1 /**
2 * Copyright 2020 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 #define LOG_TAG "NN_RAND_MODEL"
18
19 #include <android-base/logging.h>
20 #include <jni.h>
21
22 #include <algorithm>
23 #include <fstream>
24 #include <memory>
25 #include <optional>
26 #include <random>
27 #include <set>
28 #include <sstream>
29 #include <string>
30 #include <vector>
31
32 #include "GeneratedTestUtils.h"
33 #include "fuzzing/OperationManager.h"
34 #include "fuzzing/RandomGraphGenerator.h"
35 #include "fuzzing/RandomGraphGeneratorUtils.h"
36
JNI_OnLoad(JavaVM * vm,void * reserved)37 extern "C" JNIEXPORT jint JNICALL JNI_OnLoad(JavaVM* vm, void* reserved) {
38 android::base::InitLogging(nullptr, android::base::LogdLogger());
39 android::base::SetMinimumLogSeverity(android::base::INFO);
40 return JNI_VERSION_1_6;
41 }
42
43 enum RandomModelExecutionResult {
44 kSuccess = 0,
45 kFailedCompilation,
46 kFailedExecution,
47 kFailedOtherNnApiCall,
48 // The following conditions are for internal retry
49 kInvalidModelGenerated,
50 kUnsupportedModelGenerated
51 };
52
53 class FuzzerLogRAII {
54 public:
FuzzerLogRAII(const std::string & nnapiLogPath)55 FuzzerLogRAII(const std::string& nnapiLogPath) {
56 using android::nn::fuzzing_test::alignedString;
57 using android::nn::fuzzing_test::Logger;
58 using android::nn::fuzzing_test::LoggerStream;
59
60 NN_FUZZER_LOG_WRITE_FATAL_TO_SYSLOG(LOG_TAG);
61
62 mFuzzerLogOpen = false;
63 if (!nnapiLogPath.empty()) {
64 // Checking if we can write to target file
65 std::ofstream os;
66 os.open(nnapiLogPath);
67
68 if (os.fail()) {
69 LOG(ERROR) << "Opening file " << nnapiLogPath << " failed";
70 } else {
71 NN_FUZZER_LOG_INIT(nnapiLogPath);
72 LOG(INFO) << "Logging NNAPI to file " << nnapiLogPath;
73 mFuzzerLogOpen = true;
74 }
75 }
76 }
~FuzzerLogRAII()77 ~FuzzerLogRAII() {
78 if (mFuzzerLogOpen) {
79 using android::nn::fuzzing_test::alignedString;
80 using android::nn::fuzzing_test::Logger;
81 using android::nn::fuzzing_test::LoggerStream;
82
83 NN_FUZZER_LOG_CLOSE;
84 }
85 }
86
87 private:
88 bool mFuzzerLogOpen;
89 };
90
getOperationsInModel(const test_helper::TestModel & testModel)91 std::vector<test_helper::TestOperationType> getOperationsInModel(
92 const test_helper::TestModel& testModel) {
93 std::vector<test_helper::TestOperationType> result;
94 testModel.forEachSubgraph(
95 [&result](const test_helper::TestSubgraph& subgraph) {
96 for (const auto& operation : subgraph.operations) {
97 result.push_back(operation.type);
98 }
99 });
100
101 return result;
102 }
103
findDeviceByName(const char * deviceName)104 const ANeuralNetworksDevice* findDeviceByName(const char* deviceName) {
105 if (!deviceName) return nullptr;
106
107 std::string deviceNameStr(deviceName);
108 uint32_t numDevices = 0;
109 ANeuralNetworks_getDeviceCount(&numDevices);
110
111 for (uint32_t i = 0; i < numDevices; i++) {
112 ANeuralNetworksDevice* device = nullptr;
113 const char* buffer = nullptr;
114 int getDeviceResult = ANeuralNetworks_getDevice(i, &device);
115 if (getDeviceResult != ANEURALNETWORKS_NO_ERROR) {
116 LOG(ERROR) << "Unable to get NNAPI device " << i << ": "
117 << getDeviceResult;
118 return nullptr;
119 }
120
121 int getDeviceNameResult = ANeuralNetworksDevice_getName(device, &buffer);
122 if (getDeviceNameResult != ANEURALNETWORKS_NO_ERROR) {
123 LOG(ERROR) << "Unable to get name of NNAPI device " << i << ": "
124 << getDeviceNameResult;
125 return nullptr;
126 }
127
128 if (deviceNameStr == buffer) {
129 return device;
130 }
131 }
132
