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review.mlplatform.org / ml / armnn / bc14881a76699dd942e94265116da68a6466455e / . / src / backends / backendsCommon / test / layerTests / UnidirectionalSequenceLstmTestImpl.cpp
blob: d17dceb3f614c4c3d2033e611e3e711e088dff8e [file] [log] [blame]
Narumol Prangnawarate5339e72021-07-28 17:33:28 +0100[diff] [blame]1//
2// Copyright © 2021 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 <backendsCommon/TensorHandle.hpp>
11
12#include <backendsCommon/test/TensorCopyUtils.hpp>
13#include <backendsCommon/test/WorkloadTestUtils.hpp>
14
15#include <ResolveType.hpp>
16
17namespace {
18
19template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
20LayerTestResult<T, 3> UnidirectionalSequenceLstmLayerFloat32TestImpl(
21 armnn::IWorkloadFactory& workloadFactory,
22 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
23 const armnn::ITensorHandleFactory& tensorHandleFactory,
24 const std::vector<T>& input,
25 const std::vector<T>& outputExpected,
26 const armnn::TensorShape& inputShape,
27 const armnn::TensorShape& outputExpectedShape,
28 float qScale = 0.0f,
29 int32_t qOffset = 0,
30 armnn::DataType constantDataType = armnn::DataType::Float32) {
31 IgnoreUnused(memoryManager);
32 unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[0]);
33 unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[1]);
34 unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]);
35 unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]);
36 unsigned numUnits = outputSize;
37
38 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, ArmnnType, qScale, qOffset);
39 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
40 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
41
42 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
43
44 std::vector<T> inputVector;
45 inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));
46
47 std::vector<T> cellStateInVector(batchSize * numUnits, T());
48 std::vector<T> outputStateInVector(batchSize * outputSize, T());
49
50 std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
51
52 std::vector<T> outputVector;
53 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));
54
55 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
56 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
57 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
58 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
59 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
60
61 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
62
63 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
64 armnn::WorkloadInfo info;
65
66 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
67 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
68 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
69
70 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
71
72 armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset);
73 armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);
74 armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
75
76 std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,
77 -0.117484632f, 0.3298470976f, -0.1179017122f,
78 0.214305695f, 0.42135173085f, 0.003878414626f,
79 -0.348303917f, -0.1881275477f, 0.0343011027f };
80
81 std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
82 -0.3810434485f, 0.268383264f, -0.009807467424f,
83 -0.3522925403f, -0.24275735512f, -0.28344226125f,
84 0.13512269116f, -0.4932442977f, -0.10039821991f };
85
86 std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
87 0.386399507f, -0.259465157985f, -0.16545993089f,
88 -0.4230232555f, 0.341664791103f, -0.18127849691f,
89 -0.2277662414f, -0.55275535589f, 0.34184026718f };
90
91 std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
92 0.53969591851f, 0.23393625035f, -0.27140527306f,
93 0.50009280443f, 0.07511717046f, 0.3998299249f,
94 -0.51717478049f, 0.1889653282f, -0.367323637f };
95
96 std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,
97 -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,
98 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,
99 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f };
100
101 std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
102 -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
103 -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
104 -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };
105
106 std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
107 -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
108 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
109 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };
110
111 std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
112 -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
113 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
114 -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };
115
116 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
117
118 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
119
120 std::vector<float> cellBias = { 0., 0., 0., 0. };
121
122 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
123
124 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12);
125 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
126 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
127 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
128 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16);
129 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
130 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
131 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
132 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4);
133 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4);
134 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
135 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
136
137 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
138 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
139 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
140 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
141 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
142 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
143 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
144 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
145 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
146 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
147 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
148 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
149
150 data.m_InputToInputWeights = &inputToInputWeightsTensor;
151 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
152 data.m_InputToCellWeights = &inputToCellWeightsTensor;
153 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
154 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
155 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
156 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
157 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
158 data.m_InputGateBias = &inputGateBiasTensor;
159 data.m_ForgetGateBias = &forgetGateBiasTensor;
160 data.m_CellBias = &cellBiasTensor;
161 data.m_OutputGateBias = &outputGateBiasTensor;
162
163 // Flags to set test configuration
164 data.m_Parameters.m_ClippingThresCell = 10;
165 data.m_Parameters.m_ClippingThresProj = 0;
166 data.m_Parameters.m_ActivationFunc = 4;
167 data.m_Parameters.m_CifgEnabled = false;
168 data.m_Parameters.m_PeepholeEnabled = false;
169 data.m_Parameters.m_ProjectionEnabled = false;
170 data.m_Parameters.m_TimeMajor = false;
171
172 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
173 inputHandle->Allocate();
174 outputStateInHandle->Allocate();
175 cellStateInHandle->Allocate();
176
177 outputHandle->Allocate();
178
179 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
180 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
181 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
182
183 workload->Execute();
184
185 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
186
187 return LayerTestResult<T, 3>(actualOutput,
188 outputVector,
189 outputHandle->GetShape(),
190 outputTensorInfo.GetShape());
191}
192
193template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
194LayerTestResult<T, 3>
195UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl(
196 armnn::IWorkloadFactory& workloadFactory,
197 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
198 const armnn::ITensorHandleFactory& tensorHandleFactory,
199 const std::vector<T>& input,
200 const std::vector<T>& outputExpected,
201 const armnn::TensorShape& inputShape,
202 const armnn::TensorShape& outputExpectedShape,
203 float qScale = 0.0f,
204 int32_t qOffset = 0,
205 armnn::DataType constantDataType = armnn::DataType::Float32) {
206 IgnoreUnused(memoryManager);
207 unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[1]);
208 unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[0]);
209 unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]);
210 unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]);
211 unsigned numUnits = outputSize;
212
213 armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, ArmnnType, qScale, qOffset);
214 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
215 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
216
217 armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, ArmnnType, qScale, qOffset);
218
219 std::vector<T> inputVector;
220 inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));
221
222 std::vector<T> cellStateInVector(batchSize * numUnits, T());
223 std::vector<T> outputStateInVector(batchSize * outputSize, T());
224
225 std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
226
227 std::vector<T> outputVector;
228 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));
229
230 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
231 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
232 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
233 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
234 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
235
236 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
237
238 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
239 armnn::WorkloadInfo info;
240
241 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
242 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
