tests/validation/NEON/ActivationLayer.cpp

/*
 * Copyright (c) 2017-2023 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
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 */
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/Traits.h"
#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
#include "arm_compute/runtime/RuntimeContext.h"
#include "arm_compute/runtime/Tensor.h"
#include "arm_compute/runtime/TensorAllocator.h"
#include "src/common/cpuinfo/CpuIsaInfo.h"
#include "src/cpu/kernels/CpuActivationKernel.h"
#include "tests/NEON/Accessor.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/ActivationFunctionsDataset.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
#include "tests/validation/Validation.h"
#include "tests/validation/fixtures/ActivationLayerFixture.h"

#include "arm_compute/Acl.hpp"
#include "support/AclRequires.h"

namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
RelativeTolerance<float> tolerance_float_sqrt(0.0001f);

/** Define relative tolerance of the activation layer.
 *
 * @param[in] data_type  The data type used.
 * @param[in] activation The activation function used.
 *
 * @return Relative tolerance depending on the activation function.
 */
RelativeTolerance<float> relative_tolerance(DataType data_type, ActivationLayerInfo::ActivationFunction activation)
{
    switch(activation)
    {
        case ActivationLayerInfo::ActivationFunction::LOGISTIC:
        case ActivationLayerInfo::ActivationFunction::ELU:
        case ActivationLayerInfo::ActivationFunction::SQRT:
        case ActivationLayerInfo::ActivationFunction::TANH:
        case ActivationLayerInfo::ActivationFunction::HARD_SWISH:
        case ActivationLayerInfo::ActivationFunction::SWISH:
        case ActivationLayerInfo::ActivationFunction::GELU:
            switch(data_type)
            {
                case DataType::F16:
#if defined(ENABLE_SVE)
                    return RelativeTolerance<float>(0.25f);
#else  // !defined(ENABLE_SVE)
                    return RelativeTolerance<float>(0.1f);
#endif // defined(ENABLE_SVE)
                default:
                    return RelativeTolerance<float>(0.05f);
            }
        case ActivationLayerInfo::ActivationFunction::SOFT_RELU:
            switch(data_type)
            {
                case DataType::F16:
#if defined(ENABLE_SVE)
                    return RelativeTolerance<float>(0.9f);
#else  // !defined(ENABLE_SVE)
                    return RelativeTolerance<float>(0.01f);
#endif // defined(ENABLE_SVE)
                default:
                    return RelativeTolerance<float>(0.00001f);
            }
        default:
            return RelativeTolerance<float>(0.f);
    }
}

/** Define absolute tolerance of the activation layer.
 *
 * @param[in] data_type  The data type used.
 * @param[in] activation The activation function used.
 *
 * @return Absolute tolerance depending on the activation function.
 */
AbsoluteTolerance<float> absolute_tolerance(DataType data_type, ActivationLayerInfo::ActivationFunction activation)
{
    switch(activation)
    {
        case ActivationLayerInfo::ActivationFunction::LOGISTIC:
        case ActivationLayerInfo::ActivationFunction::SQRT:
        case ActivationLayerInfo::ActivationFunction::TANH:
        case ActivationLayerInfo::ActivationFunction::SWISH:
        case ActivationLayerInfo::ActivationFunction::HARD_SWISH:
            switch(data_type)
            {
                case DataType::F16:
#if defined(ENABLE_SVE)
                    return AbsoluteTolerance<float>(0.25f);
#else  // !defined(ENABLE_SVE)
                    return AbsoluteTolerance<float>(0.01f);
#endif // defined(ENABLE_SVE)
                default:
                    return AbsoluteTolerance<float>(0.00001f);
            }
        case ActivationLayerInfo::ActivationFunction::SOFT_RELU:
            switch(data_type)
            {
                case DataType::F16:
#if defined(ENABLE_SVE)
                    return AbsoluteTolerance<float>(0.9f);
#else  // !defined(ENABLE_SVE)
                    return AbsoluteTolerance<float>(0.01f);
#endif // defined(ENABLE_SVE)
                default:
                    return AbsoluteTolerance<float>(0.00001f);
            }
        default:
            return AbsoluteTolerance<float>(0.f);
    }
}

/** Define absolute tolerance of the activation layer for qasymm8.
 *
 * @param[in] activation The activation function used.
 *
 * @return Absolute tolerance depending on the activation function.
 */
AbsoluteTolerance<uint8_t> tolerance_qasymm8(ActivationLayerInfo::ActivationFunction activation)
{
    switch(activation)
    {
        case ActivationLayerInfo::ActivationFunction::LOGISTIC:
        case ActivationLayerInfo::ActivationFunction::SQRT:
        case ActivationLayerInfo::ActivationFunction::TANH:
        case ActivationLayerInfo::ActivationFunction::HARD_SWISH:
        case ActivationLayerInfo::ActivationFunction::SOFT_RELU:
        case ActivationLayerInfo::ActivationFunction::LEAKY_RELU:
            return AbsoluteTolerance<uint8_t>(1);
        default:
            return AbsoluteTolerance<uint8_t>(0);
    }
}

constexpr AbsoluteTolerance<int16_t> tolerance_qsymm16(1);