133 LOG(ERROR) << "No device with name " << deviceNameStr;
134 return nullptr;
135 }
136
getNnApiReferenceDevice()137 const ANeuralNetworksDevice* getNnApiReferenceDevice() {
138 return findDeviceByName("nnapi-reference");
139 }
140
141 class RandomGraphGenerator {
142 public:
RandomGraphGenerator(const ANeuralNetworksDevice * device,const std::string & deviceName,const std::string & testName,uint32_t numOperations,uint32_t dimensionRange,std::string nnapiLogPath,std::string failedModelDumpPath)143 RandomGraphGenerator(const ANeuralNetworksDevice* device,
144 const std::string& deviceName,
145 const std::string& testName, uint32_t numOperations,
146 uint32_t dimensionRange, std::string nnapiLogPath,
147 std::string failedModelDumpPath)
148 : mTestName(testName),
149 mDevice(device),
150 mDeviceName(deviceName),
151 mNnApiReference(getNnApiReferenceDevice()),
152 mSupportedOpsFilter(),
153 mNumOperations(numOperations),
154 mDimensionRange(dimensionRange),
155 nnapiFuzzerLogRAII(nnapiLogPath),
156 mFailedModelDumpPath(failedModelDumpPath) {}
157
init()158 RandomModelExecutionResult init() {
159 // Limiting the ops in the generator to a subset we know the target device
160 // supports to avoid failing the test because we are unable to find a
161 // suitable model to compile.
162 RandomModelExecutionResult filterInitResult;
163 filterInitResult =
164 HalVersionsSupportedByDevice(&mSupportedOpsFilter.versions);
165 if (filterInitResult != kSuccess) return filterInitResult;
166
167 filterInitResult =
168 OperandTypesSupportedByDevice(&mSupportedOpsFilter.dataTypes);
169 if (filterInitResult != kSuccess) return filterInitResult;
170
171 return OperationsSupportedByDevice(mSupportedOpsFilter,
172 &mSupportedOpsFilter.opcodes);
173 }
174
runRandomModel(bool compilationOnly)175 RandomModelExecutionResult runRandomModel(bool compilationOnly) {
176 using android::nn::generated_tests::createModel;
177 using android::nn::generated_tests::createRequest;
178 using android::nn::generated_tests::GeneratedModel;
179 using android::nn::test_wrapper::Compilation;
180 using android::nn::test_wrapper::Execution;
181 using android::nn::wrapper::Result;
182
183 std::optional<test_helper::TestModel> testModel =
184 createRandomModel(mSupportedOpsFilter);
185 if (!testModel) {
186 LOG(ERROR) << mTestName << ": No model generated";
187 return kInvalidModelGenerated;
188 }
189
190 GeneratedModel model;
191 createModel(*testModel, &model);
192 if (!model.isValid()) {
193 LOG(ERROR) << mTestName << ": Randomly generated model is not valid";
194 return kInvalidModelGenerated;
195 }
196 auto modelFinishResult = model.finish();
197 if (modelFinishResult != Result::NO_ERROR) {
198 LOG(ERROR) << mTestName << ": Failed to finish model, result is "
199 << static_cast<int>(modelFinishResult);
200 return kInvalidModelGenerated;
201 }
202
203 bool fullySupportedModel = false;
204 if (mDevice) {
205 std::unique_ptr<bool[]> opsSupportedFlags =
206 std::make_unique<bool[]>(mNumOperations);
207 std::fill(opsSupportedFlags.get(),
208 opsSupportedFlags.get() + mNumOperations, false);
209 // Check if the device fully supports the graph.
210 int supportedOpResult =
211 ANeuralNetworksModel_getSupportedOperationsForDevices(
212 model.getHandle(), &mDevice, 1, opsSupportedFlags.get());
213 if (supportedOpResult != ANEURALNETWORKS_NO_ERROR) {
214 return kFailedOtherNnApiCall;
215 }
216
217 // accepting the model even if partially supported since we found that it
218 // is extremely difficult to have fully supported models.
