Gitiles
Code Review Sign In
review.mlplatform.org / ml / armnn / 639fb0437d1a5a8a6ea737fed5a16b554dfffead / . / src / backends / backendsCommon / test / LstmTestImpl.hpp
blob: dae9c8a3f1df4b26667e85cd670c11e900443662 [file] [log] [blame]
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]1//
2// Copyright © 2017 Arm Ltd. All rights reserved.
David Beckecb56cd2018-09-05 12:52:57 +0100[diff] [blame]3// SPDX-License-Identifier: MIT
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]4//
5#pragma once
6
Aron Virginas-Tarc9cc8042018-11-01 16:15:57 +0000[diff] [blame]7#include "QuantizeHelper.hpp"
Aron Virginas-Tar5caf9072018-11-14 18:35:18 +0000[diff] [blame]8#include "WorkloadTestUtils.hpp"
Aron Virginas-Tarc9cc8042018-11-01 16:15:57 +0000[diff] [blame]9
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]10#include <armnn/ArmNN.hpp>
11#include <armnn/Tensor.hpp>
12#include <armnn/TypesUtils.hpp>
13
David Beckac42efd2018-09-26 17:41:13 +0100[diff] [blame]14#include <test/TensorHelpers.hpp>
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]15
Aron Virginas-Tarc9cc8042018-11-01 16:15:57 +0000[diff] [blame]16#include <backendsCommon/CpuTensorHandle.hpp>
17#include <backendsCommon/WorkloadFactory.hpp>
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]18
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]19template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
20LayerTestResult<T, 2>
21LstmNoCifgNoPeepholeNoProjectionTestImpl(
Aron Virginas-Tar5caf9072018-11-14 18:35:18 +0000[diff] [blame]22 armnn::IWorkloadFactory& workloadFactory,
23 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]24 const boost::multi_array<T, 2>& input,
25 const boost::multi_array<T, 2>& outputExpected,
26 float qScale = 0.0f,
27 int32_t qOffset = 0,
28 armnn::DataType constantDataType = armnn::DataType::Float32)
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]29{
30 unsigned int batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]);
31 unsigned int inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]);
32 unsigned int outputSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
33 // cellSize and outputSize have the same size when there is no projection.
34 unsigned numUnits = outputSize;
35
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]36 armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset );
37 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset);
38 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]39
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]40 armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 4}, ArmnnType, qScale, qOffset);
41 armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
42 armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
43 armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]44
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]45 LayerTestResult<T, 2> ret(outputTensorInfo);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]46
47 std::vector<float> inputVector;
48 inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
49 auto inputTensor = MakeTensor<float,2>(inputTensorInfo, inputVector);
50
51 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
52 auto cellStateInTensor = MakeTensor<float,2>(cellStateInTensorInfo, cellStateInVector);
53
54 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
55 auto outputStateInTensor = MakeTensor<float,2>(outputStateInTensorInfo, outputStateInVector);
56
Matteo Martincigha65b7ae2018-11-14 12:39:55 +0000[diff] [blame]57 std::vector<float> scratchBufferVector(batchSize * numUnits * 4, 0.f);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]58 auto scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);
59
60 std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);
61 auto outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);
62
63 std::vector<float> cellStateOutVector(batchSize * numUnits, 0.f);
64 auto cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);
65
66 std::vector<float> outputVector;
67 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));
68 ret.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputVector);
69
70 std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
71 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
72 workloadFactory.CreateTensorHandle(cellStateInTensorInfo);
73 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
74 workloadFactory.CreateTensorHandle(outputStateInTensorInfo);
75
76 std::unique_ptr<armnn::ITensorHandle> scratchHandle = workloadFactory.CreateTensorHandle(scratchBufferTensorInfo);
77 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
78 workloadFactory.CreateTensorHandle(outputStateOutTensorInfo);
79 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
80 workloadFactory.CreateTensorHandle(cellStateOutTensorInfo);
81 std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
82
83
84 armnn::LstmQueueDescriptor data;
85 armnn::WorkloadInfo info;
86
87 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
88 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
89 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
90
91 AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get());
92 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
93 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
94 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
95
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]96 armnn::TensorInfo tensorInfo4({numUnits}, constantDataType , qScale, qOffset);
97 armnn::TensorInfo tensorInfo8({numUnits, 2}, constantDataType, qScale, qOffset);
98 armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]99
100 auto inputToInputWeights = MakeTensor<float, 2>(tensorInfo8, {-0.45018822f, -0.02338299f, -0.0870589f,
101 -0.34550029f, 0.04266912f, -0.15680569f,
102 -0.34856534f, 0.43890524f});
103
104 auto inputToForgetWeights = MakeTensor<float, 2>(tensorInfo8, {0.09701663f, 0.20334584f, -0.50592935f,
105 -0.31343272f, -0.40032279f, 0.44781327f,
106 0.01387155f, -0.35593212f});
107
108 auto inputToCellWeights = MakeTensor<float, 2>(tensorInfo8, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f,
109 -0.20583314f, 0.44344562f, 0.22077113f,
110 -0.29909778f});
111
112 auto inputToOutputWeights = MakeTensor<float, 2>(tensorInfo8, {-0.25065863f, -0.28290087f, 0.04613829f,
113 0.40525138f, 0.44272184f, 0.03897077f,
114 -0.1556896f, 0.19487578f});
115
116 auto recurrentToInputWeights = MakeTensor<float, 2>(tensorInfo16, {-0.0063535f, -0.2042388f, 0.31454784f,
117 -0.35746509f, 0.28902304f, 0.08183324f,
118 -0.16555229f, 0.02286911f, -0.13566875f,
119 0.03034258f, 0.48091322f, -0.12528998f,
120 0.24077177f, -0.51332325f, -0.33502164f,
121 0.10629296f});
122
123 auto recurrentToForgetWeights = MakeTensor<float, 2>(tensorInfo16, {-0.48684245f, -0.06655136f, 0.42224967f,
124 0.2112639f, 0.27654213f, 0.20864892f,
125 -0.07646349f, 0.45877004f, 0.00141793f,
126 -0.14609534f, 0.36447752f, 0.09196436f,
127 0.28053468f, 0.01560611f, -0.20127171f,
128 -0.01140004f});
129
130 auto recurrentToCellWeights = MakeTensor<float, 2>(tensorInfo16, {-0.3407414f, 0.24443203f, -0.2078532f,
131 0.26320225f, 0.05695659f, -0.00123841f,
132 -0.4744786f, -0.35869038f, -0.06418842f,
133 -0.13502428f, -0.501764f, 0.22830659f,
134 -0.46367589f, 0.26016325f, -0.03894562f,
135 -0.16368064f});
136
137 auto recurrentToOutputWeights = MakeTensor<float, 2>(tensorInfo16, {0.43385774f, -0.17194885f, 0.2718237f,
138 0.09215671f, 0.24107647f, -0.39835793f,
139 0.18212086f, 0.01301402f, 0.48572797f,
140 -0.50656658f, 0.20047462f, -0.20607421f,
141 -0.51818722f, -0.15390486f, 0.0468148f,
142 0.39922136f});
143
144 auto cellToInputWeights = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
145
146 auto inputGateBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
147
148 auto forgetGateBias = MakeTensor<float, 1>(tensorInfo4, {1., 1., 1., 1.});
149
150 auto cellBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
151
152 auto outputGateBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
