blob: 2460fbad86b85e08c0e1f35a61b2b7440034cda5 [file] [log] [blame]
Lev Proleevc185e882020-12-15 19:25:32 +00001/*
2 * Copyright (C) 2021 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 "GeneratedTestHarness.h"
18
19#include <aidl/android/hardware/neuralnetworks/ErrorStatus.h>
Michael Butler7fc7e372021-03-10 22:51:53 -080020#include <aidl/android/hardware/neuralnetworks/RequestMemoryPool.h>
Lev Proleevc185e882020-12-15 19:25:32 +000021#include <android-base/logging.h>
22#include <android/binder_auto_utils.h>
23#include <android/sync.h>
24#include <gtest/gtest.h>
25
26#include <algorithm>
27#include <chrono>
28#include <iostream>
29#include <iterator>
30#include <numeric>
31#include <vector>
32
33#include <MemoryUtils.h>
34#include <android/binder_status.h>
35#include <nnapi/Result.h>
36#include <nnapi/SharedMemory.h>
37#include <nnapi/Types.h>
38#include <nnapi/hal/aidl/Conversions.h>
39#include <nnapi/hal/aidl/Utils.h>
40
41#include "Callbacks.h"
42#include "TestHarness.h"
43#include "Utils.h"
44#include "VtsHalNeuralnetworks.h"
45
46namespace aidl::android::hardware::neuralnetworks::vts::functional {
47
48namespace nn = ::android::nn;
49using namespace test_helper;
50using implementation::PreparedModelCallback;
51
52namespace {
53
54enum class OutputType { FULLY_SPECIFIED, UNSPECIFIED, INSUFFICIENT, MISSED_DEADLINE };
55
56struct TestConfig {
57 Executor executor;
58 bool measureTiming;
59 OutputType outputType;
60 MemoryType memoryType;
Xusong Wang72e06c22022-01-11 14:25:55 -080061 bool reusable;
Lev Proleevc185e882020-12-15 19:25:32 +000062 // `reportSkipping` indicates if a test should print an info message in case
63 // it is skipped. The field is set to true by default and is set to false in
64 // quantization coupling tests to suppress skipping a test
65 bool reportSkipping;
Xusong Wang72e06c22022-01-11 14:25:55 -080066 TestConfig(Executor executor, bool measureTiming, OutputType outputType, MemoryType memoryType,
67 bool reusable)
Lev Proleevc185e882020-12-15 19:25:32 +000068 : executor(executor),
69 measureTiming(measureTiming),
70 outputType(outputType),
71 memoryType(memoryType),
Xusong Wang72e06c22022-01-11 14:25:55 -080072 reusable(reusable),
Lev Proleevc185e882020-12-15 19:25:32 +000073 reportSkipping(true) {}
74 TestConfig(Executor executor, bool measureTiming, OutputType outputType, MemoryType memoryType,
Xusong Wang72e06c22022-01-11 14:25:55 -080075 bool reusable, bool reportSkipping)
Lev Proleevc185e882020-12-15 19:25:32 +000076 : executor(executor),
77 measureTiming(measureTiming),
78 outputType(outputType),
79 memoryType(memoryType),
Xusong Wang72e06c22022-01-11 14:25:55 -080080 reusable(reusable),
Lev Proleevc185e882020-12-15 19:25:32 +000081 reportSkipping(reportSkipping) {}
82};
83
Xusong Wang72e06c22022-01-11 14:25:55 -080084std::string toString(OutputType type) {
85 switch (type) {
86 case OutputType::FULLY_SPECIFIED:
87 return "FULLY_SPECIFIED";
88 case OutputType::UNSPECIFIED:
89 return "UNSPECIFIED";
90 case OutputType::INSUFFICIENT:
91 return "INSUFFICIENT";
92 case OutputType::MISSED_DEADLINE:
93 return "MISSED_DEADLINE";
94 }
95}
96
97std::string toString(const TestConfig& config) {
98 std::stringstream ss;
99 ss << "TestConfig{.executor=" << toString(config.executor)
100 << ", .measureTiming=" << (config.measureTiming ? "true" : "false")
101 << ", .outputType=" << toString(config.outputType)
102 << ", .memoryType=" << toString(config.memoryType)
103 << ", .reusable=" << (config.reusable ? "true" : "false") << "}";
104 return ss.str();
105}
106
Lev Proleevc185e882020-12-15 19:25:32 +0000107enum class IOType { INPUT, OUTPUT };
108
109class DeviceMemoryAllocator {
110 public:
111 DeviceMemoryAllocator(const std::shared_ptr<IDevice>& device,
112 const std::shared_ptr<IPreparedModel>& preparedModel,
113 const TestModel& testModel)
114 : kDevice(device), kPreparedModel(preparedModel), kTestModel(testModel) {}
115
116 // Allocate device memory for a target input/output operand.
117 // Return {IBuffer object, token} if successful.
118 // Return {nullptr, 0} if device memory is not supported.
119 template <IOType ioType>
120 std::pair<std::shared_ptr<IBuffer>, int32_t> allocate(uint32_t index) {
121 std::pair<std::shared_ptr<IBuffer>, int32_t> buffer;
122 allocateInternal<ioType>(index, &buffer);
123 return buffer;
124 }
125
126 private:
127 template <IOType ioType>
128 void allocateInternal(int32_t index, std::pair<std::shared_ptr<IBuffer>, int32_t>* result) {
129 ASSERT_NE(result, nullptr);
130
131 // Prepare arguments.
Xusong Wang3633d072021-03-19 13:58:24 -0700132 BufferRole role = {.modelIndex = 0, .ioIndex = index, .probability = 1.0f};
Lev Proleevc185e882020-12-15 19:25:32 +0000133 std::vector<BufferRole> inputRoles, outputRoles;
134 if constexpr (ioType == IOType::INPUT) {
135 inputRoles = {role};
136 } else {
137 outputRoles = {role};
138 }
139
140 // Allocate device memory.
141 DeviceBuffer buffer;
142 IPreparedModelParcel parcel;
143 parcel.preparedModel = kPreparedModel;
144 const auto ret = kDevice->allocate({}, {parcel}, inputRoles, outputRoles, &buffer);
145
146 // Check allocation results.
147 if (ret.isOk()) {
148 ASSERT_NE(buffer.buffer, nullptr);
149 ASSERT_GT(buffer.token, 0);
150 } else {
151 ASSERT_EQ(ret.getExceptionCode(), EX_SERVICE_SPECIFIC);
152 ASSERT_EQ(static_cast<ErrorStatus>(ret.getServiceSpecificError()),
153 ErrorStatus::GENERAL_FAILURE);
154 buffer.buffer = nullptr;
155 buffer.token = 0;
156 }
157
158 // Initialize input data from TestBuffer.