219 // We could consider a minimum number (or percentage of total number) of
220 // operations to be supported to consider the model acceptable. For the
221 // moment we just accept any model that has any supported op.
222 bool supported = std::any_of(opsSupportedFlags.get(),
223 opsSupportedFlags.get() + mNumOperations,
224 [](bool v) { return v; });
225 if (!supported) {
226 return kUnsupportedModelGenerated;
227 }
228
229 fullySupportedModel = std::all_of(
230 opsSupportedFlags.get(), opsSupportedFlags.get() + mNumOperations,
231 [](bool v) { return v; });
232 }
233
234 std::vector<const ANeuralNetworksDevice*> devices;
235 if (mDevice) {
236 devices.push_back(mDevice);
237 if (!fullySupportedModel) {
238 // If model is not fully supported we allow NNAPI to use reference
239 // implementation. This is to avoid having this test constantly
240 // nullified by the inability of finding a fully supported model.
241 LOG(VERBOSE) << "Allowing model to be partially executed on NNAPI reference device";
242 devices.push_back(mNnApiReference);
243 }
244 }
245
246 auto [compilationResult, compilation] = CreateCompilation(model, devices);
247 if (compilationResult != Result::NO_ERROR) {
248 LOG(WARNING) << mTestName << ": Compilation preparation failed with result "
249 << static_cast<int>(compilationResult);
250
251 dumpModel(*testModel);
252 return kFailedCompilation;
253 }
254 compilationResult = compilation.finish();
255 if (compilationResult != Result::NO_ERROR) {
256 LOG(WARNING) << mTestName << ": Compilation failed with result "
257 << static_cast<int>(compilationResult);
258
259 dumpModel(*testModel);
260 return kFailedCompilation;
261 }
262
263 if (!compilationOnly) {
264 Execution execution(&compilation);
265 std::vector<test_helper::TestBuffer> outputs;
266 createRequest(*testModel, &execution, &outputs);
267
268 // Compute result.
269 Result executeReturn = execution.compute();
270 if (executeReturn != Result::NO_ERROR) {
271 LOG(WARNING) << mTestName << ": Execution failed with result "
272 << static_cast<int>(executeReturn);
273
274 dumpModel(*testModel);
275 return kFailedExecution;
276 }
277 }
278
279 return kSuccess;
280 }
281
282 const std::string mTestName;
283
284 private:
285 android::nn::fuzzing_test::RandomGraph mRandomGraph;
286 std::random_device mSeedGenerator;
287 const ANeuralNetworksDevice* mDevice;
288 // empty string if mDevice is null
289 const std::string mDeviceName;
290 const ANeuralNetworksDevice* mNnApiReference;
291 android::nn::fuzzing_test::OperationFilter mSupportedOpsFilter;
292 const uint32_t mNumOperations;
293 const uint32_t mDimensionRange;
294 FuzzerLogRAII nnapiFuzzerLogRAII;
295 const std::string mFailedModelDumpPath;
296
createRandomModel(const android::nn::fuzzing_test::OperationFilter & opFilter)297 std::optional<test_helper::TestModel> createRandomModel(
298 const android::nn::fuzzing_test::OperationFilter& opFilter) {
299 android::nn::fuzzing_test::OperationManager::get()->applyFilter(opFilter);
300
301 auto seed = mSeedGenerator();
302 if (!mRandomGraph.generate(seed, mNumOperations, mDimensionRange)) {
303 return std::nullopt;
304 }
305
306 return {mRandomGraph.createTestModel()};
307 }
308
HalVersionsSupportedByDevice(std::vector<test_helper::TestHalVersion> * result)309 RandomModelExecutionResult HalVersionsSupportedByDevice(
310 std::vector<test_helper::TestHalVersion>* result) {
311 if (!mDevice) {
312 return kSuccess;
313 }
314
315 int64_t featureLevel;
316 auto getDeviceFeatureLevelResult =
317 ANeuralNetworksDevice_getFeatureLevel(mDevice, &featureLevel);
318 if (getDeviceFeatureLevelResult != ANEURALNETWORKS_NO_ERROR) {
319 LOG(ERROR) << mTestName << ": Unable to query device feature level";