153
154 armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo8);
155 armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo8);
156 armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo8);
157 armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo8);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]158 armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo16);
Matteo Martincigha65b7ae2018-11-14 12:39:55 +0000[diff] [blame]159 armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]160 armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
161 armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
162 armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo4);
163 armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo4);
164 armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo4);
165 armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo4);
166 armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo4);
167
168 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);
169 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
170 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
171 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
172 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);
173 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
174 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
175 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
176 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]);
177 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]);
178 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
179 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
180 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
181
182 data.m_InputToInputWeights = &inputToInputWeightsTensor;
183 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
184 data.m_InputToCellWeights = &inputToCellWeightsTensor;
185 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
186 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
187 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
188 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
189 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
190 data.m_CellToInputWeights = &cellToInputWeightsTensor;
191 data.m_InputGateBias = &inputGateBiasTensor;
192 data.m_ForgetGateBias = &forgetGateBiasTensor;
193 data.m_CellBias = &cellBiasTensor;
194 data.m_OutputGateBias = &outputGateBiasTensor;
195
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]196 // Flags to set test configuration
197 data.m_Parameters.m_ActivationFunc = 4;
198 data.m_Parameters.m_CifgEnabled = false;
199 data.m_Parameters.m_PeepholeEnabled = false;
200 data.m_Parameters.m_ProjectionEnabled = false;
201
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]202 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info);
203 inputHandle->Allocate();
204 outputStateInHandle->Allocate();
205 cellStateInHandle->Allocate();
206
207 scratchHandle->Allocate();
208 outputStateOutHandle->Allocate();
209 cellStateOutHandle->Allocate();
210 outputHandle->Allocate();
211
212 CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
213 CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
214 CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
215
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]216 workload->Execute();
217
218 CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
219
220 return ret;
221}
222
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]223template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
224LayerTestResult<T, 2>
Matteo Martincigha65b7ae2018-11-14 12:39:55 +0000[diff] [blame]225LstmLayerNoCifgWithPeepholeWithProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory,
226 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]227 const boost::multi_array<T, 2>& input,
228 const boost::multi_array<T, 2>& outputExpected,
229 float qScale = 0.0f,
230 int32_t qOffset = 0,
231 armnn::DataType constantDataType = armnn::DataType::Float32)
Aron Virginas-Tar5caf9072018-11-14 18:35:18 +0000[diff] [blame]232{
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]233 unsigned int batchSize = 2;
234 unsigned int outputSize = 16;
235 unsigned int inputSize = 5;
236 unsigned numUnits = 20;
237
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]238 armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset);
239 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset);
240 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]241
Matteo Martincigha65b7ae2018-11-14 12:39:55 +0000[diff] [blame]242 // Scratch buffer size without CIFG [batchSize, numUnits * 4]
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]243 armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 4}, ArmnnType, qScale, qOffset);
244 armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
245 armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
246 armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]247
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]248 LayerTestResult<T, 2> ret(outputTensorInfo);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]249
250 std::vector<float> inputVector;
251 inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
252 auto inputTensor = MakeTensor<float,2>(inputTensorInfo, inputVector);
253
254 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
255 auto cellStateInTensor = MakeTensor<float,2>(cellStateInTensorInfo, cellStateInVector);
256
257 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
258 auto outputStateInTensor = MakeTensor<float,2>(outputStateInTensorInfo, outputStateInVector);
259
Matteo Martincigha65b7ae2018-11-14 12:39:55 +0000[diff] [blame]260 std::vector<float> scratchBufferVector(batchSize * numUnits * 4, 0.f);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]261 auto scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);
262
263 std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);
264 auto outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);
265
266 std::vector<float> cellStateOutVector(batchSize * numUnits, 0.f);
267 auto cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);
268
269 std::vector<float> outputVector;
270 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));
271 ret.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputVector);
272
273 std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
274 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
275 workloadFactory.CreateTensorHandle(cellStateInTensorInfo);
276 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
277 workloadFactory.CreateTensorHandle(outputStateInTensorInfo);
278
279 std::unique_ptr<armnn::ITensorHandle> scratchHandle = workloadFactory.CreateTensorHandle(scratchBufferTensorInfo);
280 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
281 workloadFactory.CreateTensorHandle(outputStateOutTensorInfo);
282 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
283 workloadFactory.CreateTensorHandle(cellStateOutTensorInfo);
284 std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
285
286 armnn::LstmQueueDescriptor data;
287 armnn::WorkloadInfo info;
288
289 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
290 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
291 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
David Beckac42efd2018-09-26 17:41:13 +0100[diff] [blame]292
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]293 AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get());
294 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
295 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
296 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
297
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]298 armnn::TensorInfo tensorInfo16({outputSize}, constantDataType, qScale, qOffset);
299 armnn::TensorInfo tensorInfo20({numUnits}, constantDataType, qScale, qOffset);
300 armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, constantDataType, qScale, qOffset);
