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Revert "Copy VTS tests from v1.2 to v1.3" am: 9613b4649b

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am: 432f6d1609

Change-Id: I1725a29dfff75400345573abd808273f72fda1ae
This commit is contained in:
Lev Proleev
2019-10-10 07:27:07 -07:00
committed by android-build-merger
parent 8bde98b027 432f6d1609
commit fe2cd91115
13 changed files with 0 additions and 4095 deletions
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16
neuralnetworks/1.3/vts/OWNERS
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# Neuralnetworks team
butlermichael@google.com
dgross@google.com
jeanluc@google.com
levp@google.com
miaowang@google.com
mikie@google.com
mks@google.com
pszczepaniak@google.com
slavash@google.com
vddang@google.com
xusongw@google.com
# VTS team
yim@google.com
yuexima@google.com

114
neuralnetworks/1.3/vts/functional/BasicTests.cpp
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/*
* Copyright (C) 2018 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#define LOG_TAG "neuralnetworks_hidl_hal_test"
#include "VtsHalNeuralnetworks.h"
namespace android::hardware::neuralnetworks::V1_2::vts::functional {
using V1_0::DeviceStatus;
using V1_0::ErrorStatus;
using V1_0::PerformanceInfo;
// create device test
TEST_P(NeuralnetworksHidlTest, CreateDevice) {}
// status test
TEST_P(NeuralnetworksHidlTest, StatusTest) {
Return<DeviceStatus> status = kDevice->getStatus();
ASSERT_TRUE(status.isOk());
EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
}
// initialization
TEST_P(NeuralnetworksHidlTest, GetCapabilitiesTest) {
using OperandPerformance = Capabilities::OperandPerformance;
Return<void> ret = kDevice->getCapabilities_1_2([](ErrorStatus status,
const Capabilities& capabilities) {
EXPECT_EQ(ErrorStatus::NONE, status);
auto isPositive = [](const PerformanceInfo& perf) {
return perf.execTime > 0.0f && perf.powerUsage > 0.0f;
};
EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceScalar));
EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceTensor));
const auto& opPerf = capabilities.operandPerformance;
EXPECT_TRUE(std::all_of(
opPerf.begin(), opPerf.end(),
[isPositive](const OperandPerformance& a) { return isPositive(a.info); }));
EXPECT_TRUE(std::is_sorted(opPerf.begin(), opPerf.end(),
[](const OperandPerformance& a, const OperandPerformance& b) {
return a.type < b.type;
}));
});
EXPECT_TRUE(ret.isOk());
}
// device version test
TEST_P(NeuralnetworksHidlTest, GetDeviceVersionStringTest) {
Return<void> ret =
kDevice->getVersionString([](ErrorStatus status, const hidl_string& version) {
EXPECT_EQ(ErrorStatus::NONE, status);
EXPECT_LT(0, version.size());
});
EXPECT_TRUE(ret.isOk());
}
// device type test
TEST_P(NeuralnetworksHidlTest, GetDeviceTypeTest) {
Return<void> ret = kDevice->getType([](ErrorStatus status, DeviceType type) {
EXPECT_EQ(ErrorStatus::NONE, status);
EXPECT_TRUE(type == DeviceType::OTHER || type == DeviceType::CPU ||
type == DeviceType::GPU || type == DeviceType::ACCELERATOR);
});
EXPECT_TRUE(ret.isOk());
}
// device supported extensions test
TEST_P(NeuralnetworksHidlTest, GetDeviceSupportedExtensionsTest) {
Return<void> ret = kDevice->getSupportedExtensions(
[](ErrorStatus status, const hidl_vec<Extension>& extensions) {
EXPECT_EQ(ErrorStatus::NONE, status);
for (auto& extension : extensions) {
std::string extensionName = extension.name;
EXPECT_FALSE(extensionName.empty());
for (char c : extensionName) {
EXPECT_TRUE(('a' <= c && c <= 'z') || ('0' <= c && c <= '9') || c == '_' ||
c == '.')
<< "Extension name contains an illegal character: " << c;
}
EXPECT_NE(extensionName.find('.'), std::string::npos)
<< "Extension name must start with the reverse domain name of the "
"vendor";
}
});
EXPECT_TRUE(ret.isOk());
}
// getNumberOfCacheFilesNeeded test
TEST_P(NeuralnetworksHidlTest, getNumberOfCacheFilesNeeded) {
Return<void> ret = kDevice->getNumberOfCacheFilesNeeded(
[](ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache) {
EXPECT_EQ(ErrorStatus::NONE, status);
EXPECT_LE(numModelCache,
static_cast<uint32_t>(Constant::MAX_NUMBER_OF_CACHE_FILES));
EXPECT_LE(numDataCache, static_cast<uint32_t>(Constant::MAX_NUMBER_OF_CACHE_FILES));
});
EXPECT_TRUE(ret.isOk());
}
} // namespace android::hardware::neuralnetworks::V1_2::vts::functional

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neuralnetworks/1.3/vts/functional/Callbacks.cpp
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/*
* Copyright (C) 2019 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#define LOG_TAG "Callbacks"
#include "1.2/Callbacks.h"
#include <android-base/logging.h>
#include <limits>
namespace android::hardware::neuralnetworks::V1_2::implementation {
using V1_0::ErrorStatus;
constexpr Timing kNoTiming = {.timeOnDevice = std::numeric_limits<uint64_t>::max(),
.timeInDriver = std::numeric_limits<uint64_t>::max()};
// PreparedModelCallback methods begin here
Return<void> PreparedModelCallback::notify(ErrorStatus errorStatus,
const sp<V1_0::IPreparedModel>& preparedModel) {
{
std::lock_guard<std::mutex> hold(mMutex);
// quick-return if object has already been notified
if (mNotified) {
return Void();
}
// store results and mark as notified
mErrorStatus = errorStatus;
mPreparedModel = preparedModel;
mNotified = true;
}
mCondition.notify_all();
return Void();
}
Return<void> PreparedModelCallback::notify_1_2(ErrorStatus errorStatus,
const sp<V1_2::IPreparedModel>& preparedModel) {
return notify(errorStatus, preparedModel);
}
void PreparedModelCallback::wait() const {
std::unique_lock<std::mutex> lock(mMutex);
mCondition.wait(lock, [this] { return mNotified; });
}
ErrorStatus PreparedModelCallback::getStatus() const {
wait();
return mErrorStatus;
}
sp<V1_0::IPreparedModel> PreparedModelCallback::getPreparedModel() const {
wait();
return mPreparedModel;
}
// ExecutionCallback methods begin here
Return<void> ExecutionCallback::notify(ErrorStatus errorStatus) {
notifyInternal(errorStatus, {}, kNoTiming);
return Void();
}
Return<void> ExecutionCallback::notify_1_2(ErrorStatus errorStatus,
const hidl_vec<OutputShape>& outputShapes,
const Timing& timing) {
if (errorStatus == ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) {
// outputShapes must not be empty if OUTPUT_INSUFFICIENT_SIZE.
if (outputShapes.size() == 0) {
LOG(ERROR) << "Notified with empty output shape vector when OUTPUT_INSUFFICIENT_SIZE";
notifyInternal(ErrorStatus::GENERAL_FAILURE, {}, kNoTiming);
return Void();
}
} else if (errorStatus != ErrorStatus::NONE) {
// outputShapes must be empty if errorStatus is neither NONE nor OUTPUT_INSUFFICIENT_SIZE.
if (outputShapes.size() != 0) {
LOG(ERROR) << "Notified with non-empty output shape vector when error status is "
"neither NONE nor OUTPUT_INSUFFICIENT_SIZE";
notifyInternal(ErrorStatus::GENERAL_FAILURE, {}, kNoTiming);
return Void();
}
}
notifyInternal(errorStatus, outputShapes, timing);
return Void();
}
void ExecutionCallback::wait() const {
std::unique_lock<std::mutex> lock(mMutex);
mCondition.wait(lock, [this] { return mNotified; });
}
ErrorStatus ExecutionCallback::getStatus() const {
wait();
return mErrorStatus;
}
const std::vector<OutputShape>& ExecutionCallback::getOutputShapes() const {
wait();
return mOutputShapes;
}
Timing ExecutionCallback::getTiming() const {
wait();
return mTiming;
}
void ExecutionCallback::notifyInternal(ErrorStatus errorStatus,
const hidl_vec<OutputShape>& outputShapes,
const Timing& timing) {
{
std::lock_guard<std::mutex> hold(mMutex);
// quick-return if object has already been notified
if (mNotified) {
return;
}
mErrorStatus = errorStatus;
mOutputShapes = outputShapes;
mTiming = timing;
mNotified = true;
}
mCondition.notify_all();
}
} // namespace android::hardware::neuralnetworks::V1_2::implementation

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neuralnetworks/1.3/vts/functional/CompilationCachingTests.cpp
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neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
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/*
* Copyright (C) 2019 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "GeneratedTestHarness.h"
#include <android-base/logging.h>
#include <android/hardware/neuralnetworks/1.0/IDevice.h>
#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
#include <android/hardware/neuralnetworks/1.0/IPreparedModel.h>
#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
#include <android/hardware/neuralnetworks/1.0/types.h>
#include <android/hardware/neuralnetworks/1.1/IDevice.h>
#include <android/hardware/neuralnetworks/1.2/IDevice.h>
#include <android/hardware/neuralnetworks/1.2/IExecutionCallback.h>
#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h>
#include <android/hidl/allocator/1.0/IAllocator.h>
#include <android/hidl/memory/1.0/IMemory.h>
#include <hidlmemory/mapping.h>
#include <gtest/gtest.h>
#include <algorithm>
#include <iostream>
#include <numeric>
#include "1.0/Utils.h"
#include "1.2/Callbacks.h"
#include "ExecutionBurstController.h"
#include "MemoryUtils.h"
#include "TestHarness.h"
#include "Utils.h"
#include "VtsHalNeuralnetworks.h"
namespace android::hardware::neuralnetworks::V1_2::vts::functional {
using namespace test_helper;
using hidl::memory::V1_0::IMemory;
using implementation::ExecutionCallback;
using implementation::PreparedModelCallback;
using V1_0::DataLocation;
using V1_0::ErrorStatus;
using V1_0::OperandLifeTime;
using V1_0::Request;
using V1_1::ExecutionPreference;
using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
enum class OutputType { FULLY_SPECIFIED, UNSPECIFIED, INSUFFICIENT };
Model createModel(const TestModel& testModel) {
// Model operands.
hidl_vec<Operand> operands(testModel.operands.size());
size_t constCopySize = 0, constRefSize = 0;
for (uint32_t i = 0; i < testModel.operands.size(); i++) {
const auto& op = testModel.operands[i];
DataLocation loc = {};
if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
loc = {.poolIndex = 0,
.offset = static_cast<uint32_t>(constCopySize),
.length = static_cast<uint32_t>(op.data.size())};
constCopySize += op.data.alignedSize();
} else if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) {
loc = {.poolIndex = 0,
.offset = static_cast<uint32_t>(constRefSize),
.length = static_cast<uint32_t>(op.data.size())};
constRefSize += op.data.alignedSize();
}
Operand::ExtraParams extraParams;
if (op.type == TestOperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
extraParams.channelQuant(SymmPerChannelQuantParams{
.scales = op.channelQuant.scales, .channelDim = op.channelQuant.channelDim});
}
operands[i] = {.type = static_cast<OperandType>(op.type),
.dimensions = op.dimensions,
.numberOfConsumers = op.numberOfConsumers,
.scale = op.scale,
.zeroPoint = op.zeroPoint,
.lifetime = static_cast<OperandLifeTime>(op.lifetime),
.location = loc,
.extraParams = std::move(extraParams)};
}
// Model operations.
hidl_vec<Operation> operations(testModel.operations.size());
std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(),
[](const TestOperation& op) -> Operation {
return {.type = static_cast<OperationType>(op.type),
.inputs = op.inputs,
.outputs = op.outputs};
});
// Constant copies.