159 if constexpr (ioType == IOType::INPUT) {
160 if (buffer.buffer != nullptr) {
161 // TestBuffer -> Shared memory.
162 const auto& testBuffer =
163 kTestModel.main.operands[kTestModel.main.inputIndexes[index]].data;
164 ASSERT_GT(testBuffer.size(), 0);
165 const auto sharedMemory = nn::createSharedMemory(testBuffer.size()).value();
166 const auto memory = utils::convert(sharedMemory).value();
167 const auto mapping = nn::map(sharedMemory).value();
168 uint8_t* inputPtr = static_cast<uint8_t*>(std::get<void*>(mapping.pointer));
169 ASSERT_NE(inputPtr, nullptr);
170 const uint8_t* begin = testBuffer.get<uint8_t>();
171 const uint8_t* end = begin + testBuffer.size();
172 std::copy(begin, end, inputPtr);
173
174 // Shared memory -> IBuffer.
175 auto ret = buffer.buffer->copyFrom(memory, {});
176 ASSERT_TRUE(ret.isOk());
177 }
178 }
179 *result = {std::move(buffer.buffer), buffer.token};
180 }
181
182 const std::shared_ptr<IDevice> kDevice;
183 const std::shared_ptr<IPreparedModel> kPreparedModel;
184 const TestModel& kTestModel;
185};
186
187Subgraph createSubgraph(const TestSubgraph& testSubgraph, uint32_t* constCopySize,
188 std::vector<const TestBuffer*>* constCopies, uint32_t* constRefSize,
189 std::vector<const TestBuffer*>* constReferences) {
190 CHECK(constCopySize != nullptr);
191 CHECK(constCopies != nullptr);
192 CHECK(constRefSize != nullptr);
193 CHECK(constReferences != nullptr);
194
195 // Operands.
196 std::vector<Operand> operands(testSubgraph.operands.size());
197 for (uint32_t i = 0; i < testSubgraph.operands.size(); i++) {
198 const auto& op = testSubgraph.operands[i];
199
200 DataLocation loc = {};
201 if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
202 loc = {
203 .poolIndex = 0,
204 .offset = *constCopySize,
205 .length = static_cast<int64_t>(op.data.size()),
206 };
207 constCopies->push_back(&op.data);
208 *constCopySize += op.data.alignedSize();
209 } else if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) {
210 loc = {
211 .poolIndex = 0,
212 .offset = *constRefSize,
213 .length = static_cast<int64_t>(op.data.size()),
214 };
215 constReferences->push_back(&op.data);
216 *constRefSize += op.data.alignedSize();
217 } else if (op.lifetime == TestOperandLifeTime::SUBGRAPH) {
218 loc = {
219 .poolIndex = 0,
220 .offset = *op.data.get<uint32_t>(),
221 .length = 0,
222 };
223 }
224
225 std::optional<OperandExtraParams> extraParams;
226 if (op.type == TestOperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
227 using Tag = OperandExtraParams::Tag;
228 extraParams = OperandExtraParams::make<Tag::channelQuant>(SymmPerChannelQuantParams{
229 .scales = op.channelQuant.scales,
230 .channelDim = static_cast<int32_t>(op.channelQuant.channelDim)});
231 }
232
233 operands[i] = {.type = static_cast<OperandType>(op.type),
234 .dimensions = utils::toSigned(op.dimensions).value(),
235 .scale = op.scale,
236 .zeroPoint = op.zeroPoint,
237 .lifetime = static_cast<OperandLifeTime>(op.lifetime),
238 .location = loc,
239 .extraParams = std::move(extraParams)};
240 }
241
242 // Operations.
243 std::vector<Operation> operations(testSubgraph.operations.size());
244 std::transform(testSubgraph.operations.begin(), testSubgraph.operations.end(),
245 operations.begin(), [](const TestOperation& op) -> Operation {
246 return {.type = static_cast<OperationType>(op.type),
247 .inputs = utils::toSigned(op.inputs).value(),
248 .outputs = utils::toSigned(op.outputs).value()};
249 });
250
251 return {.operands = std::move(operands),
252 .operations = std::move(operations),
253 .inputIndexes = utils::toSigned(testSubgraph.inputIndexes).value(),
254 .outputIndexes = utils::toSigned(testSubgraph.outputIndexes).value()};
255}
256
257void copyTestBuffers(const std::vector<const TestBuffer*>& buffers, uint8_t* output) {
258 uint32_t offset = 0;
259 for (const TestBuffer* buffer : buffers) {
260 const uint8_t* begin = buffer->get<uint8_t>();
261 const uint8_t* end = begin + buffer->size();
262 std::copy(begin, end, output + offset);
263 offset += buffer->alignedSize();
264 }
265}
266
267} // namespace
268
269void waitForSyncFence(int syncFd) {
270 constexpr int kInfiniteTimeout = -1;
271 ASSERT_GT(syncFd, 0);
272 int r = sync_wait(syncFd, kInfiniteTimeout);
273 ASSERT_GE(r, 0);
274}
275
276Model createModel(const TestModel& testModel) {
277 uint32_t constCopySize = 0;
278 uint32_t constRefSize = 0;
279 std::vector<const TestBuffer*> constCopies;
280 std::vector<const TestBuffer*> constReferences;
281
282 Subgraph mainSubgraph = createSubgraph(testModel.main, &constCopySize, &constCopies,
283 &constRefSize, &constReferences);
284 std::vector<Subgraph> refSubgraphs(testModel.referenced.size());
285 std::transform(testModel.referenced.begin(), testModel.referenced.end(), refSubgraphs.begin(),
286 [&constCopySize, &constCopies, &constRefSize,
287 &constReferences](const TestSubgraph& testSubgraph) {
288 return createSubgraph(testSubgraph, &constCopySize, &constCopies,
289 &constRefSize, &constReferences);
290 });
291
292 // Constant copies.
293 std::vector<uint8_t> operandValues(constCopySize);
294 copyTestBuffers(constCopies, operandValues.data());
295
296 // Shared memory.