301 armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, constantDataType, qScale, qOffset);
302 armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, constantDataType, qScale, qOffset);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]303
304 auto inputToInputWeights =
305 MakeTensor<float, 2>(tensorInfo20x5, {0.021393683f,0.06124551f, 0.046905167f,-0.014657677f,-0.03149463f,
306 0.09171803f, 0.14647801f,0.10797193f, -0.0057968358f,0.0019193048f,
307 -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f,
308 -0.022599787f,-0.09121155f, -0.008675967f, -0.045206103f,-0.0821282f,
309 -0.008045952f,0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f,
310 -0.06453243f,0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f,
311 -0.1671009f, -0.15519552f, -0.16819797f,-0.13971269f,-0.11953059f,
312 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f,
313 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f,
314 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f,
315 -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f,
316 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f,
317 -0.028545704f,0.024939531f,0.050929718f,0.0076203286f,-0.0029723682f,
318 -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f,-0.16327988f,
319 -0.2273378f, -0.0993647f, -0.017155107f,0.0023917493f,0.049272764f,
320 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f,
321 -0.04544234f, -0.0497073f,-0.07135631f, -0.048929106f,-0.004042012f,
322 -0.009284026f, 0.018042054f, 0.0036860977f,-0.07427302f, -0.11434604f,
323 -0.018995456f, 0.031487543f, 0.012834908f,0.019977754f,0.044256654f,
324 -0.39292613f, -0.18519334f, -0.11651281f,-0.06809892f, 0.011373677f
325 });
326
327 auto inputToForgetWeights =
328 MakeTensor<float, 2>(tensorInfo20x5, {-0.0018401089f, -0.004852237f,0.03698424f, 0.014181704f,0.028273236f,
329 -0.016726194f, -0.05249759f,-0.10204261f, 0.00861066f,-0.040979505f,
330 -0.009899187f,0.01923892f,-0.028177269f, -0.08535103f,-0.14585495f,
331 0.10662567f,-0.01909731f,-0.017883534f,-0.0047269356f,-0.045103323f,
332 0.0030784295f,0.076784775f,0.07463696f, 0.094531395f,0.0814421f,
333 -0.12257899f, -0.033945758f,-0.031303465f, 0.045630626f,0.06843887f,
334 -0.13492945f, -0.012480007f,-0.0811829f, -0.07224499f,-0.09628791f,
335 0.045100946f,0.0012300825f, 0.013964662f, 0.099372394f,0.02543059f,
336 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f,0.052625068f,
337 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f,0.07241905f,
338 0.018668644f, -0.037377294f,-0.06277783f,-0.08833636f,-0.040120605f,
339 -0.011405586f,-0.007808335f,-0.010301386f,-0.005102167f,0.027717464f,
340 0.05483423f, 0.11449111f, 0.11289652f,0.10939839f, 0.13396506f,
341 -0.08402166f,-0.01901462f, -0.044678304f,-0.07720565f,0.014350063f,
342 -0.11757958f, -0.0652038f, -0.08185733f,-0.076754324f,-0.092614375f,
343 0.10405491f, 0.052960336f, 0.035755895f,0.035839386f,-0.012540553f,
344 0.036881298f, 0.02913376f, 0.03420159f,0.05448447f,-0.054523353f,
345 0.02582715f, 0.02327355f, -0.011857179f,-0.0011980024f,-0.034641717f,
346 -0.026125094f,-0.17582615f,-0.15923657f,-0.27486774f,-0.0006143371f,
347 0.0001771948f, -8.470171e-05f, 0.02651807f,0.045790765f,0.06956496f
348 });
349
350 auto inputToCellWeights =
351 MakeTensor<float, 2>(tensorInfo20x5, {-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,
352 -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,
353 -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,
354 -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,
355 -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,
356 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f,
357 -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f,
358 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f,
359 -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f,
360 -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f,
361 -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f,
362 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f,
363 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f,
364 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f,
365 -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f,
366 -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f,
367 -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f,
368 -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f,
369 -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f,
370 -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f,
371 0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f,
372 0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f,
373 0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f,
374 0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f,
375 0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f
376 });
377
378 auto inputToOutputWeights =
379 MakeTensor<float, 2>(tensorInfo20x5, {-0.0998932f, -0.07201956f, -0.052803773f,-0.15629593f,-0.15001918f,
380 -0.07650751f,0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f,
381 0.029517552f, -0.04751393f, 0.010350531f,-0.02664851f, -0.016839722f,
382 -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f,-0.0129761f,
383 -0.021737747f,-0.038305793f,-0.06870586f, -0.01481247f,-0.001285394f,
384 0.10124236f, 0.083122835f, 0.053313006f,-0.062235646f,-0.075637154f,
385 -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f,
386 -0.086665764f,-0.037162706f,-0.038880914f,-0.035832845f,-0.014481564f,
387 -0.09825003f,-0.12048569f,-0.097665586f,-0.05287633f, -0.0964047f,
388 -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f,0.050649304f,
389 0.05798951f, -0.021852335f,-0.099848844f,0.014740475f,-0.078897946f,
390 0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f,
391 0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f,0.06945813f,
392 -0.078907564f,-0.06707616f, -0.11844508f, -0.09986688f,-0.07509403f,
393 0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f,0.14639415f,
394 0.0015017595f, -0.014267382f, -0.03417257f,0.012711468f,0.0028300495f,
395 -0.024758482f, -0.05098548f,-0.0821182f, 0.014225672f, 0.021544158f,
396 0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f,0.06476295f,
397 -0.022907063f,0.027562456f,0.040185735f, 0.019567577f,-0.015598739f,
398 -0.049097303f, -0.017121866f, -0.083368234f,-0.02332002f,-0.0840956f
399 });
400
401 auto inputGateBias =
402 MakeTensor<float, 1>(tensorInfo20, {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f,
403 -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f,
404 -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f,
405 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f
406 });
407
408 auto forgetGateBias =
409 MakeTensor<float, 1>(tensorInfo20, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f,
410 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f,
411 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f,
412 -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f,
413 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f
414 });
415
416 auto cellBias =
417 MakeTensor<float, 1>(tensorInfo20, {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f,
418 -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f,
419 -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f,
420 -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f,
421 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f
422 });
423
424 auto outputGateBias =
425 MakeTensor<float, 1>(tensorInfo20, {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f,
426 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f,
427 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f,
428 -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f
429 });
430
431 auto recurrentToInputWeights =
432 MakeTensor<float, 2>(tensorInfo20x16, {-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,
433 -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f,
434 -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f,
435 -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f,
