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Commit 0fff59b9 authored by Slava Shklyaev's avatar Slava Shklyaev
Browse files

Add control flow support to NNAPI VTS tests 

See change I98a3edd1.

Bug: 148077633
Bug: 148601177
Bug: 136735929
Test: VtsHalNeuralnetworksV1_0TargetTest
Test: VtsHalNeuralnetworksV1_1TargetTest
Test: VtsHalNeuralnetworksV1_2TargetTest
Test: VtsHalNeuralnetworksV1_3TargetTest
Change-Id: I1e436cdba404b68026a45797ac4fb3a34f8be76a
Merged-In: I1e436cdba404b68026a45797ac4fb3a34f8be76a
(cherry picked from commit 1f98e2e9)
parent 315e9b89

neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp

+13 −12
Original line number Diff line number Diff line
@@ -42,10 +42,11 @@ using implementation::PreparedModelCallback; 


Model createModel(const TestModel& testModel) {

    // Model operands.

    hidl_vec<Operand> operands(testModel.operands.size());

    CHECK_EQ(testModel.referenced.size(), 0u);  // Not supported in 1.0.

    hidl_vec<Operand> operands(testModel.main.operands.size());

    size_t constCopySize = 0, constRefSize = 0;

    for (uint32_t i = 0; i < testModel.operands.size(); i++) {

        const auto& op = testModel.operands[i];

    for (uint32_t i = 0; i < testModel.main.operands.size(); i++) {

        const auto& op = testModel.main.operands[i];



        DataLocation loc = {};

        if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
@@ -70,9 +71,9 @@ Model createModel(const TestModel& testModel) { 
    }



    // Model operations.

    hidl_vec<Operation> operations(testModel.operations.size());

    std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(),

                   [](const TestOperation& op) -> Operation {

    hidl_vec<Operation> operations(testModel.main.operations.size());

    std::transform(testModel.main.operations.begin(), testModel.main.operations.end(),

                   operations.begin(), [](const TestOperation& op) -> Operation {

                       return {.type = static_cast<OperationType>(op.type),

                               .inputs = op.inputs,

                               .outputs = op.outputs};
@@ -80,8 +81,8 @@ Model createModel(const TestModel& testModel) { 


    // Constant copies.

    hidl_vec<uint8_t> operandValues(constCopySize);

    for (uint32_t i = 0; i < testModel.operands.size(); i++) {

        const auto& op = testModel.operands[i];

    for (uint32_t i = 0; i < testModel.main.operands.size(); i++) {

        const auto& op = testModel.main.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();
@@ -102,8 +103,8 @@ Model createModel(const TestModel& testModel) { 
                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];

        for (uint32_t i = 0; i < testModel.main.operands.size(); i++) {

            const auto& op = testModel.main.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();
@@ -114,8 +115,8 @@ Model createModel(const TestModel& testModel) { 


    return {.operands = std::move(operands),

            .operations = std::move(operations),

            .inputIndexes = testModel.inputIndexes,

            .outputIndexes = testModel.outputIndexes,

            .inputIndexes = testModel.main.inputIndexes,

            .outputIndexes = testModel.main.outputIndexes,

            .operandValues = std::move(operandValues),

            .pools = std::move(pools)};

}

neuralnetworks/1.0/vts/functional/Utils.cpp

+8 −8
Original line number Diff line number Diff line
@@ -42,10 +42,10 @@ constexpr uint32_t kOutputPoolIndex = 1; 


Request createRequest(const TestModel& testModel) {

    // Model inputs.

    hidl_vec<RequestArgument> inputs(testModel.inputIndexes.size());

    hidl_vec<RequestArgument> inputs(testModel.main.inputIndexes.size());

    size_t inputSize = 0;

    for (uint32_t i = 0; i < testModel.inputIndexes.size(); i++) {

        const auto& op = testModel.operands[testModel.inputIndexes[i]];

    for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {

        const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];

        if (op.data.size() == 0) {

            // Omitted input.