Michael Butlerfadeb8a2021-02-07 00:11:13 -0800297 std::vector<nn::SharedMemory> pools = {};
Lev Proleevc185e882020-12-15 19:25:32 +0000298 if (constRefSize > 0) {
299 const auto pool = nn::createSharedMemory(constRefSize).value();
300 pools.push_back(pool);
301
302 // load data
303 const auto mappedMemory = nn::map(pool).value();
304 uint8_t* mappedPtr = static_cast<uint8_t*>(std::get<void*>(mappedMemory.pointer));
305 CHECK(mappedPtr != nullptr);
306
307 copyTestBuffers(constReferences, mappedPtr);
308 }
309
310 std::vector<Memory> aidlPools;
311 aidlPools.reserve(pools.size());
312 for (auto& pool : pools) {
313 auto aidlPool = utils::convert(pool).value();
314 aidlPools.push_back(std::move(aidlPool));
315 }
316
317 return {.main = std::move(mainSubgraph),
318 .referenced = std::move(refSubgraphs),
319 .operandValues = std::move(operandValues),
320 .pools = std::move(aidlPools),
321 .relaxComputationFloat32toFloat16 = testModel.isRelaxed};
322}
323
324static bool isOutputSizeGreaterThanOne(const TestModel& testModel, uint32_t index) {
325 const auto byteSize = testModel.main.operands[testModel.main.outputIndexes[index]].data.size();
326 return byteSize > 1u;
327}
328
329static void makeOutputInsufficientSize(uint32_t outputIndex, Request* request) {
Xusong Wang16858a62021-02-17 21:59:39 -0800330 auto& loc = request->outputs[outputIndex].location;
331 ASSERT_GT(loc.length, 1u);
332 loc.length -= 1u;
333 // Test that the padding is not used for output data.
334 loc.padding += 1u;
Lev Proleevc185e882020-12-15 19:25:32 +0000335}
336
337static void makeOutputDimensionsUnspecified(Model* model) {
338 for (auto i : model->main.outputIndexes) {
339 auto& dims = model->main.operands[i].dimensions;
340 std::fill(dims.begin(), dims.end(), 0);
341 }
342}
343
344// Manages the lifetime of memory resources used in an execution.
345class ExecutionContext {
346 public:
347 ExecutionContext(std::shared_ptr<IDevice> device, std::shared_ptr<IPreparedModel> preparedModel)
348 : kDevice(std::move(device)), kPreparedModel(std::move(preparedModel)) {}
349
350 std::optional<Request> createRequest(const TestModel& testModel, MemoryType memoryType);
351 std::vector<TestBuffer> getOutputBuffers(const TestModel& testModel,
352 const Request& request) const;
353
354 private:
355 // Get a TestBuffer with data copied from an IBuffer object.
356 void getBuffer(const std::shared_ptr<IBuffer>& buffer, size_t size,
357 TestBuffer* testBuffer) const;
358
359 static constexpr uint32_t kInputPoolIndex = 0;
360 static constexpr uint32_t kOutputPoolIndex = 1;
361 static constexpr uint32_t kDeviceMemoryBeginIndex = 2;
362
363 const std::shared_ptr<IDevice> kDevice;
364 const std::shared_ptr<IPreparedModel> kPreparedModel;
365 std::unique_ptr<TestMemoryBase> mInputMemory, mOutputMemory;
366 std::vector<std::shared_ptr<IBuffer>> mBuffers;
367};
368
Xusong Wang16858a62021-02-17 21:59:39 -0800369// Returns the number of bytes needed to round up "size" to the nearest multiple of "multiple".
370static uint32_t roundUpBytesNeeded(uint32_t size, uint32_t multiple) {
371 CHECK(multiple != 0);
372 return ((size + multiple - 1) / multiple) * multiple - size;
373}
374
Lev Proleevc185e882020-12-15 19:25:32 +0000375std::optional<Request> ExecutionContext::createRequest(const TestModel& testModel,
376 MemoryType memoryType) {
377 // Memory pools are organized as:
378 // - 0: Input shared memory pool
379 // - 1: Output shared memory pool
380 // - [2, 2+i): Input device memories
381 // - [2+i, 2+i+o): Output device memories
382 DeviceMemoryAllocator allocator(kDevice, kPreparedModel, testModel);
383 std::vector<int32_t> tokens;
384 mBuffers.clear();
385
386 // Model inputs.
387 std::vector<RequestArgument> inputs(testModel.main.inputIndexes.size());
388 size_t inputSize = 0;
389 for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
390 const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];
391 if (op.data.size() == 0) {
392 // Omitted input.
393 inputs[i] = {.hasNoValue = true};
394 continue;
395 } else if (memoryType == MemoryType::DEVICE) {
396 SCOPED_TRACE("Input index = " + std::to_string(i));
397 auto [buffer, token] = allocator.allocate<IOType::INPUT>(i);
398 if (buffer != nullptr) {
399 DataLocation loc = {.poolIndex = static_cast<int32_t>(mBuffers.size() +
400 kDeviceMemoryBeginIndex)};
401 mBuffers.push_back(std::move(buffer));
402 tokens.push_back(token);
403 inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
404 continue;
405 }
406 }
407
408 // Reserve shared memory for input.
Xusong Wang16858a62021-02-17 21:59:39 -0800409 inputSize += roundUpBytesNeeded(inputSize, nn::kDefaultRequestMemoryAlignment);
410 const auto padding = roundUpBytesNeeded(op.data.size(), nn::kDefaultRequestMemoryPadding);
Lev Proleevc185e882020-12-15 19:25:32 +0000411 DataLocation loc = {.poolIndex = kInputPoolIndex,
412 .offset = static_cast<int64_t>(inputSize),
Xusong Wang16858a62021-02-17 21:59:39 -0800413 .length = static_cast<int64_t>(op.data.size()),
414 .padding = static_cast<int64_t>(padding)};
415 inputSize += (op.data.size() + padding);
Lev Proleevc185e882020-12-15 19:25:32 +0000416 inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
417 }
418
419 // Model outputs.
420 std::vector<RequestArgument> outputs(testModel.main.outputIndexes.size());
421 size_t outputSize = 0;
422 for (uint32_t i = 0; i < testModel.main.outputIndexes.size(); i++) {
423 const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]];
424 if (memoryType == MemoryType::DEVICE) {
425 SCOPED_TRACE("Output index = " + std::to_string(i));
426 auto [buffer, token] = allocator.allocate<IOType::OUTPUT>(i);
427 if (buffer != nullptr) {
428 DataLocation loc = {.poolIndex = static_cast<int32_t>(mBuffers.size() +
429 kDeviceMemoryBeginIndex)};
430 mBuffers.push_back(std::move(buffer));
431 tokens.push_back(token);
432 outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
433 continue;
434 }
435 }
436
437 // In the case of zero-sized output, we should at least provide a one-byte buffer.