436 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f,
437 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f,
438 -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f,
439 0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f,
440 -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f,
441 0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f,
442 -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f,
443 0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f,
444 -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f,
445 0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f,
446 -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f,
447 -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f,
448 -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f,
449 -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f,
450 -0.10820036f, -0.04666372f, 0.022746278f, -0.07851417f,
451 0.01068115f, 0.032956902f, 0.022433773f, 0.0026891115f,
452 0.08944216f, -0.0685835f, 0.010513544f, 0.07228705f,
453 0.02032331f, -0.059686817f, -0.0005566496f, -0.086984694f,
454 0.040414046f, -0.1380399f, 0.094208956f, -0.05722982f,
455 0.012092817f, -0.04989123f, -0.086576f, -0.003399834f,
456 -0.04696032f, -0.045747425f, 0.10091314f, 0.048676282f,
457 -0.029037097f, 0.031399418f, -0.0040285117f, 0.047237843f,
458 0.09504992f, 0.041799378f, -0.049185462f, -0.031518843f,
459 -0.10516937f, 0.026374253f, 0.10058866f, -0.0033195973f,
460 -0.041975245f, 0.0073591834f, 0.0033782164f, -0.004325073f,
461 -0.10167381f, 0.042500053f, -0.01447153f, 0.06464186f,
462 -0.017142897f, 0.03312627f, 0.009205989f, 0.024138335f,
463 -0.011337001f, 0.035530265f, -0.010912711f, 0.0706555f,
464 -0.005894094f, 0.051841937f, -0.1401738f, -0.02351249f,
465 0.0365468f, 0.07590991f, 0.08838724f, 0.021681072f,
466 -0.10086113f, 0.019608743f, -0.06195883f, 0.077335775f,
467 0.023646897f, -0.095322326f, 0.02233014f, 0.09756986f,
468 -0.048691444f, -0.009579111f, 0.07595467f, 0.11480546f,
469 -0.09801813f, 0.019894179f, 0.08502348f, 0.004032281f,
470 0.037211012f, 0.068537936f, -0.048005626f, -0.091520436f,
471 -0.028379958f, -0.01556313f, 0.06554592f, -0.045599163f,
472 -0.01672207f, -0.020169014f, -0.011877351f, -0.20212261f,
473 0.010889619f, 0.0047078193f, 0.038385306f, 0.08540671f,
474 -0.017140968f, -0.0035865551f, 0.016678626f, 0.005633034f,
475 0.015963363f, 0.00871737f, 0.060130805f, 0.028611384f,
476 0.10109069f, -0.015060172f, -0.07894427f, 0.06401885f,
477 0.011584063f, -0.024466386f, 0.0047652307f, -0.09041358f,
478 0.030737216f, -0.0046374933f, 0.14215417f, -0.11823516f,
479 0.019899689f, 0.006106124f, -0.027092824f, 0.0786356f,
480 0.05052217f, -0.058925f, -0.011402121f, -0.024987547f,
481 -0.0013661642f, -0.06832946f, -0.015667673f, -0.1083353f,
482 -0.00096863037f, -0.06988685f, -0.053350925f, -0.027275559f,
483 -0.033664223f, -0.07978348f, -0.025200296f, -0.017207067f,
484 -0.058403496f, -0.055697463f, 0.005798788f, 0.12965427f,
485 -0.062582195f, 0.0013350133f, -0.10482091f, 0.0379771f,
486 0.072521195f, -0.0029455067f, -0.13797039f, -0.03628521f,
487 0.013806405f, -0.017858358f, -0.01008298f, -0.07700066f,
488 -0.017081132f, 0.019358726f, 0.0027079724f, 0.004635139f,
489 0.062634714f, -0.02338735f, -0.039547626f, -0.02050681f,
490 0.03385117f, -0.083611414f, 0.002862572f, -0.09421313f,
491 0.058618143f, -0.08598433f, 0.00972939f, 0.023867095f,
492 -0.053934585f, -0.023203006f, 0.07452513f, -0.048767887f,
493 -0.07314807f, -0.056307215f, -0.10433547f, -0.06440842f,
494 0.04328182f, 0.04389765f, -0.020006588f, -0.09076438f,
495 -0.11652589f, -0.021705797f, 0.03345259f, -0.010329105f,
496 -0.025767034f, 0.013057034f, -0.07316461f, -0.10145612f,
497 0.06358255f, 0.18531723f, 0.07759293f, 0.12006465f,
498 0.1305557f, 0.058638252f, -0.03393652f, 0.09622831f,
499 -0.16253184f, -2.4580743e-06f, 0.079869635f, -0.070196845f,
500 -0.005644518f, 0.06857898f, -0.12598175f, -0.035084512f,
501 0.03156317f, -0.12794146f, -0.031963028f, 0.04692781f,
502 0.030070418f, 0.0071660685f, -0.095516115f, -0.004643372f,
503 0.040170413f, -0.062104587f, -0.0037324072f, 0.0554317f,
504 0.08184801f, -0.019164372f, 0.06791302f, 0.034257166f,
505 -0.10307039f, 0.021943003f, 0.046745934f, 0.0790918f,
506 -0.0265588f, -0.007824208f, 0.042546265f, -0.00977924f,
507 -0.0002440307f, -0.017384544f, -0.017990116f, 0.12252321f,
508 -0.014512694f, -0.08251313f, 0.08861942f, 0.13589665f,
509 0.026351685f, 0.012641483f, 0.07466548f, 0.044301085f,
510 -0.045414884f, -0.051112458f, 0.03444247f, -0.08502782f,
511 -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f
512 });
513
514 auto recurrentToForgetWeights =
515 MakeTensor<float, 2>(tensorInfo20x16, {-0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f,
516 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f,
517 -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f,
518 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f,
519 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f,
520 -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f,
521 -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f,
522 0.061878487f, -0.04729229f, 0.034919553f, -0.07585433f,
523 -0.04421272f, -0.044019096f, 0.085488975f, 0.04058006f,
524 -0.06890133f, -0.030951202f, -0.024628663f, -0.07672815f,
525 0.034293607f, 0.08556707f, -0.05293577f, -0.033561368f,
526 -0.04899627f, 0.0241671f, 0.015736353f, -0.095442444f,
527 -0.029564252f, 0.016493602f, -0.035026584f, 0.022337519f,
528 -0.026871363f, 0.004780428f, 0.0077918363f, -0.03601621f,
529 0.016435321f, -0.03263031f, -0.09543275f, -0.047392778f,
530 0.013454138f, 0.028934088f, 0.01685226f, -0.086110644f,
531 -0.046250615f, -0.01847454f, 0.047608484f, 0.07339695f,
532 0.034546845f, -0.04881143f, 0.009128804f, -0.08802852f,
533 0.03761666f, 0.008096139f, -0.014454086f, 0.014361001f,
534 -0.023502491f, -0.0011840804f, -0.07607001f, 0.001856849f,
535 -0.06509276f, -0.006021153f, -0.08570962f, -0.1451793f,
536 0.060212336f, 0.055259194f, 0.06974018f, 0.049454916f,
537 -0.027794661f, -0.08077226f, -0.016179763f, 0.1169753f,
538 0.17213494f, -0.0056326236f, -0.053934924f, -0.0124349f,
539 -0.11520337f, 0.05409887f, 0.088759385f, 0.0019655675f,
540 0.0042065294f, 0.03881498f, 0.019844765f, 0.041858196f,
541 -0.05695512f, 0.047233116f, 0.038937137f, -0.06542224f,
542 0.014429736f, -0.09719407f, 0.13908425f, -0.05379757f,
543 0.012321099f, 0.082840554f, -0.029899208f, 0.044217527f,
544 0.059855383f, 0.07711018f, -0.045319796f, 0.0948846f,
545 -0.011724666f, -0.0033288454f, -0.033542685f, -0.04764985f,
546 -0.13873616f, 0.040668588f, 0.034832682f, -0.015319203f,
547 -0.018715994f, 0.046002675f, 0.0599172f, -0.043107376f,
548 0.0294216f, -0.002314414f, -0.022424703f, 0.0030315618f,
549 0.0014641669f, 0.0029166266f, -0.11878115f, 0.013738511f,
550 0.12375372f, -0.0006038222f, 0.029104086f, 0.087442465f,
551 0.052958444f, 0.07558703f, 0.04817258f, 0.044462286f,
552 -0.015213451f, -0.08783778f, -0.0561384f, -0.003008196f,
553 0.047060397f, -0.002058388f, 0.03429439f, -0.018839769f,
554 0.024734668f, 0.024614193f, -0.042046934f, 0.09597743f,
555 -0.0043254104f, 0.04320769f, 0.0064070094f, -0.0019131786f,
556 -0.02558259f, -0.022822596f, -0.023273505f, -0.02464396f,
557 -0.10991725f, -0.006240552f, 0.0074488563f, 0.024044557f,
558 0.04383914f, -0.046476185f, 0.028658995f, 0.060410924f,
559 0.050786525f, 0.009452605f, -0.0073054377f, -0.024810238f,
560 0.0052906186f, 0.0066939713f, -0.0020913032f, 0.014515517f,
561 0.015898481f, 0.021362653f, -0.030262267f, 0.016587038f,
562 -0.011442813f, 0.041154444f, -0.007631438f, -0.03423484f,
563 -0.010977775f, 0.036152758f, 0.0066366293f, 0.11915515f,
564 0.02318443f, -0.041350313f, 0.021485701f, -0.10906167f,
565 -0.028218046f, -0.00954771f, 0.020531068f, -0.11995105f,
566 -0.03672871f, 0.024019798f, 0.014255957f, -0.05221243f,
567 -0.00661567f, -0.04630967f, 0.033188973f, 0.10107534f,
568 -0.014027541f, 0.030796422f, -0.10270911f, -0.035999842f,
569 0.15443139f, 0.07684145f, 0.036571592f, -0.035900835f,