320 return kFailedOtherNnApiCall;
321 }
322
323 if (featureLevel == 27) *result = {test_helper::TestHalVersion::V1_0};
324 if (featureLevel == 28) *result = {test_helper::TestHalVersion::V1_1};
325 if (featureLevel == 29) *result = {test_helper::TestHalVersion::V1_2};
326
327 return kSuccess;
328 }
329
OperandTypesSupportedByDevice(std::vector<test_helper::TestOperandType> * result)330 RandomModelExecutionResult OperandTypesSupportedByDevice(
331 std::vector<test_helper::TestOperandType>* result) {
332 if (!mDevice) {
333 return kSuccess;
334 }
335
336 int32_t deviceType;
337 auto getDeviceTypeResult =
338 ANeuralNetworksDevice_getType(mDevice, &deviceType);
339 if (getDeviceTypeResult != ANEURALNETWORKS_NO_ERROR) {
340 LOG(ERROR) << mTestName << ": Unable to query device type";
341 return kFailedOtherNnApiCall;
342 }
343 using test_helper::TestOperandType;
344 switch (deviceType) {
345 case ANEURALNETWORKS_DEVICE_GPU:
346 // No quantized types
347 *result = {
348 TestOperandType::FLOAT32, TestOperandType::INT32,
349 TestOperandType::UINT32, TestOperandType::TENSOR_FLOAT32,
350 TestOperandType::TENSOR_INT32, TestOperandType::BOOL,
351 TestOperandType::TENSOR_FLOAT16, TestOperandType::TENSOR_BOOL8,
352 TestOperandType::FLOAT16};
353 break;
354 case ANEURALNETWORKS_DEVICE_CPU:
355 case ANEURALNETWORKS_DEVICE_ACCELERATOR:
356 result->clear(); // no filter
357 break;
358 case ANEURALNETWORKS_DEVICE_UNKNOWN:
359 case ANEURALNETWORKS_DEVICE_OTHER:
360 if (mDeviceName.find("dsp") != std::string::npos) {
361 *result = {TestOperandType::INT32,
362 TestOperandType::UINT32,
363 TestOperandType::TENSOR_INT32,
364 TestOperandType::BOOL,
365 TestOperandType::TENSOR_BOOL8,
366 TestOperandType::TENSOR_QUANT8_ASYMM,
367 TestOperandType::TENSOR_QUANT16_SYMM,
368 TestOperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL,
369 TestOperandType::TENSOR_QUANT16_ASYMM,
370 TestOperandType::TENSOR_QUANT8_SYMM,
371 TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED};
372 break;
373 }
374 FALLTHROUGH_INTENDED;
375 default:
376 result->clear(); // no filter
377 }
378 return kSuccess;
379 }
380
381 /// Finds some operations supported by the device
OperationsSupportedByDevice(const android::nn::fuzzing_test::OperationFilter & basicFilter,std::vector<test_helper::TestOperationType> * result)382 RandomModelExecutionResult OperationsSupportedByDevice(
383 const android::nn::fuzzing_test::OperationFilter& basicFilter,
384 std::vector<test_helper::TestOperationType>* result) {
385 if (!mDevice) {
386 return kSuccess;
387 }
388
389 constexpr int kNumOfAttempts = 50;
390 std::set<test_helper::TestOperationType> supportedOps;
391 for (int i = 0; i < kNumOfAttempts; i++) {
392 std::optional<test_helper::TestModel> testModel =
393 createRandomModel(basicFilter);
394 if (!testModel) {
395 LOG(ERROR)
396 << mTestName
397 << ": Unable to generate a model trying to understand the ops "
398 "supported by target device";
399 continue;
400 }
401
402 android::nn::generated_tests::GeneratedModel model;
403 createModel(*testModel, &model);
404 if (!model.isValid()) {
405 LOG(WARNING) << mTestName << ": Randomly generated model is not valid";
406 continue;
407 }
408 auto modelFinishResult = model.finish();
409 if (modelFinishResult != android::nn::wrapper::Result::NO_ERROR) {
410 LOG(WARNING) << "Model::finish call failed, result is "
411 << static_cast<int>(modelFinishResult);
412 continue;
413 }
414
415 std::unique_ptr<bool[]> opsSupportedFlags =
416 std::make_unique<bool[]>(mNumOperations);
417 std::fill(opsSupportedFlags.get(),
418 opsSupportedFlags.get() + mNumOperations, false);
419
420 // Check if the device fully supports the graph.