438 // This is because zero-sized tensors are only supported internally to the driver, or
439 // reported in output shapes. It is illegal for the client to pre-specify a zero-sized
440 // tensor as model output. Otherwise, we will have two semantic conflicts:
441 // - "Zero dimension" conflicts with "unspecified dimension".
442 // - "Omitted operand buffer" conflicts with "zero-sized operand buffer".
443 size_t bufferSize = std::max<size_t>(op.data.size(), 1);
444
445 // Reserve shared memory for output.
Xusong Wang16858a62021-02-17 21:59:39 -0800446 outputSize += roundUpBytesNeeded(outputSize, nn::kDefaultRequestMemoryAlignment);
447 const auto padding = roundUpBytesNeeded(bufferSize, nn::kDefaultRequestMemoryPadding);
Lev Proleevc185e882020-12-15 19:25:32 +0000448 DataLocation loc = {.poolIndex = kOutputPoolIndex,
449 .offset = static_cast<int64_t>(outputSize),
Xusong Wang16858a62021-02-17 21:59:39 -0800450 .length = static_cast<int64_t>(bufferSize),
451 .padding = static_cast<int64_t>(padding)};
452 outputSize += (bufferSize + padding);
Lev Proleevc185e882020-12-15 19:25:32 +0000453 outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
454 }
455
456 if (memoryType == MemoryType::DEVICE && mBuffers.empty()) {
457 return std::nullopt;
458 }
459
460 // Memory pools.
461 if (memoryType == MemoryType::BLOB_AHWB) {
462 mInputMemory = TestBlobAHWB::create(std::max<size_t>(inputSize, 1));
463 mOutputMemory = TestBlobAHWB::create(std::max<size_t>(outputSize, 1));
464 } else {
Xusong Wang378a9382021-05-21 14:58:40 -0700465 mInputMemory = TestAshmem::create(std::max<size_t>(inputSize, 1), /*aidlReadonly=*/true);
466 mOutputMemory = TestAshmem::create(std::max<size_t>(outputSize, 1), /*aidlReadonly=*/false);
Lev Proleevc185e882020-12-15 19:25:32 +0000467 }
468 CHECK_NE(mInputMemory, nullptr);
469 CHECK_NE(mOutputMemory, nullptr);
470 std::vector<RequestMemoryPool> pools;
471 pools.reserve(kDeviceMemoryBeginIndex + mBuffers.size());
472
473 auto copiedInputMemory = utils::clone(*mInputMemory->getAidlMemory());
474 CHECK(copiedInputMemory.has_value()) << copiedInputMemory.error().message;
475 auto copiedOutputMemory = utils::clone(*mOutputMemory->getAidlMemory());
476 CHECK(copiedOutputMemory.has_value()) << copiedOutputMemory.error().message;
477
478 pools.push_back(RequestMemoryPool::make<RequestMemoryPool::Tag::pool>(
479 std::move(copiedInputMemory).value()));
480 pools.push_back(RequestMemoryPool::make<RequestMemoryPool::Tag::pool>(
481 std::move(copiedOutputMemory).value()));
482 for (const auto& token : tokens) {
483 pools.push_back(RequestMemoryPool::make<RequestMemoryPool::Tag::token>(token));
484 }
485
486 // Copy input data to the input shared memory pool.
487 uint8_t* inputPtr = mInputMemory->getPointer();
488 for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
489 if (!inputs[i].hasNoValue && inputs[i].location.poolIndex == kInputPoolIndex) {
490 const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];
491 const uint8_t* begin = op.data.get<uint8_t>();
492 const uint8_t* end = begin + op.data.size();
493 std::copy(begin, end, inputPtr + inputs[i].location.offset);
494 }
495 }
496 return Request{
497 .inputs = std::move(inputs), .outputs = std::move(outputs), .pools = std::move(pools)};
498}
499
500std::vector<TestBuffer> ExecutionContext::getOutputBuffers(const TestModel& testModel,
501 const Request& request) const {
502 // Copy out output results.
503 uint8_t* outputPtr = mOutputMemory->getPointer();
504 std::vector<TestBuffer> outputBuffers;
505 for (uint32_t i = 0; i < request.outputs.size(); i++) {
506 const auto& outputLoc = request.outputs[i].location;
507 if (outputLoc.poolIndex == kOutputPoolIndex) {
508 outputBuffers.emplace_back(outputLoc.length, outputPtr + outputLoc.offset);
509 } else {
510 const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]];
511 if (op.data.size() == 0) {
512 outputBuffers.emplace_back(0, nullptr);
513 } else {
514 SCOPED_TRACE("Output index = " + std::to_string(i));
515 const uint32_t bufferIndex = outputLoc.poolIndex - kDeviceMemoryBeginIndex;
516 TestBuffer buffer;
517 getBuffer(mBuffers[bufferIndex], op.data.size(), &buffer);
518 outputBuffers.push_back(std::move(buffer));
519 }
520 }
521 }
522 return outputBuffers;
523}
524
525// Get a TestBuffer with data copied from an IBuffer object.
526void ExecutionContext::getBuffer(const std::shared_ptr<IBuffer>& buffer, size_t size,
527 TestBuffer* testBuffer) const {
528 // IBuffer -> Shared memory.
529 auto sharedMemory = nn::createSharedMemory(size).value();
530 auto aidlMemory = utils::convert(sharedMemory).value();
531 const auto ret = buffer->copyTo(aidlMemory);
532 ASSERT_TRUE(ret.isOk());
533
534 // Shared memory -> TestBuffer.
535 const auto outputMemory = nn::map(sharedMemory).value();
536 const uint8_t* outputPtr = std::visit(
537 [](auto* ptr) { return static_cast<const uint8_t*>(ptr); }, outputMemory.pointer);
538 ASSERT_NE(outputPtr, nullptr);
539 ASSERT_NE(testBuffer, nullptr);
540 *testBuffer = TestBuffer(size, outputPtr);
541}
542
543static bool hasZeroSizedOutput(const TestModel& testModel) {
544 return std::any_of(testModel.main.outputIndexes.begin(), testModel.main.outputIndexes.end(),
545 [&testModel](uint32_t index) {
546 return testModel.main.operands[index].data.size() == 0;
547 });
548}
549
550void EvaluatePreparedModel(const std::shared_ptr<IDevice>& device,
551 const std::shared_ptr<IPreparedModel>& preparedModel,
552 const TestModel& testModel, const TestConfig& testConfig,
553 bool* skipped = nullptr) {
554 if (skipped != nullptr) {
555 *skipped = false;
556 }
557 // If output0 does not have size larger than one byte, we can not test with insufficient buffer.