570 -0.0034699554f, 0.06209149f, 0.015920248f, -0.031122351f,
571 -0.03858649f, 0.01849943f, 0.13872518f, 0.01503974f,
572 0.069941424f, -0.06948533f, -0.0088794185f, 0.061282158f,
573 -0.047401894f, 0.03100163f, -0.041533746f, -0.10430945f,
574 0.044574402f, -0.01425562f, -0.024290353f, 0.034563623f,
575 0.05866852f, 0.023947537f, -0.09445152f, 0.035450947f,
576 0.02247216f, -0.0042998926f, 0.061146557f, -0.10250651f,
577 0.020881841f, -0.06747029f, 0.10062043f, -0.0023941975f,
578 0.03532124f, -0.016341697f, 0.09685456f, -0.016764693f,
579 0.051808182f, 0.05875331f, -0.04536488f, 0.001626336f,
580 -0.028892258f, -0.01048663f, -0.009793449f, -0.017093895f,
581 0.010987891f, 0.02357273f, -0.00010856845f, 0.0099760275f,
582 -0.001845119f, -0.03551521f, 0.0018358806f, 0.05763657f,
583 -0.01769146f, 0.040995963f, 0.02235177f, -0.060430344f,
584 0.11475477f, -0.023854522f, 0.10071741f, 0.0686208f,
585 -0.014250481f, 0.034261297f, 0.047418304f, 0.08562733f,
586 -0.030519066f, 0.0060542435f, 0.014653856f, -0.038836084f,
587 0.04096551f, 0.032249358f, -0.08355519f, -0.026823482f,
588 0.056386515f, -0.010401743f, -0.028396193f, 0.08507674f,
589 0.014410365f, 0.020995233f, 0.17040324f, 0.11511526f,
590 0.02459721f, 0.0066619175f, 0.025853224f, -0.023133837f,
591 -0.081302024f, 0.017264642f, -0.009585969f, 0.09491168f,
592 -0.051313367f, 0.054532815f, -0.014298593f, 0.10657464f,
593 0.007076659f, 0.10964551f, 0.0409152f, 0.008275321f,
594 -0.07283536f, 0.07937492f, 0.04192024f, -0.1075027f
595 });
596
597 auto recurrentToCellWeights =
598 MakeTensor<float, 2>(tensorInfo20x16, {-0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,
599 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f,
600 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f,
601 -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f,
602 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f,
603 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f,
604 -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f,
605 -0.019443132f, -0.030755889f, -0.0040000007f, 0.04465846f,
606 -0.021585021f, 0.0031670958f, 0.0053199246f, -0.056117613f,
607 -0.10893326f, 0.076739706f, -0.08509834f, -0.027997585f,
608 0.037871376f, 0.01449768f, -0.09002357f, -0.06111149f,
609 -0.046195522f, 0.0422062f, -0.005683705f, -0.1253618f,
610 -0.012925729f, -0.04890792f, 0.06985068f, 0.037654128f,
611 0.03398274f, -0.004781977f, 0.007032333f, -0.031787455f,
612 0.010868644f, -0.031489216f, 0.09525667f, 0.013939797f,
613 0.0058680447f, 0.0167067f, 0.02668468f, -0.04797466f,
614 -0.048885044f, -0.12722108f, 0.035304096f, 0.06554885f,
615 0.00972396f, -0.039238118f, -0.05159735f, -0.11329045f,
616 0.1613692f, -0.03750952f, 0.06529313f, -0.071974665f,
617 -0.11769596f, 0.015524369f, -0.0013754242f, -0.12446318f,
618 0.02786344f, -0.014179351f, 0.005264273f, 0.14376344f,
619 0.015983658f, 0.03406988f, -0.06939408f, 0.040699873f,
620 0.02111075f, 0.09669095f, 0.041345075f, -0.08316494f,
621 -0.07684199f, -0.045768797f, 0.032298047f, -0.041805092f,
622 0.0119405f, 0.0061010392f, 0.12652606f, 0.0064572375f,
623 -0.024950314f, 0.11574242f, 0.04508852f, -0.04335324f,
624 0.06760663f, -0.027437469f, 0.07216407f, 0.06977076f,
625 -0.05438599f, 0.034033038f, -0.028602652f, 0.05346137f,
626 0.043184172f, -0.037189785f, 0.10420091f, 0.00882477f,
627 -0.054019816f, -0.074273005f, -0.030617684f, -0.0028467078f,
628 0.024302477f, -0.0038869337f, 0.005332455f, 0.0013399826f,
629 0.04361412f, -0.007001822f, 0.09631092f, -0.06702025f,
630 -0.042049985f, -0.035070654f, -0.04103342f, -0.10273396f,
631 0.0544271f, 0.037184782f, -0.13150354f, -0.0058036847f,
632 -0.008264958f, 0.042035464f, 0.05891794f, 0.029673764f,
633 0.0063542654f, 0.044788733f, 0.054816857f, 0.062257513f,
634 -0.00093483756f, 0.048938446f, -0.004952862f, -0.007730018f,
635 -0.04043371f, -0.017094059f, 0.07229206f, -0.023670016f,
636 -0.052195564f, -0.025616996f, -0.01520939f, 0.045104615f,
637 -0.007376126f, 0.003533447f, 0.006570588f, 0.056037236f,
638 0.12436656f, 0.051817212f, 0.028532185f, -0.08686856f,
639 0.11868599f, 0.07663395f, -0.07323171f, 0.03463402f,
640 -0.050708205f, -0.04458982f, -0.11590894f, 0.021273347f,
641 0.1251325f, -0.15313013f, -0.12224372f, 0.17228661f,
642 0.023029093f, 0.086124025f, 0.006445803f, -0.03496501f,
643 0.028332196f, 0.04449512f, -0.042436164f, -0.026587414f,
644 -0.006041347f, -0.09292539f, -0.05678812f, 0.03897832f,
645 0.09465633f, 0.008115513f, -0.02171956f, 0.08304309f,
646 0.071401566f, 0.019622514f, 0.032163795f, -0.004167056f,
647 0.02295182f, 0.030739572f, 0.056506045f, 0.004612461f,
648 0.06524936f, 0.059999723f, 0.046395954f, -0.0045512207f,
649 -0.1335546f, -0.030136576f, 0.11584653f, -0.014678886f,
650 0.0020118146f, -0.09688814f, -0.0790206f, 0.039770417f,
651 -0.0329582f, 0.07922767f, 0.029322514f, 0.026405897f,
652 0.04207835f, -0.07073373f, 0.063781224f, 0.0859677f,
653 -0.10925287f, -0.07011058f, 0.048005477f, 0.03438226f,
654 -0.09606514f, -0.006669445f, -0.043381985f, 0.04240257f,
655 -0.06955775f, -0.06769346f, 0.043903265f, -0.026784198f,
656 -0.017840602f, 0.024307009f, -0.040079936f, -0.019946516f,
657 0.045318738f, -0.12233574f, 0.026170589f, 0.0074471775f,
658 0.15978073f, 0.10185836f, 0.10298046f, -0.015476589f,
659 -0.039390966f, -0.072174534f, 0.0739445f, -0.1211869f,
660 -0.0347889f, -0.07943156f, 0.014809798f, -0.12412325f,
661 -0.0030663363f, 0.039695457f, 0.0647603f, -0.08291318f,
662 -0.018529687f, -0.004423833f, 0.0037507233f, 0.084633216f,
663 -0.01514876f, -0.056505352f, -0.012800942f, -0.06994386f,
664 0.012962922f, -0.031234352f, 0.07029052f, 0.016418684f,
665 0.03618972f, 0.055686004f, -0.08663945f, -0.017404709f,
666 -0.054761406f, 0.029065743f, 0.052404847f, 0.020238016f,
667 0.0048197987f, -0.0214882f, 0.07078733f, 0.013016777f,
668 0.06262858f, 0.009184685f, 0.020785125f, -0.043904778f,
669 -0.0270329f, -0.03299152f, -0.060088247f, -0.015162964f,
670 -0.001828936f, 0.12642565f, -0.056757294f, 0.013586685f,
671 0.09232601f, -0.035886683f, 0.06000002f, 0.05229691f,
672 -0.052580316f, -0.082029596f, -0.010794592f, 0.012947712f,
673 -0.036429964f, -0.085508935f, -0.13127148f, -0.017744139f,
674 0.031502828f, 0.036232427f, -0.031581745f, 0.023051167f,
675 -0.05325106f, -0.03421577f, 0.028793324f, -0.034633752f,
676 -0.009881397f, -0.043551125f, -0.018609839f, 0.0019097115f,
677 -0.008799762f, 0.056595087f, 0.0022273948f, 0.055752404f
678 });
679
680 auto recurrentToOutputWeights =
681 MakeTensor<float, 2>(tensorInfo20x16, {0.025825322f, -0.05813119f, 0.09495884f,-0.045984812f, -0.01255415f,
682 -0.0026479573f,-0.08196161f,-0.054914974f,-0.0046604523f,
683 -0.029587349f, -0.044576716f, -0.07480124f, -0.082868785f,
684 0.023254942f, 0.027502948f, -0.0039728214f, -0.08683098f,
685 -0.08116779f, -0.014675607f, -0.037924774f, -0.023314456f,
686 -0.007401714f, -0.09255757f, 0.029460307f, -0.08829125f,
687 -0.005139627f, -0.08989442f, -0.0555066f, 0.13596267f,
688 -0.025062224f, -0.048351806f, -0.03850004f, 0.07266485f,
689 -0.022414139f, 0.05940088f, 0.075114764f, 0.09597592f,
690 -0.010211725f, -0.0049794707f, -0.011523867f, -0.025980417f,
691 0.072999895f, 0.11091378f, -0.081685916f, 0.014416728f,
692 0.043229222f, 0.034178585f, -0.07530371f, 0.035837382f,
693 -0.085607f, -0.007721233f, -0.03287832f, -0.043848954f,
694 -0.06404588f, -0.06632928f, -0.073643476f, 0.008214239f,
695 -0.045984086f, 0.039764922f, 0.03474462f, 0.060612556f,
696 -0.080590084f, 0.049127717f, 0.04151091f, -0.030063879f,
697 0.008801774f, -0.023021035f, -0.019558564f, 0.05158114f,
698 -0.010947698f, -0.011825728f, 0.0075720972f, 0.0699727f,
699 -0.0039981045f, 0.069350146f, 0.08799282f, 0.016156472f,
700 0.035502106f, 0.11695009f, 0.006217345f, 0.13392477f,
701 -0.037875112f, 0.025745004f, 0.08940699f, -0.00924166f,
702 0.0046702605f, -0.036598757f, -0.08811812f, 0.10522024f,
703 -0.032441203f, 0.008176899f, -0.04454919f, 0.07058152f,
704 0.0067963637f, 0.039206743f, 0.03259838f, 0.03725492f,
705 -0.09515802f, 0.013326398f, -0.052055415f, -0.025676316f,
