Modify 1.0 VTS tests to consume test struct directly. am: 8487c5e21f (3076ffc8) · Commits · e / os / android_hardware_interfaces

neuralnetworks/1.0/vts/functional/Android.bp

+2 −4

Original line number	Diff line number	Diff line
		@@ -32,12 +32,11 @@ cc_library_static {
		"android.hidl.memory@1.0",
		"libgmock",
		"libhidlmemory",
		"libneuralnetworks_generated_test_harness",
		"libneuralnetworks_utils",
		],
		header_libs: [
		"libneuralnetworks_headers",
		"libneuralnetworks_generated_test_harness_headers",
		"libneuralnetworks_generated_tests",
		],
		}

		@@ -60,13 +59,12 @@ cc_defaults {
		"android.hidl.memory@1.0",
		"libgmock",
		"libhidlmemory",
		"libneuralnetworks_generated_test_harness",
		"libneuralnetworks_utils",
		"VtsHalNeuralNetworksV1_0_utils",
		],
		header_libs: [
		"libneuralnetworks_headers",
		"libneuralnetworks_generated_test_harness_headers",
		"libneuralnetworks_generated_tests",
		],
		test_suites: ["general-tests"],
		}

neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp

+100 −125

Original line number	Diff line number	Diff line
		@@ -15,6 +15,7 @@
		*/

		#include "GeneratedTestHarness.h"

		#include "1.0/Callbacks.h"
		#include "1.0/Utils.h"
		#include "MemoryUtils.h"
		@@ -28,6 +29,7 @@
		#include <android/hidl/memory/1.0/IMemory.h>
		#include <hidlmemory/mapping.h>

		#include <gtest/gtest.h>
		#include <iostream>

		namespace android {
		@@ -36,6 +38,7 @@ namespace neuralnetworks {
		namespace V1_0 {
		namespace generated_tests {

		using namespace test_helper;
		using ::android::hardware::neuralnetworks::V1_0::ErrorStatus;
		using ::android::hardware::neuralnetworks::V1_0::IDevice;
		using ::android::hardware::neuralnetworks::V1_0::IPreparedModel;
		@@ -45,137 +48,111 @@ using ::android::hardware::neuralnetworks::V1_0::RequestArgument;
		using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
		using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
		using ::android::hidl::memory::V1_0::IMemory;
		using ::test_helper::compare;
		using ::test_helper::filter;
		using ::test_helper::for_all;
		using ::test_helper::MixedTyped;
		using ::test_helper::MixedTypedExample;
		using ::test_helper::resize_accordingly;

		// Top level driver for models and examples generated by test_generator.py
		// Test driver for those generated from ml/nn/runtime/test/spec
		void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored,
		const std::vector<MixedTypedExample>& examples, float fpAtol,
		float fpRtol) {
		const uint32_t INPUT = 0;
		const uint32_t OUTPUT = 1;

		int example_no = 1;
		for (auto& example : examples) {
		SCOPED_TRACE(example_no++);
		const MixedTyped& inputs = example.operands.first;
		const MixedTyped& golden = example.operands.second;

		CHECK(inputs.float16Operands.empty()) << "float16 is not supported in 1.0";

		std::vector<RequestArgument> inputs_info, outputs_info;
		uint32_t inputSize = 0, outputSize = 0;
		// This function only partially specifies the metadata (vector of RequestArguments).
		// The contents are copied over below.
		for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) {
		if (inputs_info.size() <= static_cast<size_t>(index)) inputs_info.resize(index + 1);
		RequestArgument arg = {
		.location = {.poolIndex = INPUT,
		.offset = 0,
		.length = static_cast<uint32_t>(s)},
		.dimensions = {},
		};
		RequestArgument arg_empty = {
		.hasNoValue = true,
		};
		inputs_info[index] = s ? arg : arg_empty;
		inputSize += s;
		});
		// Compute offset for inputs 1 and so on
		{
		size_t offset = 0;
		for (auto& i : inputs_info) {
		if (!i.hasNoValue) i.location.offset = offset;
		offset += i.location.length;
		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();
		}