421 int supportedOpResult =
422 ANeuralNetworksModel_getSupportedOperationsForDevices(
423 model.getHandle(), &mDevice, 1, opsSupportedFlags.get());
424 if (supportedOpResult != ANEURALNETWORKS_NO_ERROR) {
425 return kFailedOtherNnApiCall;
426 }
427
428 std::vector<test_helper::TestOperationType> opsInModel =
429 getOperationsInModel(*testModel);
430 for (int opIndex = 0; opIndex < mNumOperations; opIndex++) {
431 test_helper::TestOperationType currOp = opsInModel[opIndex];
432 if (opsSupportedFlags[opIndex]) {
433 supportedOps.insert(currOp);
434 }
435 }
436 }
437 std::copy(supportedOps.begin(), supportedOps.end(),
438 std::back_inserter(*result));
439
440 if (result->empty()) {
441 LOG(WARNING)
442 << mTestName
443 << ": Could not find any operation supported by target device."
444 << " Returning no filter.";
445 } else {
446 LOG(INFO) << mTestName << ": Filtering to " << result->size()
447 << " supported operations";
448 }
449
450 return kSuccess;
451 }
452
dumpModel(const test_helper::TestModel & testModel)453 void dumpModel(const test_helper::TestModel& testModel) {
454 if (mFailedModelDumpPath.empty()) return;
455
456 LOG(INFO) << mTestName << ": Dumping model failing tests to "
457 << mFailedModelDumpPath;
458
459 std::ofstream os(mFailedModelDumpPath);
460 ASSERT_TRUE(os.is_open());
461 os << "# Generated from " << mTestName << ". Do not edit.\n\n";
462 test_helper::SpecDumper dumper(testModel, os);
463 dumper.dumpTestModel();
464 }
465
466 std::pair<android::nn::wrapper::Result,
467 android::nn::test_wrapper::Compilation>
CreateCompilation(const android::nn::generated_tests::GeneratedModel & model,const std::vector<const ANeuralNetworksDevice * > & devices)468 CreateCompilation(const android::nn::generated_tests::GeneratedModel& model,
469 const std::vector<const ANeuralNetworksDevice*>& devices) {
470 using android::nn::test_wrapper::Compilation;
471 if (!devices.empty())
472 return Compilation::createForDevices(&model, devices);
473 else
474 return {android::nn::wrapper::Result::NO_ERROR, Compilation(&model)};
475 }
476 };
477
478 extern "C" JNIEXPORT jint JNICALL
Java_com_android_nn_crashtest_core_RandomGraphTest_runRandomModel(JNIEnv * env,jclass,jlong _generatorHandle,jboolean _compilationOnly,jlong _maxModelSearchTimeSeconds)479 Java_com_android_nn_crashtest_core_RandomGraphTest_runRandomModel(
480 JNIEnv* env, jclass /* static method */, jlong _generatorHandle,
481 jboolean _compilationOnly, jlong _maxModelSearchTimeSeconds) {
482 RandomGraphGenerator* graphGenerator =
483 reinterpret_cast<RandomGraphGenerator*>(_generatorHandle);
484
485 std::time_t startTime = std::time(nullptr);
486
487 int result = kSuccess;
488 int modelSearchAttempt = 0;
489 while (std::difftime(std::time(nullptr), startTime) <
490 _maxModelSearchTimeSeconds) {
491 modelSearchAttempt++;
492
493 result = graphGenerator->runRandomModel(_compilationOnly);
494
495 // if by chance we generated an invalid model or a model that couldn't run
496 // on the target accelerator we will try again.