243 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
244
245 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
246
247 armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset);
248 armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);
249 armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
250
251 std::vector<float> inputToInputWeights = { 0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f,
252 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f,
253 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f,
254 -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f };
255
256 std::vector<float> inputToForgetWeights = { -0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f,
257 -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f,
258 -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f,
259 -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f };
260
261 std::vector<float> inputToCellWeights = { -0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f,
262 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f,
263 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f,
264 -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f };
265
266 std::vector<float> inputToOutputWeights = { -0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f,
267 -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f,
268 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f,
269 -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f };
270
271 std::vector<float> recurrentToInputWeights = { 0.23788475990f, -0.24948765337f, 0.50044941902f, 0.14431896805f,
272 -0.115940228137f, -0.717082679f, -0.17208620906f, 0.17850610617f,
273 -0.16702319684f, -0.11384502053f, -0.309785276245f, -0.3316611672f,
274 0.52380162477f, -0.06839632987f, -0.391478359627f, -0.10756178963f };
275
276 std::vector<float> recurrentToForgetWeights = { 0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f,
277 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f,
278 -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f,
279 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f };
280
281 std::vector<float> recurrentToCellWeights = { 0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f,
282 -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f,
283 -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f,
284 -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f };
285
286 std::vector<float> recurrentToOutputWeights = { -0.079031050201f, 0.041414566286f, -0.583727357285f, 0.1025384515f,
287 -0.172372072937f, 0.09214124082f, 0.178184121827f, -0.2439443916f,
288 0.104485116899f, 0.2600405514f, 0.064414866268f, 0.24141204357f,
289 0.281875759363f, -0.14234502664f, 0.15126448862f, -0.24421440064f };
290
291 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
292
293 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
294
295 std::vector<float> cellBias = { 0., 0., 0., 0. };
296
297 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
298
299 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12);
300 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
301 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
302 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
303 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16);
304 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
305 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
306 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
307 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4);
308 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4);
309 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
310 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
311
312 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
313 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
314 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
315 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
316 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
317 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
318 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
319 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
320 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
321 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
322 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
323 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
324
325 data.m_InputToInputWeights = &inputToInputWeightsTensor;
326 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
327 data.m_InputToCellWeights = &inputToCellWeightsTensor;
328 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
329 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
330 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
331 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
332 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
333 data.m_InputGateBias = &inputGateBiasTensor;
334 data.m_ForgetGateBias = &forgetGateBiasTensor;
335 data.m_CellBias = &cellBiasTensor;
336 data.m_OutputGateBias = &outputGateBiasTensor;
337
338 // Flags to set test configuration
339 data.m_Parameters.m_ClippingThresCell = 10;
340 data.m_Parameters.m_ClippingThresProj = 0;
341 data.m_Parameters.m_ActivationFunc = 4;
342 data.m_Parameters.m_CifgEnabled = false;
343 data.m_Parameters.m_PeepholeEnabled = false;
344 data.m_Parameters.m_ProjectionEnabled = false;
345 data.m_Parameters.m_TimeMajor = true;
346
347 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
348 inputHandle->Allocate();
349 outputStateInHandle->Allocate();
350 cellStateInHandle->Allocate();
351
352 outputHandle->Allocate();
353
354 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
355 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
356 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
357
358 workload->Execute();
359
360 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
361
362 return LayerTestResult<T, 3>(actualOutput,
363 outputVector,
364 outputHandle->GetShape(),
365 outputTensorInfo.GetShape());
366}
367
368} // anonymous namespace
369
370LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32Test(
371 armnn::IWorkloadFactory& workloadFactory,
372 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
373 const armnn::ITensorHandleFactory& tensorHandleFactory) {
374 armnn::TensorInfo inputInfo({3, 2, 3}, armnn::DataType::Float32);
375 std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
376 3., 2., 1., 2., 3., 4.,
377 5., 4., 3., 2., 1., 2. };
378
379 armnn::TensorInfo outputInfo({3, 2, 4}, armnn::DataType::Float32);
380 std::vector<float> expectedOutput = { -0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f,
381 -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f,
382 -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f,
383 -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f,
384 -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f,
385 -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f };
386 return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>(
387 workloadFactory, memoryManager, tensorHandleFactory,
388 input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
389}
390
391LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32TimeMajorTest(
392 armnn::IWorkloadFactory& workloadFactory,
393 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
394 const armnn::ITensorHandleFactory& tensorHandleFactory) {
395 armnn::TensorInfo inputInfo({2, 3, 3}, armnn::DataType::Float32);
396 std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
397 3., 2., 1., 2., 3., 4.,
398 5., 4., 3., 2., 1., 2. };
399
400 armnn::TensorInfo outputInfo({2, 3, 4}, armnn::DataType::Float32);
401 std::vector<float> expectedOutput = { 0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f,
402 0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f,
403 -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f,
404 0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f,
405 0.11171641f, 0.043119f , 0.0762981f , -0.01228541f,
406 0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f };
407 return UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl<armnn::DataType::Float32>(
408 workloadFactory, memoryManager, tensorHandleFactory,
409 input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
410}
411
412LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest(
413 armnn::IWorkloadFactory& workloadFactory,
414 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
415 const armnn::ITensorHandleFactory& tensorHandleFactory)
416{
417 IgnoreUnused(memoryManager);
418 unsigned int batchSize = 2;
419 unsigned int timeSize = 3;
420 unsigned int outputSize = 5;
421 unsigned int inputSize = 4;
422 unsigned numUnits = 6;
423
424 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
425 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
426 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
427 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
428
429 const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
430 3., 2., 1., 2., 3., 4.,
431 5., 4., 3., 2., 1., 2.,
432 1., 2., 3., 4., 5., 4.};
433
434 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
435 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
436
437 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
438
439 const std::vector<float> expectedOutput = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f,
440 -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f,
441 -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f,
442 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f,
443 -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f,
444 -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f };
445
446 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
447 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
448 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
449 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
450 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
451 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
452
453 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
454 armnn::WorkloadInfo info;
455
456 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
457 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
458 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
459 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
460