		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};
		}

		MixedTyped test; // holding test results

		// Go through all outputs, initialize RequestArgument descriptors
		resize_accordingly(golden, test);
		for_all(golden, [&outputs_info, &outputSize](int index, auto, auto s) {
		if (outputs_info.size() <= static_cast<size_t>(index)) outputs_info.resize(index + 1);
		RequestArgument arg = {
		.location = {.poolIndex = OUTPUT,
		.offset = 0,
		.length = static_cast<uint32_t>(s)},
		.dimensions = {},
		};
		outputs_info[index] = arg;
		outputSize += s;
		// 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};
		});
		// Compute offset for outputs 1 and so on
		{
		size_t offset = 0;
		for (auto& i : outputs_info) {
		i.location.offset = offset;
		offset += i.location.length;

		// 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);
		}
		}
		std::vector<hidl_memory> pools = {nn::allocateSharedMemory(inputSize),
		nn::allocateSharedMemory(outputSize)};
		ASSERT_NE(0ull, pools[INPUT].size());
		ASSERT_NE(0ull, pools[OUTPUT].size());

		// 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> inputMemory = mapMemory(pools[INPUT]);
		sp<IMemory> outputMemory = mapMemory(pools[OUTPUT]);
		ASSERT_NE(nullptr, inputMemory.get());
		ASSERT_NE(nullptr, outputMemory.get());
		char* inputPtr = reinterpret_cast<char>(static_cast<void>(inputMemory->getPointer()));
		char* outputPtr = reinterpret_cast<char>(static_cast<void>(outputMemory->getPointer()));
		ASSERT_NE(nullptr, inputPtr);
		ASSERT_NE(nullptr, outputPtr);
		inputMemory->update();
		outputMemory->update();

		// Go through all inputs, copy the values
		for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) {
		char* begin = (char*)p;
		char* end = begin + s;
		// TODO: handle more than one input
		std::copy(begin, end, inputPtr + inputs_info[index].location.offset);
		});
		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);
		}
		}
		}

		inputMemory->commit();
		outputMemory->commit();
		return {.operands = std::move(operands),
		.operations = std::move(operations),
		.inputIndexes = testModel.inputIndexes,
		.outputIndexes = testModel.outputIndexes,
		.operandValues = std::move(operandValues),
		.pools = std::move(pools)};
		}

		const Request request = {.inputs = inputs_info, .outputs = outputs_info, .pools = pools};
		// Top level driver for models and examples generated by test_generator.py
		// Test driver for those generated from ml/nn/runtime/test/spec
		void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel) {
		const Request request = createRequest(testModel);

		// launch execution
		// Launch execution.
		sp<ExecutionCallback> executionCallback = new ExecutionCallback();
		ASSERT_NE(nullptr, executionCallback.get());
		Return<ErrorStatus> executionLaunchStatus =
		preparedModel->execute(request, executionCallback);
		Return<ErrorStatus> executionLaunchStatus = preparedModel->execute(request, executionCallback);
		ASSERT_TRUE(executionLaunchStatus.isOk());
		EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));

		// retrieve execution status
		// Retrieve execution status.
		executionCallback->wait();
		ASSERT_EQ(ErrorStatus::NONE, executionCallback->getStatus());

		// validate results
		outputMemory->read();
		copy_back(&test, outputs_info, outputPtr);
		outputMemory->commit();
		// Filter out don't cares
		MixedTyped filtered_golden = filter(golden, is_ignored);
		MixedTyped filtered_test = filter(test, is_ignored);
		// Retrieve execution results.
		const std::vector<TestBuffer> outputs = getOutputBuffers(request);

		// We want "close-enough" results for float
		compare(filtered_golden, filtered_test, fpAtol, fpRtol);
		}
		// We want "close-enough" results.
		checkResults(testModel, outputs);
		}

		void Execute(const sp<IDevice>& device, std::function<Model(void)> create_model,
		std::function<bool(int)> is_ignored, const std::vector<MixedTypedExample>& examples) {
		Model model = create_model();
		void Execute(const sp<IDevice>& device, const TestModel& testModel) {
		Model model = createModel(testModel);

		// see if service can handle model
		bool fullySupportsModel = false;
		@@ -190,7 +167,6 @@ void Execute(const sp<IDevice>& device, std::function<Model(void)> create_model,

