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
2026-02-01 16:09:42 +00:00 · 2020-01-31 15:14:24 +00:00
parent 8a9b306571
commit 1f98e2e929
9 changed files with 155 additions and 114 deletions
--- a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -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)};
 }
--- a/neuralnetworks/1.0/vts/functional/Utils.cpp
+++ b/neuralnetworks/1.0/vts/functional/Utils.cpp
@@ -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();
--- a/neuralnetworks/1.1/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.1/vts/functional/GeneratedTestHarness.cpp
@@ -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};
--- a/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp
+++ b/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp
@@ -207,10 +207,10 @@ TestModel createLargeTestModelImpl(TestOperationType op, uint32_t len) {
    };

    return {
-            .operands = std::move(operands),
-            .operations = std::move(operations),
-            .inputIndexes = {1},
-            .outputIndexes = {len * 2 + 1},
+            .main = {.operands = std::move(operands),
+                     .operations = std::move(operations),
+                     .inputIndexes = {1},
+                     .outputIndexes = {len * 2 + 1}},
            .isRelaxed = false,
    };
 }
--- a/neuralnetworks/1.2/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.2/vts/functional/GeneratedTestHarness.cpp
@@ -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);
    }
--- a/neuralnetworks/1.3/vts/functional/CompilationCachingTests.cpp
+++ b/neuralnetworks/1.3/vts/functional/CompilationCachingTests.cpp
@@ -209,10 +209,10 @@ TestModel createLargeTestModelImpl(TestOperationType op, uint32_t len) {
    };

    return {
-            .operands = std::move(operands),
-            .operations = std::move(operations),
-            .inputIndexes = {1},
-            .outputIndexes = {len * 2 + 1},
+            .main = {.operands = std::move(operands),
+                     .operations = std::move(operations),
+                     .inputIndexes = {1},
+                     .outputIndexes = {len * 2 + 1}},
            .isRelaxed = false,
    };
 }
--- a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
@@ -169,7 +169,8 @@ class DeviceMemoryAllocator {
        if constexpr (ioType == IOType::INPUT) {
            if (buffer != nullptr) {
                // TestBuffer -> Shared memory.
-                const auto& testBuffer = kTestModel.operands[kTestModel.inputIndexes[index]].data;
+                const auto& testBuffer =
+                        kTestModel.main.operands[kTestModel.main.inputIndexes[index]].data;
                ASSERT_GT(testBuffer.size(), 0);
                hidl_memory tmp = nn::allocateSharedMemory(testBuffer.size());
                sp<IMemory> inputMemory = mapMemory(tmp);
@@ -195,26 +196,42 @@ class DeviceMemoryAllocator {
    const TestModel& kTestModel;
 };

-}  // namespace
+Subgraph createSubgraph(const TestSubgraph& testSubgraph, uint32_t* constCopySize,
+                        std::vector<const TestBuffer*>* constCopies, uint32_t* constRefSize,
+                        std::vector<const TestBuffer*>* constReferences) {
+    CHECK(constCopySize != nullptr);
+    CHECK(constCopies != nullptr);
+    CHECK(constRefSize != nullptr);
+    CHECK(constReferences != nullptr);

-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];
+    // Operands.
+    hidl_vec<Operand> operands(testSubgraph.operands.size());
+    for (uint32_t i = 0; i < testSubgraph.operands.size(); i++) {
+        const auto& op = testSubgraph.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();
+            loc = {
+                    .poolIndex = 0,
+                    .offset = *constCopySize,
+                    .length = static_cast<uint32_t>(op.data.size()),
+            };
+            constCopies->push_back(&op.data);
+            *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();
+            loc = {
+                    .poolIndex = 0,
+                    .offset = *constRefSize,
+                    .length = static_cast<uint32_t>(op.data.size()),
+            };
+            constReferences->push_back(&op.data);
+            *constRefSize += op.data.alignedSize();
+        } else if (op.lifetime == TestOperandLifeTime::SUBGRAPH) {
+            loc = {
+                    .poolIndex = 0,
+                    .offset = *op.data.get<uint32_t>(),
+                    .length = 0,
+            };
        }

        V1_2::Operand::ExtraParams extraParams;
@@ -233,25 +250,52 @@ Model createModel(const TestModel& testModel) {
                       .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 {
+    // Operations.
+    hidl_vec<Operation> operations(testSubgraph.operations.size());
+    std::transform(testSubgraph.operations.begin(), testSubgraph.operations.end(),
+                   operations.begin(), [](const TestOperation& op) -> Operation {
                       return {.type = static_cast<OperationType>(op.type),
                               .inputs = op.inputs,
                               .outputs = op.outputs};
                   });

