Copy VTS tests from v1.2 to v1.3
So that it's easier to see what actually has changed in VTS tests for
version 1.3
Bug: 139120468
Test: m
Change-Id: I09797f5f3898501a008186a22dd411b00e9e2c67
Merged-In: I09797f5f3898501a008186a22dd411b00e9e2c67
(cherry picked from commit 3b13b55ac1532647ee6f261489d23ca4269c1440)
diff --git a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
new file mode 100644
index 0000000..2beec98
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
@@ -0,0 +1,408 @@
+/*
+ * 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