hidl_vec<uint8_t> operandValues(constCopySize);
for (uint32_t i = 0; i < testModel.operands.size(); i++) {
const auto& op = testModel.operands[i];
if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
const uint8_t* begin = op.data.get<uint8_t>();
const uint8_t* end = begin + op.data.size();
std::copy(begin, end, operandValues.data() + operands[i].location.offset);
}
}
// Shared memory.
hidl_vec<hidl_memory> pools = {};
if (constRefSize > 0) {
hidl_vec_push_back(&pools, nn::allocateSharedMemory(constRefSize));
CHECK_NE(pools[0].size(), 0u);
// load data
sp<IMemory> mappedMemory = mapMemory(pools[0]);
CHECK(mappedMemory.get() != nullptr);
uint8_t* mappedPtr =
reinterpret_cast<uint8_t*>(static_cast<void*>(mappedMemory->getPointer()));
CHECK(mappedPtr != nullptr);
for (uint32_t i = 0; i < testModel.operands.size(); i++) {
const auto& op = testModel.operands[i];
if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) {
const uint8_t* begin = op.data.get<uint8_t>();
const uint8_t* end = begin + op.data.size();
std::copy(begin, end, mappedPtr + operands[i].location.offset);
}
}
}
return {.operands = std::move(operands),
.operations = std::move(operations),
.inputIndexes = testModel.inputIndexes,
.outputIndexes = testModel.outputIndexes,
.operandValues = std::move(operandValues),
.pools = std::move(pools),
.relaxComputationFloat32toFloat16 = testModel.isRelaxed};
}
static bool isOutputSizeGreaterThanOne(const TestModel& testModel, uint32_t index) {
const auto byteSize = testModel.operands[testModel.outputIndexes[index]].data.size();
return byteSize > 1u;
}
static void makeOutputInsufficientSize(uint32_t outputIndex, Request* request) {
auto& length = request->outputs[outputIndex].location.length;
ASSERT_GT(length, 1u);
length -= 1u;
}
static void makeOutputDimensionsUnspecified(Model* model) {
for (auto i : model->outputIndexes) {
auto& dims = model->operands[i].dimensions;
std::fill(dims.begin(), dims.end(), 0);
}
}
static Return<ErrorStatus> ExecutePreparedModel(const sp<IPreparedModel>& preparedModel,
const Request& request, MeasureTiming measure,
sp<ExecutionCallback>& callback) {
return preparedModel->execute_1_2(request, measure, callback);
}
static Return<ErrorStatus> ExecutePreparedModel(const sp<IPreparedModel>& preparedModel,
const Request& request, MeasureTiming measure,
hidl_vec<OutputShape>* outputShapes,
Timing* timing) {
ErrorStatus result;
Return<void> ret = preparedModel->executeSynchronously(
request, measure,
[&result, outputShapes, timing](ErrorStatus error, const hidl_vec<OutputShape>& shapes,
const Timing& time) {
result = error;
*outputShapes = shapes;
*timing = time;
});
if (!ret.isOk()) {
return ErrorStatus::GENERAL_FAILURE;
}
return result;
}
static std::shared_ptr<::android::nn::ExecutionBurstController> CreateBurst(
const sp<IPreparedModel>& preparedModel) {
return android::nn::ExecutionBurstController::create(preparedModel, /*blocking=*/true);
}
enum class Executor { ASYNC, SYNC, BURST };
void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
Executor executor, MeasureTiming measure, OutputType outputType) {
// If output0 does not have size larger than one byte, we can not test with insufficient buffer.
if (outputType == OutputType::INSUFFICIENT && !isOutputSizeGreaterThanOne(testModel, 0)) {
return;
}
Request request = createRequest(testModel);
if (outputType == OutputType::INSUFFICIENT) {
makeOutputInsufficientSize(/*outputIndex=*/0, &request);
}
ErrorStatus executionStatus;
hidl_vec<OutputShape> outputShapes;
Timing timing;
switch (executor) {
case Executor::ASYNC: {
SCOPED_TRACE("asynchronous");
// launch execution
sp<ExecutionCallback> executionCallback = new ExecutionCallback();
Return<ErrorStatus> executionLaunchStatus =
ExecutePreparedModel(preparedModel, request, measure, executionCallback);
ASSERT_TRUE(executionLaunchStatus.isOk());
EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
// retrieve execution status
executionCallback->wait();
executionStatus = executionCallback->getStatus();
outputShapes = executionCallback->getOutputShapes();
timing = executionCallback->getTiming();
break;
}
case Executor::SYNC: {
SCOPED_TRACE("synchronous");
// execute
Return<ErrorStatus> executionReturnStatus =
ExecutePreparedModel(preparedModel, request, measure, &outputShapes, &timing);
ASSERT_TRUE(executionReturnStatus.isOk());
executionStatus = static_cast<ErrorStatus>(executionReturnStatus);
break;
}
case Executor::BURST: {
SCOPED_TRACE("burst");
// create burst
const std::shared_ptr<::android::nn::ExecutionBurstController> controller =
CreateBurst(preparedModel);
ASSERT_NE(nullptr, controller.get());
// create memory keys
std::vector<intptr_t> keys(request.pools.size());
for (size_t i = 0; i < keys.size(); ++i) {
keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
}
// execute burst
std::tie(executionStatus, outputShapes, timing) =
controller->compute(request, measure, keys);
break;
}
}
if (outputType != OutputType::FULLY_SPECIFIED &&
executionStatus == ErrorStatus::GENERAL_FAILURE) {
LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
"execute model that it does not support.";
std::cout << "[ ] Early termination of test because vendor service cannot "
"execute model that it does not support."
<< std::endl;
GTEST_SKIP();
}
if (measure == MeasureTiming::NO) {
EXPECT_EQ(UINT64_MAX, timing.timeOnDevice);
EXPECT_EQ(UINT64_MAX, timing.timeInDriver);
} else {
if (timing.timeOnDevice != UINT64_MAX && timing.timeInDriver != UINT64_MAX) {
EXPECT_LE(timing.timeOnDevice, timing.timeInDriver);
}
}
switch (outputType) {
case OutputType::FULLY_SPECIFIED:
// If the model output operands are fully specified, outputShapes must be either
// either empty, or have the same number of elements as the number of outputs.
ASSERT_EQ(ErrorStatus::NONE, executionStatus);
ASSERT_TRUE(outputShapes.size() == 0 ||
outputShapes.size() == testModel.outputIndexes.size());
break;
case OutputType::UNSPECIFIED:
// If the model output operands are not fully specified, outputShapes must have
// the same number of elements as the number of outputs.
ASSERT_EQ(ErrorStatus::NONE, executionStatus);
ASSERT_EQ(outputShapes.size(), testModel.outputIndexes.size());
break;
case OutputType::INSUFFICIENT:
ASSERT_EQ(ErrorStatus::OUTPUT_INSUFFICIENT_SIZE, executionStatus);
ASSERT_EQ(outputShapes.size(), testModel.outputIndexes.size());
ASSERT_FALSE(outputShapes[0].isSufficient);
return;
}
// Go through all outputs, check returned output shapes.
for (uint32_t i = 0; i < outputShapes.size(); i++) {
EXPECT_TRUE(outputShapes[i].isSufficient);
const auto& expect = testModel.operands[testModel.outputIndexes[i]].dimensions;
const std::vector<uint32_t> actual = outputShapes[i].dimensions;
EXPECT_EQ(expect, actual);
}
// Retrieve execution results.
const std::vector<TestBuffer> outputs = getOutputBuffers(request);
// We want "close-enough" results.
checkResults(testModel, outputs);
}
void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
bool testDynamicOutputShape) {
if (testDynamicOutputShape) {
EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
OutputType::UNSPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
OutputType::UNSPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
OutputType::UNSPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
OutputType::UNSPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
OutputType::UNSPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
OutputType::UNSPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
OutputType::INSUFFICIENT);
EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
OutputType::INSUFFICIENT);
EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
OutputType::INSUFFICIENT);
EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
OutputType::INSUFFICIENT);
EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
OutputType::INSUFFICIENT);
EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
OutputType::INSUFFICIENT);
} else {
EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
OutputType::FULLY_SPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
OutputType::FULLY_SPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
OutputType::FULLY_SPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
OutputType::FULLY_SPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
OutputType::FULLY_SPECIFIED);
EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
OutputType::FULLY_SPECIFIED);
}
}
void Execute(const sp<IDevice>& device, const TestModel& testModel, bool testDynamicOutputShape) {
Model model = createModel(testModel);
if (testDynamicOutputShape) {
makeOutputDimensionsUnspecified(&model);
}
sp<IPreparedModel> preparedModel;
createPreparedModel(device, model, &preparedModel);
if (preparedModel == nullptr) return;
EvaluatePreparedModel(preparedModel, testModel, testDynamicOutputShape);
}
void GeneratedTestBase::SetUp() {
testing::TestWithParam<GeneratedTestParam>::SetUp();
ASSERT_NE(kDevice, nullptr);
}
std::vector<NamedModel> getNamedModels(const FilterFn& filter) {
return TestModelManager::get().getTestModels(filter);
}
std::string printGeneratedTest(const testing::TestParamInfo<GeneratedTestParam>& info) {
const auto& [namedDevice, namedModel] = info.param;
return gtestCompliantName(getName(namedDevice) + "_" + getName(namedModel));
}
// Tag for the generated tests
class GeneratedTest : public GeneratedTestBase {};
// Tag for the dynamic output shape tests
class DynamicOutputShapeTest : public GeneratedTest {};
TEST_P(GeneratedTest, Test) {
Execute(kDevice, kTestModel, /*testDynamicOutputShape=*/false);
}
TEST_P(DynamicOutputShapeTest, Test) {
Execute(kDevice, kTestModel, /*testDynamicOutputShape=*/true);
}
INSTANTIATE_GENERATED_TEST(GeneratedTest,
[](const TestModel& testModel) { return !testModel.expectFailure; });
INSTANTIATE_GENERATED_TEST(DynamicOutputShapeTest,
[](const TestModel& testModel) { return !testModel.expectFailure; });
} // namespace android::hardware::neuralnetworks::V1_2::vts::functional

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neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h
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/*
* Copyright (C) 2019 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef ANDROID_HARDWARE_NEURALNETWORKS_V1_2_GENERATED_TEST_HARNESS_H
#define ANDROID_HARDWARE_NEURALNETWORKS_V1_2_GENERATED_TEST_HARNESS_H
#include <android/hardware/neuralnetworks/1.2/IDevice.h>
#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
#include <android/hardware/neuralnetworks/1.2/types.h>
#include <functional>
#include <vector>
#include "1.0/Utils.h"
#include "TestHarness.h"
#include "VtsHalNeuralnetworks.h"
namespace android::hardware::neuralnetworks::V1_2::vts::functional {
using NamedModel = Named<const test_helper::TestModel*>;
using GeneratedTestParam = std::tuple<NamedDevice, NamedModel>;
class GeneratedTestBase : public testing::TestWithParam<GeneratedTestParam> {
protected:
void SetUp() override;
const sp<IDevice> kDevice = getData(std::get<NamedDevice>(GetParam()));
const test_helper::TestModel& kTestModel = *getData(std::get<NamedModel>(GetParam()));
};
using FilterFn = std::function<bool(const test_helper::TestModel&)>;
std::vector<NamedModel> getNamedModels(const FilterFn& filter);
std::string printGeneratedTest(const testing::TestParamInfo<GeneratedTestParam>& info);
#define INSTANTIATE_GENERATED_TEST(TestSuite, filter) \
INSTANTIATE_TEST_SUITE_P(TestGenerated, TestSuite, \
testing::Combine(testing::ValuesIn(getNamedDevices()), \
testing::ValuesIn(getNamedModels(filter))), \
printGeneratedTest)
// Tag for the validation tests, instantiated in VtsHalNeuralnetworks.cpp.