461 armnn::TensorInfo tensorInfo5({outputSize}, armnn::DataType::Float32);
462 armnn::TensorInfo tensorInfo6({numUnits}, armnn::DataType::Float32);
463 armnn::TensorInfo tensorInfo6x4({numUnits, inputSize}, armnn::DataType::Float32);
464 armnn::TensorInfo tensorInfo6x5({numUnits, outputSize}, armnn::DataType::Float32);
465 armnn::TensorInfo tensorInfo5x6({outputSize, numUnits}, armnn::DataType::Float32);
466
467 std::vector<float> inputToInputWeights = { 0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f,
468 -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f,
469 -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f,
470 -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f,
471 -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f,
472 -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f };
473
474 std::vector<float> inputToForgetWeights = { -0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f,
475 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f,
476 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f,
477 -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f,
478 -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f,
479 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f};
480
481 std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,
482 -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,
483 -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,
484 -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,
485 -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,
486 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f };
487
488 std::vector<float> inputToOutputWeights = { -0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f,
489 -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f,
490 -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f,
491 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f,
492 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f,
493 -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f };
494
495 std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f,
496 0.10380666f, 0.053110216f, -0.06928846f };
497
498 std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f,
499 0.23027696f, 0.11098921f, 0.08989442f };
500
501 std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f,
502 0.033463873f, -0.1483596f, 0.029460307f };
503
504 std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f,
505 0.12648113f, 0.027195795f, 0.35373217f };
506
507 std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,
508 -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f,
509 -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f,
510 -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f,
511 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f,
512 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f,
513 -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f,
514 0.14283475f, -0.07390571f };
515
516 std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,
517 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f,
518 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f,
519 -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f,
520 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f,
521 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f,
522 -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f,
523 -0.019443132f, -0.030755889f };
524
525 std::vector<float> recurrentToForgetWeights = { -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f,
526 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f,
527 -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f,
528 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f,
529 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f,
530 -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f,
531 -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f,
532 0.061878487f, -0.04729229f };
533
534 std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f,
535 -0.045984812f,-0.01255415f, -0.0026479573f,
536 -0.08196161f, -0.054914974f, -0.0046604523f,
537 -0.029587349f, -0.044576716f, -0.07480124f,
538 -0.082868785f, 0.023254942f, 0.027502948f,
539 -0.0039728214f, -0.08683098f, -0.08116779f,
540 -0.014675607f, -0.037924774f, -0.023314456f,
541 -0.007401714f, -0.09255757f, 0.029460307f,
542 -0.08829125f, -0.005139627f, -0.08989442f,
543 -0.0555066f, 0.13596267f, 0.025062224f };
544
545 std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f,
546 0.018586371f, -0.037586458f, -0.15312155f };
547
548 std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f,
549 -0.012770197f, 0.041331276f, -0.072311886f };
550
551 std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f,
552 0.002913762f, 0.17764764f, -0.5495371f };
553
554 std::vector<float> projectionWeights = { -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,
555 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,
556 -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,
557 -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,
558 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,
559 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f };
560
561 std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
562
563 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo6x4);
564 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo6x4);
565 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo6x4);
566 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo6x4);
567 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo6x5);
568 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo6x5);
569 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo6x5);
570 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo6x5);
571 armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo6);
572 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo6);
573 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo6);
574 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo6);
575 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo6);
576 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo6);
577 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo6);
578 armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo5x6);
579 armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo5);
580
581 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
582 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
583 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
584 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
585 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
586 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
587 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
588 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
589 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
590 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
591 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
592 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
593 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
594 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
595 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
596 AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
597 AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
598
599 data.m_InputToInputWeights = &inputToInputWeightsTensor;
600 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
601 data.m_InputToCellWeights = &inputToCellWeightsTensor;
602 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
603 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
604 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
605 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
606 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
607 data.m_CellToInputWeights = &cellToInputWeightsTensor;
608 data.m_InputGateBias = &inputGateBiasTensor;
609 data.m_ForgetGateBias = &forgetGateBiasTensor;
610 data.m_CellBias = &cellBiasTensor;
611 data.m_OutputGateBias = &outputGateBiasTensor;
612 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
613 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
614 data.m_ProjectionWeights = &projectionWeightsTensor;
615 data.m_ProjectionBias = &projectionBiasTensor;
616
617 // Flags to set test configuration
618 data.m_Parameters.m_ActivationFunc = 4;
619 data.m_Parameters.m_CifgEnabled = false;
620 data.m_Parameters.m_PeepholeEnabled = true;
621 data.m_Parameters.m_ProjectionEnabled = true;
622 data.m_Parameters.m_LayerNormEnabled = false;
623 data.m_Parameters.m_TimeMajor = false;
624 data.m_Parameters.m_ClippingThresCell = 10.0f;
625
626
627 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
628 inputHandle->Allocate();
629 outputStateInHandle->Allocate();
630 cellStateInHandle->Allocate();
631 outputHandle->Allocate();
632
633 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
634 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
635 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
636
637 workload->Execute();
638
639 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
640
641 return LayerTestResult<float, 3>(actualOutput,
642 expectedOutput,
643 outputHandle->GetShape(),
644 outputTensorInfo.GetShape());
645}
646
647LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest(
648 armnn::IWorkloadFactory& workloadFactory,
649 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
650 const armnn::ITensorHandleFactory& tensorHandleFactory)
651{
652 IgnoreUnused(memoryManager);
653 unsigned int batchSize = 3;
654 unsigned int timeSize = 2;
655 unsigned int outputSize = 4;
656 unsigned int inputSize = 3;
657 unsigned numUnits = 5;
658
659 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
660 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
661 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
662 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
663
664 const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
665 3., 2., 1., 2., 3., 4.,
666 5., 4., 3., 2., 1., 2. };
667
668 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
669 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
670
671 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
672
673 const std::vector<float> expectedOutput = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f,
674 0.11458f, 0.0407109f, 0.300327f, 0.174301f,
675 0.0864761f, 0.0362912f, 0.178635f, 0.115689f,
676 0.108008f, 0.0386623f, 0.273471f, 0.167115f,