/** CNN data types */
const auto CNNDataTypes = framework::dataset::make("DataType",
{
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
    DataType::F16,
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
    DataType::F32,
});

const auto NeonActivationFunctionsDataset = concat(datasets::ActivationFunctions(),
                                                   framework::dataset::make("ActivationFunction", { ActivationLayerInfo::ActivationFunction::HARD_SWISH, ActivationLayerInfo::ActivationFunction::SWISH }));

/** Input data sets. */
const auto ActivationDataset = combine(combine(framework::dataset::make("InPlace", { false, true }), NeonActivationFunctionsDataset), framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));

template <typename T, ARM_COMPUTE_REQUIRES_TA(arm_compute::utils::traits::is_floating_point<T>::value)>
void test_float_sqrt_boundary_value()
{
    constexpr auto vector_size = uint32_t{ 16 };

    auto data_type = DataType::F32;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
    data_type = std::is_same<T, half>::value ? DataType::F16 : data_type;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

    const auto boundary_value_vector = std::vector<T>
    {
        std::numeric_limits<T>::min(),
        T(0),
        std::numeric_limits<T>::epsilon(),
        std::numeric_limits<T>::max(),
    };

    // the following size ensures that the whole logic (vector + left-over) to be tested
    // using all boundary values iff boundary_value_vecotr.size() is smaller than vector_size.
    auto shape = TensorShape{ vector_size + boundary_value_vector.size() };
    auto info  = ActivationLayerInfo{ ActivationLayerInfo::ActivationFunction::SQRT };
    auto src   = create_tensor<Tensor>(shape, data_type);

    auto act = NEActivationLayer{};
    act.configure(&src, nullptr, info);
    src.allocator()->allocate();
    library->fill_static_values(Accessor(src), boundary_value_vector);
    act.run();

    auto reference_src = SimpleTensor<T> { shape, data_type };
    library->fill_static_values(reference_src, boundary_value_vector);
    auto reference_dst = reference::activation_layer<T>(reference_src, info);

    validate(Accessor(src), reference_dst, tolerance_float_sqrt);
}
} // namespace

TEST_SUITE(NEON)
TEST_SUITE(ActivationLayer)

/** Test case for memory injection in @ref cpu::CpuWinogradConv2d.
 *
 * Configure the operator once and inject memory at run-time in multiple executions.
 *
 * Checks performed in order:
 * - Both runs compute the same output
 */
TEST_CASE(ActivationAPI, framework::DatasetMode::ALL)
{
    acl::StatusCode err = acl::StatusCode::Success;

    // Create context & Queue
    acl::Context ctx(acl::Target::Cpu, &err);
    ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success);

    acl::Queue queue(ctx, &err);
    ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success);

    // Create activation operator
    acl::TensorDescriptor src_info({ 2, 3 }, acl::DataType::Float32);
    acl::TensorDescriptor dst_info({ 2, 3 }, acl::DataType::Float32);
    acl::ActivationDesc   desc{ AclRelu, 6.f, 0.f, false };

    acl::Activation act(ctx, src_info, dst_info, desc, &err);
    ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success);

    // Create tensors and feed
    acl::Tensor src(ctx, src_info, &err);
    ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success);
    acl::Tensor dst(ctx, dst_info, &err);
    ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success);

    acl::TensorPack pack(ctx);
    err = pack.add(src, ACL_SRC);
    err = pack.add(dst, ACL_DST);
    ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success);

    // Execute operator
    err = act.run(queue, pack);
    ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success);
}

// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
    framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),     // Mismatching data types
                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),     // Mismatching shapes
                                          }),
    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                          })),
    framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
                                                 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
                                                 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
                                               })),
    framework::dataset::make("Expected", { false, true, false})),
    input_info, output_info, act_info, expected)
{
    bool is_valid = bool(NEActivationLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), act_info));
    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}

DATA_TEST_CASE(KernelSelection, framework::DatasetMode::ALL, concat(concat(
               combine(framework::dataset::make("CpuExt", std::string("NEON")),
                       framework::dataset::make("DataType", { DataType::F32,
                                                              DataType::F16,
                                                              DataType::QASYMM8,
                                                              DataType::QASYMM8_SIGNED,
                                                              DataType::QSYMM16
                                                            })),
                combine(framework::dataset::make("CpuExt", std::string("SVE")),
                        framework::dataset::make("DataType", { DataType::F32,
                                                               DataType::F16,
                                                             }))),
                combine(framework::dataset::make("CpuExt", std::string("SVE2")),
                        framework::dataset::make("DataType", { DataType::QASYMM8,
                                                               DataType::QASYMM8_SIGNED,
                                                               DataType::QSYMM16
                                                             }))),
               cpu_ext, data_type)
{
    using namespace cpu::kernels;

