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 "NeuralNetworksWrapper.h" 20 #include "gmock/gmock-matchers.h" 21 #include "gtest/gtest.h" 22 23 using ::testing::FloatNear; 24 using ::testing::Matcher; 25 26 namespace android { 27 namespace nn { 28 namespace wrapper { 29 30 namespace { 31 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[] = {0.12609188, -0.46347019, -0.89598465, 47 0.12609188, -0.46347019, -0.89598465, 48 49 0.14278367, -1.64410412, -0.75222826, 50 0.14278367, -1.64410412, -0.75222826, 51 52 0.49837467, 0.19278903, 0.26584083, 53 0.49837467, 0.19278903, 0.26584083, 54 55 -0.11186574, 0.13164264, -0.05349274, 56 -0.11186574, 0.13164264, -0.05349274, 57 58 -0.68892461, 0.37783599, 0.18263303, 59 -0.68892461, 0.37783599, 0.18263303, 60 61 -0.81299269, -0.86831826, 1.43940818, 62 -0.81299269, -0.86831826, 1.43940818, 63 64 -1.45006323, -0.82251364, -1.69082689, 65 -1.45006323, -0.82251364, -1.69082689, 66 67 0.03966608, -0.24936394, -0.77526885, 68 0.03966608, -0.24936394, -0.77526885, 69 70 0.11771342, -0.23761693, -0.65898693, 71 0.11771342, -0.23761693, -0.65898693, 72 73 -0.89477462, 1.67204106, -0.53235275, 74 -0.89477462, 1.67204106, -0.53235275}; 75 76 static float svdf_input_rank2[] = { 77 0.12609188, -0.46347019, -0.89598465, 78 0.35867718, 0.36897406, 0.73463392, 79 80 0.14278367, -1.64410412, -0.75222826, 81 -0.57290924, 0.12729003, 0.7567004, 82 83 0.49837467, 0.19278903, 0.26584083, 84 0.17660543, 0.52949083, -0.77931279, 85 86 -0.11186574, 0.13164264, -0.05349274, 87 -0.72674477, -0.5683046, 0.55900657, 88 89 -0.68892461, 0.37783599, 0.18263303, 90 -0.63690937, 0.44483393, -0.71817774, 91 92 -0.81299269, -0.86831826, 1.43940818, 93 -0.95760226, 1.82078898, 0.71135032, 94 95 -1.45006323, -0.82251364, -1.69082689, 96 -1.65087092, -1.89238167, 1.54172635, 97 98 0.03966608, -0.24936394, -0.77526885, 99 2.06740379, -1.51439476, 1.43768692, 100 101 0.11771342, -0.23761693, -0.65898693, 102 0.31088525, -1.55601168, -0.87661445, 103 104 -0.89477462, 1.67204106, -0.53235275, 105 -0.6230064, 0.29819036, 1.06939757, 106 }; 107 108 static float svdf_golden_output[] = { 109 0.014899, -0.0517661, -0.143725, -0.00271883, 110 0.014899, -0.0517661, -0.143725, -0.00271883, 111 112 0.068281, -0.162217, -0.152268, 0.00323521, 113 0.068281, -0.162217, -0.152268, 0.00323521, 114 115 -0.0317821, -0.0333089, 0.0609602, 0.0333759, 116 -0.0317821, -0.0333089, 0.0609602, 0.0333759, 117 118 -0.00623099, -0.077701, -0.391193, -0.0136691, 119 -0.00623099, -0.077701, -0.391193, -0.0136691, 120 121 0.201551, -0.164607, -0.179462, -0.0592739, 122 0.201551, -0.164607, -0.179462, -0.0592739, 123 124 0.0886511, -0.0875401, -0.269283, 0.0281379, 125 0.0886511, -0.0875401, -0.269283, 0.0281379, 126 127 -0.201174, -0.586145, -0.628624, -0.0330412, 128 -0.201174, -0.586145, -0.628624, -0.0330412, 129 130 -0.0839096, -0.299329, 0.108746, 0.109808, 131 -0.0839096, -0.299329, 0.108746, 0.109808, 132 133 0.419114, -0.237824, -0.422627, 0.175115, 134 0.419114, -0.237824, -0.422627, 0.175115, 135 136 0.36726, -0.522303, -0.456502, -0.175475, 137 0.36726, -0.522303, -0.456502, -0.175475}; 138 139 static float svdf_golden_output_rank_2[] = { 140 -0.09623547, -0.10193135, 0.11083051, -0.0347917, 141 0.1141196, 0.12965347, -0.12652366, 0.01007236, 142 143 -0.16396809, -0.21247184, 0.11259045, -0.04156673, 144 0.10132131, -0.06143532, -0.00924693, 0.10084561, 145 146 0.01257364, 0.0506071, -0.19287863, -0.07162561, 147 -0.02033747, 