            inputs[i] = {.hasNoValue = true};
@@ -59,10 +59,10 @@ Request createRequest(const TestModel& testModel) { 
    }



    // Model outputs.

    hidl_vec<RequestArgument> outputs(testModel.outputIndexes.size());

    hidl_vec<RequestArgument> outputs(testModel.main.outputIndexes.size());

    size_t outputSize = 0;

    for (uint32_t i = 0; i < testModel.outputIndexes.size(); i++) {

        const auto& op = testModel.operands[testModel.outputIndexes[i]];

    for (uint32_t i = 0; i < testModel.main.outputIndexes.size(); i++) {

        const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]];



        // In the case of zero-sized output, we should at least provide a one-byte buffer.

        // This is because zero-sized tensors are only supported internally to the driver, or
@@ -90,8 +90,8 @@ Request createRequest(const TestModel& testModel) { 
    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]];

    for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {

        const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];

        if (op.data.size() > 0) {

            const uint8_t* begin = op.data.get<uint8_t>();

            const uint8_t* end = begin + op.data.size();

neuralnetworks/1.1/vts/functional/GeneratedTestHarness.cpp

+13 −12
Original line number Diff line number Diff line
@@ -49,10 +49,11 @@ using V1_0::implementation::PreparedModelCallback; 


Model createModel(const TestModel& testModel) {

    // Model operands.

    hidl_vec<Operand> operands(testModel.operands.size());

    CHECK_EQ(testModel.referenced.size(), 0u);  // Not supported in 1.1.

    hidl_vec<Operand> operands(testModel.main.operands.size());

    size_t constCopySize = 0, constRefSize = 0;

    for (uint32_t i = 0; i < testModel.operands.size(); i++) {

        const auto& op = testModel.operands[i];

    for (uint32_t i = 0; i < testModel.main.operands.size(); i++) {

        const auto& op = testModel.main.operands[i];



        DataLocation loc = {};

        if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
@@ -77,9 +78,9 @@ Model createModel(const TestModel& testModel) { 
    }



    // Model operations.

    hidl_vec<Operation> operations(testModel.operations.size());

    std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(),

                   [](const TestOperation& op) -> Operation {

    hidl_vec<Operation> operations(testModel.main.operations.size());

    std::transform(testModel.main.operations.begin(), testModel.main.operations.end(),

                   operations.begin(), [](const TestOperation& op) -> Operation {

                       return {.type = static_cast<OperationType>(op.type),

                               .inputs = op.inputs,

                               .outputs = op.outputs};
@@ -87,8 +88,8 @@ Model createModel(const TestModel& testModel) { 


    // Constant copies.

    hidl_vec<uint8_t> operandValues(constCopySize);

    for (uint32_t i = 0; i < testModel.operands.size(); i++) {

        const auto& op = testModel.operands[i];

    for (uint32_t i = 0; i < testModel.main.operands.size(); i++) {

        const auto& op = testModel.main.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();
@@ -109,8 +110,8 @@ Model createModel(const TestModel& testModel) { 
                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];

        for (uint32_t i = 0; i < testModel.main.operands.size(); i++) {

            const auto& op = testModel.main.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();
@@ -121,8 +122,8 @@ Model createModel(const TestModel& testModel) { 


    return {.operands = std::move(operands),

            .operations = std::move(operations),

            .inputIndexes = testModel.inputIndexes,

            .outputIndexes = testModel.outputIndexes,

            .inputIndexes = testModel.main.inputIndexes,

            .outputIndexes = testModel.main.outputIndexes,

            .operandValues = std::move(operandValues),

            .pools = std::move(pools),

            .relaxComputationFloat32toFloat16 = testModel.isRelaxed};

neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp

+4 −4
Original line number Diff line number Diff line
@@ -207,10 +207,10 @@ TestModel createLargeTestModelImpl(TestOperationType op, uint32_t len) { 
    };



    return {

            .operands = std::move(operands),

            .main = {.operands = std::move(operands),

                     .operations = std::move(operations),

                     .inputIndexes = {1},

            .outputIndexes = {len * 2 + 1},

                     .outputIndexes = {len * 2 + 1}},

            .isRelaxed = false,

    };