706 0.03198509f, -0.015951829f, -0.058556724f, 0.036879618f,
707 0.043357447f, 0.028362012f, -0.05908629f, 0.0059240665f,
708 -0.04995891f, -0.019187413f,0.0276265f, -0.01628143f, 0.0025863599f,
709 0.08800015f, 0.035250366f, -0.022165963f, -0.07328642f,
710 -0.009415526f, -0.07455109f, 0.11690406f, 0.0363299f,
711 0.07411125f, 0.042103454f, -0.009660886f, 0.019076364f,
712 0.018299393f, -0.046004917f, 0.08891175f,0.0431396f, -0.026327137f,
713 -0.051502608f, 0.08979574f, -0.051670972f, 0.04940282f,
714 -0.07491107f, -0.021240504f, 0.022596184f, -0.034280192f,
715 0.060163025f, -0.058211457f, -0.051837247f, -0.01349775f,
716 -0.04639988f, -0.035936575f, -0.011681591f, 0.064818054f,
717 0.0073146066f, -0.021745546f, -0.043124277f, -0.06471268f,
718 -0.07053354f, -0.029321948f, -0.05330136f, 0.016933719f,
719 -0.053782392f, 0.13747959f, -0.1361751f, -0.11569455f,
720 0.0033329215f, 0.05693899f, -0.053219706f, 0.063698f,
721 0.07977434f, -0.07924483f, 0.06936997f, 0.0034815092f,
722 -0.007305279f, -0.037325785f, -0.07251102f, -0.033633437f,
723 -0.08677009f, 0.091591336f, -0.14165086f, 0.021752775f,
724 0.019683983f, 0.0011612234f, -0.058154266f, 0.049996935f,
725 0.0288841f, -0.0024567875f, -0.14345716f, 0.010955264f,-0.10234828f,
726 0.1183656f, -0.0010731248f, -0.023590032f,-0.072285876f,-0.0724771f,
727 -0.026382286f, -0.0014920527f, 0.042667855f, 0.0018776858f,
728 0.02986552f, 0.009814309f, 0.0733756f, 0.12289186f,
729 0.018043943f, -0.0458958f, 0.049412545f, 0.033632483f,
730 0.05495232f, 0.036686596f, -0.013781798f, -0.010036754f,
731 0.02576849f, -0.08307328f, 0.010112348f, 0.042521734f,
732 -0.05869831f, -0.071689695f, 0.03876447f, -0.13275425f, -0.0352966f,
733 -0.023077697f, 0.10285965f, 0.084736146f, 0.15568255f,
734 -0.00040734606f, 0.027835453f, -0.10292561f, -0.032401145f,
735 0.10053256f, -0.026142767f, -0.08271222f, -0.0030240538f,
736 -0.016368777f, 0.1070414f, 0.042672627f, 0.013456989f,
737 -0.0437609f, -0.022309763f, 0.11576483f, 0.04108048f,
738 0.061026827f, -0.0190714f, -0.0869359f, 0.037901703f, 0.0610107f,
739 0.07202949f, 0.01675338f, 0.086139716f, -0.08795751f,
740 -0.014898893f, -0.023771819f, -0.01965048f, 0.007955471f,
741 -0.043740474f, 0.03346837f, -0.10549954f, 0.090567775f,
742 0.042013682f, -0.03176985f, 0.12569028f, -0.02421228f,
743 -0.029526481f, 0.023851605f, 0.031539805f, 0.05292009f,
744 -0.02344001f, -0.07811758f, -0.08834428f, 0.10094801f,
745 0.16594367f, -0.06861939f, -0.021256343f, -0.041093912f,
746 -0.06669611f, 0.035498552f, 0.021757556f, -0.09302526f,
747 -0.015403468f, -0.06614931f, -0.051798206f, -0.013874718f,
748 0.03630673f, 0.010412845f, -0.08077351f, 0.046185967f,
749 0.0035662893f, 0.03541868f, -0.094149634f, -0.034814864f,
750 0.003128424f, -0.020674974f, -0.03944324f, -0.008110165f,
751 -0.11113267f, 0.08484226f, 0.043586485f, 0.040582247f,
752 0.0968012f, -0.065249965f, -0.028036479f, 0.0050708856f,
753 0.0017462453f, 0.0326779f, 0.041296225f, 0.09164146f,
754 -0.047743853f, -0.015952192f, -0.034451712f, 0.084197424f,
755 -0.05347844f, -0.11768019f, 0.085926116f, -0.08251791f,
756 -0.045081906f, 0.0948852f, 0.068401024f, 0.024856757f,
757 0.06978981f, -0.057309967f, -0.012775832f, -0.0032452994f,
758 0.01977615f, -0.041040014f, -0.024264973f,0.063464895f, 0.05431621f
759 });
760
761 auto cellToInputWeights =
762 MakeTensor<float, 1>(tensorInfo20, {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f,
763 -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f,
764 -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f,-0.052169047f,
765 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f
766 });
767
768
769 auto cellToForgetWeights =
770 MakeTensor<float, 1>(tensorInfo20, {-0.01998659f,-0.15568835f,-0.24248174f, -0.012770197f, 0.041331276f,
771 -0.072311886f, -0.052123554f,-0.0066330447f,-0.043891653f,0.036225766f,
772 -0.047248036f, 0.021479502f,0.033189066f, 0.11952997f, -0.020432774f,
773 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f
774 });
775
776 auto cellToOutputWeights =
777 MakeTensor<float, 1>(tensorInfo20, {0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f,
778 -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f,
779 -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f,
780 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f
781 });
782
783 auto projectionWeights =
784 MakeTensor<float, 2>(tensorInfo16x20,
785 {-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,
786 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,
787 -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,
788 -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,
789 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,
790 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f,
791 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f,
792 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f,
793 -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f,
794 -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f,
795 -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f,
796 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f,
797 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f,
798 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f,
799 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f,
800 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f,
801 -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f,
802 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f,
803 -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f,
804 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f,
805 -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f,
806 -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f,
807 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f,
808 -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f,
809 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f,
810 -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f,
811 -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f,
812 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f,
813 -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f,
814 -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f,
815 -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f,
816 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f,
817 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f,
818 -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f,
819 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f,
820 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f,
821 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f,
822 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f,
823 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f,
824 -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f,
825 -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f,
826 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f,
827 -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f,
828 -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f,
829 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f,
830 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f,
831 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f,
832 -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f,
833 -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f,
834 -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f,
835 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f,
836 -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f,
837 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f,
838 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f,
839 -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f,
840 -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f,
841 -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f,
842 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f,
843 -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f,
844 -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f,