677 0.0859545f, 0.0331481f, 0.186051f, 0.11888f,
678 0.106649f, 0.0276847f, 0.229863f, 0.166958f };
679
680 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
681 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
682 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
683 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
684 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
685
686 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
687
688 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
689 armnn::WorkloadInfo info;
690
691 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
692 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
693 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
694
695 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
696
697 armnn::TensorInfo tensorInfo4({outputSize}, armnn::DataType::Float32);
698 armnn::TensorInfo tensorInfo5({numUnits}, armnn::DataType::Float32);
699 armnn::TensorInfo tensorInfo5x3({numUnits, inputSize}, armnn::DataType::Float32);
700 armnn::TensorInfo tensorInfo5x4({numUnits, outputSize}, armnn::DataType::Float32);
701 armnn::TensorInfo tensorInfo4x5({outputSize, numUnits}, armnn::DataType::Float32);
702
703 std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,
704 -0.117484632f, 0.3298470976f, -0.1179017122f,
705 0.214305695f, 0.42135173085f, 0.003878414626f,
706 -0.348303917f, -0.1881275477f, 0.0343011027f,
707 -0.38837709614f, -0.05636804124f, 0.4259087456f};
708
709 std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
710 -0.3810434485f, 0.268383264f, -0.009807467424f,
711 -0.3522925403f, -0.24275735512f, -0.28344226125f,
712 0.13512269116f, -0.4932442977f, -0.10039821991f,
713 0.2726137042f, 0.09216640889f, -0.06551410215f};
714
715 std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
716 0.386399507f, -0.259465157985f, -0.16545993089f,
717 -0.4230232555f, 0.341664791103f, -0.18127849691f,
718 -0.2277662414f, -0.55275535589f, 0.34184026718f,
719 0.3954237699f, -0.19407111404f, 0.30412107706f};
720
721 std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
722 0.53969591851f, 0.23393625035f, -0.27140527306f,
723 0.50009280443f, 0.07511717046f, 0.3998299249f,
724 -0.51717478049f, 0.1889653282f, -0.367323637f,
725 -0.12584099173f, -0.12319286912f, 0.2407919466f};
726
727 std::vector<float> inputGateBias{ 0.03f, 0.15f, 0.22f, 0.38f, 0.05f };
728 std::vector<float> forgetGateBias{ 0.1f, -0.3f, -0.2f, 0.1f, 0.4f };
729 std::vector<float> cellBias{ -0.05f, 0.72f, 0.25f, 0.08f, 0.1f };
730 std::vector<float> outputGateBias{ 0.05f, -0.01f, 0.2f, 0.1f, -0.2f };
731
732 std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,
733 -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,
734 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,
735 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f,
736 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f };
737
738 std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
739 -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
740 -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
741 -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f,
742 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f };
743
744 std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
745 -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
746 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
747 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f,
748 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f };
749
750 std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
751 -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
752 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
753 -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f,
754 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f };
755
756 std::vector<float> cellToInputWeights { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f };
757 std::vector<float> cellToForgetWeights { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f };
758 std::vector<float> cellToOutputWeights { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f };
759
760 std::vector<float> projectionWeights{ -0.1f, 0.2f, 0.01f, -0.2f,
761 0.1f, 0.5f, 0.3f, 0.08f,
762 0.07f, 0.2f, -0.4f, 0.2f,
763 0.5f, -0.4f, 0.3f, -0.2f,
764 0.3f, 0.08f, -0.07f, 0.2f};
765
766 std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
767
768 std::vector<float> inputLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.8f };
769 std::vector<float> forgetLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.2f };
770 std::vector<float> cellLayerNormWeights{ 0.7f, 0.2f, 0.3f, 0.8f, 0.5f };
771 std::vector<float> outputLayerNormWeights{ 0.6f, 0.2f, 0.2f, 0.5f, 0.1f };
772
773 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo5x3);
774 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo5x3);
775 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo5x3);
776 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo5x3);
777 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo5x4);
778 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo5x4);
779 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo5x4);
780 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo5x4);
781 armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo5);
782 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo5);
783 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo5);
784 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo5);
785 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo5);
786 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo5);
787 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo5);
788 armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo4x5);
789 armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo4);
790
791 armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfo5);
792 armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfo5);
793 armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfo5);
794 armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfo5);
795
796 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
797 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
798 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
799 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
800 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
801 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
802 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
803 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
804 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
805 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
806 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
807 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
808 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
809 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
810 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
811 AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
812 AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
813
814 AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data());
815 AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
816 AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
817 AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());
818
819 data.m_InputToInputWeights = &inputToInputWeightsTensor;
820 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
821 data.m_InputToCellWeights = &inputToCellWeightsTensor;
822 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
823 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
824 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
825 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
826 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
827 data.m_CellToInputWeights = &cellToInputWeightsTensor;
828 data.m_InputGateBias = &inputGateBiasTensor;
829 data.m_ForgetGateBias = &forgetGateBiasTensor;
830 data.m_CellBias = &cellBiasTensor;
831 data.m_OutputGateBias = &outputGateBiasTensor;
832 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
833 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
834 data.m_ProjectionWeights = &projectionWeightsTensor;
835 data.m_ProjectionBias = &projectionBiasTensor;
836
837 data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;
838 data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor;
839 data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor;
840 data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor;
841
842 // Flags to set test configuration
843 data.m_Parameters.m_ActivationFunc = 4;
844 data.m_Parameters.m_CifgEnabled = false;
845 data.m_Parameters.m_PeepholeEnabled = true;
846 data.m_Parameters.m_ProjectionEnabled = true;
847 data.m_Parameters.m_LayerNormEnabled = true;
848 data.m_Parameters.m_TimeMajor = false;
849 data.m_Parameters.m_ClippingThresCell = 10.0f;
850
851 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
852 inputHandle->Allocate();
853 outputStateInHandle->Allocate();
854 cellStateInHandle->Allocate();
855 outputHandle->Allocate();
856
857 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
858 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
859 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
860
861 workload->Execute();
862
863 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
864
865 return LayerTestResult<float, 3>(actualOutput,
866 expectedOutput,
867 outputHandle->GetShape(),
868 outputTensorInfo.GetShape());
869}
870
871LayerTestResult<float, 3> UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(
872 armnn::IWorkloadFactory& workloadFactory,
873 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
874 const armnn::ITensorHandleFactory& tensorHandleFactory)
875{
876 IgnoreUnused(memoryManager);
877 unsigned int batchSize = 3;
878 unsigned int timeSize = 2;
879 unsigned int inputSize = 3;
880 unsigned int outputSize = 4;
881 unsigned numUnits = outputSize;
882
883 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
884 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
885 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
886
887 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
888
889 std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
890 3., 2., 1., 2., 3., 4.,
891 5., 4., 3., 2., 1., 2. };
892
893 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