558 if (testConfig.outputType == OutputType::INSUFFICIENT &&
559 !isOutputSizeGreaterThanOne(testModel, 0)) {
560 return;
561 }
562
563 ExecutionContext context(device, preparedModel);
564 auto maybeRequest = context.createRequest(testModel, testConfig.memoryType);
565 // Skip if testing memory domain but no device memory has been allocated.
566 if (!maybeRequest.has_value()) {
567 return;
568 }
569
570 Request request = std::move(maybeRequest).value();
571
572 constexpr uint32_t kInsufficientOutputIndex = 0;
573 if (testConfig.outputType == OutputType::INSUFFICIENT) {
574 makeOutputInsufficientSize(kInsufficientOutputIndex, &request);
575 }
576
Lev Proleev8df7d6e2021-04-14 20:54:27 +0100577 int64_t loopTimeoutDurationNs = kOmittedTimeoutDuration;
Lev Proleevc185e882020-12-15 19:25:32 +0000578 // OutputType::MISSED_DEADLINE is only used by
579 // TestKind::INTINITE_LOOP_TIMEOUT tests to verify that an infinite loop is
580 // aborted after a timeout.
581 if (testConfig.outputType == OutputType::MISSED_DEADLINE) {
582 // Override the default loop timeout duration with a small value to
583 // speed up test execution.
584 constexpr int64_t kMillisecond = 1'000'000;
Lev Proleev8df7d6e2021-04-14 20:54:27 +0100585 loopTimeoutDurationNs = 1 * kMillisecond;
Lev Proleevc185e882020-12-15 19:25:32 +0000586 }
587
Xusong Wang72e06c22022-01-11 14:25:55 -0800588 std::shared_ptr<IExecution> execution;
589 if (testConfig.reusable) {
590 const auto ret = preparedModel->createReusableExecution(request, testConfig.measureTiming,
591 loopTimeoutDurationNs, &execution);
592 ASSERT_TRUE(ret.isOk()) << static_cast<nn::ErrorStatus>(ret.getServiceSpecificError());
593 ASSERT_NE(nullptr, execution.get());
594 }
Lev Proleevc185e882020-12-15 19:25:32 +0000595
Xusong Wang72e06c22022-01-11 14:25:55 -0800596 const auto executeAndCheckResults = [&preparedModel, &execution, &testConfig, &testModel,
597 &context, &request, loopTimeoutDurationNs, skipped]() {
598 ErrorStatus executionStatus;
599 std::vector<OutputShape> outputShapes;
600 Timing timing = kNoTiming;
601 switch (testConfig.executor) {
602 case Executor::SYNC: {
603 SCOPED_TRACE("synchronous");
Michael Butler7fc7e372021-03-10 22:51:53 -0800604
Xusong Wang72e06c22022-01-11 14:25:55 -0800605 ExecutionResult executionResult;
606 // execute
607 ::ndk::ScopedAStatus ret;
608 if (testConfig.reusable) {
609 ret = execution->executeSynchronously(kNoDeadline, &executionResult);
Michael Butler7fc7e372021-03-10 22:51:53 -0800610 } else {
Xusong Wang72e06c22022-01-11 14:25:55 -0800611 ret = preparedModel->executeSynchronously(request, testConfig.measureTiming,
612 kNoDeadline, loopTimeoutDurationNs,
613 &executionResult);
Michael Butler7fc7e372021-03-10 22:51:53 -0800614 }
Xusong Wang72e06c22022-01-11 14:25:55 -0800615 ASSERT_TRUE(ret.isOk() || ret.getExceptionCode() == EX_SERVICE_SPECIFIC)
616 << ret.getDescription();
617 if (ret.isOk()) {
618 executionStatus = executionResult.outputSufficientSize
619 ? ErrorStatus::NONE
620 : ErrorStatus::OUTPUT_INSUFFICIENT_SIZE;
621 outputShapes = std::move(executionResult.outputShapes);
622 timing = executionResult.timing;
623 } else {
624 executionStatus = static_cast<ErrorStatus>(ret.getServiceSpecificError());
625 }
626 break;
Michael Butler7fc7e372021-03-10 22:51:53 -0800627 }
Xusong Wang72e06c22022-01-11 14:25:55 -0800628 case Executor::BURST: {
629 SCOPED_TRACE("burst");
Michael Butler7fc7e372021-03-10 22:51:53 -0800630
Xusong Wang72e06c22022-01-11 14:25:55 -0800631 // create burst
632 std::shared_ptr<IBurst> burst;
633 auto ret = preparedModel->configureExecutionBurst(&burst);
Michael Butler7fc7e372021-03-10 22:51:53 -0800634 ASSERT_TRUE(ret.isOk()) << ret.getDescription();
Xusong Wang72e06c22022-01-11 14:25:55 -0800635 ASSERT_NE(nullptr, burst.get());
Michael Butler7fc7e372021-03-10 22:51:53 -0800636
Xusong Wang72e06c22022-01-11 14:25:55 -0800637 // associate a unique slot with each memory pool
638 int64_t currentSlot = 0;
639 std::vector<int64_t> slots;
640 slots.reserve(request.pools.size());
641 for (const auto& pool : request.pools) {
642 if (pool.getTag() == RequestMemoryPool::Tag::pool) {
643 slots.push_back(currentSlot++);
644 } else {
645 EXPECT_EQ(pool.getTag(), RequestMemoryPool::Tag::token);
646 slots.push_back(-1);
647 }
Lev Proleevc185e882020-12-15 19:25:32 +0000648 }
Xusong Wang72e06c22022-01-11 14:25:55 -0800649
650 ExecutionResult executionResult;
651 // execute
652 ret = burst->executeSynchronously(request, slots, testConfig.measureTiming,
653 kNoDeadline, loopTimeoutDurationNs,
654 &executionResult);
655 ASSERT_TRUE(ret.isOk() || ret.getExceptionCode() == EX_SERVICE_SPECIFIC)
656 << ret.getDescription();
657 if (ret.isOk()) {
658 executionStatus = executionResult.outputSufficientSize
659 ? ErrorStatus::NONE
660 : ErrorStatus::OUTPUT_INSUFFICIENT_SIZE;
661 outputShapes = std::move(executionResult.outputShapes);
662 timing = executionResult.timing;
663 } else {
664 executionStatus = static_cast<ErrorStatus>(ret.getServiceSpecificError());
665 }
666
667 // Mark each slot as unused after the execution. This is unnecessary because the
668 // burst is freed after this scope ends, but this is here to test the functionality.