		// launch prepare model
		sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
		ASSERT_NE(nullptr, preparedModelCallback.get());
		Return<ErrorStatus> prepareLaunchStatus = device->prepareModel(model, preparedModelCallback);
		ASSERT_TRUE(prepareLaunchStatus.isOk());
		ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
		@@ -213,8 +189,7 @@ void Execute(const sp<IDevice>& device, std::function<Model(void)> create_model,
		EXPECT_EQ(ErrorStatus::NONE, prepareReturnStatus);
		ASSERT_NE(nullptr, preparedModel.get());

		float fpAtol = 1e-5f, fpRtol = 5.0f * 1.1920928955078125e-7f;
		EvaluatePreparedModel(preparedModel, is_ignored, examples, fpAtol, fpRtol);
		EvaluatePreparedModel(preparedModel, testModel);
		}

		} // namespace generated_tests

neuralnetworks/1.0/vts/functional/GeneratedTestHarness.h

+2 −3

Original line number	Diff line number	Diff line
		@@ -26,10 +26,9 @@ namespace neuralnetworks {
		namespace V1_0 {
		namespace generated_tests {

		using ::test_helper::MixedTypedExample;
		Model createModel(const ::test_helper::TestModel& testModel);

		void Execute(const sp<V1_0::IDevice>& device, std::function<V1_0::Model(void)> create_model,
		std::function<bool(int)> is_ignored, const std::vector<MixedTypedExample>& examples);
		void Execute(const sp<V1_0::IDevice>& device, const ::test_helper::TestModel& testModel);

		} // namespace generated_tests
		} // namespace V1_0

neuralnetworks/1.0/vts/functional/GeneratedTests.h

+1 −10

Original line number	Diff line number	Diff line
		@@ -14,20 +14,11 @@
		* limitations under the License.
		*/

		#include <android/hidl/memory/1.0/IMemory.h>
		#include <hidlmemory/mapping.h>

		#include "1.0/Utils.h"
		#include "GeneratedTestHarness.h"
		#include "MemoryUtils.h"
		#include "TestHarness.h"
		#include "VtsHalNeuralnetworks.h"

		namespace android::hardware::neuralnetworks::V1_0::vts::functional {

		std::vector<Request> createRequests(const std::vector<::test_helper::MixedTypedExample>& examples);

		} // namespace android::hardware::neuralnetworks::V1_0::vts::functional

		namespace android::hardware::neuralnetworks::V1_0::generated_tests {

		using namespace android::hardware::neuralnetworks::V1_0::vts::functional;

neuralnetworks/1.0/vts/functional/Utils.cpp

+80 −26

Original line number	Diff line number	Diff line
		@@ -14,45 +14,99 @@
		* limitations under the License.
		*/

		#include "GeneratedTestHarness.h"
		#include "1.0/Utils.h"

		#include "MemoryUtils.h"
		#include "TestHarness.h"

		#include <android-base/logging.h>
		#include <android/hardware/neuralnetworks/1.0/types.h>
		#include <android/hidl/allocator/1.0/IAllocator.h>
		#include <android/hidl/memory/1.0/IMemory.h>
		#include <hidlmemory/mapping.h>

		#include <cstring>
		#include <map>
		#include <vector>

		namespace android {
		namespace hardware {
		namespace neuralnetworks {

		using namespace test_helper;
		using ::android::hardware::neuralnetworks::V1_0::DataLocation;
		using ::android::hardware::neuralnetworks::V1_0::Request;
		using ::android::hardware::neuralnetworks::V1_0::RequestArgument;
		using ::test_helper::for_each;
		using ::test_helper::MixedTyped;

		template <typename T>
		void copy_back_(std::map<int, std::vector<T>>* dst, const std::vector<RequestArgument>& ra,
		char* src) {
		for_each<T>(*dst, [&ra, src](int index, std::vector<T>& m) {
		ASSERT_EQ(m.size(), ra[index].location.length / sizeof(T));
		char* begin = src + ra[index].location.offset;
		memcpy(m.data(), begin, ra[index].location.length);
		});
		}