894 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
895
896 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
897
898 std::vector<float> outputVector = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f,
899 -0.0300169f, -0.195717f, -0.528679f, -0.0818106f,
900 -0.0332748f, 0.155429f, -0.353966f, -0.0801505f,
901 -0.032312f, -0.0407911f, -0.435053f, -0.0932317f,
902 -0.0108233f, 0.165584f, -0.640424f, -0.0447535f,
903 -0.031675f, 0.125987f, -0.526695f, -0.110093f };
904
905 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
906 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
907 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
908 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
909 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
910
911 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
912
913 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
914 armnn::WorkloadInfo info;
915
916 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
917 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
918 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
919
920 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
921
922 armnn::TensorInfo tensorInfo4({numUnits}, armnn::DataType::Float32);
923 armnn::TensorInfo tensorInfo12({numUnits, 3}, armnn::DataType::Float32);
924 armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::DataType::Float32);
925
926 std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
927 -0.3810434485f, 0.268383264f, -0.009807467424f,
928 -0.3522925403f, -0.24275735512f, -0.28344226125f,
929 0.13512269116f, -0.4932442977f, -0.10039821991f };
930
931 std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
932 0.386399507f, -0.259465157985f, -0.16545993089f,
933 -0.4230232555f, 0.341664791103f, -0.18127849691f,
934 -0.2277662414f, -0.55275535589f, 0.34184026718f };
935
936 std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
937 0.53969591851f, 0.23393625035f, -0.27140527306f,
938 0.50009280443f, 0.07511717046f, 0.3998299249f,
939 -0.51717478049f, 0.1889653282f, -0.367323637f };
940
941 std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
942 -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
943 -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
944 -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };
945
946 std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
947 -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
948 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
949 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };
950
951 std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
952 -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
953 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
954 -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };
955
956 std::vector<float> cellToForgetWeights{ 0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f };
957
958 std::vector<float> cellToOutputWeights{ -0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f };
959
960 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
961
962 std::vector<float> cellBias = { 0., 0., 0., 0. };
963
964 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
965
966 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
967 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
968 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
969 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
970 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
971 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
972 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo4);
973 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo4);
974 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4);
975 armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
976 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
977
978 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
979 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
980 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
981 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
982 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
983 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
984 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
985 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
986 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
987 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
988 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
989
990 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
991 data.m_InputToCellWeights = &inputToCellWeightsTensor;
992 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
993 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
994 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
995 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
996 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
997 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
998 data.m_ForgetGateBias = &forgetGateBiasTensor;
999 data.m_CellBias = &cellBiasTensor;
1000 data.m_OutputGateBias = &outputGateBiasTensor;
1001
1002 // Flags to set test configuration
1003 data.m_Parameters.m_ClippingThresCell = 10;
1004 data.m_Parameters.m_ClippingThresProj = 0;
1005 data.m_Parameters.m_ActivationFunc = 4;
1006 data.m_Parameters.m_CifgEnabled = true;
1007 data.m_Parameters.m_PeepholeEnabled = true;
1008 data.m_Parameters.m_ProjectionEnabled = false;
1009 data.m_Parameters.m_TimeMajor = false;
1010
1011 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
1012 inputHandle->Allocate();
1013 outputStateInHandle->Allocate();
1014 cellStateInHandle->Allocate();
1015
1016 outputHandle->Allocate();
1017
1018 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1019 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1020 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1021
1022 workload->Execute();
1023
1024 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1025
1026 return LayerTestResult<float, 3>(actualOutput,
1027 outputVector,
1028 outputHandle->GetShape(),
1029 outputTensorInfo.GetShape());
1030}
Narumol Prangnawaratbd575b22021-08-31 16:53:54 +0100[diff] [blame]1031
1032LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8Test(
1033 armnn::IWorkloadFactory& workloadFactory,
1034 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1035 const armnn::ITensorHandleFactory& tensorHandleFactory)
1036{
1037 IgnoreUnused(memoryManager);
1038 unsigned int batchSize = 3;
1039 unsigned int timeSize = 2;
1040 unsigned int inputSize = 3;
1041 unsigned int outputSize = 4;
1042 unsigned numUnits = outputSize;
1043
1044 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
1045 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
1046 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
1047
1048 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1049
1050 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1051 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1052 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1053
1054 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1055 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1056
1057 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1058
1059 const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120569f, -0.0116868f,
1060 -0.0350714f, -0.0343202f, -0.047504f, -0.0569789f,
1061 -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f,
1062 -0.0294759f, -0.0129935f, -0.0444175f, -0.0444354f,
1063 -0.0280855f, 0.00545101f, -0.051422f, -0.0463838f,
1064 -0.0310702f, 0.00915739f, -0.0625207f, -0.0482648f };
1065
1066 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1067 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1068 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1069 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1070 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1071
1072 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1073
1074 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1075 armnn::WorkloadInfo info;
1076
1077 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1078 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1079 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1080
1081 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1082
1083 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
1084 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1085 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1086
1087 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 };
1088 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1089 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1090 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1091
1092 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 };
1093 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1094 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1095 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1096
1097 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
1098 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
1099 std::vector<float> cellBias = { 0., 0., 0., 0. };
1100 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
1101
1102 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput);
1103 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
1104 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
1105 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
1106 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput);
1107 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
1108 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
1109 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
1110 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp);
1111 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
1112 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
1113 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
1114
1115 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
1116 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1117 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1118 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1119 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
1120 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1121 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1122 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1123 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