// TODO: Clean up the hierarchy for ValidationTest.
class ValidationTest : public GeneratedTestBase {};
Model createModel(const test_helper::TestModel& testModel);
void PrepareModel(const sp<IDevice>& device, const Model& model, sp<IPreparedModel>* preparedModel);
void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel,
const test_helper::TestModel& testModel, bool testDynamicOutputShape);
} // namespace android::hardware::neuralnetworks::V1_2::vts::functional
#endif // ANDROID_HARDWARE_NEURALNETWORKS_V1_2_GENERATED_TEST_HARNESS_H

141
neuralnetworks/1.3/vts/functional/TestAssertions.cpp
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/*
* Copyright (C) 2019 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <android/hardware/neuralnetworks/1.2/types.h>
#include "TestHarness.h"
namespace android::hardware::neuralnetworks::V1_2 {
// Make sure that the HIDL enums are compatible with the values defined in
// frameworks/ml/nn/tools/test_generator/test_harness/include/TestHarness.h.
using namespace test_helper;
#define CHECK_TEST_ENUM(EnumType, enumValue) \
static_assert(static_cast<EnumType>(Test##EnumType::enumValue) == EnumType::enumValue)
CHECK_TEST_ENUM(OperandType, FLOAT32);
CHECK_TEST_ENUM(OperandType, INT32);
CHECK_TEST_ENUM(OperandType, UINT32);
CHECK_TEST_ENUM(OperandType, TENSOR_FLOAT32);
CHECK_TEST_ENUM(OperandType, TENSOR_INT32);
CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_ASYMM);
CHECK_TEST_ENUM(OperandType, BOOL);
CHECK_TEST_ENUM(OperandType, TENSOR_QUANT16_SYMM);
CHECK_TEST_ENUM(OperandType, TENSOR_FLOAT16);
CHECK_TEST_ENUM(OperandType, TENSOR_BOOL8);
CHECK_TEST_ENUM(OperandType, FLOAT16);
CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_SYMM_PER_CHANNEL);
CHECK_TEST_ENUM(OperandType, TENSOR_QUANT16_ASYMM);
CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_SYMM);
CHECK_TEST_ENUM(OperationType, ADD);
CHECK_TEST_ENUM(OperationType, AVERAGE_POOL_2D);
CHECK_TEST_ENUM(OperationType, CONCATENATION);
CHECK_TEST_ENUM(OperationType, CONV_2D);
CHECK_TEST_ENUM(OperationType, DEPTHWISE_CONV_2D);
CHECK_TEST_ENUM(OperationType, DEPTH_TO_SPACE);
CHECK_TEST_ENUM(OperationType, DEQUANTIZE);
CHECK_TEST_ENUM(OperationType, EMBEDDING_LOOKUP);
CHECK_TEST_ENUM(OperationType, FLOOR);
CHECK_TEST_ENUM(OperationType, FULLY_CONNECTED);
CHECK_TEST_ENUM(OperationType, HASHTABLE_LOOKUP);
CHECK_TEST_ENUM(OperationType, L2_NORMALIZATION);
CHECK_TEST_ENUM(OperationType, L2_POOL_2D);
CHECK_TEST_ENUM(OperationType, LOCAL_RESPONSE_NORMALIZATION);
CHECK_TEST_ENUM(OperationType, LOGISTIC);
CHECK_TEST_ENUM(OperationType, LSH_PROJECTION);
CHECK_TEST_ENUM(OperationType, LSTM);
CHECK_TEST_ENUM(OperationType, MAX_POOL_2D);
CHECK_TEST_ENUM(OperationType, MUL);
CHECK_TEST_ENUM(OperationType, RELU);
CHECK_TEST_ENUM(OperationType, RELU1);
CHECK_TEST_ENUM(OperationType, RELU6);
CHECK_TEST_ENUM(OperationType, RESHAPE);
CHECK_TEST_ENUM(OperationType, RESIZE_BILINEAR);
CHECK_TEST_ENUM(OperationType, RNN);
CHECK_TEST_ENUM(OperationType, SOFTMAX);
CHECK_TEST_ENUM(OperationType, SPACE_TO_DEPTH);
CHECK_TEST_ENUM(OperationType, SVDF);
CHECK_TEST_ENUM(OperationType, TANH);
CHECK_TEST_ENUM(OperationType, BATCH_TO_SPACE_ND);
CHECK_TEST_ENUM(OperationType, DIV);
CHECK_TEST_ENUM(OperationType, MEAN);
CHECK_TEST_ENUM(OperationType, PAD);
CHECK_TEST_ENUM(OperationType, SPACE_TO_BATCH_ND);
CHECK_TEST_ENUM(OperationType, SQUEEZE);
CHECK_TEST_ENUM(OperationType, STRIDED_SLICE);
CHECK_TEST_ENUM(OperationType, SUB);
CHECK_TEST_ENUM(OperationType, TRANSPOSE);
CHECK_TEST_ENUM(OperationType, ABS);
CHECK_TEST_ENUM(OperationType, ARGMAX);
CHECK_TEST_ENUM(OperationType, ARGMIN);
CHECK_TEST_ENUM(OperationType, AXIS_ALIGNED_BBOX_TRANSFORM);
CHECK_TEST_ENUM(OperationType, BIDIRECTIONAL_SEQUENCE_LSTM);
CHECK_TEST_ENUM(OperationType, BIDIRECTIONAL_SEQUENCE_RNN);
CHECK_TEST_ENUM(OperationType, BOX_WITH_NMS_LIMIT);
CHECK_TEST_ENUM(OperationType, CAST);
CHECK_TEST_ENUM(OperationType, CHANNEL_SHUFFLE);
CHECK_TEST_ENUM(OperationType, DETECTION_POSTPROCESSING);
CHECK_TEST_ENUM(OperationType, EQUAL);
CHECK_TEST_ENUM(OperationType, EXP);
CHECK_TEST_ENUM(OperationType, EXPAND_DIMS);
CHECK_TEST_ENUM(OperationType, GATHER);
CHECK_TEST_ENUM(OperationType, GENERATE_PROPOSALS);
CHECK_TEST_ENUM(OperationType, GREATER);
CHECK_TEST_ENUM(OperationType, GREATER_EQUAL);
CHECK_TEST_ENUM(OperationType, GROUPED_CONV_2D);
CHECK_TEST_ENUM(OperationType, HEATMAP_MAX_KEYPOINT);
CHECK_TEST_ENUM(OperationType, INSTANCE_NORMALIZATION);
CHECK_TEST_ENUM(OperationType, LESS);
CHECK_TEST_ENUM(OperationType, LESS_EQUAL);
CHECK_TEST_ENUM(OperationType, LOG);
CHECK_TEST_ENUM(OperationType, LOGICAL_AND);
CHECK_TEST_ENUM(OperationType, LOGICAL_NOT);
CHECK_TEST_ENUM(OperationType, LOGICAL_OR);
CHECK_TEST_ENUM(OperationType, LOG_SOFTMAX);
CHECK_TEST_ENUM(OperationType, MAXIMUM);
CHECK_TEST_ENUM(OperationType, MINIMUM);
CHECK_TEST_ENUM(OperationType, NEG);
CHECK_TEST_ENUM(OperationType, NOT_EQUAL);
CHECK_TEST_ENUM(OperationType, PAD_V2);
CHECK_TEST_ENUM(OperationType, POW);
CHECK_TEST_ENUM(OperationType, PRELU);
CHECK_TEST_ENUM(OperationType, QUANTIZE);
CHECK_TEST_ENUM(OperationType, QUANTIZED_16BIT_LSTM);
CHECK_TEST_ENUM(OperationType, RANDOM_MULTINOMIAL);
CHECK_TEST_ENUM(OperationType, REDUCE_ALL);
CHECK_TEST_ENUM(OperationType, REDUCE_ANY);
CHECK_TEST_ENUM(OperationType, REDUCE_MAX);
CHECK_TEST_ENUM(OperationType, REDUCE_MIN);
CHECK_TEST_ENUM(OperationType, REDUCE_PROD);
CHECK_TEST_ENUM(OperationType, REDUCE_SUM);
CHECK_TEST_ENUM(OperationType, ROI_ALIGN);
CHECK_TEST_ENUM(OperationType, ROI_POOLING);
CHECK_TEST_ENUM(OperationType, RSQRT);
CHECK_TEST_ENUM(OperationType, SELECT);
CHECK_TEST_ENUM(OperationType, SIN);
CHECK_TEST_ENUM(OperationType, SLICE);
CHECK_TEST_ENUM(OperationType, SPLIT);
CHECK_TEST_ENUM(OperationType, SQRT);
CHECK_TEST_ENUM(OperationType, TILE);
CHECK_TEST_ENUM(OperationType, TOPK_V2);
CHECK_TEST_ENUM(OperationType, TRANSPOSE_CONV_2D);
CHECK_TEST_ENUM(OperationType, UNIDIRECTIONAL_SEQUENCE_LSTM);
CHECK_TEST_ENUM(OperationType, UNIDIRECTIONAL_SEQUENCE_RNN);
CHECK_TEST_ENUM(OperationType, RESIZE_NEAREST_NEIGHBOR);
#undef CHECK_TEST_ENUM
} // namespace android::hardware::neuralnetworks::V1_2

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neuralnetworks/1.3/vts/functional/ValidateBurst.cpp
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/*
* Copyright (C) 2019 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#define LOG_TAG "neuralnetworks_hidl_hal_test"
#include "VtsHalNeuralnetworks.h"
#include "1.2/Callbacks.h"
#include "ExecutionBurstController.h"
#include "ExecutionBurstServer.h"
#include "GeneratedTestHarness.h"
#include "TestHarness.h"
#include "Utils.h"
#include <android-base/logging.h>
#include <cstring>
namespace android::hardware::neuralnetworks::V1_2::vts::functional {
using nn::ExecutionBurstController;
using nn::RequestChannelSender;
using nn::ResultChannelReceiver;
using V1_0::ErrorStatus;
using V1_0::Request;
using ExecutionBurstCallback = ExecutionBurstController::ExecutionBurstCallback;
// This constant value represents the length of an FMQ that is large enough to
// return a result from a burst execution for all of the generated test cases.
constexpr size_t kExecutionBurstChannelLength = 1024;
// This constant value represents a length of an FMQ that is not large enough
// to return a result from a burst execution for some of the generated test
// cases.
constexpr size_t kExecutionBurstChannelSmallLength = 8;
///////////////////////// UTILITY FUNCTIONS /////////////////////////
static bool badTiming(Timing timing) {
return timing.timeOnDevice == UINT64_MAX && timing.timeInDriver == UINT64_MAX;
}
static void createBurst(const sp<IPreparedModel>& preparedModel, const sp<IBurstCallback>& callback,
std::unique_ptr<RequestChannelSender>* sender,
std::unique_ptr<ResultChannelReceiver>* receiver,
sp<IBurstContext>* context,
size_t resultChannelLength = kExecutionBurstChannelLength) {
ASSERT_NE(nullptr, preparedModel.get());
ASSERT_NE(nullptr, sender);
ASSERT_NE(nullptr, receiver);
ASSERT_NE(nullptr, context);
// create FMQ objects
auto [fmqRequestChannel, fmqRequestDescriptor] =
RequestChannelSender::create(kExecutionBurstChannelLength, /*blocking=*/true);
auto [fmqResultChannel, fmqResultDescriptor] =
ResultChannelReceiver::create(resultChannelLength, /*blocking=*/true);
ASSERT_NE(nullptr, fmqRequestChannel.get());
ASSERT_NE(nullptr, fmqResultChannel.get());
ASSERT_NE(nullptr, fmqRequestDescriptor);
ASSERT_NE(nullptr, fmqResultDescriptor);
// configure burst
ErrorStatus errorStatus;
sp<IBurstContext> burstContext;
const Return<void> ret = preparedModel->configureExecutionBurst(
callback, *fmqRequestDescriptor, *fmqResultDescriptor,
[&errorStatus, &burstContext](ErrorStatus status, const sp<IBurstContext>& context) {
errorStatus = status;
burstContext = context;
});
ASSERT_TRUE(ret.isOk());
ASSERT_EQ(ErrorStatus::NONE, errorStatus);
ASSERT_NE(nullptr, burstContext.get());
// return values
*sender = std::move(fmqRequestChannel);
*receiver = std::move(fmqResultChannel);
*context = burstContext;
}
static void createBurstWithResultChannelLength(
const sp<IPreparedModel>& preparedModel, size_t resultChannelLength,
std::shared_ptr<ExecutionBurstController>* controller) {
ASSERT_NE(nullptr, preparedModel.get());
ASSERT_NE(nullptr, controller);
// create FMQ objects
std::unique_ptr<RequestChannelSender> sender;
std::unique_ptr<ResultChannelReceiver> receiver;
sp<ExecutionBurstCallback> callback = new ExecutionBurstCallback();
sp<IBurstContext> context;
ASSERT_NO_FATAL_FAILURE(createBurst(preparedModel, callback, &sender, &receiver, &context,
resultChannelLength));
ASSERT_NE(nullptr, sender.get());
ASSERT_NE(nullptr, receiver.get());
ASSERT_NE(nullptr, context.get());
// return values
*controller = std::make_shared<ExecutionBurstController>(std::move(sender), std::move(receiver),
context, callback);
}
// Primary validation function. This function will take a valid serialized
// request, apply a mutation to it to invalidate the serialized request, then
// pass it to interface calls that use the serialized request. Note that the
// serialized request here is passed by value, and any mutation to the
// serialized request does not leave this function.