0.22673416, 0.15487903, 0.02525555, 148 149 -0.1411963, -0.37054959, 0.01774767, 0.05867489, 150 0.09607603, -0.0141301, -0.08995658, 0.12867066, 151 152 -0.27142537, -0.16955489, 0.18521598, -0.12528358, 153 0.00331409, 0.11167502, 0.02218599, -0.07309391, 154 155 0.09593632, -0.28361851, -0.0773851, 0.17199151, 156 -0.00075242, 0.33691186, -0.1536046, 0.16572715, 157 158 -0.27916506, -0.27626723, 0.42615682, 0.3225764, 159 -0.37472126, -0.55655634, -0.05013514, 0.289112, 160 161 -0.24418658, 0.07540751, -0.1940318, -0.08911639, 162 0.00732617, 0.46737891, 0.26449674, 0.24888524, 163 164 -0.17225097, -0.54660404, -0.38795233, 0.08389944, 165 0.07736043, -0.28260678, 0.15666828, 1.14949894, 166 167 -0.57454878, -0.64704704, 0.73235172, -0.34616736, 168 0.21120001, -0.22927976, 0.02455296, -0.35906726, 169 }; 170 171 #define FOR_ALL_INPUT_AND_WEIGHT_TENSORS(ACTION) \ 172 ACTION(Input) \ 173 ACTION(WeightsFeature) \ 174 ACTION(WeightsTime) \ 175 ACTION(Bias) \ 176 ACTION(StateIn) 177 178 // For all output and intermediate states 179 #define FOR_ALL_OUTPUT_TENSORS(ACTION) \ 180 ACTION(StateOut) \ 181 ACTION(Output) 182 183 // Derived class of SingleOpModel, which is used to test SVDF TFLite op. 184 class SVDFOpModel { 185 public: 186 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, 187 uint32_t memory_size, uint32_t rank) 188 : batches_(batches), 189 units_(units), 190 input_size_(input_size), 191 memory_size_(memory_size), 192 rank_(rank) { 193 std::vector<std::vector<uint32_t>> input_shapes{ 194 {batches_, input_size_}, // Input tensor 195 {units_ * rank_, input_size_}, // weights_feature tensor 196 {units_ * rank_, memory_size_}, // weights_time tensor 197 {units_}, // bias tensor 198 {batches_, memory_size * units_ * rank_}, // state in tensor 199 }; 200 std::vector<uint32_t> inputs; 201 auto it = input_shapes.begin(); 202 203 // Input and weights 204 #define AddInput(X) \ 205 OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it++); \ 206 inputs.push_back(model_.addOperand(&X##OpndTy)); 207 208 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(AddInput); 209 210 #undef AddInput 211 212 // Parameters 213 OperandType RankParamTy(Type::INT32, {}); 214 inputs.push_back(model_.addOperand(&RankParamTy)); 215 OperandType ActivationParamTy(Type::INT32, {}); 216 inputs.push_back(model_.addOperand(&ActivationParamTy)); 217 218 // Output and other intermediate state 219 std::vector<std::vector<uint32_t>> output_shapes{{batches_, memory_size_ * units_ * rank_}, 220 {batches_, units_}}; 221 std::vector<uint32_t> outputs; 222 223 auto it2 = output_shapes.begin(); 224 225 #define AddOutput(X) \ 226 OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it2++); \ 227 outputs.push_back(model_.addOperand(&X##OpndTy)); 228 229 FOR_ALL_OUTPUT_TENSORS(AddOutput); 230 231 #undef AddOutput 232 233 Input_.insert(Input_.end(), batches_ * input_size_, 0.f); 234 StateIn_.insert(StateIn_.end(), batches_ * units_ * rank_ * memory_size_, 0.f); 235 236 auto multiAll = [](const std::vector<uint32_t> &dims) -> uint32_t { 237 uint32_t sz = 1; 238 for(uint32_t d:dims) { sz *= d; } 239 return sz; 240 }; 241 242 it2 = output_shapes.begin(); 243 244 #define ReserveOutput(X) X##_.insert(X##_.end(), multiAll(*it2++), 0.f); 245 246 FOR_ALL_OUTPUT_TENSORS(ReserveOutput); 247 248 model_.addOperation(ANEURALNETWORKS_SVDF, inputs, outputs); 249 model_.identifyInputsAndOutputs(inputs, outputs); 250 251 model_.finish(); 252 } 253 254 void Invoke() { 255 