
2083 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
2084 armnn::WorkloadInfo info;
2085
2086 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
2087 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
2088 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
2089
2090 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
2091 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
2092 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
2093
2094 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
2095 armnn::TensorInfo tensorInfoNum({numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
2096 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
2097 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
2098
2099 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
2100 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
2101 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
2102
2103 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
2104 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
2105 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
2106
2107 std::vector<int8_t> cellToForgetWeights = { 47, -52, -24, 31 };
2108 std::vector<int8_t> cellToOutputWeights = { -17, 82, 85, -77 };
2109
2110 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
2111 std::vector<float> cellBias = { 0., 0., 0., 0. };
2112 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
2113
2114 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
2115 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
2116 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
2117 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
2118 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
2119 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
2120 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNum);
2121 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNum);
2122 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
2123 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
2124 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
2125
2126 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
2127 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
2128 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
2129 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
2130 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
2131 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
2132 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
2133 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
2134 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
2135 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
2136 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
2137
2138 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
2139 data.m_InputToCellWeights = &inputToCellWeightsTensor;
2140 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
2141 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
2142 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
2143 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
2144 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
2145 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
2146 data.m_ForgetGateBias = &forgetGateBiasTensor;
2147 data.m_CellBias = &cellBiasTensor;
2148 data.m_OutputGateBias = &outputGateBiasTensor;
2149
2150 // Flags to set test configuration
2151 data.m_Parameters.m_ClippingThresCell = 10;
2152 data.m_Parameters.m_ClippingThresProj = 0;
2153 data.m_Parameters.m_ActivationFunc = 4;
2154 data.m_Parameters.m_CifgEnabled = true;
2155 data.m_Parameters.m_PeepholeEnabled = true;
2156 data.m_Parameters.m_ProjectionEnabled = false;
2157 data.m_Parameters.m_TimeMajor = false;
2158
2159 std::unique_ptr<armnn::IWorkload> workload
2160 = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info);
2161 inputHandle->Allocate();
2162 outputStateInHandle->Allocate();
2163 cellStateInHandle->Allocate();
2164
2165 outputStateOutHandle->Allocate();
2166 cellStateOutHandle->Allocate();
2167 outputHandle->Allocate();
2168
2169 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
2170 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
2171 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
2172
2173 workload->Execute();
2174
2175 CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
2176 CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
2177 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
2178
2179 return LayerTestResult<float, 3>(actualOutput,
2180 outputVector,
2181 outputHandle->GetShape(),
2182 outputTensorInfo.GetShape());
2183 }
2184