static void validate(RequestChannelSender* sender, ResultChannelReceiver* receiver,
const std::string& message, std::vector<FmqRequestDatum> serialized,
const std::function<void(std::vector<FmqRequestDatum>*)>& mutation) {
mutation(&serialized);
// skip if packet is too large to send
if (serialized.size() > kExecutionBurstChannelLength) {
return;
}
SCOPED_TRACE(message);
// send invalid packet
ASSERT_TRUE(sender->sendPacket(serialized));
// receive error
auto results = receiver->getBlocking();
ASSERT_TRUE(results.has_value());
const auto [status, outputShapes, timing] = std::move(*results);
EXPECT_NE(ErrorStatus::NONE, status);
EXPECT_EQ(0u, outputShapes.size());
EXPECT_TRUE(badTiming(timing));
}
// For validation, valid packet entries are mutated to invalid packet entries,
// or invalid packet entries are inserted into valid packets. This function
// creates pre-set invalid packet entries for convenience.
static std::vector<FmqRequestDatum> createBadRequestPacketEntries() {
const FmqRequestDatum::PacketInformation packetInformation = {
/*.packetSize=*/10, /*.numberOfInputOperands=*/10, /*.numberOfOutputOperands=*/10,
/*.numberOfPools=*/10};
const FmqRequestDatum::OperandInformation operandInformation = {
/*.hasNoValue=*/false, /*.location=*/{}, /*.numberOfDimensions=*/10};
const int32_t invalidPoolIdentifier = std::numeric_limits<int32_t>::max();
std::vector<FmqRequestDatum> bad(7);
bad[0].packetInformation(packetInformation);
bad[1].inputOperandInformation(operandInformation);
bad[2].inputOperandDimensionValue(0);
bad[3].outputOperandInformation(operandInformation);
bad[4].outputOperandDimensionValue(0);
bad[5].poolIdentifier(invalidPoolIdentifier);
bad[6].measureTiming(MeasureTiming::YES);
return bad;
}
// For validation, valid packet entries are mutated to invalid packet entries,
// or invalid packet entries are inserted into valid packets. This function
// retrieves pre-set invalid packet entries for convenience. This function
// caches these data so they can be reused on subsequent validation checks.
static const std::vector<FmqRequestDatum>& getBadRequestPacketEntries() {
static const std::vector<FmqRequestDatum> bad = createBadRequestPacketEntries();
return bad;
}
///////////////////////// REMOVE DATUM ////////////////////////////////////
static void removeDatumTest(RequestChannelSender* sender, ResultChannelReceiver* receiver,
const std::vector<FmqRequestDatum>& serialized) {
for (size_t index = 0; index < serialized.size(); ++index) {
const std::string message = "removeDatum: removed datum at index " + std::to_string(index);
validate(sender, receiver, message, serialized,
[index](std::vector<FmqRequestDatum>* serialized) {
serialized->erase(serialized->begin() + index);
});
}
}
///////////////////////// ADD DATUM ////////////////////////////////////
static void addDatumTest(RequestChannelSender* sender, ResultChannelReceiver* receiver,
const std::vector<FmqRequestDatum>& serialized) {
const std::vector<FmqRequestDatum>& extra = getBadRequestPacketEntries();
for (size_t index = 0; index <= serialized.size(); ++index) {
for (size_t type = 0; type < extra.size(); ++type) {
const std::string message = "addDatum: added datum type " + std::to_string(type) +
" at index " + std::to_string(index);
validate(sender, receiver, message, serialized,
[index, type, &extra](std::vector<FmqRequestDatum>* serialized) {
serialized->insert(serialized->begin() + index, extra[type]);
});
}
}
}
///////////////////////// MUTATE DATUM ////////////////////////////////////
static bool interestingCase(const FmqRequestDatum& lhs, const FmqRequestDatum& rhs) {
using Discriminator = FmqRequestDatum::hidl_discriminator;
const bool differentValues = (lhs != rhs);
const bool sameDiscriminator = (lhs.getDiscriminator() == rhs.getDiscriminator());
const auto discriminator = rhs.getDiscriminator();
const bool isDimensionValue = (discriminator == Discriminator::inputOperandDimensionValue ||
discriminator == Discriminator::outputOperandDimensionValue);
return differentValues && !(sameDiscriminator && isDimensionValue);
}
static void mutateDatumTest(RequestChannelSender* sender, ResultChannelReceiver* receiver,
const std::vector<FmqRequestDatum>& serialized) {
const std::vector<FmqRequestDatum>& change = getBadRequestPacketEntries();
for (size_t index = 0; index < serialized.size(); ++index) {
for (size_t type = 0; type < change.size(); ++type) {
if (interestingCase(serialized[index], change[type])) {
const std::string message = "mutateDatum: changed datum at index " +
std::to_string(index) + " to datum type " +
std::to_string(type);
validate(sender, receiver, message, serialized,
[index, type, &change](std::vector<FmqRequestDatum>* serialized) {
(*serialized)[index] = change[type];
});
}
}
}
}
///////////////////////// BURST VALIATION TESTS ////////////////////////////////////
static void validateBurstSerialization(const sp<IPreparedModel>& preparedModel,
const Request& request) {
// create burst
std::unique_ptr<RequestChannelSender> sender;
std::unique_ptr<ResultChannelReceiver> receiver;
sp<ExecutionBurstCallback> callback = new ExecutionBurstCallback();
sp<IBurstContext> context;
ASSERT_NO_FATAL_FAILURE(createBurst(preparedModel, callback, &sender, &receiver, &context));
ASSERT_NE(nullptr, sender.get());
ASSERT_NE(nullptr, receiver.get());
ASSERT_NE(nullptr, context.get());
// load memory into callback slots
std::vector<intptr_t> keys;
keys.reserve(request.pools.size());
std::transform(request.pools.begin(), request.pools.end(), std::back_inserter(keys),
[](const auto& pool) { return reinterpret_cast<intptr_t>(&pool); });
const std::vector<int32_t> slots = callback->getSlots(request.pools, keys);
// ensure slot std::numeric_limits<int32_t>::max() doesn't exist (for
// subsequent slot validation testing)
ASSERT_TRUE(std::all_of(slots.begin(), slots.end(), [](int32_t slot) {
return slot != std::numeric_limits<int32_t>::max();
}));
// serialize the request
const auto serialized = android::nn::serialize(request, MeasureTiming::YES, slots);
// validations
removeDatumTest(sender.get(), receiver.get(), serialized);
addDatumTest(sender.get(), receiver.get(), serialized);
mutateDatumTest(sender.get(), receiver.get(), serialized);
}
// This test validates that when the Result message size exceeds length of the
// result FMQ, the service instance gracefully fails and returns an error.
static void validateBurstFmqLength(const sp<IPreparedModel>& preparedModel,
const Request& request) {
// create regular burst
std::shared_ptr<ExecutionBurstController> controllerRegular;
ASSERT_NO_FATAL_FAILURE(createBurstWithResultChannelLength(
preparedModel, kExecutionBurstChannelLength, &controllerRegular));
ASSERT_NE(nullptr, controllerRegular.get());
// create burst with small output channel
std::shared_ptr<ExecutionBurstController> controllerSmall;
ASSERT_NO_FATAL_FAILURE(createBurstWithResultChannelLength(
preparedModel, kExecutionBurstChannelSmallLength, &controllerSmall));
ASSERT_NE(nullptr, controllerSmall.get());
// load memory into callback slots
std::vector<intptr_t> keys(request.pools.size());
for (size_t i = 0; i < keys.size(); ++i) {
keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
}
// collect serialized result by running regular burst
const auto [statusRegular, outputShapesRegular, timingRegular] =
controllerRegular->compute(request, MeasureTiming::NO, keys);
// skip test if regular burst output isn't useful for testing a failure
// caused by having too small of a length for the result FMQ
const std::vector<FmqResultDatum> serialized =
android::nn::serialize(statusRegular, outputShapesRegular, timingRegular);
if (statusRegular != ErrorStatus::NONE ||
serialized.size() <= kExecutionBurstChannelSmallLength) {
return;
}
// by this point, execution should fail because the result channel isn't
// large enough to return the serialized result
const auto [statusSmall, outputShapesSmall, timingSmall] =
controllerSmall->compute(request, MeasureTiming::NO, keys);
EXPECT_NE(ErrorStatus::NONE, statusSmall);
EXPECT_EQ(0u, outputShapesSmall.size());
EXPECT_TRUE(badTiming(timingSmall));
}
static bool isSanitized(const FmqResultDatum& datum) {
using Discriminator = FmqResultDatum::hidl_discriminator;
// check to ensure the padding values in the returned
// FmqResultDatum::OperandInformation are initialized to 0
if (datum.getDiscriminator() == Discriminator::operandInformation) {
static_assert(
offsetof(FmqResultDatum::OperandInformation, isSufficient) == 0,
"unexpected value for offset of FmqResultDatum::OperandInformation::isSufficient");
static_assert(
sizeof(FmqResultDatum::OperandInformation::isSufficient) == 1,
"unexpected value for size of FmqResultDatum::OperandInformation::isSufficient");
static_assert(offsetof(FmqResultDatum::OperandInformation, numberOfDimensions) == 4,
"unexpected value for offset of "
"FmqResultDatum::OperandInformation::numberOfDimensions");
static_assert(sizeof(FmqResultDatum::OperandInformation::numberOfDimensions) == 4,
"unexpected value for size of "
"FmqResultDatum::OperandInformation::numberOfDimensions");
static_assert(sizeof(FmqResultDatum::OperandInformation) == 8,
"unexpected value for size of "
"FmqResultDatum::OperandInformation");
constexpr size_t paddingOffset =
offsetof(FmqResultDatum::OperandInformation, isSufficient) +
sizeof(FmqResultDatum::OperandInformation::isSufficient);
constexpr size_t paddingSize =
offsetof(FmqResultDatum::OperandInformation, numberOfDimensions) - paddingOffset;
FmqResultDatum::OperandInformation initialized{};
std::memset(&initialized, 0, sizeof(initialized));
const char* initializedPaddingStart =
reinterpret_cast<const char*>(&initialized) + paddingOffset;
const char* datumPaddingStart =
reinterpret_cast<const char*>(&datum.operandInformation()) + paddingOffset;
return std::memcmp(datumPaddingStart, initializedPaddingStart, paddingSize) == 0;
}
// there are no other padding initialization checks required, so return true
// for any sum-type that isn't FmqResultDatum::OperandInformation
return true;
}
static void validateBurstSanitized(const sp<IPreparedModel>& preparedModel,
const Request& request) {
// create burst
std::unique_ptr<RequestChannelSender> sender;
std::unique_ptr<ResultChannelReceiver> receiver;
sp<ExecutionBurstCallback> callback = new ExecutionBurstCallback();
sp<IBurstContext> context;
ASSERT_NO_FATAL_FAILURE(createBurst(preparedModel, callback, &sender, &receiver, &context));
ASSERT_NE(nullptr, sender.get());
ASSERT_NE(nullptr, receiver.get());
ASSERT_NE(nullptr, context.get());
// load memory into callback slots
std::vector<intptr_t> keys;
keys.reserve(request.pools.size());
std::transform(request.pools.begin(), request.pools.end(), std::back_inserter(keys),