1124 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1125 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1126 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1127
1128 data.m_InputToInputWeights = &inputToInputWeightsTensor;
1129 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1130 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1131 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1132 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
1133 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1134 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1135 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1136 data.m_InputGateBias = &inputGateBiasTensor;
1137 data.m_ForgetGateBias = &forgetGateBiasTensor;
1138 data.m_CellBias = &cellBiasTensor;
1139 data.m_OutputGateBias = &outputGateBiasTensor;
1140
1141 // Flags to set test configuration
1142 data.m_Parameters.m_ClippingThresCell = 10;
1143 data.m_Parameters.m_ClippingThresProj = 0;
1144 data.m_Parameters.m_ActivationFunc = 4;
1145 data.m_Parameters.m_CifgEnabled = false;
1146 data.m_Parameters.m_PeepholeEnabled = false;
1147 data.m_Parameters.m_ProjectionEnabled = false;
1148 data.m_Parameters.m_TimeMajor = false;
1149
1150 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
1151 inputHandle->Allocate();
1152 outputStateInHandle->Allocate();
1153 cellStateInHandle->Allocate();
1154
1155 outputHandle->Allocate();
1156
1157 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1158 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1159 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1160
1161 workload->Execute();
1162
1163 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1164
1165 return LayerTestResult<float, 3>(actualOutput,
1166 outputVector,
1167 outputHandle->GetShape(),
1168 outputTensorInfo.GetShape());
1169}
1170
1171LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8TimeMajorTest(
1172 armnn::IWorkloadFactory& workloadFactory,
1173 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1174 const armnn::ITensorHandleFactory& tensorHandleFactory)
1175{
1176 IgnoreUnused(memoryManager);
1177 unsigned int batchSize = 3;
1178 unsigned int timeSize = 2;
1179 unsigned int inputSize = 3;
1180 unsigned int outputSize = 4;
1181 unsigned numUnits = outputSize;
1182
1183 armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, armnn::DataType::Float32);
1184 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
1185 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
1186
1187 armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, armnn::DataType::Float32);
1188
1189 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1190 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1191 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1192
1193 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1194 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1195
1196 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1197
1198 const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120122f, -0.0116868f,
1199 -0.0261295f, -0.0188487f, -0.0345463f, -0.049733f,
1200 -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f,
1201 -0.0291863f, -0.0369402f, -0.0354071f, -0.0296529f,
1202 -0.0419539f, -0.00617731f, -0.0814796f, -0.0804005f,
1203 -0.0244737f, 0.0119905f, -0.0457527f, -0.0331862f };
1204 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1205 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1206 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1207 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1208 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1209
1210 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1211
1212 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1213 armnn::WorkloadInfo info;
1214
1215 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1216 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1217 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1218
1219 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1220
1221 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
1222 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1223 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1224
1225 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 };
1226 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1227 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1228 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1229
1230 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 };
1231 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1232 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1233 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1234
1235
1236 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
1237 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
1238 std::vector<float> cellBias = { 0., 0., 0., 0. };
1239 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
1240
1241 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput);
1242 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
1243 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
1244 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
1245 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput);
1246 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
1247 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
1248 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
1249 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp);
1250 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
1251 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
1252 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
1253
1254 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
1255 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1256 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1257 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1258 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
1259 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1260 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1261 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1262 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
1263 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1264 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1265 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1266
1267 data.m_InputToInputWeights = &inputToInputWeightsTensor;
1268 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1269 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1270 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1271 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
1272 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1273 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1274 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1275 data.m_InputGateBias = &inputGateBiasTensor;
1276 data.m_ForgetGateBias = &forgetGateBiasTensor;
1277 data.m_CellBias = &cellBiasTensor;
1278 data.m_OutputGateBias = &outputGateBiasTensor;
1279
1280 // Flags to set test configuration
1281 data.m_Parameters.m_ClippingThresCell = 10;
1282 data.m_Parameters.m_ClippingThresProj = 0;
1283 data.m_Parameters.m_ActivationFunc = 4;
1284 data.m_Parameters.m_CifgEnabled = false;
1285 data.m_Parameters.m_PeepholeEnabled = false;
1286 data.m_Parameters.m_ProjectionEnabled = false;
1287 data.m_Parameters.m_TimeMajor = true;
1288
1289 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
1290 inputHandle->Allocate();
1291 outputStateInHandle->Allocate();
1292 cellStateInHandle->Allocate();
1293
1294 outputHandle->Allocate();
1295
1296 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1297 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1298 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1299
1300 workload->Execute();
1301
1302 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1303
1304 return LayerTestResult<float, 3>(actualOutput,
1305 outputVector,
1306 outputHandle->GetShape(),
1307 outputTensorInfo.GetShape());
1308}
1309
1310LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionTest(
1311 armnn::IWorkloadFactory& workloadFactory,
1312 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1313 const armnn::ITensorHandleFactory& tensorHandleFactory)
1314{
1315 IgnoreUnused(memoryManager);
1316 unsigned int batchSize = 3;
1317 unsigned int timeSize = 2;
1318 unsigned int outputSize = 4;
1319 unsigned int inputSize = 3;
1320 unsigned numUnits = 4;
1321
1322 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
1323 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
1324 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
1325 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1326
1327 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1328 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1329 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1330
1331 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1332 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1333
1334 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1335
1336 const std::vector<float> expectedOutput = { 0.612103f, 1.56788f, 0.31966f, 1.42956f,
1337 0.909718f, 3.07916f, -0.560586f, 3.8907f,
1338 0.753671f, 1.77485f, 0.365122f, 1.60077f,
1339 0.812644f, 2.79092f, -0.605396f, 3.61742f,
1340 0.791857f, 1.64353f, 0.316588f, 1.55192f,
1341 0.807265f, 2.47012f, -0.539598f, 3.25654f };
1342
1343 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1344 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1345 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1346 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1347 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1348 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1349
1350 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1351 armnn::WorkloadInfo info;
1352
1353 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1354 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1355 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1356 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1357
1358 armnn::TensorInfo tensorInfoOut({outputSize}, armnn::DataType::Float32);
1359 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
1360 armnn::TensorInfo tensorInfoNum({numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