+    return {.operands = std::move(operands),
+            .operations = std::move(operations),
+            .inputIndexes = testSubgraph.inputIndexes,
+            .outputIndexes = testSubgraph.outputIndexes};
+}
+
+void copyTestBuffers(const std::vector<const TestBuffer*>& buffers, uint8_t* output) {
+    uint32_t offset = 0;
+    for (const TestBuffer* buffer : buffers) {
+        const uint8_t* begin = buffer->get<uint8_t>();
+        const uint8_t* end = begin + buffer->size();
+        std::copy(begin, end, output + offset);
+        offset += buffer->alignedSize();
+    }
+}
+
+}  // namespace
+
+Model createModel(const TestModel& testModel) {
+    uint32_t constCopySize = 0;
+    uint32_t constRefSize = 0;
+    std::vector<const TestBuffer*> constCopies;
+    std::vector<const TestBuffer*> constReferences;
+
+    Subgraph mainSubgraph = createSubgraph(testModel.main, &constCopySize, &constCopies,
+                                           &constRefSize, &constReferences);
+    hidl_vec<Subgraph> refSubgraphs(testModel.referenced.size());
+    std::transform(testModel.referenced.begin(), testModel.referenced.end(), refSubgraphs.begin(),
+                   [&constCopySize, &constCopies, &constRefSize,
+                    &constReferences](const TestSubgraph& testSubgraph) {
+                       return createSubgraph(testSubgraph, &constCopySize, &constCopies,
+                                             &constRefSize, &constReferences);
+                   });
+
    // 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);
-        }
-    }
+    copyTestBuffers(constCopies, operandValues.data());

    // Shared memory.
    hidl_vec<hidl_memory> pools = {};
@@ -266,27 +310,18 @@ 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];
-            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);
-            }
-        }
+        copyTestBuffers(constReferences, mappedPtr);
    }

-    return {.main = {.operands = std::move(operands),
-                     .operations = std::move(operations),
-                     .inputIndexes = testModel.inputIndexes,
-                     .outputIndexes = testModel.outputIndexes},
+    return {.main = std::move(mainSubgraph),
+            .referenced = std::move(refSubgraphs),
            .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;
 }

@@ -320,10 +355,10 @@ static std::pair<Request, std::vector<sp<IBuffer>>> createRequest(
    std::vector<uint32_t> tokens;

    // 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};
@@ -350,10 +385,10 @@ static std::pair<Request, std::vector<sp<IBuffer>>> createRequest(
    }

    // 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]];
        if (preferDeviceMemory) {
            SCOPED_TRACE("Output index = " + std::to_string(i));
            auto [buffer, token] = allocator.allocate<IOType::OUTPUT>(i);
@@ -398,9 +433,9 @@ static std::pair<Request, std::vector<sp<IBuffer>>> createRequest(
    CHECK(inputMemory.get() != nullptr);
    uint8_t* inputPtr = static_cast<uint8_t*>(static_cast<void*>(inputMemory->getPointer()));
    CHECK(inputPtr != nullptr);
-    for (uint32_t i = 0; i < testModel.inputIndexes.size(); i++) {
+    for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
        if (!inputs[i].hasNoValue && inputs[i].location.poolIndex == kInputPoolIndex) {
-            const auto& op = testModel.operands[testModel.inputIndexes[i]];
+            const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];
            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);
@@ -443,7 +478,7 @@ static std::vector<TestBuffer> getOutputBuffers(const TestModel& testModel, cons
        if (outputLoc.poolIndex == kOutputPoolIndex) {
            outputBuffers.emplace_back(outputLoc.length, outputPtr + outputLoc.offset);
        } else {
-            const auto& op = testModel.operands[testModel.outputIndexes[i]];
+            const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]];
            if (op.data.size() == 0) {
                outputBuffers.emplace_back();
            } else {
@@ -638,17 +673,17 @@ void EvaluatePreparedModel(const sp<IDevice>& device, const sp<IPreparedModel>&
            // 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;
    }
@@ -656,7 +691,7 @@ void EvaluatePreparedModel(const sp<IDevice>& device, const sp<IPreparedModel>&
    // 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);
    }
@@ -862,7 +897,7 @@ INSTANTIATE_GENERATED_TEST(FencedComputeTest,
                           [](const TestModel& testModel) { return !testModel.expectFailure; });

 INSTANTIATE_GENERATED_TEST(QuantizationCouplingTest, [](const TestModel& testModel) {
-    return testModel.hasQuant8CoupledOperands() && testModel.operations.size() == 1;
+    return testModel.hasQuant8CoupledOperands() && testModel.main.operations.size() == 1;
 });

 }  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
--- a/neuralnetworks/1.3/vts/functional/QualityOfServiceTests.cpp
+++ b/neuralnetworks/1.3/vts/functional/QualityOfServiceTests.cpp
@@ -237,12 +237,13 @@ void runExecutionTest(const sp<IPreparedModel>& preparedModel, const TestModel&

    // 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_TRUE(outputShapes.size() == 0 || outputShapes.size() == testModel.outputIndexes.size());
+    ASSERT_TRUE(outputShapes.size() == 0 ||
+                outputShapes.size() == testModel.main.outputIndexes.size());

    // 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);
    }
--- a/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
+++ b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
@@ -182,6 +182,7 @@ static float getInvalidScale(OperandType type) {
        case OperandType::TENSOR_FLOAT16:
        case OperandType::TENSOR_FLOAT32:
        case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+        case OperandType::SUBGRAPH:
            return 1.0f;
        case OperandType::TENSOR_INT32:
            return -1.0f;
@@ -220,6 +221,7 @@ static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
        case OperandType::TENSOR_FLOAT32:
        case OperandType::TENSOR_INT32:
        case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+        case OperandType::SUBGRAPH:
            return {1};
        case OperandType::TENSOR_QUANT8_ASYMM:
            return {-1, 256};