[](const auto& pool) { return reinterpret_cast<intptr_t>(&pool); });
const std::vector<int32_t> slots = callback->getSlots(request.pools, keys);
// send valid request
ASSERT_TRUE(sender->send(request, MeasureTiming::YES, slots));
// receive valid result
auto serialized = receiver->getPacketBlocking();
ASSERT_TRUE(serialized.has_value());
// sanitize result
ASSERT_TRUE(std::all_of(serialized->begin(), serialized->end(), isSanitized))
<< "The result serialized data is not properly sanitized";
}
///////////////////////////// ENTRY POINT //////////////////////////////////
void validateBurst(const sp<IPreparedModel>& preparedModel, const Request& request) {
ASSERT_NO_FATAL_FAILURE(validateBurstSerialization(preparedModel, request));
ASSERT_NO_FATAL_FAILURE(validateBurstFmqLength(preparedModel, request));
ASSERT_NO_FATAL_FAILURE(validateBurstSanitized(preparedModel, request));
}
} // namespace android::hardware::neuralnetworks::V1_2::vts::functional

713
neuralnetworks/1.3/vts/functional/ValidateModel.cpp
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@@ -1,713 +0,0 @@
/*
* Copyright (C) 2018 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#define LOG_TAG "neuralnetworks_hidl_hal_test"
#include "1.0/Utils.h"
#include "1.2/Callbacks.h"
#include "GeneratedTestHarness.h"
#include "VtsHalNeuralnetworks.h"
namespace android::hardware::neuralnetworks::V1_2::vts::functional {
using implementation::PreparedModelCallback;
using V1_0::ErrorStatus;
using V1_0::OperandLifeTime;
using V1_1::ExecutionPreference;
using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
///////////////////////// UTILITY FUNCTIONS /////////////////////////
static void validateGetSupportedOperations(const sp<IDevice>& device, const std::string& message,
const Model& model) {
SCOPED_TRACE(message + " [getSupportedOperations_1_2]");
Return<void> ret = device->getSupportedOperations_1_2(
model, [&](ErrorStatus status, const hidl_vec<bool>&) {
EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
});
EXPECT_TRUE(ret.isOk());
}
static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
const Model& model, ExecutionPreference preference) {
SCOPED_TRACE(message + " [prepareModel_1_2]");
sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
Return<ErrorStatus> prepareLaunchStatus =
device->prepareModel_1_2(model, preference, hidl_vec<hidl_handle>(),
hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
ASSERT_TRUE(prepareLaunchStatus.isOk());
ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
preparedModelCallback->wait();
ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
sp<IPreparedModel> preparedModel = getPreparedModel_1_2(preparedModelCallback);
ASSERT_EQ(nullptr, preparedModel.get());
}
static bool validExecutionPreference(ExecutionPreference preference) {
return preference == ExecutionPreference::LOW_POWER ||
preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
preference == ExecutionPreference::SUSTAINED_SPEED;
}
// Primary validation function. This function will take a valid model, apply a
// mutation to it to invalidate the model, then pass it to interface calls that
// use the model. Note that the model here is passed by value, and any mutation
// to the model does not leave this function.
static void validate(const sp<IDevice>& device, const std::string& message, Model model,
const std::function<void(Model*)>& mutation,
ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER) {
mutation(&model);
if (validExecutionPreference(preference)) {
validateGetSupportedOperations(device, message, model);
}
validatePrepareModel(device, message, model, preference);
}
static uint32_t addOperand(Model* model) {
return hidl_vec_push_back(&model->operands,
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 0,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
});
}
static uint32_t addOperand(Model* model, OperandLifeTime lifetime) {
uint32_t index = addOperand(model);
model->operands[index].numberOfConsumers = 1;
model->operands[index].lifetime = lifetime;
return index;
}
///////////////////////// VALIDATE MODEL OPERAND TYPE /////////////////////////
static const uint32_t invalidOperandTypes[] = {
static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MIN) - 1,
static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MAX) + 1,
static_cast<uint32_t>(OperandTypeRange::OEM_MIN) - 1,
static_cast<uint32_t>(OperandTypeRange::OEM_MAX) + 1,
};
static void mutateOperandTypeTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
for (uint32_t invalidOperandType : invalidOperandTypes) {
const std::string message = "mutateOperandTypeTest: operand " +
std::to_string(operand) + " set to value " +
std::to_string(invalidOperandType);
validate(device, message, model, [operand, invalidOperandType](Model* model) {
model->operands[operand].type = static_cast<OperandType>(invalidOperandType);
});
}
}
}
///////////////////////// VALIDATE OPERAND RANK /////////////////////////
static uint32_t getInvalidRank(OperandType type) {
switch (type) {
case OperandType::FLOAT16:
case OperandType::FLOAT32:
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
return 1;
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_INT32:
case OperandType::TENSOR_QUANT8_ASYMM:
case OperandType::TENSOR_QUANT8_SYMM:
case OperandType::TENSOR_QUANT16_ASYMM:
case OperandType::TENSOR_QUANT16_SYMM:
case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
return 0;
default:
return 0;
}
}
static void mutateOperandRankTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
const uint32_t invalidRank = getInvalidRank(model.operands[operand].type);
if (invalidRank == 0) {
continue;
}
const std::string message = "mutateOperandRankTest: operand " + std::to_string(operand) +
" has rank of " + std::to_string(invalidRank);
validate(device, message, model, [operand, invalidRank](Model* model) {
model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0);
});
}
}
///////////////////////// VALIDATE OPERAND SCALE /////////////////////////
static float getInvalidScale(OperandType type) {
switch (type) {
case OperandType::FLOAT16:
case OperandType::FLOAT32:
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
return 1.0f;
case OperandType::TENSOR_INT32:
return -1.0f;
case OperandType::TENSOR_QUANT8_SYMM:
case OperandType::TENSOR_QUANT8_ASYMM:
case OperandType::TENSOR_QUANT16_ASYMM:
case OperandType::TENSOR_QUANT16_SYMM:
return 0.0f;
default:
return 0.0f;
}
}
static void mutateOperandScaleTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
const float invalidScale = getInvalidScale(model.operands[operand].type);
const std::string message = "mutateOperandScaleTest: operand " + std::to_string(operand) +
" has scale of " + std::to_string(invalidScale);
validate(device, message, model, [operand, invalidScale](Model* model) {
model->operands[operand].scale = invalidScale;
});
}
}
///////////////////////// VALIDATE OPERAND ZERO POINT /////////////////////////
static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
switch (type) {
case OperandType::FLOAT16:
case OperandType::FLOAT32:
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_INT32:
case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
return {1};
case OperandType::TENSOR_QUANT8_ASYMM:
return {-1, 256};
case OperandType::TENSOR_QUANT8_SYMM:
return {-129, -1, 1, 128};
case OperandType::TENSOR_QUANT16_ASYMM:
return {-1, 65536};
case OperandType::TENSOR_QUANT16_SYMM:
return {-32769, -1, 1, 32768};
default:
return {};
}
}
static void mutateOperandZeroPointTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
const std::vector<int32_t> invalidZeroPoints =
getInvalidZeroPoints(model.operands[operand].type);
for (int32_t invalidZeroPoint : invalidZeroPoints) {
const std::string message = "mutateOperandZeroPointTest: operand " +
std::to_string(operand) + " has zero point of " +
std::to_string(invalidZeroPoint);
validate(device, message, model, [operand, invalidZeroPoint](Model* model) {
model->operands[operand].zeroPoint = invalidZeroPoint;
});
}
}
}
///////////////////////// VALIDATE EXTRA ??? /////////////////////////
// TODO: Operand::lifetime
// TODO: Operand::location
///////////////////////// VALIDATE OPERATION OPERAND TYPE /////////////////////////
static void mutateOperand(Operand* operand, OperandType type) {
Operand newOperand = *operand;
newOperand.type = type;
switch (type) {
case OperandType::FLOAT16:
case OperandType::FLOAT32:
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
newOperand.dimensions = hidl_vec<uint32_t>();
newOperand.scale = 0.0f;
newOperand.zeroPoint = 0;
break;
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
newOperand.dimensions =
operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
newOperand.scale = 0.0f;
newOperand.zeroPoint = 0;
break;
case OperandType::TENSOR_INT32:
newOperand.dimensions =
operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
newOperand.zeroPoint = 0;
break;
case OperandType::TENSOR_QUANT8_ASYMM:
case OperandType::TENSOR_QUANT8_SYMM:
case OperandType::TENSOR_QUANT16_ASYMM:
case OperandType::TENSOR_QUANT16_SYMM:
newOperand.dimensions =
operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
newOperand.scale = operand->scale != 0.0f ? operand->scale : 1.0f;
break;
case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL: {
newOperand.dimensions =
operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
newOperand.scale = 0.0f;
newOperand.zeroPoint = 0;
SymmPerChannelQuantParams channelQuant;
channelQuant.channelDim = 0;
channelQuant.scales = hidl_vec<float>(
operand->dimensions.size() > 0 ? static_cast<size_t>(operand->dimensions[0])
: 0);
for (size_t i = 0; i < channelQuant.scales.size(); ++i) {
channelQuant.scales[i] = 1.0f;
}
newOperand.extraParams.channelQuant(std::move(channelQuant));
} break;
case OperandType::OEM:
case OperandType::TENSOR_OEM_BYTE:
default:
break;
}
*operand = newOperand;
}
static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, const Model& model) {
// Do not test OEM types
if (type == model.operands[operand].type || type == OperandType::OEM ||
type == OperandType::TENSOR_OEM_BYTE) {
return true;
}
for (const Operation& operation : model.operations) {
// Skip mutateOperationOperandTypeTest for the following operations.
// - LSH_PROJECTION's second argument is allowed to have any type.
// - ARGMIN and ARGMAX's first argument can be any of
// TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
// - CAST's argument can be any of TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
// - RANDOM_MULTINOMIAL's argument can be either TENSOR_FLOAT16 or TENSOR_FLOAT32.
// - DEQUANTIZE input can be any of
// TENSOR_(QUANT8_ASYMM|QUANT8_SYMM|QUANT8_SYMM_PER_CHANNEL), output can
// be of either TENSOR_FLOAT16 or TENSOR_FLOAT32.