497 if (result != kInvalidModelGenerated &&
498 result != kUnsupportedModelGenerated) {
499 break;
500 }
501 }
502
503 if (result == kInvalidModelGenerated ||
504 result == kUnsupportedModelGenerated) {
505 LOG(WARNING) << graphGenerator->mTestName
506 << ": Max time to search for a model of "
507 << static_cast<long>(_maxModelSearchTimeSeconds)
508 << "seconds reached. Aborting test at attempt "
509 << modelSearchAttempt;
510 }
511
512 return result;
513 }
514
515 extern "C" JNIEXPORT jlong JNICALL
com_android_nn_crashtest_core_RandomGraphTest_RandomGraphTest_createRandomGraphGenerator(JNIEnv * env,jclass,jstring _nnApiDeviceName,jint _numOperations,jint _dimensionRange,jstring _testName,jstring _nnapiLogPath,jstring _failedModelDumpPath)516 com_android_nn_crashtest_core_RandomGraphTest_RandomGraphTest_createRandomGraphGenerator(
517 JNIEnv* env, jclass /* static method */, jstring _nnApiDeviceName,
518 jint _numOperations, jint _dimensionRange, jstring _testName,
519 jstring _nnapiLogPath, jstring _failedModelDumpPath) {
520 const char* nnApiDeviceName =
521 _nnApiDeviceName ? env->GetStringUTFChars(_nnApiDeviceName, nullptr)
522 : nullptr;
523
524 std::string nnApiDeviceNameStr{nnApiDeviceName ? nnApiDeviceName : ""};
525 const ANeuralNetworksDevice* device = nullptr;
526 if (nnApiDeviceName) {
527 device = findDeviceByName(nnApiDeviceName);
528 if (!device) {
529 LOG(ERROR) << ": Unable to find accelerator " << nnApiDeviceName;
530 env->ReleaseStringUTFChars(_nnApiDeviceName, nnApiDeviceName);
531 return reinterpret_cast<jlong>(nullptr);
532 }
533 env->ReleaseStringUTFChars(_nnApiDeviceName, nnApiDeviceName);
534 }
535
536 std::string testName{"no-test-name"};
537 if (_testName) {
538 const char* testNameBuf = env->GetStringUTFChars(_testName, nullptr);
539 testName = testNameBuf;
540 env->ReleaseStringUTFChars(_testName, testNameBuf);
541 }
542
543 std::string nnapiLogPath;
544 if (_nnapiLogPath) {
545 const char* nnapiLogPathTmp =
546 env->GetStringUTFChars(_nnapiLogPath, nullptr);
547 nnapiLogPath = nnapiLogPathTmp;
548 env->ReleaseStringUTFChars(_nnapiLogPath, nnapiLogPathTmp);
549 }
550
551 std::string failedModelDumpPath;
552 if (_failedModelDumpPath) {
553 const char* failedModelDumpPathTmp =
554 env->GetStringUTFChars(_failedModelDumpPath, nullptr);
555 failedModelDumpPath = failedModelDumpPathTmp;
556 env->ReleaseStringUTFChars(_failedModelDumpPath, failedModelDumpPathTmp);
557 }
558
559 uint32_t numOperations = static_cast<uint32_t>(_numOperations);
560 uint32_t dimensionRange = static_cast<uint32_t>(_dimensionRange);
561
562 RandomGraphGenerator* result = new RandomGraphGenerator(
563 device, nnApiDeviceNameStr, testName, numOperations, dimensionRange,
564 nnapiLogPath, failedModelDumpPath);
565
566 if (result->init() != kSuccess) {
567 delete result;
568 return reinterpret_cast<jlong>(nullptr);
569 }
570
571 return reinterpret_cast<jlong>(result);
572 }
573
574 extern "C" JNIEXPORT void JNICALL
com_android_nn_crashtest_core_RandomGraphTest_RandomGraphTest_destroyRandomGraphGenerator(JNIEnv * env,jclass,jlong generatorHandle)575 com_android_nn_crashtest_core_RandomGraphTest_RandomGraphTest_destroyRandomGraphGenerator(
576 JNIEnv* env, jclass /* static method */, jlong generatorHandle) {
577 RandomGraphGenerator* graphGenerator =
578 reinterpret_cast<RandomGraphGenerator*>(generatorHandle);
579 delete graphGenerator;
580 }
581