1 /*
2 * Copyright (C) 2017 The Android Open Source Project
3 *
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * http://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 */
16
17 #include "SVDF.h"
18
19 #include <gmock/gmock-matchers.h>
20 #include <gtest/gtest.h>
21 #include "NeuralNetworksWrapper.h"
22
23 using ::testing::FloatNear;
24 using ::testing::Matcher;
25
26 namespace android {
27 namespace nn {
28 namespace wrapper {
29
30 namespace {
31
ArrayFloatNear(const std::vector<float> & values,float max_abs_error=1.e-6)32 std::vector<Matcher<float>> ArrayFloatNear(const std::vector<float>& values,
33 float max_abs_error = 1.e-6) {
34 std::vector<Matcher<float>> matchers;
35 matchers.reserve(values.size());
36 for (const float& v : values) {
37 matchers.emplace_back(FloatNear(v, max_abs_error));
38 }
39 return matchers;
40 }
41
42 } // namespace
43
44 using ::testing::ElementsAreArray;
45
46 static float svdf_input[] = {
47 0.12609188, -0.46347019, -0.89598465, 0.12609188, -0.46347019, -0.89598465,
48
49 0.14278367, -1.64410412, -0.75222826, 0.14278367, -1.64410412, -0.75222826,
50
51 0.49837467, 0.19278903, 0.26584083, 0.49837467, 0.19278903, 0.26584083,
52
53 -0.11186574, 0.13164264, -0.05349274, -0.11186574, 0.13164264, -0.05349274,
54
55 -0.68892461, 0.37783599, 0.18263303, -0.68892461, 0.37783599, 0.18263303,
56
57 -0.81299269, -0.86831826, 1.43940818, -0.81299269, -0.86831826, 1.43940818,
58
59 -1.45006323, -0.82251364, -1.69082689, -1.45006323, -0.82251364, -1.69082689,
60
61 0.03966608, -0.24936394, -0.77526885, 0.03966608, -0.24936394, -0.77526885,
62
63 0.11771342, -0.23761693, -0.65898693, 0.11771342, -0.23761693, -0.65898693,
64
65 -0.89477462, 1.67204106, -0.53235275, -0.89477462, 1.67204106, -0.53235275};
66
67 static float svdf_input_rank2[] = {
68 0.12609188, -0.46347019, -0.89598465, 0.35867718, 0.36897406, 0.73463392,
69
70 0.14278367, -1.64410412, -0.75222826, -0.57290924, 0.12729003, 0.7567004,
71
72 0.49837467, 0.19278903, 0.26584083, 0.17660543, 0.52949083, -0.77931279,
73
74 -0.11186574, 0.13164264, -0.05349274, -0.72674477, -0.5683046, 0.55900657,
75
76 -0.68892461, 0.37783599, 0.18263303, -0.63690937, 0.44483393, -0.71817774,
77
78 -0.81299269, -0.86831826, 1.43940818, -0.95760226, 1.82078898, 0.71135032,
79
80 -1.45006323, -0.82251364, -1.69082689, -1.65087092, -1.89238167, 1.54172635,
81
82 0.03966608, -0.24936394, -0.77526885, 2.06740379, -1.51439476, 1.43768692,
83
84 0.11771342, -0.23761693, -0.65898693, 0.31088525, -1.55601168, -0.87661445,
85
86 -0.89477462, 1.67204106, -0.53235275, -0.6230064, 0.29819036, 1.06939757,
87 };
88
89 static float svdf_golden_output[] = {0.014899, -0.0517661, -0.143725, -0.00271883,
90 0.014899, -0.0517661, -0.143725, -0.00271883,
91
92 0.068281, -0.162217, -0.152268, 0.00323521,
93 0.068281, -0.162217, -0.152268, 0.00323521,
94
95 -0.0317821, -0.0333089, 0.0609602, 0.0333759,
96 -0.0317821, -0.0333089, 0.0609602, 0.0333759,
97
98 -0.00623099, -0.077701, -0.391193, -0.0136691,
99 -0.00623099, -0.077701, -0.391193, -0.0136691,
100
101 0.201551, -0.164607, -0.179462, -0.0592739,