// - QUANTIZE input can be either TENSOR_FLOAT16 or TENSOR_FLOAT32
// - CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
// - DEPTHWISE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
// - GROUPED_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
// - TRANSPOSE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
switch (operation.type) {
case OperationType::LSH_PROJECTION: {
if (operand == operation.inputs[1]) {
return true;
}
} break;
case OperationType::CAST:
case OperationType::ARGMAX:
case OperationType::ARGMIN: {
if (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32 ||
type == OperandType::TENSOR_INT32 || type == OperandType::TENSOR_QUANT8_ASYMM) {
return true;
}
} break;
case OperationType::QUANTIZE:
case OperationType::RANDOM_MULTINOMIAL: {
if (operand == operation.inputs[0] &&
(type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
return true;
}
} break;
case OperationType::DEQUANTIZE: {
if (operand == operation.inputs[0] &&
(type == OperandType::TENSOR_QUANT8_ASYMM ||
type == OperandType::TENSOR_QUANT8_SYMM ||
type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
return true;
}
if (operand == operation.outputs[0] &&
(type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
return true;
}
} break;
case OperationType::TRANSPOSE_CONV_2D:
case OperationType::GROUPED_CONV_2D:
case OperationType::DEPTHWISE_CONV_2D:
case OperationType::CONV_2D: {
if (operand == operation.inputs[1] &&
(type == OperandType::TENSOR_QUANT8_ASYMM ||
type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
return true;
}
} break;
default:
break;
}
}
return false;
}
static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
for (OperandType invalidOperandType : hidl_enum_range<OperandType>{}) {
if (mutateOperationOperandTypeSkip(operand, invalidOperandType, model)) {
continue;
}
const std::string message = "mutateOperationOperandTypeTest: operand " +
std::to_string(operand) + " set to type " +
toString(invalidOperandType);
validate(device, message, model, [operand, invalidOperandType](Model* model) {
mutateOperand(&model->operands[operand], invalidOperandType);
});
}
}
}
///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////
static const uint32_t invalidOperationTypes[] = {
static_cast<uint32_t>(OperationTypeRange::FUNDAMENTAL_MAX) + 1,
static_cast<uint32_t>(OperationTypeRange::OEM_MIN) - 1,
static_cast<uint32_t>(OperationTypeRange::OEM_MAX) + 1,
};
static void mutateOperationTypeTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
for (uint32_t invalidOperationType : invalidOperationTypes) {
const std::string message = "mutateOperationTypeTest: operation " +
std::to_string(operation) + " set to value " +
std::to_string(invalidOperationType);
validate(device, message, model, [operation, invalidOperationType](Model* model) {
model->operations[operation].type =
static_cast<OperationType>(invalidOperationType);
});
}
}
}
///////////////////////// VALIDATE MODEL OPERATION INPUT OPERAND INDEX /////////////////////////
static void mutateOperationInputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
const uint32_t invalidOperand = model.operands.size();
for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
const std::string message = "mutateOperationInputOperandIndexTest: operation " +
std::to_string(operation) + " input " +
std::to_string(input);
validate(device, message, model, [operation, input, invalidOperand](Model* model) {
model->operations[operation].inputs[input] = invalidOperand;
});
}
}
}
///////////////////////// VALIDATE MODEL OPERATION OUTPUT OPERAND INDEX /////////////////////////
static void mutateOperationOutputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
const uint32_t invalidOperand = model.operands.size();
for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
const std::string message = "mutateOperationOutputOperandIndexTest: operation " +
std::to_string(operation) + " output " +
std::to_string(output);
validate(device, message, model, [operation, output, invalidOperand](Model* model) {
model->operations[operation].outputs[output] = invalidOperand;
});
}
}
}
///////////////////////// REMOVE OPERAND FROM EVERYTHING /////////////////////////
static void removeValueAndDecrementGreaterValues(hidl_vec<uint32_t>* vec, uint32_t value) {
if (vec) {
// remove elements matching "value"
auto last = std::remove(vec->begin(), vec->end(), value);
vec->resize(std::distance(vec->begin(), last));
// decrement elements exceeding "value"
std::transform(vec->begin(), vec->end(), vec->begin(),
[value](uint32_t v) { return v > value ? v-- : v; });
}
}
static void removeOperand(Model* model, uint32_t index) {
hidl_vec_removeAt(&model->operands, index);
for (Operation& operation : model->operations) {
removeValueAndDecrementGreaterValues(&operation.inputs, index);
removeValueAndDecrementGreaterValues(&operation.outputs, index);
}
removeValueAndDecrementGreaterValues(&model->inputIndexes, index);
removeValueAndDecrementGreaterValues(&model->outputIndexes, index);
}
static bool removeOperandSkip(size_t operand, const Model& model) {
for (const Operation& operation : model.operations) {
// Skip removeOperandTest for the following operations.
// - SPLIT's outputs are not checked during prepareModel.
if (operation.type == OperationType::SPLIT) {
for (const size_t outOprand : operation.outputs) {
if (operand == outOprand) {
return true;
}
}
}
// BIDIRECTIONAL_SEQUENCE_LSTM and BIDIRECTIONAL_SEQUENCE_RNN can have either one or two
// outputs depending on their mergeOutputs parameter.
if (operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_LSTM ||
operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_RNN) {
for (const size_t outOprand : operation.outputs) {
if (operand == outOprand) {
return true;
}
}
}
}
return false;
}
static void removeOperandTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
if (removeOperandSkip(operand, model)) {
continue;
}
const std::string message = "removeOperandTest: operand " + std::to_string(operand);
validate(device, message, model,
[operand](Model* model) { removeOperand(model, operand); });
}
}
///////////////////////// REMOVE OPERATION /////////////////////////
static void removeOperation(Model* model, uint32_t index) {
for (uint32_t operand : model->operations[index].inputs) {
model->operands[operand].numberOfConsumers--;
}
hidl_vec_removeAt(&model->operations, index);
}
static void removeOperationTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
const std::string message = "removeOperationTest: operation " + std::to_string(operation);
validate(device, message, model,
[operation](Model* model) { removeOperation(model, operation); });
}
}
///////////////////////// REMOVE OPERATION INPUT /////////////////////////
static bool removeOperationInputSkip(const Operation& op, size_t input) {
// Skip removeOperationInputTest for the following operations.
// - CONCATENATION has at least 2 inputs, with the last element being INT32.
// - CONV_2D, DEPTHWISE_CONV_2D, MAX_POOL_2D, AVERAGE_POOL_2D, L2_POOL_2D, RESIZE_BILINEAR,
// SPACE_TO_DEPTH, SPACE_TO_DEPTH, SPACE_TO_BATCH_ND, BATCH_TO_SPACE_ND can have an optional
// layout parameter.
// - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional axis
// parameter.
switch (op.type) {
case OperationType::CONCATENATION: {
if (op.inputs.size() > 2 && input != op.inputs.size() - 1) {
return true;
}
} break;
case OperationType::DEPTHWISE_CONV_2D: {
if ((op.inputs.size() == 12 && input == 11) || (op.inputs.size() == 9 && input == 8)) {
return true;
}
} break;
case OperationType::CONV_2D:
case OperationType::AVERAGE_POOL_2D:
case OperationType::MAX_POOL_2D:
case OperationType::L2_POOL_2D: {
if ((op.inputs.size() == 11 && input == 10) || (op.inputs.size() == 8 && input == 7)) {
return true;
}
} break;
case OperationType::RESIZE_BILINEAR: {
if (op.inputs.size() == 4 && input == 3) {
return true;
}
} break;
case OperationType::SPACE_TO_DEPTH:
case OperationType::DEPTH_TO_SPACE:
case OperationType::BATCH_TO_SPACE_ND: {
if (op.inputs.size() == 3 && input == 2) {
return true;
}
} break;
case OperationType::SPACE_TO_BATCH_ND: {
if (op.inputs.size() == 4 && input == 3) {
return true;
}
} break;
case OperationType::L2_NORMALIZATION: {
if (op.inputs.size() == 2 && input == 1) {
return true;
}
} break;
case OperationType::LOCAL_RESPONSE_NORMALIZATION: {
if (op.inputs.size() == 6 && input == 5) {
return true;
}
} break;
case OperationType::SOFTMAX: {
if (op.inputs.size() == 3 && input == 2) {
return true;
}
} break;
default:
break;
}
return false;
}
static void removeOperationInputTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
const Operation& op = model.operations[operation];
if (removeOperationInputSkip(op, input)) {
continue;
}
const std::string message = "removeOperationInputTest: operation " +
std::to_string(operation) + ", input " +
std::to_string(input);
validate(device, message, model, [operation, input](Model* model) {
uint32_t operand = model->operations[operation].inputs[input];
model->operands[operand].numberOfConsumers--;
hidl_vec_removeAt(&model->operations[operation].inputs, input);
});
}
}
}
///////////////////////// REMOVE OPERATION OUTPUT /////////////////////////
static void removeOperationOutputTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
const std::string message = "removeOperationOutputTest: operation " +
std::to_string(operation) + ", output " +
std::to_string(output);
validate(device, message, model, [operation, output](Model* model) {
hidl_vec_removeAt(&model->operations[operation].outputs, output);
});
}
}
}
///////////////////////// MODEL VALIDATION /////////////////////////
// TODO: remove model input
// TODO: remove model output
// TODO: add unused operation
///////////////////////// ADD OPERATION INPUT /////////////////////////
static bool addOperationInputSkip(const Operation& op) {
// Skip addOperationInputTest for the following operations.
// - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional INT32 axis
// parameter.
if ((op.type == OperationType::L2_NORMALIZATION && op.inputs.size() == 1) ||
(op.type == OperationType::LOCAL_RESPONSE_NORMALIZATION && op.inputs.size() == 5) ||
(op.type == OperationType::SOFTMAX && op.inputs.size() == 2)) {
return true;
}
return false;
}
static void addOperationInputTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
if (addOperationInputSkip(model.operations[operation])) {
continue;
}
const std::string message = "addOperationInputTest: operation " + std::to_string(operation);
validate(device, message, model, [operation](Model* model) {
uint32_t index = addOperand(model, OperandLifeTime::MODEL_INPUT);
hidl_vec_push_back(&model->operations[operation].inputs, index);
hidl_vec_push_back(&model->inputIndexes, index);
});
}
}
///////////////////////// ADD OPERATION OUTPUT /////////////////////////
static void addOperationOutputTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
const std::string message =
"addOperationOutputTest: operation " + std::to_string(operation);
validate(device, message, model, [operation](Model* model) {
uint32_t index = addOperand(model, OperandLifeTime::MODEL_OUTPUT);
hidl_vec_push_back(&model->operations[operation].outputs, index);
hidl_vec_push_back(&model->outputIndexes, index);
});
}
}
///////////////////////// VALIDATE EXECUTION PREFERENCE /////////////////////////
static const int32_t invalidExecutionPreferences[] = {
static_cast<int32_t>(ExecutionPreference::LOW_POWER) - 1, // lower bound
static_cast<int32_t>(ExecutionPreference::SUSTAINED_SPEED) + 1, // upper bound
};
static void mutateExecutionPreferenceTest(const sp<IDevice>& device, const Model& model) {
for (int32_t preference : invalidExecutionPreferences) {
const std::string message =
"mutateExecutionPreferenceTest: preference " + std::to_string(preference);
validate(
device, message, model, [](Model*) {},
static_cast<ExecutionPreference>(preference));
}
}
////////////////////////// ENTRY POINT //////////////////////////////
void validateModel(const sp<IDevice>& device, const Model& model) {
mutateOperandTypeTest(device, model);
mutateOperandRankTest(device, model);
mutateOperandScaleTest(device, model);
mutateOperandZeroPointTest(device, model);
mutateOperationOperandTypeTest(device, model);
mutateOperationTypeTest(device, model);
mutateOperationInputOperandIndexTest(device, model);
mutateOperationOutputOperandIndexTest(device, model);
removeOperandTest(device, model);
removeOperationTest(device, model);
removeOperationInputTest(device, model);
removeOperationOutputTest(device, model);
addOperationInputTest(device, model);
addOperationOutputTest(device, model);
mutateExecutionPreferenceTest(device, model);
}
} // namespace android::hardware::neuralnetworks::V1_2::vts::functional

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/*
* Copyright (C) 2018 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#define LOG_TAG "neuralnetworks_hidl_hal_test"
#include "1.0/Utils.h"
#include "1.2/Callbacks.h"
#include "ExecutionBurstController.h"
#include "GeneratedTestHarness.h"
#include "TestHarness.h"
#include "Utils.h"
#include "VtsHalNeuralnetworks.h"
namespace android::hardware::neuralnetworks::V1_2::vts::functional {
using implementation::ExecutionCallback;
using V1_0::ErrorStatus;
using V1_0::Request;
///////////////////////// UTILITY FUNCTIONS /////////////////////////
static bool badTiming(Timing timing) {
return timing.timeOnDevice == UINT64_MAX && timing.timeInDriver == UINT64_MAX;
}
// Primary validation function. This function will take a valid request, apply a
// mutation to it to invalidate the request, then pass it to interface calls
// that use the request. Note that the request here is passed by value, and any
// mutation to the request does not leave this function.