669 for (int64_t slot : slots) {
670 ret = burst->releaseMemoryResource(slot);
671 ASSERT_TRUE(ret.isOk()) << ret.getDescription();
672 }
673
674 break;
Lev Proleevc185e882020-12-15 19:25:32 +0000675 }
Xusong Wang72e06c22022-01-11 14:25:55 -0800676 case Executor::FENCED: {
677 SCOPED_TRACE("fenced");
678 ErrorStatus result = ErrorStatus::NONE;
679 FencedExecutionResult executionResult;
680 ::ndk::ScopedAStatus ret;
681 if (testConfig.reusable) {
682 ret = execution->executeFenced({}, kNoDeadline, kNoDuration, &executionResult);
683 } else {
684 ret = preparedModel->executeFenced(request, {}, testConfig.measureTiming,
685 kNoDeadline, loopTimeoutDurationNs,
686 kNoDuration, &executionResult);
687 }
688 ASSERT_TRUE(ret.isOk() || ret.getExceptionCode() == EX_SERVICE_SPECIFIC)
689 << ret.getDescription();
690 if (!ret.isOk()) {
691 result = static_cast<ErrorStatus>(ret.getServiceSpecificError());
692 executionStatus = result;
693 } else if (executionResult.syncFence.get() != -1) {
694 std::vector<ndk::ScopedFileDescriptor> waitFor;
695 auto dupFd = dup(executionResult.syncFence.get());
696 ASSERT_NE(dupFd, -1);
697 waitFor.emplace_back(dupFd);
698 // If a sync fence is returned, try start another run waiting for the sync
699 // fence.
700 ret = preparedModel->executeFenced(request, waitFor, testConfig.measureTiming,
701 kNoDeadline, loopTimeoutDurationNs,
702 kNoDuration, &executionResult);
703 ASSERT_TRUE(ret.isOk());
704 waitForSyncFence(executionResult.syncFence.get());
705 }
706 if (result == ErrorStatus::NONE) {
707 ASSERT_NE(executionResult.callback, nullptr);
708 Timing timingFenced;
709 auto ret = executionResult.callback->getExecutionInfo(&timing, &timingFenced,
710 &executionStatus);
711 ASSERT_TRUE(ret.isOk());
712 }
713 break;
714 }
715 default: {
716 FAIL() << "Unsupported execution mode for AIDL interface.";
717 }
718 }
719
720 if (testConfig.outputType != OutputType::FULLY_SPECIFIED &&
721 executionStatus == ErrorStatus::GENERAL_FAILURE) {
722 if (skipped != nullptr) {
723 *skipped = true;
724 }
725 if (!testConfig.reportSkipping) {
726 return;
727 }
728 LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
729 "execute model that it does not support.";
730 std::cout << "[ ] Early termination of test because vendor service cannot "
731 "execute model that it does not support."
732 << std::endl;
733 GTEST_SKIP();
734 }
735 if (!testConfig.measureTiming) {
736 EXPECT_EQ(timing, kNoTiming);
737 } else {
738 if (timing.timeOnDeviceNs != -1 && timing.timeInDriverNs != -1) {
739 EXPECT_LE(timing.timeOnDeviceNs, timing.timeInDriverNs);
740 }
741 }
742
743 switch (testConfig.outputType) {
744 case OutputType::FULLY_SPECIFIED:
745 if (testConfig.executor == Executor::FENCED && hasZeroSizedOutput(testModel)) {
746 // Executor::FENCED does not support zero-sized output.
747 ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionStatus);
748 return;
749 }
750 // If the model output operands are fully specified, outputShapes must be either
751 // either empty, or have the same number of elements as the number of outputs.
752 ASSERT_EQ(ErrorStatus::NONE, executionStatus);
753 ASSERT_TRUE(outputShapes.size() == 0 ||
754 outputShapes.size() == testModel.main.outputIndexes.size());
755 break;
756 case OutputType::UNSPECIFIED:
757 if (testConfig.executor == Executor::FENCED) {
758 // For Executor::FENCED, the output shape must be fully specified.
759 ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionStatus);
760 return;
761 }
762 // If the model output operands are not fully specified, outputShapes must have
763 // the same number of elements as the number of outputs.
764 ASSERT_EQ(ErrorStatus::NONE, executionStatus);
765 ASSERT_EQ(outputShapes.size(), testModel.main.outputIndexes.size());
766 break;
767 case OutputType::INSUFFICIENT:
768 if (testConfig.executor == Executor::FENCED) {
769 // For Executor::FENCED, the output shape must be fully specified.
770 ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionStatus);
771 return;
772 }
773 ASSERT_EQ(ErrorStatus::OUTPUT_INSUFFICIENT_SIZE, executionStatus);
774 ASSERT_EQ(outputShapes.size(), testModel.main.outputIndexes.size());
775 // Check that all returned output dimensions are at least as fully specified as the
776 // union of the information about the corresponding operand in the model and in the
777 // request. In this test, all model outputs have known rank with all dimensions
778 // unspecified, and no dimensional information is provided in the request.
779 for (uint32_t i = 0; i < outputShapes.size(); i++) {
780 ASSERT_EQ(outputShapes[i].isSufficient, i != kInsufficientOutputIndex);
781 const auto& actual = outputShapes[i].dimensions;
782 const auto& golden =
783 testModel.main.operands[testModel.main.outputIndexes[i]].dimensions;
784 ASSERT_EQ(actual.size(), golden.size());
785 for (uint32_t j = 0; j < actual.size(); j++) {
786 if (actual[j] == 0) continue;
787 EXPECT_EQ(actual[j], golden[j]) << "index: " << j;
788 }
789 }
790 return;
791 case OutputType::MISSED_DEADLINE:
792 ASSERT_TRUE(executionStatus == ErrorStatus::MISSED_DEADLINE_TRANSIENT ||
793 executionStatus == ErrorStatus::MISSED_DEADLINE_PERSISTENT)
794 << "executionStatus = " << executionStatus;
795 return;
796 }
797
798 // Go through all outputs, check returned output shapes.
799 for (uint32_t i = 0; i < outputShapes.size(); i++) {
800 EXPECT_TRUE(outputShapes[i].isSufficient);
801 const auto& expect =
802 testModel.main.operands[testModel.main.outputIndexes[i]].dimensions;
803 const auto unsignedActual = nn::toUnsigned(outputShapes[i].dimensions);
804 ASSERT_TRUE(unsignedActual.has_value());
805 const std::vector<uint32_t>& actual = unsignedActual.value();
806 EXPECT_EQ(expect, actual);
807 }
808
809 // Retrieve execution results.
810 const std::vector<TestBuffer> outputs = context.getOutputBuffers(testModel, request);
811
812 // We want "close-enough" results.