ASSERT_TRUE(model_.isValid()); 256 257 Compilation compilation(&model_); 258 compilation.finish(); 259 Execution execution(&compilation); 260 261 StateIn_.swap(StateOut_); 262 263 #define SetInputOrWeight(X) \ 264 ASSERT_EQ(execution.setInput(SVDF::k##X##Tensor, X##_.data(), \ 265 sizeof(float) * X##_.size()), \ 266 Result::NO_ERROR); 267 268 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight); 269 270 #undef SetInputOrWeight 271 272 #define SetOutput(X) \ 273 EXPECT_TRUE(X##_.data() != nullptr); \ 274 ASSERT_EQ(execution.setOutput(SVDF::k##X##Tensor, X##_.data(), \ 275 sizeof(float) * X##_.size()), \ 276 Result::NO_ERROR); 277 278 FOR_ALL_OUTPUT_TENSORS(SetOutput); 279 280 #undef SetOutput 281 282 ASSERT_EQ(execution.setInput(SVDF::kRankParam, &rank_, sizeof(rank_)), 283 Result::NO_ERROR); 284 285 int activation = TfLiteFusedActivation::kTfLiteActNone; 286 ASSERT_EQ(execution.setInput(SVDF::kActivationParam, &activation, 287 sizeof(activation)), 288 Result::NO_ERROR); 289 290 ASSERT_EQ(execution.compute(), Result::NO_ERROR); 291 } 292 293 #define DefineSetter(X) \ 294 void Set##X(const std::vector<float>& f) { \ 295 X##_.insert(X##_.end(), f.begin(), f.end()); \ 296 } 297 298 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineSetter); 299 300 #undef DefineSetter 301 302 void SetInput(int offset, float* begin, float* end) { 303 for (; begin != end; begin++, offset++) { 304 Input_[offset] = *begin; 305 } 306 } 307 308 // Resets the state of SVDF op by filling it with 0's. 309 void ResetState() { 310 std::fill(StateIn_.begin(), StateIn_.end(), 0.f); 311 std::fill(StateOut_.begin(), StateOut_.end(), 0.f); 312 } 313 314 // Extracts the output tensor from the SVDF op. 315 const std::vector<float>& GetOutput() const { return Output_; } 316 317 int input_size() const { return input_size_; } 318 int num_units() const { return units_; } 319 int num_batches() const { return batches_; } 320 321 private: 322 Model model_; 323 324 const uint32_t batches_; 325 const uint32_t units_; 326 const uint32_t input_size_; 327 const uint32_t memory_size_; 328 const uint32_t rank_; 329 330 #define DefineTensor(X) std::vector<float> X##_; 331 332 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineTensor); 333 FOR_ALL_OUTPUT_TENSORS(DefineTensor); 334 335 #undef DefineTensor 336 }; 337 338 TEST(SVDFOpTest, BlackBoxTest) { 339 SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, 340 /*memory_size=*/10, /*rank=*/1); 341 svdf.SetWeightsFeature({-0.31930989, -0.36118156, 0.0079667, 0.37613347, 342 0.22197971, 0.12416199, 0.27901134, 0.27557442, 343 0.3905206, -0.36137494, -0.06634006, -0.10640851}); 344 345 svdf.SetWeightsTime( 346 {-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, 347 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, 348 349 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, 350 -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, 351 352 -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, 353 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, 354 355 -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, 356 -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657}); 357 358 svdf.SetBias({}); 359 360 svdf.ResetState(); 361 const int svdf_num_batches = svdf.num_batches(); 362 const int svdf_input_size = svdf.input_size(); 363 const int svdf_num_units = svdf.num_units(); 364 const int input_sequence_size = 365 sizeof(svdf_input) / sizeof(float) / (svdf_input_size * svdf_num_batches); 366 // Going over each input batch, setting the input tensor, invoking the SVDF op 367 // and checking the output with the expected golden values. 