1361 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1362 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1363 armnn::TensorInfo tensorInfoOutNum({outputSize, numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
1364
1365 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 };
1366 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1367 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1368 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1369
1370 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 };
1371 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1372 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1373 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1374
1375 std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f};
1376 std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.3032477f, 0.23027696f};
1377 std::vector<float> cellBias = { -0.124379363f, 0.55531194f, 0.23377132f, 0.033463873f };
1378 std::vector<float> outputGateBias = { 0.046159424f, -0.12809046f, 0.03563469f, 0.12648113f };
1379
1380 std::vector<int8_t> cellToInputWeights = { 5, 10, 25, 15 };
1381 std::vector<int8_t> cellToForgetWeights = { -5, 15, 25, 3 };
1382 std::vector<int8_t> cellToOutputWeights = { 10, -10, -5, 50 };
1383
1384 std::vector<int8_t> projectionWeights = { -25, 51, 3, -5, 25, 127, 77, 20, 18, 51, -10, 51, -25, 88, 77, -13 };
1385
1386 std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
1387
1388 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput);
1389 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
1390 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
1391 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
1392 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
1393 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput);
1394 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
1395 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
1396 armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfoNum);
1397 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp);
1398 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
1399 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
1400 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
1401 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNum);
1402 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNum);
1403 armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfoOutNum);
1404 armnn::ScopedTensorHandle projectionBiasTensor(tensorInfoOut);
1405
1406 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
1407 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1408 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1409 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1410 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
1411 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1412 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1413 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1414 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
1415 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
1416 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1417 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1418 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1419 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
1420 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
1421 AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
1422 AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
1423
1424 data.m_InputToInputWeights = &inputToInputWeightsTensor;
1425 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1426 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1427 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1428 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
1429 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1430 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1431 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1432 data.m_CellToInputWeights = &cellToInputWeightsTensor;
1433 data.m_InputGateBias = &inputGateBiasTensor;
1434 data.m_ForgetGateBias = &forgetGateBiasTensor;
1435 data.m_CellBias = &cellBiasTensor;
1436 data.m_OutputGateBias = &outputGateBiasTensor;
1437 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
1438 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
1439 data.m_ProjectionWeights = &projectionWeightsTensor;
1440 data.m_ProjectionBias = &projectionBiasTensor;
1441
1442 // Flags to set test configuration
1443 data.m_Parameters.m_ActivationFunc = 4;
1444 data.m_Parameters.m_CifgEnabled = false;
1445 data.m_Parameters.m_PeepholeEnabled = true;
1446 data.m_Parameters.m_ProjectionEnabled = true;
1447 data.m_Parameters.m_LayerNormEnabled = false;
1448 data.m_Parameters.m_TimeMajor = false;
1449 data.m_Parameters.m_ClippingThresCell = 10.0f;
1450
1451
1452 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
1453 inputHandle->Allocate();
1454 outputStateInHandle->Allocate();
1455 cellStateInHandle->Allocate();
1456 outputHandle->Allocate();
1457
1458 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1459 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1460 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1461
1462 workload->Execute();
1463
1464 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1465
1466 return LayerTestResult<float, 3>(actualOutput,
1467 expectedOutput,
1468 outputHandle->GetShape(),
1469 outputTensorInfo.GetShape());
1470}
1471
1472LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest(
1473 armnn::IWorkloadFactory& workloadFactory,
1474 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1475 const armnn::ITensorHandleFactory& tensorHandleFactory)
1476{
1477 IgnoreUnused(memoryManager);
1478 unsigned int batchSize = 3;
1479 unsigned int timeSize = 2;
1480 unsigned int outputSize = 4;
1481 unsigned int inputSize = 3;
1482 unsigned numUnits = 5;
1483
1484 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
1485 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
1486 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
1487 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1488
1489 const std::vector<float> inputVector = { 1., 8., 3., 4., 5., 4.,
1490 3., 2., 1., 2., 3., 4.,
1491 5., 4., 3., 2., 1., 2. };
1492
1493 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1494 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1495
1496 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1497
1498 const std::vector<float> expectedOutput = { 0.0471276f, 0.0168155f, 0.0789885f, 0.16550f,
1499 0.0643133f, -0.0400722f, 0.100593f, 0.197722f,
1500 0.0465562f, -0.0600682f, 0.0622087f, 0.115053f,
1501 0.056287f, -0.0566218f, 0.0856832f, 0.148484f,
1502 0.0457859f, -0.0588112f, 0.0623636f, 0.114333f,
1503 0.0509271f, -0.0754262f, 0.058600f, 0.0801288f };
1504
1505 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1506 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1507 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1508 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1509 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1510
1511 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1512
1513 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1514 armnn::WorkloadInfo info;
1515
1516 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1517 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1518 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1519
1520 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1521
1522 armnn::TensorInfo tensorInfoOut({outputSize}, armnn::DataType::Float32);
1523 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
1524 armnn::TensorInfo tensorInfoNum({numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
1525 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1526 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1527 armnn::TensorInfo tensorInfoOutNum({outputSize, numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
1528
1529 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3, 2, 2, -4 };
1530 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1, -3, -2, -4 };
1531 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3, 2, 5, -4 };
1532 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4, -4, -1, -1 };
1533
1534 std::vector<float> inputGateBias = { 0.03f, 0.15f, 0.22f, 0.38f, 0.05f };
1535 std::vector<float> forgetGateBias = { 0.1f, -0.3f, -0.2f, 0.1f, 0.4f };
1536 std::vector<float> cellBias = { -0.05f, 0.72f, 0.25f, 0.08f, 0.1f };
1537 std::vector<float> outputGateBias = { 0.05f, -0.01f, 0.2f, 0.1f, -0.2f };
1538
1539 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3,
1540 5, -1, 1, 3, -1, -1, -1, 4, 2, 3 };
1541
1542 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3,
1543 5, -1, 1, 3, -2, -1, -1, 2, 2, 1 };
1544
1545 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2,
1546 1, 2, 3, -2, 3, -3, -1, -5, 1, 3 };
1547
1548 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3,
1549 -4, -1, -1, -1, 2, -1, 5, 1, -3, -4 };
1550
1551 std::vector<int8_t> cellToInputWeights = { 5, 3, 8, -5, 2 };
1552 std::vector<int8_t> cellToForgetWeights = { -2, -7, 5, -3, 4 };
1553 std::vector<int8_t> cellToOutputWeights = { 9, -10 , -5, 5, 1 };
1554
1555 std::vector<int8_t> projectionWeights = { -1, 2, 1, -2, 1, 5, 3, 8, 7, 2,
1556 -4, 2, 5, -4, 3, -2, 3, 8, -7, 2 };
1557
1558 std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
1559
1560 std::vector<float> inputLayerNormWeights = { 0.1f, 0.2f, -0.3f, -0.1f, 0.5f };
1561 std::vector<float> forgetLayerNormWeights = { -0.1f, 0.2f, 0.3f, 0.5f, 0.2f };
1562 std::vector<float> cellLayerNormWeights = { 0.5f, 0.2f, 0.3f, 0.4f, -0.5f };
1563 std::vector<float> outputLayerNormWeights = { 0.6f, -0.2f, -0.2f, 0.5f, 0.1f };
1564
1565 armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput);
1566 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
1567 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
1568 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
1569 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
1570 armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput);
1571 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
1572 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
1573 armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfoNum);
1574 armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp);
