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Commit 650fd186 authored by Michael K. Sanders's avatar Michael K. Sanders Committed by Lev Proleev
Browse files

Adds float16 support to generated tests. 

Uses the _Float16 type (ISO/IEC TS 18661-3:2015) for storage.

Test: VtsHalNeuralnetworksV1_2TargetTest --hal_service_instance=android.hardware.neuralnetworks@1.2::IDevice/sample-all
Bug: 113563458
Change-Id: I1779b828d397b5354dc854c68d21c159cd5b582c
Merged-In: I1779b828d397b5354dc854c68d21c159cd5b582c
(cherry picked from commit efa4c814)
parent a8efddc5

neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp

+20 −19
Original line number Diff line number Diff line
@@ -45,6 +45,7 @@ using ::test_helper::for_each; 
using ::test_helper::Int32Operands;

using ::test_helper::MixedTyped;

using ::test_helper::MixedTypedExample;

using ::test_helper::MixedTypedIndex;

using ::test_helper::Quant8Operands;

using ::test_helper::resize_accordingly;
@@ -63,14 +64,16 @@ void copy_back(MixedTyped* dst, const std::vector<RequestArgument>& ra, char* sr 
    copy_back_<int32_t>(dst, ra, src);

    copy_back_<uint8_t>(dst, ra, src);

    copy_back_<int16_t>(dst, ra, src);

    static_assert(4 == std::tuple_size<MixedTyped>::value,

    copy_back_<_Float16>(dst, ra, src);

    static_assert(5 == std::tuple_size<MixedTyped>::value,

                  "Number of types in MixedTyped changed, but copy_back function wasn't updated");

}



// 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 = 1e-5f,

                           const std::vector<MixedTypedExample>& examples,

                           bool hasRelaxedFloat32Model = false, float fpAtol = 1e-5f,

                           float fpRtol = 1e-5f) {

    const uint32_t INPUT = 0;

    const uint32_t OUTPUT = 1;
@@ -78,13 +81,20 @@ void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool 
    int example_no = 1;

    for (auto& example : examples) {

        SCOPED_TRACE(example_no++);



        const MixedTyped& inputs = example.operands.first;

        const MixedTyped& golden = example.operands.second;



        const bool hasFloat16Inputs = !std::get<MixedTypedIndex<_Float16>::index>(inputs).empty();

        if (hasRelaxedFloat32Model || hasFloat16Inputs) {

            // TODO: Adjust the error limit based on testing.

            // If in relaxed mode, set the absolute tolerance to be 5ULP of FP16.

            fpAtol = 5.0f * 0.0009765625f;

            // Set the relative tolerance to be 5ULP of the corresponding FP precision.

            fpRtol = 5.0f * 0.0009765625f;

        }



        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) {
@@ -228,7 +238,8 @@ void Execute(const sp<V1_0::IDevice>& device, std::function<V1_0::Model(void)> c 
    ASSERT_NE(nullptr, preparedModel.get());



    float fpAtol = 1e-5f, fpRtol = 5.0f * 1.1920928955078125e-7f;

    EvaluatePreparedModel(preparedModel, is_ignored, examples, fpAtol, fpRtol);

    EvaluatePreparedModel(preparedModel, is_ignored, examples,

                          /*hasRelaxedFloat32Model=*/false, fpAtol, fpRtol);

}



void Execute(const sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> create_model,
@@ -272,13 +283,8 @@ void Execute(const sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> c 
    EXPECT_EQ(ErrorStatus::NONE, prepareReturnStatus);

    ASSERT_NE(nullptr, preparedModel.get());



    // TODO: Adjust the error limit based on testing.

    // If in relaxed mode, set the absolute tolerance to be 5ULP of FP16.

    float fpAtol = !model.relaxComputationFloat32toFloat16 ? 1e-5f : 5.0f * 0.0009765625f;

    // Set the relative tolerance to be 5ULP of the corresponding FP precision.

    float fpRtol = !model.relaxComputationFloat32toFloat16 ? 5.0f * 1.1920928955078125e-7f

                                                           : 5.0f * 0.0009765625f;

    EvaluatePreparedModel(preparedModel, is_ignored, examples, fpAtol, fpRtol);

    EvaluatePreparedModel(preparedModel, is_ignored, examples,

                          model.relaxComputationFloat32toFloat16);

}



// TODO: Reduce code duplication.
@@ -323,13 +329,8 @@ void Execute(const sp<V1_2::IDevice>& device, std::function<V1_2::Model(void)> c 
    EXPECT_EQ(ErrorStatus::NONE, prepareReturnStatus);

    ASSERT_NE(nullptr, preparedModel.get());



    // TODO: Adjust the error limit based on testing.

    // If in relaxed mode, set the absolute tolerance to be 5ULP of FP16.

    float fpAtol = !model.relaxComputationFloat32toFloat16 ? 1e-5f : 5.0f * 0.0009765625f;

    // Set the relative tolerance to be 5ULP of the corresponding FP precision.

    float fpRtol = !model.relaxComputationFloat32toFloat16 ? 5.0f * 1.1920928955078125e-7f

                                                           : 5.0f * 0.0009765625f;

    EvaluatePreparedModel(preparedModel, is_ignored, examples, fpAtol, fpRtol);

    EvaluatePreparedModel(preparedModel, is_ignored, examples,

                          model.relaxComputationFloat32toFloat16);

}



}  // namespace generated_tests