368 for (int i = 0; i < input_sequence_size; i++) { 369 float* batch_start = svdf_input + i * svdf_input_size * svdf_num_batches; 370 float* batch_end = batch_start + svdf_input_size * svdf_num_batches; 371 svdf.SetInput(0, batch_start, batch_end); 372 373 svdf.Invoke(); 374 375 float* golden_start = 376 svdf_golden_output + i * svdf_num_units * svdf_num_batches; 377 float* golden_end = golden_start + svdf_num_units * svdf_num_batches; 378 std::vector<float> expected; 379 expected.insert(expected.end(), golden_start, golden_end); 380 381 EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); 382 } 383 } 384 385 TEST(SVDFOpTest, BlackBoxTestRank2) { 386 SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, 387 /*memory_size=*/10, /*rank=*/2); 388 svdf.SetWeightsFeature({-0.31930989, 0.0079667, 0.39296314, 0.37613347, 389 0.12416199, 0.15785322, 0.27901134, 0.3905206, 390 0.21931258, -0.36137494, -0.10640851, 0.31053296, 391 -0.36118156, -0.0976817, -0.36916667, 0.22197971, 392 0.15294972, 0.38031587, 0.27557442, 0.39635518, 393 -0.21580373, -0.06634006, -0.02702999, 0.27072677}); 394 395 svdf.SetWeightsTime( 396 {-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, 397 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, 398 399 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, 400 -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, 401 402 -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, 403 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, 404 405 -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, 406 -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657, 407 408 -0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486, 409 0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187, 410 411 -0.26816407, -0.19905911, 0.29211238, 0.31264046, -0.28664589, 412 0.05698794, 0.11613581, 0.14078894, 0.02187902, -0.21781836, 413 414 -0.15567942, 0.08693647, -0.38256618, 0.36580828, -0.22922277, 415 -0.0226903, 0.12878349, -0.28122205, -0.10850525, -0.11955214, 416 417 0.27179423, -0.04710215, 0.31069002, 0.22672787, 0.09580326, 418 0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763}); 419 420 svdf.SetBias({}); 421 422 svdf.ResetState(); 423 const int svdf_num_batches = svdf.num_batches(); 424 const int svdf_input_size = svdf.input_size(); 425 const int svdf_num_units = svdf.num_units(); 426 const int input_sequence_size = 427 sizeof(svdf_input_rank2) / sizeof(float) / (svdf_input_size * svdf_num_batches); 428 // Going over each input batch, setting the input tensor, invoking the SVDF op 429 // and checking the output with the expected golden values. 430 for (int i = 0; i < input_sequence_size; i++) { 431 float* batch_start = svdf_input_rank2 + i * svdf_input_size * svdf_num_batches; 432 float* batch_end = batch_start + svdf_input_size * svdf_num_batches; 433 svdf.SetInput(0, batch_start, batch_end); 434 435 svdf.Invoke(); 436 437 float* golden_start = 438 svdf_golden_output_rank_2 + i * svdf_num_units * svdf_num_batches; 439 float* golden_end = golden_start + svdf_num_units * svdf_num_batches; 440 std::vector<float> expected; 441 expected.insert(expected.end(), golden_start, golden_end); 442 443 EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); 444 } 445 } 446 447 } // namespace wrapper 448 } // namespace nn 449 } // namespace android 450