845 -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f,
846 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f,
847 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f,
848 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f
849 });
850
851 std::vector<float> projectionBiasVector(outputSize, 0.f);
852 auto projectionBias = MakeTensor<float,1>(tensorInfo16, projectionBiasVector);
853
854 armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo20x5);
855 armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo20x5);
856 armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo20x5);
857 armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo20x5);
858 armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo20x16);
859 armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo20x16);
860 armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo20x16);
861 armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo20x16);
862 armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo20);
863 armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo20);
864 armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo20);
865 armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo20);
866 armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo20);
867 armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfo20);
868 armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfo20);
869 armnn::ScopedCpuTensorHandle projectionWeightsTensor(tensorInfo16x20);
870 armnn::ScopedCpuTensorHandle projectionBiasTensor(tensorInfo16);
871
872 AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);
873 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
874 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
875 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
876 AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);
877 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
878 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
879 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
880 AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]);
881 AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]);
882 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
883 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
884 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
885 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);
886 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);
887 AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, &projectionWeights[0][0]);
888 AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, &projectionBias[0]);
889
890 data.m_InputToInputWeights = &inputToInputWeightsTensor;
891 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
892 data.m_InputToCellWeights = &inputToCellWeightsTensor;
893 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
894 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
895 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
896 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
897 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
898 data.m_CellToInputWeights = &cellToInputWeightsTensor;
899 data.m_InputGateBias = &inputGateBiasTensor;
900 data.m_ForgetGateBias = &forgetGateBiasTensor;
901 data.m_CellBias = &cellBiasTensor;
902 data.m_OutputGateBias = &outputGateBiasTensor;
903 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
904 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
905 data.m_ProjectionWeights = &projectionWeightsTensor;
906 data.m_ProjectionBias = &projectionBiasTensor;
907
908 // Flags to set test configuration
909 data.m_Parameters.m_ActivationFunc = 4;
910 data.m_Parameters.m_CifgEnabled = false;
911 data.m_Parameters.m_PeepholeEnabled = true;
912 data.m_Parameters.m_ProjectionEnabled = true;
913
914
915 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info);
916 inputHandle->Allocate();
917 outputStateInHandle->Allocate();
918 cellStateInHandle->Allocate();
919
920 scratchHandle->Allocate();
921 outputStateOutHandle->Allocate();
922 cellStateOutHandle->Allocate();
923 outputHandle->Allocate();
924
925 CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
926 CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
927 CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
928
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]929 workload->Execute();
930
931 CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
932
933 return ret;
934
935}
936
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]937template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
938LayerTestResult<T, 2> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(
Aron Virginas-Tar5caf9072018-11-14 18:35:18 +0000[diff] [blame]939 armnn::IWorkloadFactory& workloadFactory,
940 const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]941 const boost::multi_array<T, 2>& input,
942 const boost::multi_array<T, 2>& outputExpected,
943 float qScale = 0.0f,
944 int32_t qOffset = 0,
945 armnn::DataType constantDataType = armnn::DataType::Float32)
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]946{
947 bool cifgEnabled = true;
948 bool peepholeEnabled = true;
949 bool projectionEnabled = false;
950 // These are not the input and the output of Lstm yet
951 unsigned int batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]);
952 unsigned int inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]);
953
954 unsigned int outputSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
955
956 const unsigned int cellSize = outputSize;
957
958 // Decide the shape of all input tensors
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]959 armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset); // change to ArmnnType
960 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
961 armnn::TensorInfo cellStateInTensorInfo({batchSize, cellSize}, ArmnnType, qScale, qOffset);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]962
Matteo Martincigha65b7ae2018-11-14 12:39:55 +0000[diff] [blame]963 unsigned int scratchBufferSize = cifgEnabled ? cellSize * 3 : cellSize * 4;
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]964 armnn::TensorInfo scratchBufferTensorInfo({batchSize, scratchBufferSize}, ArmnnType, qScale, qOffset);
965 armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
966 armnn::TensorInfo cellStateOutTensorInfo({batchSize, cellSize}, ArmnnType, qScale, qOffset);
967 armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]968
969 // List of inputs
970 std::vector<float> inputData;
971 inputData.assign(input.data(), input.data() + batchSize*inputSize);
972 auto inputTensor = MakeTensor<float,2>(inputTensorInfo, inputData);
973
974 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
975 auto outputStateInTensor = MakeTensor<float, 2>(outputStateInTensorInfo, outputStateInVector);
976
977 std::vector<float> cellStateInVector(batchSize * cellSize, 0.f);
978 auto cellStateInTensor = MakeTensor<float, 2>(cellStateInTensorInfo, cellStateInVector);
979
980
981 // Prepare all the weights in the descriptor for LSTM
982 armnn::LstmQueueDescriptor data;
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]983 armnn::TensorInfo tensorInfoInput({cellSize, inputSize}, constantDataType, qScale, qOffset);
984 armnn::TensorInfo tensorInfoOutput({cellSize, outputSize}, constantDataType, qScale, qOffset);
985 armnn::TensorInfo tensorInfoNumUnits({cellSize}, constantDataType, qScale, qOffset);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]986
987 auto inputToCellWeights = MakeTensor<float, 2>(tensorInfoInput,
988 {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f,
989 0.04717243f, 0.48944736f, -0.38535351f,
990 -0.17212132f});
991 auto inputToForgetWeights = MakeTensor<float, 2>(tensorInfoInput,
992 {-0.55291498f, -0.42866567f, 0.13056988f,
993 -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f,
994 0.33826375f});
995 auto inputToOutputWeights = MakeTensor<float, 2>(tensorInfoInput,
996 {0.10725588f, -0.02335852f, -0.55932593f,