102 0.201551, -0.164607, -0.179462, -0.0592739,
103
104 0.0886511, -0.0875401, -0.269283, 0.0281379,
105 0.0886511, -0.0875401, -0.269283, 0.0281379,
106
107 -0.201174, -0.586145, -0.628624, -0.0330412,
108 -0.201174, -0.586145, -0.628624, -0.0330412,
109
110 -0.0839096, -0.299329, 0.108746, 0.109808,
111 -0.0839096, -0.299329, 0.108746, 0.109808,
112
113 0.419114, -0.237824, -0.422627, 0.175115,
114 0.419114, -0.237824, -0.422627, 0.175115,
115
116 0.36726, -0.522303, -0.456502, -0.175475,
117 0.36726, -0.522303, -0.456502, -0.175475};
118
119 static float svdf_golden_output_rank_2[] = {
120 -0.09623547, -0.10193135, 0.11083051, -0.0347917,
121 0.1141196, 0.12965347, -0.12652366, 0.01007236,
122
123 -0.16396809, -0.21247184, 0.11259045, -0.04156673,
124 0.10132131, -0.06143532, -0.00924693, 0.10084561,
125
126 0.01257364, 0.0506071, -0.19287863, -0.07162561,
127 -0.02033747, 0.22673416, 0.15487903, 0.02525555,
128
129 -0.1411963, -0.37054959, 0.01774767, 0.05867489,
130 0.09607603, -0.0141301, -0.08995658, 0.12867066,
131
132 -0.27142537, -0.16955489, 0.18521598, -0.12528358,
133 0.00331409, 0.11167502, 0.02218599, -0.07309391,
134
135 0.09593632, -0.28361851, -0.0773851, 0.17199151,
136 -0.00075242, 0.33691186, -0.1536046, 0.16572715,
137
138 -0.27916506, -0.27626723, 0.42615682, 0.3225764,
139 -0.37472126, -0.55655634, -0.05013514, 0.289112,
140
141 -0.24418658, 0.07540751, -0.1940318, -0.08911639,
142 0.00732617, 0.46737891, 0.26449674, 0.24888524,
143
144 -0.17225097, -0.54660404, -0.38795233, 0.08389944,
145 0.07736043, -0.28260678, 0.15666828, 1.14949894,
146
147 -0.57454878, -0.64704704, 0.73235172, -0.34616736,
148 0.21120001, -0.22927976, 0.02455296, -0.35906726,
149 };
150
151 #define FOR_ALL_INPUT_AND_WEIGHT_TENSORS(ACTION) \
152 ACTION(Input) \
153 ACTION(WeightsFeature) \
154 ACTION(WeightsTime) \
155 ACTION(Bias) \
156 ACTION(StateIn)
157
158 // For all output and intermediate states
159 #define FOR_ALL_OUTPUT_TENSORS(ACTION) \
160 ACTION(StateOut) \
161 ACTION(Output)
162
163 // Derived class of SingleOpModel, which is used to test SVDF TFLite op.
164 class SVDFOpModel {
165 public:
SVDFOpModel(uint32_t batches,uint32_t units,uint32_t input_size,uint32_t memory_size,uint32_t rank)166 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, uint32_t memory_size,
167 uint32_t rank)
168 : batches_(batches),
169 units_(units),
170 input_size_(input_size),
171 memory_size_(memory_size),
172 rank_(rank) {
173 std::vector<std::vector<uint32_t>> input_shapes{
174 {batches_, input_size_}, // Input tensor
175 {units_ * rank_, input_size_}, // weights_feature tensor
176 {units_ * rank_, memory_size_}, // weights_time tensor
177 {units_}, // bias tensor
178 {batches_, memory_size * units_ * rank_}, // state in tensor
179 };
180 std::vector<uint32_t> inputs;
181 auto it = input_shapes.begin();
182
183 // Input and weights
184 #define AddInput(X) \
185 OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it++); \
186 inputs.push_back(model_.addOperand(&X##OpndTy));
187
188 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(AddInput);
189
