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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/Utils.h"
#include "1.3/Callbacks.h"
#include "1.3/Utils.h"
#include "GeneratedTestHarness.h"
#include "TestHarness.h"
#include "Utils.h"
namespace android::hardware::neuralnetworks::V1_3::vts::functional {
using HidlToken =
hidl_array<uint8_t, static_cast<uint32_t>(V1_2::Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
using implementation::PreparedModelCallback;
using V1_1::ExecutionPreference;
// internal helper function
void createPreparedModel(const sp<IDevice>& device, const Model& model,
sp<IPreparedModel>* preparedModel, bool reportSkipping) {
ASSERT_NE(nullptr, preparedModel);
*preparedModel = nullptr;
// see if service can handle model
bool fullySupportsModel = false;
const Return<void> supportedCall = device->getSupportedOperations_1_3(
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_3(
model, ExecutionPreference::FAST_SINGLE_ANSWER, kDefaultPriority, {},
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_3(preparedModelCallback);
// The getSupportedOperations_1_3 call returns a list of operations that are
// guaranteed not to fail if prepareModel_1_3 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());
if (!reportSkipping) {
return;
}
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);
const bool deviceIsResponsive = kDevice->ping().isOk();
ASSERT_TRUE(deviceIsResponsive);
}
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 Request& request);
// Forward declaration from ValidateRequest.cpp
void validateRequestFailure(const sp<IPreparedModel>& preparedModel, const Request& request);
// Forward declaration from ValidateBurst.cpp
void validateBurst(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
// Validate sync_fence handles for dispatch with valid input
void validateExecuteFenced(const sp<IPreparedModel>& preparedModel, const Request& request) {
SCOPED_TRACE("Expecting request to fail [executeFenced]");
Return<void> ret_null = preparedModel->executeFenced(
request, {hidl_handle(nullptr)}, V1_2::MeasureTiming::NO, {}, {}, {},
[](ErrorStatus error, const hidl_handle& handle,
const sp<IFencedExecutionCallback>& callback) {
ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
ASSERT_EQ(handle.getNativeHandle(), nullptr);
ASSERT_EQ(callback, nullptr);
});
ASSERT_TRUE(ret_null.isOk());
}
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);
validateExecuteFenced(preparedModel, request);
// TODO(butlermichael): Check if we need to test burst in V1_3 if the interface remains V1_2.
ASSERT_TRUE(nn::compliantWithV1_0(request));
V1_0::Request request10 = nn::convertToV1_0(request);
validateBurst(preparedModel, request10);
}
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);
ExecutionContext context;
const Request request = nn::convertToV1_3(context.createRequest(kTestModel));
if (kTestModel.expectFailure) {
validateFailure(kDevice, model, request);
} else {
validateEverything(kDevice, model, request);
}
}
INSTANTIATE_GENERATED_TEST(ValidationTest, [](const std::string& testName) {
// Skip validation for the "inputs_as_internal" and "all_tensors_as_inputs"
// generated tests.
return testName.find("inputs_as_internal") == std::string::npos &&
testName.find("all_tensors_as_inputs") == std::string::npos;
});
sp<IPreparedModel> getPreparedModel_1_3(const sp<PreparedModelCallback>& callback) {
sp<V1_0::IPreparedModel> preparedModelV1_0 = callback->getPreparedModel();
return IPreparedModel::castFrom(preparedModelV1_0).withDefault(nullptr);
}
std::string toString(Executor executor) {
switch (executor) {
case Executor::ASYNC:
return "ASYNC";
case Executor::SYNC:
return "SYNC";
case Executor::BURST:
return "BURST";
case Executor::FENCED:
return "FENCED";
default:
CHECK(false);
}
}
} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
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