813 checkResults(testModel, outputs);
814 };
815
816 executeAndCheckResults();
817
818 // For reusable execution tests, run the execution twice.
819 if (testConfig.reusable) {
820 SCOPED_TRACE("Second execution");
821 executeAndCheckResults();
Lev Proleevc185e882020-12-15 19:25:32 +0000822 }
Lev Proleevc185e882020-12-15 19:25:32 +0000823}
824
825void EvaluatePreparedModel(const std::shared_ptr<IDevice>& device,
826 const std::shared_ptr<IPreparedModel>& preparedModel,
827 const TestModel& testModel, TestKind testKind) {
828 std::vector<OutputType> outputTypesList;
829 std::vector<bool> measureTimingList;
830 std::vector<Executor> executorList;
831 std::vector<MemoryType> memoryTypeList;
Xusong Wang72e06c22022-01-11 14:25:55 -0800832 std::vector<bool> reusableList = {false};
833
834 int deviceVersion;
835 ASSERT_TRUE(device->getInterfaceVersion(&deviceVersion).isOk());
836 if (deviceVersion >= kMinAidlLevelForFL8) {
837 reusableList.push_back(true);
838 }
Lev Proleevc185e882020-12-15 19:25:32 +0000839
840 switch (testKind) {
841 case TestKind::GENERAL: {
842 outputTypesList = {OutputType::FULLY_SPECIFIED};
843 measureTimingList = {false, true};
Michael Butler7fc7e372021-03-10 22:51:53 -0800844 executorList = {Executor::SYNC, Executor::BURST};
Lev Proleevc185e882020-12-15 19:25:32 +0000845 memoryTypeList = {MemoryType::ASHMEM};
846 } break;
847 case TestKind::DYNAMIC_SHAPE: {
848 outputTypesList = {OutputType::UNSPECIFIED, OutputType::INSUFFICIENT};
849 measureTimingList = {false, true};
Michael Butler7fc7e372021-03-10 22:51:53 -0800850 executorList = {Executor::SYNC, Executor::BURST, Executor::FENCED};
Lev Proleevc185e882020-12-15 19:25:32 +0000851 memoryTypeList = {MemoryType::ASHMEM};
852 } break;
853 case TestKind::MEMORY_DOMAIN: {
854 outputTypesList = {OutputType::FULLY_SPECIFIED};
855 measureTimingList = {false};
Michael Butler7fc7e372021-03-10 22:51:53 -0800856 executorList = {Executor::SYNC, Executor::BURST, Executor::FENCED};
Lev Proleevc185e882020-12-15 19:25:32 +0000857 memoryTypeList = {MemoryType::BLOB_AHWB, MemoryType::DEVICE};
858 } break;
859 case TestKind::FENCED_COMPUTE: {
860 outputTypesList = {OutputType::FULLY_SPECIFIED};
861 measureTimingList = {false, true};
862 executorList = {Executor::FENCED};
863 memoryTypeList = {MemoryType::ASHMEM};
864 } break;
865 case TestKind::QUANTIZATION_COUPLING: {
866 LOG(FATAL) << "Wrong TestKind for EvaluatePreparedModel";
867 return;
868 } break;
869 case TestKind::INTINITE_LOOP_TIMEOUT: {
870 outputTypesList = {OutputType::MISSED_DEADLINE};
871 measureTimingList = {false, true};
Michael Butler7fc7e372021-03-10 22:51:53 -0800872 executorList = {Executor::SYNC, Executor::BURST, Executor::FENCED};
Lev Proleevc185e882020-12-15 19:25:32 +0000873 memoryTypeList = {MemoryType::ASHMEM};
874 } break;
875 }
876
877 for (const OutputType outputType : outputTypesList) {
878 for (const bool measureTiming : measureTimingList) {
879 for (const Executor executor : executorList) {
880 for (const MemoryType memoryType : memoryTypeList) {
Xusong Wang72e06c22022-01-11 14:25:55 -0800881 for (const bool reusable : reusableList) {
882 if (executor == Executor::BURST && reusable) continue;
883 const TestConfig testConfig(executor, measureTiming, outputType, memoryType,
884 reusable);
885 SCOPED_TRACE(toString(testConfig));
886 EvaluatePreparedModel(device, preparedModel, testModel, testConfig);
887 }
Lev Proleevc185e882020-12-15 19:25:32 +0000888 }
889 }
890 }
891 }
892}
893
894void EvaluatePreparedCoupledModels(const std::shared_ptr<IDevice>& device,
895 const std::shared_ptr<IPreparedModel>& preparedModel,
896 const TestModel& testModel,
897 const std::shared_ptr<IPreparedModel>& preparedCoupledModel,
898 const TestModel& coupledModel) {
899 const std::vector<OutputType> outputTypesList = {OutputType::FULLY_SPECIFIED};
900 const std::vector<bool> measureTimingList = {false, true};
Michael Butler7fc7e372021-03-10 22:51:53 -0800901 const std::vector<Executor> executorList = {Executor::SYNC, Executor::BURST, Executor::FENCED};
Lev Proleevc185e882020-12-15 19:25:32 +0000902
903 for (const OutputType outputType : outputTypesList) {
904 for (const bool measureTiming : measureTimingList) {
905 for (const Executor executor : executorList) {
906 const TestConfig testConfig(executor, measureTiming, outputType, MemoryType::ASHMEM,
Xusong Wang72e06c22022-01-11 14:25:55 -0800907 /*reusable=*/false, /*reportSkipping=*/false);
Lev Proleevc185e882020-12-15 19:25:32 +0000908 bool baseSkipped = false;
909 EvaluatePreparedModel(device, preparedModel, testModel, testConfig, &baseSkipped);
910 bool coupledSkipped = false;
911 EvaluatePreparedModel(device, preparedCoupledModel, coupledModel, testConfig,
912 &coupledSkipped);
913 ASSERT_EQ(baseSkipped, coupledSkipped);
914 if (baseSkipped) {
915 LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
916 "execute model that it does not support.";
917 std::cout << "[ ] Early termination of test because vendor service "
918 "cannot "
919 "execute model that it does not support."
