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/*
* Copyright (C) 2020 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 <gtest/gtest.h>
#include <algorithm>
#include <memory>
#include <vector>
#include "CompilationBuilder.h"
#include "ExecutionPlan.h"
#include "HalUtils.h"
#include "Manager.h"
#include "SampleDriverPartial.h"
#include "TestNeuralNetworksWrapper.h"
namespace android::nn {
namespace {
using sample_driver::SampleDriverPartial;
using Result = test_wrapper::Result;
using WrapperOperandType = test_wrapper::OperandType;
using WrapperCompilation = test_wrapper::Compilation;
using WrapperExecution = test_wrapper::Execution;
using WrapperType = test_wrapper::Type;
using WrapperModel = test_wrapper::Model;
class EmptyOperationResolver : public IOperationResolver {
public:
const OperationRegistration* findOperation(OperationType) const override { return nullptr; }
};
const char* kTestDriverName = "nnapi-test-sqrt-failing";
// A driver that only supports SQRT and fails during execution.
class FailingTestDriver : public SampleDriverPartial {
public:
// EmptyOperationResolver causes execution to fail.
FailingTestDriver() : SampleDriverPartial(kTestDriverName, &mEmptyOperationResolver) {}
hardware::Return<void> getCapabilities_1_3(getCapabilities_1_3_cb cb) override {
cb(V1_3::ErrorStatus::NONE, makeCapabilities(0.1)); // Faster than CPU.
return hardware::Void();
}
private:
std::vector<bool> getSupportedOperationsImpl(const V1_3::Model& model) const override {
std::vector<bool> supported(model.main.operations.size());
std::transform(model.main.operations.begin(), model.main.operations.end(),
supported.begin(), [](const V1_3::Operation& operation) {
return operation.type == V1_3::OperationType::SQRT;
});
return supported;
}
const EmptyOperationResolver mEmptyOperationResolver;
};
class FailingDriverTest : public ::testing::Test {
virtual void SetUp() {
DeviceManager* deviceManager = DeviceManager::get();
if (deviceManager->getUseCpuOnly() ||
!DeviceManager::partitioningAllowsFallback(deviceManager->getPartitioning())) {
GTEST_SKIP();
}
mTestDevice =
DeviceManager::forTest_makeDriverDevice(kTestDriverName, new FailingTestDriver());
deviceManager->forTest_setDevices({
mTestDevice,
DeviceManager::getCpuDevice(),
});
}
virtual void TearDown() { DeviceManager::get()->forTest_reInitializeDeviceList(); }
protected:
std::shared_ptr<Device> mTestDevice;
};
// Regression test for b/152623150.
TEST_F(FailingDriverTest, FailAfterInterpretedWhile) {
// Model:
// f = input0
// b = input1
// while CAST(b): # Identity cast.
// f = CAST(f)
// # FailingTestDriver fails here. When partial CPU fallback happens,
// # it should not loop forever.
// output0 = SQRT(f)
WrapperOperandType floatType(WrapperType::TENSOR_FLOAT32, {2});
WrapperOperandType boolType(WrapperType::TENSOR_BOOL8, {1});
WrapperModel conditionModel;
{
uint32_t f = conditionModel.addOperand(&floatType);
uint32_t b = conditionModel.addOperand(&boolType);
uint32_t out = conditionModel.addOperand(&boolType);
conditionModel.addOperation(ANEURALNETWORKS_CAST, {b}, {out});
conditionModel.identifyInputsAndOutputs({f, b}, {out});
ASSERT_EQ(conditionModel.finish(), Result::NO_ERROR);
ASSERT_TRUE(conditionModel.isValid());
}
WrapperModel bodyModel;
{
uint32_t f = bodyModel.addOperand(&floatType);
uint32_t b = bodyModel.addOperand(&boolType);
uint32_t out = bodyModel.addOperand(&floatType);