}

neuralnetworks/1.2/vts/functional/GeneratedTestHarness.cpp

+18 −17
Original line number Diff line number Diff line
@@ -75,10 +75,11 @@ struct TestConfig { 


Model createModel(const TestModel& testModel) {

    // Model operands.

    hidl_vec<Operand> operands(testModel.operands.size());

    CHECK_EQ(testModel.referenced.size(), 0u);  // Not supported in 1.1.

    hidl_vec<Operand> operands(testModel.main.operands.size());

    size_t constCopySize = 0, constRefSize = 0;

    for (uint32_t i = 0; i < testModel.operands.size(); i++) {

        const auto& op = testModel.operands[i];

    for (uint32_t i = 0; i < testModel.main.operands.size(); i++) {

        const auto& op = testModel.main.operands[i];



        DataLocation loc = {};

        if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
@@ -110,9 +111,9 @@ Model createModel(const TestModel& testModel) { 
    }



    // Model operations.

    hidl_vec<Operation> operations(testModel.operations.size());

    std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(),

                   [](const TestOperation& op) -> Operation {

    hidl_vec<Operation> operations(testModel.main.operations.size());

    std::transform(testModel.main.operations.begin(), testModel.main.operations.end(),

                   operations.begin(), [](const TestOperation& op) -> Operation {

                       return {.type = static_cast<OperationType>(op.type),

                               .inputs = op.inputs,

                               .outputs = op.outputs};
@@ -120,8 +121,8 @@ Model createModel(const TestModel& testModel) { 


    // Constant copies.

    hidl_vec<uint8_t> operandValues(constCopySize);

    for (uint32_t i = 0; i < testModel.operands.size(); i++) {

        const auto& op = testModel.operands[i];

    for (uint32_t i = 0; i < testModel.main.operands.size(); i++) {

        const auto& op = testModel.main.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();
@@ -142,8 +143,8 @@ Model createModel(const TestModel& testModel) { 
                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];

        for (uint32_t i = 0; i < testModel.main.operands.size(); i++) {

            const auto& op = testModel.main.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();
@@ -154,15 +155,15 @@ Model createModel(const TestModel& testModel) { 


    return {.operands = std::move(operands),

            .operations = std::move(operations),

            .inputIndexes = testModel.inputIndexes,

            .outputIndexes = testModel.outputIndexes,

            .inputIndexes = testModel.main.inputIndexes,

            .outputIndexes = testModel.main.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();

    const auto byteSize = testModel.main.operands[testModel.main.outputIndexes[index]].data.size();

    return byteSize > 1u;

}
@@ -302,17 +303,17 @@ void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestMo 
            // 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());

                        outputShapes.size() == testModel.main.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());

            ASSERT_EQ(outputShapes.size(), testModel.main.outputIndexes.size());

            break;

        case OutputType::INSUFFICIENT:

            ASSERT_EQ(ErrorStatus::OUTPUT_INSUFFICIENT_SIZE, executionStatus);

            ASSERT_EQ(outputShapes.size(), testModel.outputIndexes.size());

            ASSERT_EQ(outputShapes.size(), testModel.main.outputIndexes.size());

            ASSERT_FALSE(outputShapes[0].isSufficient);

            return;

    }
@@ -320,7 +321,7 @@ void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestMo 
    // 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 auto& expect = testModel.main.operands[testModel.main.outputIndexes[i]].dimensions;

        const std::vector<uint32_t> actual = outputShapes[i].dimensions;

        EXPECT_EQ(expect, actual);

    }