		void copy_back(MixedTyped* dst, const std::vector<RequestArgument>& ra, char* src) {
		copy_back_(&dst->float32Operands, ra, src);
		copy_back_(&dst->int32Operands, ra, src);
		copy_back_(&dst->quant8AsymmOperands, ra, src);
		copy_back_(&dst->quant16SymmOperands, ra, src);
		copy_back_(&dst->float16Operands, ra, src);
		copy_back_(&dst->bool8Operands, ra, src);
		copy_back_(&dst->quant8ChannelOperands, ra, src);
		copy_back_(&dst->quant16AsymmOperands, ra, src);
		copy_back_(&dst->quant8SymmOperands, ra, src);
		static_assert(9 == MixedTyped::kNumTypes,
		"Number of types in MixedTyped changed, but copy_back function wasn't updated");
		using ::android::hidl::memory::V1_0::IMemory;

		constexpr uint32_t kInputPoolIndex = 0;
		constexpr uint32_t kOutputPoolIndex = 1;

		Request createRequest(const TestModel& testModel) {
		// Model inputs.
		hidl_vec<RequestArgument> inputs(testModel.inputIndexes.size());
		size_t inputSize = 0;
		for (uint32_t i = 0; i < testModel.inputIndexes.size(); i++) {
		const auto& op = testModel.operands[testModel.inputIndexes[i]];
		if (op.data.size() == 0) {
		// Omitted input.
		inputs[i] = {.hasNoValue = true};
		} else {
		DataLocation loc = {.poolIndex = kInputPoolIndex,
		.offset = static_cast<uint32_t>(inputSize),
		.length = static_cast<uint32_t>(op.data.size())};
		inputSize += op.data.alignedSize();
		inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
		}
		}

		// Model outputs.
		hidl_vec<RequestArgument> outputs(testModel.outputIndexes.size());
		size_t outputSize = 0;
		for (uint32_t i = 0; i < testModel.outputIndexes.size(); i++) {
		const auto& op = testModel.operands[testModel.outputIndexes[i]];
		size_t dataSize = op.data.size();
		DataLocation loc = {.poolIndex = kOutputPoolIndex,
		.offset = static_cast<uint32_t>(outputSize),
		.length = static_cast<uint32_t>(dataSize)};
		outputSize += op.data.alignedSize();
		outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
		}

		// Allocate memory pools.
		hidl_vec<hidl_memory> pools = {nn::allocateSharedMemory(inputSize),
		nn::allocateSharedMemory(outputSize)};
		CHECK_NE(pools[kInputPoolIndex].size(), 0u);
		CHECK_NE(pools[kOutputPoolIndex].size(), 0u);
		sp<IMemory> inputMemory = mapMemory(pools[kInputPoolIndex]);
		CHECK(inputMemory.get() != nullptr);
		uint8_t* inputPtr = static_cast<uint8_t>(static_cast<void>(inputMemory->getPointer()));
		CHECK(inputPtr != nullptr);

		// Copy input data to the memory pool.
		for (uint32_t i = 0; i < testModel.inputIndexes.size(); i++) {
		const auto& op = testModel.operands[testModel.inputIndexes[i]];
		if (op.data.size() > 0) {
		const uint8_t* begin = op.data.get<uint8_t>();
		const uint8_t* end = begin + op.data.size();
		std::copy(begin, end, inputPtr + inputs[i].location.offset);
		}
		}

		return {.inputs = std::move(inputs), .outputs = std::move(outputs), .pools = std::move(pools)};
		}

		std::vector<TestBuffer> getOutputBuffers(const Request& request) {
		sp<IMemory> outputMemory = mapMemory(request.pools[kOutputPoolIndex]);
		CHECK(outputMemory.get() != nullptr);
		uint8_t* outputPtr = static_cast<uint8_t>(static_cast<void>(outputMemory->getPointer()));
		CHECK(outputPtr != nullptr);

		// Copy out output results.
		std::vector<TestBuffer> outputBuffers;
		for (const auto& output : request.outputs) {
		outputBuffers.emplace_back(output.location.length, outputPtr + output.location.offset);
		}

		return outputBuffers;
		}

		} // namespace neuralnetworks