920 << std::endl;
921 GTEST_SKIP();
922 }
923 }
924 }
925 }
926}
927
928void Execute(const std::shared_ptr<IDevice>& device, const TestModel& testModel,
929 TestKind testKind) {
930 Model model = createModel(testModel);
931 if (testKind == TestKind::DYNAMIC_SHAPE) {
932 makeOutputDimensionsUnspecified(&model);
933 }
934
935 std::shared_ptr<IPreparedModel> preparedModel;
936 switch (testKind) {
937 case TestKind::GENERAL:
938 case TestKind::DYNAMIC_SHAPE:
939 case TestKind::MEMORY_DOMAIN:
940 case TestKind::FENCED_COMPUTE:
941 case TestKind::INTINITE_LOOP_TIMEOUT: {
942 createPreparedModel(device, model, &preparedModel);
943 if (preparedModel == nullptr) return;
944 EvaluatePreparedModel(device, preparedModel, testModel, testKind);
945 } break;
946 case TestKind::QUANTIZATION_COUPLING: {
947 ASSERT_TRUE(testModel.hasQuant8CoupledOperands());
948 createPreparedModel(device, model, &preparedModel,
949 /*reportSkipping*/ false);
950 TestModel signedQuantizedModel = convertQuant8AsymmOperandsToSigned(testModel);
951 std::shared_ptr<IPreparedModel> preparedCoupledModel;
952 createPreparedModel(device, createModel(signedQuantizedModel), &preparedCoupledModel,
953 /*reportSkipping*/ false);
954 // If we couldn't prepare a model with unsigned quantization, we must
955 // fail to prepare a model with signed quantization as well.
956 if (preparedModel == nullptr) {
957 ASSERT_EQ(preparedCoupledModel, nullptr);
958 // If we failed to prepare both of the models, we can safely skip
959 // the test.
960 LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
961 "prepare model that it does not support.";
962 std::cout
963 << "[ ] Early termination of test because vendor service cannot "
964 "prepare model that it does not support."
965 << std::endl;
966 GTEST_SKIP();
967 }
968 ASSERT_NE(preparedCoupledModel, nullptr);
969 EvaluatePreparedCoupledModels(device, preparedModel, testModel, preparedCoupledModel,
970 signedQuantizedModel);
971 } break;
972 }
973}
974
975void GeneratedTestBase::SetUp() {
976 testing::TestWithParam<GeneratedTestParam>::SetUp();
977 ASSERT_NE(kDevice, nullptr);
Michael Butler9c3c8642021-08-23 18:14:50 -0700978 const bool deviceIsResponsive =
979 ndk::ScopedAStatus::fromStatus(AIBinder_ping(kDevice->asBinder().get())).isOk();
980 ASSERT_TRUE(deviceIsResponsive);
Ian Huaca46f972021-10-15 11:06:31 +0100981 // TODO(b/201260787): We should require old drivers to report the model as
982 // unsupported instead of simply skipping the test.
983 SkipIfDriverOlderThanTestModel();
984}
985
986void GeneratedTestBase::SkipIfDriverOlderThanTestModel() {
987 int32_t deviceVersion;
988 ASSERT_TRUE(kDevice->getInterfaceVersion(&deviceVersion).isOk());
989 const int32_t modelVersion = kTestModel.getAidlVersionInt();
990 if (deviceVersion < modelVersion) {
991 GTEST_SKIP() << "Device interface version " << deviceVersion
992 << " is older than test model's minimum supported HAL version " << modelVersion
993 << ". Skipping test.";
994 }
Lev Proleevc185e882020-12-15 19:25:32 +0000995}
996
997std::vector<NamedModel> getNamedModels(const FilterFn& filter) {
998 return TestModelManager::get().getTestModels(filter);
999}
1000
1001std::vector<NamedModel> getNamedModels(const FilterNameFn& filter) {
1002 return TestModelManager::get().getTestModels(filter);
1003}
1004
1005std::string printGeneratedTest(const testing::TestParamInfo<GeneratedTestParam>& info) {
1006 const auto& [namedDevice, namedModel] = info.param;
1007 return gtestCompliantName(getName(namedDevice) + "_" + getName(namedModel));
1008}
1009
1010// Tag for the generated tests
1011class GeneratedTest : public GeneratedTestBase {};
1012
1013// Tag for the dynamic output shape tests
1014class DynamicOutputShapeTest : public GeneratedTest {};
1015
1016// Tag for the memory domain tests
1017class MemoryDomainTest : public GeneratedTest {};
1018
1019// Tag for the fenced compute tests
1020class FencedComputeTest : public GeneratedTest {};
1021
1022// Tag for the dynamic output shape tests
1023class QuantizationCouplingTest : public GeneratedTest {};
1024
1025// Tag for the loop timeout tests
1026class InfiniteLoopTimeoutTest : public GeneratedTest {};
1027
1028TEST_P(GeneratedTest, Test) {
1029 Execute(kDevice, kTestModel, TestKind::GENERAL);
1030}
1031
1032TEST_P(DynamicOutputShapeTest, Test) {
1033 Execute(kDevice, kTestModel, TestKind::DYNAMIC_SHAPE);
1034}
1035
1036TEST_P(MemoryDomainTest, Test) {
1037 Execute(kDevice, kTestModel, TestKind::MEMORY_DOMAIN);
1038}
1039
1040TEST_P(FencedComputeTest, Test) {
1041 Execute(kDevice, kTestModel, TestKind::FENCED_COMPUTE);
1042}
1043
1044TEST_P(QuantizationCouplingTest, Test) {
1045 Execute(kDevice, kTestModel, TestKind::QUANTIZATION_COUPLING);
1046}
1047
1048TEST_P(InfiniteLoopTimeoutTest, Test) {
1049 Execute(kDevice, kTestModel, TestKind::INTINITE_LOOP_TIMEOUT);
1050}
1051
1052INSTANTIATE_GENERATED_TEST(GeneratedTest,
1053 [](const TestModel& testModel) { return !testModel.expectFailure; });
1054
1055INSTANTIATE_GENERATED_TEST(DynamicOutputShapeTest, [](const TestModel& testModel) {
1056 return !testModel.expectFailure && !testModel.hasScalarOutputs();
1057});
1058
1059INSTANTIATE_GENERATED_TEST(MemoryDomainTest,
1060 [](const TestModel& testModel) { return !testModel.expectFailure; });
1061
1062INSTANTIATE_GENERATED_TEST(FencedComputeTest,
1063 [](const TestModel& testModel) { return !testModel.expectFailure; });
1064
1065INSTANTIATE_GENERATED_TEST(QuantizationCouplingTest, [](const TestModel& testModel) {
1066 return !testModel.expectFailure && testModel.hasQuant8CoupledOperands() &&
1067 testModel.main.operations.size() == 1;
1068});
1069
1070INSTANTIATE_GENERATED_TEST(InfiniteLoopTimeoutTest, [](const TestModel& testModel) {
1071 return testModel.isInfiniteLoopTimeoutTest();
1072});
1073
1074} // namespace aidl::android::hardware::neuralnetworks::vts::functional