bodyModel.addOperation(ANEURALNETWORKS_CAST, {f}, {out});
bodyModel.identifyInputsAndOutputs({f, b}, {out});
ASSERT_EQ(bodyModel.finish(), Result::NO_ERROR);
ASSERT_TRUE(bodyModel.isValid());
}
WrapperModel model;
{
uint32_t fInput = model.addOperand(&floatType);
uint32_t bInput = model.addOperand(&boolType);
uint32_t fTmp = model.addOperand(&floatType);
uint32_t fSqrt = model.addOperand(&floatType);
uint32_t cond = model.addModelOperand(&conditionModel);
uint32_t body = model.addModelOperand(&bodyModel);
model.addOperation(ANEURALNETWORKS_WHILE, {cond, body, fInput, bInput}, {fTmp});
model.addOperation(ANEURALNETWORKS_SQRT, {fTmp}, {fSqrt});
model.identifyInputsAndOutputs({fInput, bInput}, {fSqrt});
ASSERT_TRUE(model.isValid());
ASSERT_EQ(model.finish(), Result::NO_ERROR);
}
WrapperCompilation compilation(&model);
ASSERT_EQ(compilation.finish(), Result::NO_ERROR);
const CompilationBuilder* compilationBuilder =
reinterpret_cast<CompilationBuilder*>(compilation.getHandle());
const ExecutionPlan& plan = compilationBuilder->forTest_getExecutionPlan();
const std::vector<std::shared_ptr<LogicalStep>>& steps = plan.forTest_compoundGetSteps();
ASSERT_EQ(steps.size(), 6u);
ASSERT_TRUE(steps[0]->isWhile());
ASSERT_TRUE(steps[1]->isExecution());
ASSERT_EQ(steps[1]->executionStep()->getDevice(), DeviceManager::getCpuDevice());
ASSERT_TRUE(steps[2]->isGoto());
ASSERT_TRUE(steps[3]->isExecution());
ASSERT_EQ(steps[3]->executionStep()->getDevice(), DeviceManager::getCpuDevice());
ASSERT_TRUE(steps[4]->isGoto());
ASSERT_TRUE(steps[5]->isExecution());
ASSERT_EQ(steps[5]->executionStep()->getDevice(), mTestDevice);
WrapperExecution execution(&compilation);
const float fInput[] = {12 * 12, 5 * 5};
const bool8 bInput = false;
float fSqrt[] = {0, 0};
ASSERT_EQ(execution.setInput(0, &fInput), Result::NO_ERROR);
ASSERT_EQ(execution.setInput(1, &bInput), Result::NO_ERROR);
ASSERT_EQ(execution.setOutput(0, &fSqrt), Result::NO_ERROR);
ASSERT_EQ(execution.compute(), Result::NO_ERROR);
ASSERT_EQ(fSqrt[0], 12);
ASSERT_EQ(fSqrt[1], 5);
}
// Regression test for b/155923033.
TEST_F(FailingDriverTest, SimplePlan) {
// Model:
// output0 = SQRT(input0)
//
// This results in a SIMPLE execution plan. When FailingTestDriver fails,
// partial CPU fallback should complete the execution.
WrapperOperandType floatType(WrapperType::TENSOR_FLOAT32, {2});
WrapperModel model;
{
uint32_t fInput = model.addOperand(&floatType);
uint32_t fSqrt = model.addOperand(&floatType);
model.addOperation(ANEURALNETWORKS_SQRT, {fInput}, {fSqrt});
model.identifyInputsAndOutputs({fInput}, {fSqrt});
ASSERT_TRUE(model.isValid());
ASSERT_EQ(model.finish(), Result::NO_ERROR);
}
WrapperCompilation compilation(&model);
ASSERT_EQ(compilation.finish(), Result::NO_ERROR);
const CompilationBuilder* compilationBuilder =
reinterpret_cast<CompilationBuilder*>(compilation.getHandle());
const ExecutionPlan& plan = compilationBuilder->forTest_getExecutionPlan();
ASSERT_TRUE(plan.isSimple());
WrapperExecution execution(&compilation);
const float fInput[] = {12 * 12, 5 * 5};
float fSqrt[] = {0, 0};
ASSERT_EQ(execution.setInput(0, &fInput), Result::NO_ERROR);
ASSERT_EQ(execution.setOutput(0, &fSqrt), Result::NO_ERROR);
ASSERT_EQ(execution.compute(), Result::NO_ERROR);
ASSERT_EQ(fSqrt[0], 12);
ASSERT_EQ(fSqrt[1], 5);
}
} // namespace
} // namespace android::nn
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