190 #undef AddInput
191
192 // Parameters
193 OperandType RankParamTy(Type::INT32, {});
194 inputs.push_back(model_.addOperand(&RankParamTy));
195 OperandType ActivationParamTy(Type::INT32, {});
196 inputs.push_back(model_.addOperand(&ActivationParamTy));
197
198 // Output and other intermediate state
199 std::vector<std::vector<uint32_t>> output_shapes{{batches_, memory_size_ * units_ * rank_},
200 {batches_, units_}};
201 std::vector<uint32_t> outputs;
202
203 auto it2 = output_shapes.begin();
204
205 #define AddOutput(X) \
206 OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it2++); \
207 outputs.push_back(model_.addOperand(&X##OpndTy));
208
209 FOR_ALL_OUTPUT_TENSORS(AddOutput);
210
211 #undef AddOutput
212
213 Input_.insert(Input_.end(), batches_ * input_size_, 0.f);
214 StateIn_.insert(StateIn_.end(), batches_ * units_ * rank_ * memory_size_, 0.f);
215
216 auto multiAll = [](const std::vector<uint32_t>& dims) -> uint32_t {
217 uint32_t sz = 1;
218 for (uint32_t d : dims) {
219 sz *= d;
220 }
221 return sz;
222 };
223
224 it2 = output_shapes.begin();
225
226 #define ReserveOutput(X) X##_.insert(X##_.end(), multiAll(*it2++), 0.f);
227
228 FOR_ALL_OUTPUT_TENSORS(ReserveOutput);
229
230 model_.addOperation(ANEURALNETWORKS_SVDF, inputs, outputs);
231 model_.identifyInputsAndOutputs(inputs, outputs);
232
233 model_.finish();
234 }
235
Invoke()236 void Invoke() {
237 ASSERT_TRUE(model_.isValid());
238
239 Compilation compilation(&model_);
240 compilation.finish();
241 Execution execution(&compilation);
242
243 StateIn_.swap(StateOut_);
244
245 #define SetInputOrWeight(X) \
246 ASSERT_EQ(execution.setInput(SVDF::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \
247 Result::NO_ERROR);
248
249 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight);
250
251 #undef SetInputOrWeight
252
253 #define SetOutput(X) \
254 EXPECT_TRUE(X##_.data() != nullptr); \
255 ASSERT_EQ(execution.setOutput(SVDF::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \
256 Result::NO_ERROR);
257
258 FOR_ALL_OUTPUT_TENSORS(SetOutput);
259
260 #undef SetOutput
261
262 ASSERT_EQ(execution.setInput(SVDF::kRankParam, &rank_, sizeof(rank_)), Result::NO_ERROR);
263
264 int activation = TfLiteFusedActivation::kTfLiteActNone;
265 ASSERT_EQ(execution.setInput(SVDF::kActivationParam, &activation, sizeof(activation)),
266 Result::NO_ERROR);
267
268 ASSERT_EQ(execution.compute(), Result::NO_ERROR);
269 }
270
271 #define DefineSetter(X) \
272 void Set##X(const std::vector<float>& f) { X##_.insert(X##_.end(), f.begin(), f.end()); }
273
274 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineSetter);
275
276 #undef DefineSetter
277
SetInput(int offset,float * begin,float * end)278 void SetInput(int offset, float* begin, float* end) {
279 for (; begin != end; begin++, offset++) {
280 Input_[offset] = *begin;
281 }
282 }
283
284 // Resets the state of SVDF op by filling it with 0's.
ResetState()285 void ResetState() {
286 std::fill(StateIn_.begin(), StateIn_.end(), 0.f);
287 std::fill(StateOut_.begin(), StateOut_.end(), 0.f);
288 }
289
290 // Extracts the output tensor from the SVDF op.