997 -0.09426838f, -0.44257352f, 0.54939759f,
998 0.01533556f, 0.42751634f});
999 auto cellBias = MakeTensor<float, 1>(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f});
1000 auto forgetGateBias = MakeTensor<float, 1>(tensorInfoNumUnits, {1.f, 1.f, 1.f, 1.f});
1001 auto outputGateBias = MakeTensor<float, 1>(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f});
1002
1003 auto recurrentToCellWeights = MakeTensor<float, 2>(tensorInfoOutput,
1004 {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f,
1005 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f,
1006 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f,
1007 0.21193194f});
1008 auto recurrentToForgetWeights = MakeTensor<float, 2>(tensorInfoOutput,
1009 {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f,
1010 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f,
1011 -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f});
1012
1013 auto recurrentToOutputWeights = MakeTensor<float, 2>(tensorInfoOutput,
1014 {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f,
1015 -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f,
1016 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f});
1017
1018 auto cellToForgetWeights = MakeTensor<float, 1>(tensorInfoNumUnits,
1019 {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f});
1020 auto cellToOutputWeights = MakeTensor<float, 1>(tensorInfoNumUnits,
1021 {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f});
1022
1023 armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfoInput);
1024 armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfoInput);
1025 armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfoInput);
1026
1027 armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfoNumUnits);
1028 armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfoNumUnits);
1029 armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfoNumUnits);
1030
1031 armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfoOutput);
1032 armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfoOutput);
1033 armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfoOutput);
1034
1035
1036 armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfoNumUnits);
1037 armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfoNumUnits);
1038
1039 AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
1040 AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
1041 AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
1042
1043 AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
1044 AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
1045 AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
1046
1047 AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
1048 AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
1049 AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
1050
1051 AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);
1052 AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);
1053
1054
1055 data.m_InputToCellWeights = &inputToCellWeightsTensor;
1056 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
1057 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
1058
1059 data.m_CellBias = &cellBiasTensor;
1060 data.m_ForgetGateBias = &forgetGateBiasTensor;
1061 data.m_OutputGateBias = &outputGateBiasTensor;
1062
1063 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
1064 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
1065 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
1066
1067 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
1068 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
1069
1070 // other parameters for the descriptor
1071 data.m_Parameters.m_CifgEnabled = cifgEnabled;
1072 data.m_Parameters.m_ProjectionEnabled = projectionEnabled;
1073 data.m_Parameters.m_PeepholeEnabled = peepholeEnabled;
1074
1075 data.m_Parameters.m_ActivationFunc = 4;
1076 data.m_Parameters.m_ClippingThresProj = 0.0;
1077 data.m_Parameters.m_ClippingThresCell = 0.0;
1078
1079
1080 // List of outputs
1081 std::vector<float> scratchBufferVector(batchSize * scratchBufferSize, 0.f);
1082 auto scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]1083 LayerTestResult<T, 2> ret0(scratchBufferTensorInfo);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]1084
1085 // Output state for a certain time step
1086 std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);
1087 auto outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]1088 LayerTestResult<T, 2> ret1(outputStateOutTensorInfo);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]1089
1090 // Cell state for a certain time step
1091 std::vector<float> cellStateOutVector(batchSize * cellSize, 0.f);
1092 auto cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]1093 LayerTestResult<T, 2> ret2(cellStateOutTensorInfo);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]1094
1095 // Output for a certain time step
1096 std::vector<float> outputVector(batchSize * outputSize, 0.f);
1097 auto outputTensor = MakeTensor<float, 2>(outputTensorInfo, outputVector);
1098 std::vector<float> outputData;
1099 outputData.assign(outputExpected.data(), outputExpected.data() + batchSize*outputSize);
Conor Kennedyb9971c92019-05-07 07:14:23 +0100[diff] [blame]1100 LayerTestResult<T, 2> ret3(outputTensorInfo);
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]1101 ret3.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputData);
1102
1103 // Prepare the inputs and outputs for the workload
1104 std::unique_ptr<armnn::ITensorHandle> inputHandle =
1105 workloadFactory.CreateTensorHandle(inputTensorInfo);
1106 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1107 workloadFactory.CreateTensorHandle(outputStateInTensorInfo);
1108 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1109 workloadFactory.CreateTensorHandle(cellStateInTensorInfo);
1110
1111 std::unique_ptr<armnn::ITensorHandle> scratchBufferHandle =
1112 workloadFactory.CreateTensorHandle(scratchBufferTensorInfo);
1113 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1114 workloadFactory.CreateTensorHandle(outputStateOutTensorInfo);
1115 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1116 workloadFactory.CreateTensorHandle(cellStateOutTensorInfo);
1117 std::unique_ptr<armnn::ITensorHandle> outputHandle =
1118 workloadFactory.CreateTensorHandle(outputTensorInfo);
1119
1120 armnn::WorkloadInfo info;
1121 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1122 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1123 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1124
1125 AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchBufferHandle.get());
1126 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1127 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1128 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1129
1130 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info);
1131
1132
1133 inputHandle->Allocate();
1134 outputStateInHandle->Allocate();
1135 cellStateInHandle->Allocate();
1136
1137 scratchBufferHandle->Allocate();
1138 outputStateOutHandle->Allocate();
1139 cellStateOutHandle->Allocate();
1140 outputHandle->Allocate();
1141
1142
1143 CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
1144 CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
1145 CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
1146
1147 CopyDataToITensorHandle(scratchBufferHandle.get(), &scratchBufferTensor[0][0]);
1148 CopyDataToITensorHandle(outputStateOutHandle.get(), &outputStateOutTensor[0][0]);
1149 CopyDataToITensorHandle(cellStateOutHandle.get(), &cellStateOutTensor[0][0]);
1150
telsoa01c577f2c2018-08-31 09:22:23 +0100[diff] [blame]1151 workload->Execute();
1152
1153 CopyDataFromITensorHandle(&ret0.output[0][0], scratchBufferHandle.get());
1154 CopyDataFromITensorHandle(&ret1.output[0][0], outputStateOutHandle.get());
1155 CopyDataFromITensorHandle(&ret2.output[0][0], cellStateOutHandle.get());
1156 CopyDataFromITensorHandle(&ret3.output[0][0], outputHandle.get());
1157
1158 return ret3;
1159}