1575 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
1576 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
1577 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
1578 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNum);
1579 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNum);
1580 armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfoOutNum);
1581 armnn::ScopedTensorHandle projectionBiasTensor(tensorInfoOut);
1582
1583 armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfoNumFp);
1584 armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfoNumFp);
1585 armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfoNumFp);
1586 armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfoNumFp);
1587
1588 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
1589 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1590 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1591 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1592 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
1593 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1594 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1595 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1596 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
1597 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
1598 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1599 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1600 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1601 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
1602 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
1603 AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
1604 AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
1605
1606 AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data());
1607 AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
1608 AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
1609 AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());
1610
1611 data.m_InputToInputWeights = &inputToInputWeightsTensor;
1612 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1613 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1614 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1615 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
1616 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1617 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1618 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1619 data.m_CellToInputWeights = &cellToInputWeightsTensor;
1620 data.m_InputGateBias = &inputGateBiasTensor;
1621 data.m_ForgetGateBias = &forgetGateBiasTensor;
1622 data.m_CellBias = &cellBiasTensor;
1623 data.m_OutputGateBias = &outputGateBiasTensor;
1624 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
1625 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
1626 data.m_ProjectionWeights = &projectionWeightsTensor;
1627 data.m_ProjectionBias = &projectionBiasTensor;
1628
1629 data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;
1630 data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor;
1631 data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor;
1632 data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor;
1633
1634 // Flags to set test configuration
1635 data.m_Parameters.m_ActivationFunc = 4;
1636 data.m_Parameters.m_CifgEnabled = false;
1637 data.m_Parameters.m_PeepholeEnabled = true;
1638 data.m_Parameters.m_ProjectionEnabled = true;
1639 data.m_Parameters.m_LayerNormEnabled = true;
1640 data.m_Parameters.m_TimeMajor = false;
1641 data.m_Parameters.m_ClippingThresCell = 10.0f;
1642
1643 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
1644 inputHandle->Allocate();
1645 outputStateInHandle->Allocate();
1646 cellStateInHandle->Allocate();
1647 outputHandle->Allocate();
1648
1649 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1650 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1651 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1652
1653 workload->Execute();
1654
1655 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1656
1657 return LayerTestResult<float, 3>(actualOutput,
1658 expectedOutput,
1659 outputHandle->GetShape(),
1660 outputTensorInfo.GetShape());
1661}
1662
1663LayerTestResult<float, 3> UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest(
1664 armnn::IWorkloadFactory& workloadFactory,
1665 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
1666 const armnn::ITensorHandleFactory& tensorHandleFactory)
1667{
1668 IgnoreUnused(memoryManager);
1669 unsigned int batchSize = 3;
1670 unsigned int timeSize = 2;
1671 unsigned int inputSize = 3;
1672 unsigned int outputSize = 4;
1673 unsigned numUnits = outputSize;
1674
1675 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
1676 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
1677 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
1678
1679 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
1680
1681 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1682 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1683 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1684
1685 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1686 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1687
1688 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1689
1690 const std::vector<float> outputVector = { -0.0072104f, -0.00991171f, -0.00650478f, -0.00713055f,
1691 -0.0191782f, -0.0161269f, -0.0233683f, -0.054299f,
1692 -0.00783725f, 0.00635271f, -0.0126718f, -0.022613f,
1693 -0.0161351f, -0.00775868f, -0.021054f, -0.0339778f,
1694 -0.0146392f, 0.00330261f, -0.0258733f, -0.0407797f,
1695 -0.0174297f, 0.0050105f, -0.0266275f, -0.0362564f };
1696
1697 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
1698 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1699 tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
1700 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1701 tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
1702
1703 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
1704
1705 armnn::UnidirectionalSequenceLstmQueueDescriptor data;
1706 armnn::WorkloadInfo info;
1707
1708 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1709 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1710 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1711
1712 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1713
1714 armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
1715 armnn::TensorInfo tensorInfoNum({numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0);
1716 armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1717 armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0);
1718
1719 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1720 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1721 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1722
1723 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1724 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1725 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1726
1727 std::vector<int8_t> cellToForgetWeights = { 47, -52, -24, 31 };
1728 std::vector<int8_t> cellToOutputWeights = { -17, 82, 85, -77 };
1729
1730 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
1731 std::vector<float> cellBias = { 0., 0., 0., 0. };
1732 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
1733
1734 armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput);
1735 armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput);
1736 armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput);
1737 armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput);
1738 armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput);
1739 armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput);
1740 armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNum);
1741 armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNum);
1742 armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp);
1743 armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp);
1744 armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp);
1745
1746 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
1747 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
1748 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
1749 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
1750 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
1751 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
1752 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
1753 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
1754 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
1755 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
1756 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
1757
1758 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1759 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1760 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1761 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1762 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1763 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1764 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
1765 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
1766 data.m_ForgetGateBias = &forgetGateBiasTensor;
1767 data.m_CellBias = &cellBiasTensor;
1768 data.m_OutputGateBias = &outputGateBiasTensor;
1769
1770 // Flags to set test configuration
1771 data.m_Parameters.m_ClippingThresCell = 10;
1772 data.m_Parameters.m_ClippingThresProj = 0;
1773 data.m_Parameters.m_ActivationFunc = 4;
1774 data.m_Parameters.m_CifgEnabled = true;
1775 data.m_Parameters.m_PeepholeEnabled = true;
1776 data.m_Parameters.m_ProjectionEnabled = false;
1777 data.m_Parameters.m_TimeMajor = false;
1778
1779 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
1780 inputHandle->Allocate();
1781 outputStateInHandle->Allocate();
1782 cellStateInHandle->Allocate();
1783
1784 outputHandle->Allocate();
1785
1786 CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1787 CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1788 CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1789
1790 workload->Execute();
1791
1792 CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1793
1794 return LayerTestResult<float, 3>(actualOutput,
1795 outputVector,
1796 outputHandle->GetShape(),
1797 outputTensorInfo.GetShape());
1798}