GetOutput() const291 const std::vector<float>& GetOutput() const { return Output_; }
292
input_size() const293 int input_size() const { return input_size_; }
num_units() const294 int num_units() const { return units_; }
num_batches() const295 int num_batches() const { return batches_; }
296
297 private:
298 Model model_;
299
300 const uint32_t batches_;
301 const uint32_t units_;
302 const uint32_t input_size_;
303 const uint32_t memory_size_;
304 const uint32_t rank_;
305
306 #define DefineTensor(X) std::vector<float> X##_;
307
308 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineTensor);
309 FOR_ALL_OUTPUT_TENSORS(DefineTensor);
310
311 #undef DefineTensor
312 };
313
TEST(SVDFOpTest,BlackBoxTest)314 TEST(SVDFOpTest, BlackBoxTest) {
315 SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3,
316 /*memory_size=*/10, /*rank=*/1);
317 svdf.SetWeightsFeature({-0.31930989, -0.36118156, 0.0079667, 0.37613347, 0.22197971, 0.12416199,
318 0.27901134, 0.27557442, 0.3905206, -0.36137494, -0.06634006,
319 -0.10640851});
320
321 svdf.SetWeightsTime({-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156,
322 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199,
323
324 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518,
325 -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296,
326
327 -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236,
328 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846,
329
330 -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166,
331 -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657});
332
333 svdf.SetBias({});
334
335 svdf.ResetState();
336 const int svdf_num_batches = svdf.num_batches();
337 const int svdf_input_size = svdf.input_size();
338 const int svdf_num_units = svdf.num_units();
339 const int input_sequence_size =
340 sizeof(svdf_input) / sizeof(float) / (svdf_input_size * svdf_num_batches);
341 // Going over each input batch, setting the input tensor, invoking the SVDF op
342 // and checking the output with the expected golden values.
343 for (int i = 0; i < input_sequence_size; i++) {
344 float* batch_start = svdf_input + i * svdf_input_size * svdf_num_batches;
345 float* batch_end = batch_start + svdf_input_size * svdf_num_batches;
346 svdf.SetInput(0, batch_start, batch_end);
347
348 svdf.Invoke();
349
350 float* golden_start = svdf_golden_output + i * svdf_num_units * svdf_num_batches;
351 float* golden_end = golden_start + svdf_num_units * svdf_num_batches;
352 std::vector<float> expected;
353 expected.insert(expected.end(), golden_start, golden_end);
354
355 EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected)));
356 }
357 }
358
TEST(SVDFOpTest,BlackBoxTestRank2)359 TEST(SVDFOpTest, BlackBoxTestRank2) {
360 SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3,
361 /*memory_size=*/10, /*rank=*/2);
362 svdf.SetWeightsFeature({-0.31930989, 0.0079667, 0.39296314, 0.37613347, 0.12416199,
363 0.15785322, 0.27901134, 0.3905206, 0.21931258, -0.36137494,
364 -0.10640851, 0.31053296, -0.36118156, -0.0976817, -0.36916667,
365 0.22197971, 0.15294972, 0.38031587, 0.27557442, 0.39635518,
366 -0.21580373, -0.06634006, -0.02702999, 0.27072677});
367
368 svdf.SetWeightsTime({-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156,
369 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199,
370
371 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518,
372 -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296,
373
374 -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236,
375 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846,
376
377 -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166,
378 -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657,
379
380 -0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486,
381 0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187,
382
383 -0.26816407, -0.19905911, 0.29211238, 0.31264046, -0.28664589,
384 0.05698794, 0.11613581, 0.14078894, 0.02187902, -0.21781836,
385
386 -0.15567942, 0.08693647, -0.38256618, 0.36580828, -0.22922277,
387 -0.0226903, 0.12878349, -0.28122205, -0.10850525, -0.11955214,
388
389 0.27179423, -0.04710215, 0.31069002, 0.22672787, 0.09580326,
390 0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763});
391
392 svdf.SetBias({});
393
394 svdf.ResetState();
395 const int svdf_num_batches = svdf.num_batches();
396 const int svdf_input_size = svdf.input_size();
397 const int svdf_num_units = svdf.num_units();
398 const int input_sequence_size =
399 sizeof(svdf_input_rank2) / sizeof(float) / (svdf_input_size * svdf_num_batches);
400 // Going over each input batch, setting the input tensor, invoking the SVDF op
401 // and checking the output with the expected golden values.
402 for (int i = 0; i < input_sequence_size; i++) {
403 float* batch_start = svdf_input_rank2 + i * svdf_input_size * svdf_num_batches;
404 float* batch_end = batch_start + svdf_input_size * svdf_num_batches;
405 svdf.SetInput(0, batch_start, batch_end);
406
407 svdf.Invoke();
408
409 float* golden_start = svdf_golden_output_rank_2 + i * svdf_num_units * svdf_num_batches;
410 float* golden_end = golden_start + svdf_num_units * svdf_num_batches;
411 std::vector<float> expected;
412 expected.insert(expected.end(), golden_start, golden_end);
413
414 EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected)));
415 }
416 }
417
418 } // namespace wrapper
419 } // namespace nn
420 } // namespace android
421