    cpuinfo::CpuIsaInfo cpu_isa{};
    cpu_isa.neon = (cpu_ext == "NEON");
    cpu_isa.sve  = (cpu_ext == "SVE");
    cpu_isa.sve2 = (cpu_ext == "SVE2");
    cpu_isa.fp16 = (data_type == DataType::F16);

    const auto *selected_impl = CpuActivationKernel::get_implementation(ActivationDataTypeISASelectorData{data_type, CPUModel::GENERIC, cpu_isa,ActivationLayerInfo::ActivationFunction::BOUNDED_RELU}, cpu::KernelSelectionType::Preferred);

    ARM_COMPUTE_ERROR_ON_NULLPTR(selected_impl);

    std::string expected = lower_string(cpu_ext) + "_" + cpu_impl_dt(data_type) + "_activation";
    std::string actual   = selected_impl->name;

    ARM_COMPUTE_EXPECT_EQUAL(expected, actual, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*

template <typename T>
using NEActivationLayerFixture = ActivationValidationFixture<Tensor, Accessor, NEActivationLayer, T>;

TEST_SUITE(Float)
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)
TEST_CASE(SqrtBoundaryValue, framework::DatasetMode::ALL)
{
    test_float_sqrt_boundary_value<half>();
}
FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(datasets::SmallShapes(), ActivationDataset),
                                                                                                      framework::dataset::make("DataType",
                                                                                                              DataType::F16)))
{
    // Validate output
    validate(Accessor(_target), _reference, relative_tolerance(_data_type, _function), 0.f, absolute_tolerance(_data_type, _function));
}
TEST_SUITE_END() // FP16
#endif           /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

TEST_SUITE(FP32)
TEST_CASE(SqrtBoundaryValue, framework::DatasetMode::ALL)
{
    test_float_sqrt_boundary_value<float>();
}
FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(datasets::SmallShapes(), ActivationDataset), framework::dataset::make("DataType",
                                                                                                       DataType::F32)))

{
    // Validate output
    validate(Accessor(_target), _reference, relative_tolerance(_data_type, _function), 0.f, absolute_tolerance(_data_type, _function));
}
TEST_SUITE_END() // FP32
TEST_SUITE_END() // Float

template <typename T>
using NEActivationLayerQuantizedFixture = ActivationValidationQuantizedFixture<Tensor, Accessor, NEActivationLayer, T>;

/** Input data sets. */
const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationFunction",
{
    ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU,
    ActivationLayerInfo::ActivationFunction::RELU,
    ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
    ActivationLayerInfo::ActivationFunction::LOGISTIC,
    ActivationLayerInfo::ActivationFunction::TANH,
    ActivationLayerInfo::ActivationFunction::LEAKY_RELU,
});

const auto QuantizedActivationDataset = combine(combine(framework::dataset::make("InPlace", { false }),
                                                        concat(QuantizedActivationFunctionsDataset, framework::dataset::make("ActivationFunction", ActivationLayerInfo::ActivationFunction::HARD_SWISH))),
                                                framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));

TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallShapes(), QuantizedActivationDataset),
                                                                                                                  framework::dataset::make("DataType",
                                                                                                                          DataType::QASYMM8)),
                                                                                                                  framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.1f, 128.0f) })))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_qasymm8(_function));
}
TEST_SUITE_END() // QASYMM8

TEST_SUITE(QASYMM8_SIGNED)
FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallShapes(), QuantizedActivationDataset),
                                                                                                                 framework::dataset::make("DataType",
                                                                                                                         DataType::QASYMM8_SIGNED)),
                                                                                                                 framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10.0f) })))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_qasymm8(_function));
}
TEST_SUITE_END() // QASYMM8_SIGNED

/** Input data sets. */
const auto Int16QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationFunction",
{
    ActivationLayerInfo::ActivationFunction::LOGISTIC,
    ActivationLayerInfo::ActivationFunction::TANH,
    ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU,
});
const auto Int16QuantizedActivationDataset = combine(combine(framework::dataset::make("InPlace", { false }), Int16QuantizedActivationFunctionsDataset),
                                                     framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));

TEST_SUITE(QSYMM16)
FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerQuantizedFixture<int16_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallShapes(), Int16QuantizedActivationDataset),
                                                                                                                  framework::dataset::make("DataType",
                                                                                                                          DataType::QSYMM16)),
                                                                                                                  framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 32768.f, 0.f) })))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_qsymm16);
}
TEST_SUITE_END() // QSYMM16
TEST_SUITE_END() // Quantized

TEST_SUITE_END() // ActivationLayer
TEST_SUITE_END() // Neon
} // namespace validation
} // namespace test
} // namespace arm_compute