static void validate(const sp<IPreparedModel>& preparedModel, const std::string& message,
Request request, const std::function<void(Request*)>& mutation) {
mutation(&request);
// We'd like to test both with timing requested and without timing
// requested. Rather than running each test both ways, we'll decide whether
// to request timing by hashing the message. We do not use std::hash because
// it is not guaranteed stable across executions.
char hash = 0;
for (auto c : message) {
hash ^= c;
};
MeasureTiming measure = (hash & 1) ? MeasureTiming::YES : MeasureTiming::NO;
// asynchronous
{
SCOPED_TRACE(message + " [execute_1_2]");
sp<ExecutionCallback> executionCallback = new ExecutionCallback();
Return<ErrorStatus> executeLaunchStatus =
preparedModel->execute_1_2(request, measure, executionCallback);
ASSERT_TRUE(executeLaunchStatus.isOk());
ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
executionCallback->wait();
ErrorStatus executionReturnStatus = executionCallback->getStatus();
const auto& outputShapes = executionCallback->getOutputShapes();
Timing timing = executionCallback->getTiming();
ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
ASSERT_EQ(outputShapes.size(), 0);
ASSERT_TRUE(badTiming(timing));
}
// synchronous
{
SCOPED_TRACE(message + " [executeSynchronously]");
Return<void> executeStatus = preparedModel->executeSynchronously(
request, measure,
[](ErrorStatus error, const hidl_vec<OutputShape>& outputShapes,
const Timing& timing) {
ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
EXPECT_EQ(outputShapes.size(), 0);
EXPECT_TRUE(badTiming(timing));
});
ASSERT_TRUE(executeStatus.isOk());
}
// burst
{
SCOPED_TRACE(message + " [burst]");
// create burst
std::shared_ptr<::android::nn::ExecutionBurstController> burst =
android::nn::ExecutionBurstController::create(preparedModel, /*blocking=*/true);
ASSERT_NE(nullptr, burst.get());
// create memory keys
std::vector<intptr_t> keys(request.pools.size());
for (size_t i = 0; i < keys.size(); ++i) {
keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
}
// execute and verify
ErrorStatus error;
std::vector<OutputShape> outputShapes;
Timing timing;
std::tie(error, outputShapes, timing) = burst->compute(request, measure, keys);
EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
EXPECT_EQ(outputShapes.size(), 0);
EXPECT_TRUE(badTiming(timing));
// additional burst testing
if (request.pools.size() > 0) {
// valid free
burst->freeMemory(keys.front());
// negative test: invalid free of unknown (blank) memory
burst->freeMemory(intptr_t{});
// negative test: double free of memory
burst->freeMemory(keys.front());
}
}
}
///////////////////////// REMOVE INPUT ////////////////////////////////////
static void removeInputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
for (size_t input = 0; input < request.inputs.size(); ++input) {
const std::string message = "removeInput: removed input " + std::to_string(input);
validate(preparedModel, message, request,
[input](Request* request) { hidl_vec_removeAt(&request->inputs, input); });
}
}
///////////////////////// REMOVE OUTPUT ////////////////////////////////////
static void removeOutputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
for (size_t output = 0; output < request.outputs.size(); ++output) {
const std::string message = "removeOutput: removed Output " + std::to_string(output);
validate(preparedModel, message, request,
[output](Request* request) { hidl_vec_removeAt(&request->outputs, output); });
}
}
///////////////////////////// ENTRY POINT //////////////////////////////////
void validateRequest(const sp<IPreparedModel>& preparedModel, const Request& request) {
removeInputTest(preparedModel, request);
removeOutputTest(preparedModel, request);
}
void validateRequestFailure(const sp<IPreparedModel>& preparedModel, const Request& request) {
SCOPED_TRACE("Expecting request to fail [executeSynchronously]");
Return<void> executeStatus = preparedModel->executeSynchronously(
request, MeasureTiming::NO,
[](ErrorStatus error, const hidl_vec<OutputShape>& outputShapes, const Timing& timing) {
ASSERT_NE(ErrorStatus::NONE, error);
EXPECT_EQ(outputShapes.size(), 0);
EXPECT_TRUE(badTiming(timing));
});
ASSERT_TRUE(executeStatus.isOk());
}
} // namespace android::hardware::neuralnetworks::V1_2::vts::functional

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/*
* Copyright (C) 2018 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#define LOG_TAG "neuralnetworks_hidl_hal_test"
#include "VtsHalNeuralnetworks.h"
#include <android-base/logging.h>
#include <hidl/ServiceManagement.h>
#include <string>
#include <utility>
#include "1.0/Callbacks.h"
#include "1.0/Utils.h"
#include "GeneratedTestHarness.h"
#include "TestHarness.h"
namespace android::hardware::neuralnetworks::V1_2::vts::functional {
using implementation::PreparedModelCallback;
using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
using V1_0::ErrorStatus;
using V1_0::Request;
using V1_1::ExecutionPreference;
// internal helper function
void createPreparedModel(const sp<IDevice>& device, const Model& model,
sp<IPreparedModel>* preparedModel) {
ASSERT_NE(nullptr, preparedModel);
*preparedModel = nullptr;
// see if service can handle model
bool fullySupportsModel = false;
const Return<void> supportedCall = device->getSupportedOperations_1_2(
model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
ASSERT_EQ(ErrorStatus::NONE, status);
ASSERT_NE(0ul, supported.size());
fullySupportsModel = std::all_of(supported.begin(), supported.end(),
[](bool valid) { return valid; });
});
ASSERT_TRUE(supportedCall.isOk());
// launch prepare model
const sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
const Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_2(
model, ExecutionPreference::FAST_SINGLE_ANSWER, hidl_vec<hidl_handle>(),
hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
ASSERT_TRUE(prepareLaunchStatus.isOk());
ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
// retrieve prepared model
preparedModelCallback->wait();
const ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
*preparedModel = getPreparedModel_1_2(preparedModelCallback);
// The getSupportedOperations_1_2 call returns a list of operations that are
// guaranteed not to fail if prepareModel_1_2 is called, and
// 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
// If a driver has any doubt that it can prepare an operation, it must
// return false. So here, if a driver isn't sure if it can support an
// operation, but reports that it successfully prepared the model, the test
// can continue.
if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
ASSERT_EQ(nullptr, preparedModel->get());
LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot prepare "
"model that it does not support.";
std::cout << "[ ] Early termination of test because vendor service cannot "
"prepare model that it does not support."
<< std::endl;
GTEST_SKIP();
}
ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
ASSERT_NE(nullptr, preparedModel->get());
}
void NeuralnetworksHidlTest::SetUp() {
testing::TestWithParam<NeuralnetworksHidlTestParam>::SetUp();
ASSERT_NE(kDevice, nullptr);
}
static NamedDevice makeNamedDevice(const std::string& name) {
return {name, IDevice::getService(name)};
}
static std::vector<NamedDevice> getNamedDevicesImpl() {
// Retrieves the name of all service instances that implement IDevice,
// including any Lazy HAL instances.
const std::vector<std::string> names = hardware::getAllHalInstanceNames(IDevice::descriptor);
// Get a handle to each device and pair it with its name.
std::vector<NamedDevice> namedDevices;
namedDevices.reserve(names.size());
std::transform(names.begin(), names.end(), std::back_inserter(namedDevices), makeNamedDevice);
return namedDevices;
}
const std::vector<NamedDevice>& getNamedDevices() {
const static std::vector<NamedDevice> devices = getNamedDevicesImpl();
return devices;
}
std::string printNeuralnetworksHidlTest(
const testing::TestParamInfo<NeuralnetworksHidlTestParam>& info) {
return gtestCompliantName(getName(info.param));
}
INSTANTIATE_DEVICE_TEST(NeuralnetworksHidlTest);
// Forward declaration from ValidateModel.cpp
void validateModel(const sp<IDevice>& device, const Model& model);
// Forward declaration from ValidateRequest.cpp
void validateRequest(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
// Forward declaration from ValidateRequest.cpp
void validateRequestFailure(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
// Forward declaration from ValidateBurst.cpp
void validateBurst(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
void validateEverything(const sp<IDevice>& device, const Model& model, const Request& request) {
validateModel(device, model);
// Create IPreparedModel.
sp<IPreparedModel> preparedModel;
createPreparedModel(device, model, &preparedModel);
if (preparedModel == nullptr) return;
validateRequest(preparedModel, request);
validateBurst(preparedModel, request);
}
void validateFailure(const sp<IDevice>& device, const Model& model, const Request& request) {
// TODO: Should this always succeed?
// What if the invalid input is part of the model (i.e., a parameter).
validateModel(device, model);
// Create IPreparedModel.
sp<IPreparedModel> preparedModel;
createPreparedModel(device, model, &preparedModel);
if (preparedModel == nullptr) return;
validateRequestFailure(preparedModel, request);
}
TEST_P(ValidationTest, Test) {
const Model model = createModel(kTestModel);
const Request request = createRequest(kTestModel);
if (kTestModel.expectFailure) {
validateFailure(kDevice, model, request);
} else {
validateEverything(kDevice, model, request);
}
}
INSTANTIATE_GENERATED_TEST(ValidationTest, [](const test_helper::TestModel&) { return true; });
sp<IPreparedModel> getPreparedModel_1_2(const sp<implementation::PreparedModelCallback>& callback) {
sp<V1_0::IPreparedModel> preparedModelV1_0 = callback->getPreparedModel();
return IPreparedModel::castFrom(preparedModelV1_0).withDefault(nullptr);
}
} // namespace android::hardware::neuralnetworks::V1_2::vts::functional

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neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h
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/*
* Copyright (C) 2018 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef ANDROID_HARDWARE_NEURALNETWORKS_V1_2_VTS_HAL_NEURALNETWORKS_H
#define ANDROID_HARDWARE_NEURALNETWORKS_V1_2_VTS_HAL_NEURALNETWORKS_H
#include <android/hardware/neuralnetworks/1.2/IDevice.h>
#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
#include <android/hardware/neuralnetworks/1.2/types.h>
#include <gtest/gtest.h>
#include "1.0/Utils.h"
#include "1.2/Callbacks.h"
namespace android::hardware::neuralnetworks::V1_2::vts::functional {
using NamedDevice = Named<sp<IDevice>>;
using NeuralnetworksHidlTestParam = NamedDevice;
class NeuralnetworksHidlTest : public testing::TestWithParam<NeuralnetworksHidlTestParam> {
protected:
void SetUp() override;
const sp<IDevice> kDevice = getData(GetParam());
};
const std::vector<NamedDevice>& getNamedDevices();
std::string printNeuralnetworksHidlTest(
const testing::TestParamInfo<NeuralnetworksHidlTestParam>& info);
#define INSTANTIATE_DEVICE_TEST(TestSuite) \
INSTANTIATE_TEST_SUITE_P(PerInstance, TestSuite, testing::ValuesIn(getNamedDevices()), \
printNeuralnetworksHidlTest)
// Create an IPreparedModel object. If the model cannot be prepared,
// "preparedModel" will be nullptr instead.
void createPreparedModel(const sp<IDevice>& device, const Model& model,
sp<IPreparedModel>* preparedModel);
// Utility function to get PreparedModel from callback and downcast to V1_2.
sp<IPreparedModel> getPreparedModel_1_2(const sp<implementation::PreparedModelCallback>& callback);
} // namespace android::hardware::neuralnetworks::V1_2::vts::functional
#endif // ANDROID_HARDWARE_NEURALNETWORKS_V1_2_VTS_HAL_NEURALNETWORKS_H

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/*
* Copyright (C) 2018 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef ANDROID_HARDWARE_NEURALNETWORKS_V1_2_CALLBACKS_H
#define ANDROID_HARDWARE_NEURALNETWORKS_V1_2_CALLBACKS_H
#include <android-base/thread_annotations.h>
#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
#include <android/hardware/neuralnetworks/1.2/IExecutionCallback.h>
#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h>
#include <hidl/Status.h>
#include <condition_variable>
#include <mutex>
/*
* The Callback classes are used internally by the NeuralNetworks runtime to
* synchronize between different threads. An asynchronous task is launched
* paired with a callback object. When a client thread requires the output being
* generated by the asynchronous task, the client thread can wait for the result
* and be blocked until it has completed. Any wait may safely be called
* concurrently, even on the same callback object. When the asynchronous task
* has finished its workload, it must immediately call "notify*". If the
* asynchronous task has failed to launch, the function that tried to launch the
* asynchronous task must immediately call "notify*". This "notify*" call
* awakens any client threads waiting on the callback object.
*
* These classes exist to enable synchronization across HIDL. When
* synchronization is only required in the same process, consider using
* std::future, std::mutex, std::condition_variable, or std::experimental::latch
* instead.
*/
namespace android::hardware::neuralnetworks::V1_2::implementation {
/**
* The PreparedModelCallback class is used to receive the error status of
* preparing a model as well as the prepared model from a task executing
* asynchronously with respect to the runtime. If a calling thread calls wait
* or get* on a PreparedModelCallback object and the corresponding asynchronous
* task has not finished preparing the model, the calling thread will block
* until the asynchronous task has either called notify or notify_1_2.
*
* If the callback object is notified more than once, only the results of the
* first call to notify* are used, and the results from subsequent calls are
* discarded.
*
* This callback object is passed as an argument to IDevice::prepareModel*.
*/
class PreparedModelCallback : public IPreparedModelCallback {
public:
/**
* IPreparedModelCallback::notify marks the callback object with the return
* status of the asynchronous model preparation along with the prepared
* model, and allows all prior and future wait calls on the
* PreparedModelCallback object to proceed.
*
* Either IPreparedModelCallback::notify or
* IPreparedModelCallback::notify_1_2 must be called on a given
* PreparedModelCallback object.
*
* If the callback object is notified more than once, only the results of
* the first call to notify* are used, and the results from subsequent calls
* are discarded.
*
* @param status Error status returned from asynchronously preparing the
* model; will be:
* - NONE if the asynchronous preparation was successful
* - DEVICE_UNAVAILABLE if driver is offline or busy
* - GENERAL_FAILURE if there is an unspecified error
* - INVALID_ARGUMENT if the input model is invalid
* @param preparedModel Returned model that has been prepared for execution,
* nullptr if the model was unable to be prepared.
*/
Return<void> notify(V1_0::ErrorStatus status,
const sp<V1_0::IPreparedModel>& preparedModel) override;
/**
* IPreparedModelCallback::notify_1_2 marks the callback object with the
* return status of the asynchronous model preparation along with the
* prepared model, and allows all prior and future wait calls on the
* PreparedModelCallback object to proceed.
*
* Either IPreparedModelCallback::notify or
* IPreparedModelCallback::notify_1_2 must be called on a given
* PreparedModelCallback object.
*
* If the callback object is notified more than once, only the results of
* the first call to notify* are used, and the results from subsequent calls
* are discarded.
*
* @param status Error status returned from asynchronously preparing the
* model; will be:
* - NONE if the asynchronous preparation was successful
* - DEVICE_UNAVAILABLE if driver is offline or busy
* - GENERAL_FAILURE if there is an unspecified error
* - INVALID_ARGUMENT if the input model is invalid
* @param preparedModel Returned model that has been prepared for execution,
* nullptr if the model was unable to be prepared.
*/
Return<void> notify_1_2(V1_0::ErrorStatus status,
const sp<V1_2::IPreparedModel>& preparedModel) override;
/**
* PreparedModelCallback::wait blocks until notify* has been called on the
* callback object.
*/
void wait() const;
/**
* Retrieves the error status returned from the asynchronous task launched
* by IDevice::prepareModel*. If IDevice::prepareModel* has not finished
* asynchronously preparing the model, this call will block until the
* asynchronous task notifies the object.
*
* @return status Error status returned from asynchronously preparing the
* model; will be:
* - NONE if the asynchronous preparation was successful
* - DEVICE_UNAVAILABLE if driver is offline or busy
* - GENERAL_FAILURE if there is an unspecified error
* - INVALID_ARGUMENT if the input model is invalid
*/
V1_0::ErrorStatus getStatus() const;
/**
* Retrieves the model that has been prepared for execution from the
* asynchronous task launched by IDevice::prepareModel*. If
* IDevice::prepareModel* has not finished asynchronously preparing the
* model, this call will block until the asynchronous task notifies the
* object.
*
* @return preparedModel Returned model that has been prepared for
* execution, nullptr if the model was unable to be prepared.
*/
sp<V1_0::IPreparedModel> getPreparedModel() const;
private:
mutable std::mutex mMutex;
mutable std::condition_variable mCondition;
bool mNotified GUARDED_BY(mMutex) = false;
V1_0::ErrorStatus mErrorStatus = V1_0::ErrorStatus::GENERAL_FAILURE;
sp<V1_0::IPreparedModel> mPreparedModel;
};
/**
* The ExecutionCallback class is used to receive the results of the execution
* from a task executing asynchronously with respect to the runtime. If a
* calling thread calls wait or get* on a ExecutionCallback object and the
* corresponding asynchronous task has not finished the execution, the calling
* thread will block until the asynchronous task has either called notify or
* notify_1_2.
*
* If the callback object is notified more than once, only the results of the
* first call to notify* are used, and the results from subsequent calls are
* discarded.
*
* This callback object is passed as an argument to IPreparedModel::execute*.
*/
class ExecutionCallback : public IExecutionCallback {
public:
/**
* IExecutionCallback::notify marks the callback object with the return
* status of the asynchronous execution that held this callback and enables
* all prior and future wait calls on the ExecutionCallback object to
* proceed.
*
* Either IExecutionCallback::notify or IExecutionCallback::notify_1_2 must
* be called on a given ExecutionCallback object.
*
* If the callback object is notified more than once, only the results of
* the first call to notify* are used, and the results from subsequent calls
* are discarded.
*
* @param status Error status returned from launching the asynchronous task
* (if the launch fails) or from the asynchronous task itself (if the
* launch succeeds). Must be:
* - NONE if the asynchronous execution was successful
* - DEVICE_UNAVAILABLE if driver is offline or busy
* - GENERAL_FAILURE if there is an unspecified error
* - OUTPUT_INSUFFICIENT_SIZE if provided output buffer is not large
* enough to store the resultant values
* - INVALID_ARGUMENT if the input request is invalid
*/
Return<void> notify(V1_0::ErrorStatus status) override;
/**
* IExecutionCallback::notify_1_2 marks the callback object with the results
* (error status, dynamic output shapes, and timing information) of the
* asynchronous execution that held this callback and enables all prior and
* future wait calls on the ExecutionCallback object to proceed.
*
* Either IExecutionCallback::notify or IExecutionCallback::notify_1_2 must
* be called on a given ExecutionCallback object.
*
* If the callback object is notified more than once, only the results of
* the first call to notify* are used, and the results from subsequent calls
* are discarded.
*
* @param status Error status returned from launching the asynchronous task
* (if the launch fails) or from the asynchronous task itself (if the
* launch succeeds). Must be:
* - NONE if the asynchronous execution was successful
* - DEVICE_UNAVAILABLE if driver is offline or busy
* - GENERAL_FAILURE if the asynchronous task resulted in an unspecified
* error
* - OUTPUT_INSUFFICIENT_SIZE if at least one output operand buffer is
* not large enough to store the corresponding output
* - INVALID_ARGUMENT if one of the input arguments to prepareModel is
* invalid
* @param outputShapes A list of shape information of model output operands.
* The index into "outputShapes" corresponds to the index of the output
* operand in the Request outputs vector. outputShapes must be empty
* unless the status is either NONE or OUTPUT_INSUFFICIENT_SIZE.
* @param Timing Duration of execution. Unless MeasureTiming::YES was passed
* when launching the execution and status is NONE, all times must be
* reported as UINT64_MAX. A driver may choose to report any time as
* UINT64_MAX, indicating that particular measurement is not available.
*/
Return<void> notify_1_2(V1_0::ErrorStatus status, const hidl_vec<OutputShape>& outputShapes,
const Timing& timing) override;
// An overload of the latest notify interface to hide the version from ExecutionBuilder.
Return<void> notify(V1_0::ErrorStatus status, const hidl_vec<OutputShape>& outputShapes,
const Timing& timing) {
return notify_1_2(status, outputShapes, timing);
}
/**
* ExecutionCallback::wait blocks until notify* has been called on the
* callback object.
*/
void wait() const;
/**
* Retrieves the error status returned from the asynchronous task launched
* by either IPreparedModel::execute or IPreparedModel::execute_1_2. If
* IPreparedModel::execute or IPreparedModel::execute_1_2 has not finished
* asynchronously executing, this call will block until the asynchronous
* task notifies the object.
*
* @return status Error status returned from launching the asynchronous task
* (if the launch fails) or from the asynchronous task itself (if the
* launch succeeds). Must be:
* - NONE if the asynchronous execution was successful
* - DEVICE_UNAVAILABLE if driver is offline or busy
* - GENERAL_FAILURE if the asynchronous task resulted in an unspecified
* error
* - OUTPUT_INSUFFICIENT_SIZE if at least one output operand buffer is
* not large enough to store the corresponding output
* - INVALID_ARGUMENT if one of the input arguments to prepareModel is
* invalid
*/
V1_0::ErrorStatus getStatus() const;
/**
* Retrieves the output shapes returned from the asynchronous task launched
* by IPreparedModel::execute_1_2. If IPreparedModel::execute_1_2 has not
* finished asynchronously executing, this call will block until the
* asynchronous task notifies the object.
*
* If the asynchronous task was launched by IPreparedModel::execute, an
* empty vector will be returned.
*
* @return outputShapes A list of shape information of model output
* operands. The index into "outputShapes" corresponds to the index of
* the output operand in the Request outputs vector. outputShapes must
* be empty unless the status is either NONE or
* OUTPUT_INSUFFICIENT_SIZE. outputShaps may be empty if the status is
* NONE and all model output operands are fully-specified at execution
* time. outputShapes must have the same number of elements as the
* number of model output operands if the status is
* OUTPUT_INSUFFICIENT_SIZE, or if the status is NONE and the model has
* at least one output operand that is not fully-specified.
*/
const std::vector<OutputShape>& getOutputShapes() const;
/**
* Retrieves the duration of execution of the asynchronous task launched by
* IPreparedModel::execute_1_2. If IPreparedModel::execute_1_2 has not
* finished asynchronously executing, this call will block until the
* asynchronous task notifies the object.
*
* If the asynchronous task was launched by IPreparedModel::execute, every
* time must be UINT64_MAX.
*
* @return timing Duration of the execution. Every time must be UINT64_MAX
* unless the status is NONE.
*/
Timing getTiming() const;
private:
/*
* ExecutionCallback::notifyInternal stores the results of the execution
* (status, output shapes, and timing information) in the ExecutionCallback
* object before any call to wait or get* return. It then enables all prior
* and future wait calls on the ExecutionCallback object to proceed.
*/
void notifyInternal(V1_0::ErrorStatus errorStatus, const hidl_vec<OutputShape>& outputShapes,
const Timing& timing);
// members
mutable std::mutex mMutex;
mutable std::condition_variable mCondition;
bool mNotified GUARDED_BY(mMutex) = false;
V1_0::ErrorStatus mErrorStatus = V1_0::ErrorStatus::GENERAL_FAILURE;
std::vector<OutputShape> mOutputShapes = {};
Timing mTiming = {};
};
} // namespace android::hardware::neuralnetworks::V1_2::implementation
#endif // ANDROID_HARDWARE_NEURALNETWORKS_V1_2_CALLBACKS_H