/*
 * Copyright (c) 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.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

#include "arm_compute/runtime/CL/CLTensor.h"
#include "src/gpu/cl/kernels/ClMatMulNativeKernel.h"
#include "tests/datasets/LargeMatMulDataset.h"
#include "tests/datasets/SmallMatMulDataset.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
#include "tests/validation/Validation.h"
#include "tests/validation/fixtures/MatMulKernelFixture.h"
#include "tests/validation/reference/Permute.h"

#include <tuple>

namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
constexpr float          abs_tolerance_f32(
    0.0001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for floating point data types in case using relative tolerance fails because of small values */
constexpr float abs_tolerance_f16(
    0.001f);                                                   /**< Absolute tolerance value for comparing reference's output against implementation's output for fp16  data types in case using relative tolerance fails because of small values */
RelativeTolerance<half_float::half> tolerance_f16(half(0.01)); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
} // namespace

/** M0 values to test --precommit*/
const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 });

/** N0 values to test --precommit*/
const auto n0_values_precommit = framework::dataset::make("N0", { 2, 4 });

/** K0 values to test --precommit*/
const auto k0_values_precommit = framework::dataset::make("K0", { 2, 3 });

/** M0 values to test --nightly*/
const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 });
const auto m0_values_nightly_lhs_t  = framework::dataset::make("M0", { 1, 2, 3, 4, 8 });

/** N0 values to test --nightly*/
const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 });
const auto n0_values_nightly_rhs_t  = framework::dataset::make("N0", { 1, 2, 3, 4, 8 });

/** K0 values to test --nightly*/
const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 8, 16 });
const auto k0_values_nightly_rhs_t         = framework::dataset::make("K0", { 1, 2, 3, 4, 8 });
const auto k0_values_nightly_lhs_t_rhs_nt  = framework::dataset::make("K0", { 1, 2, 3, 4, 5, 6, 7, 8 });

template <typename T>
using CLMatMulKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeKernel>;

template <typename T>
using CLMatMulKernelBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulNativeKernel>;

TEST_SUITE(CL)
TEST_SUITE(MatMulKernel)
TEST_SUITE(Validate)

TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
{
    using MatMulConfigurationPair = std::pair<MatMulKernelInfo, bool>;

    const std::vector<MatMulConfigurationPair> supported_block_sizes =
    {
        // MatMulKernelInfo(adj_lhs, adj_rhs, M0, N0, K0, export_rhs_to_cl_image = false)
        // Lhs not-transposed, Rhs-not-transposed
        { MatMulKernelInfo(false, false, 0, 1, 1), false },  // M0 should be > 0
        { MatMulKernelInfo(false, false, 3, 5, 1), false },  // N0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(false, false, 3, 6, 1), false },  // N0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(false, false, 3, 3, 17), false }, // K0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(false, false, 3, 3, 7), false },  // K0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(false, false, 9, 1, 2), true },
        { MatMulKernelInfo(false, false, 3, 16, 3), true },
        { MatMulKernelInfo(false, false, 7, 3, 4), true },
        { MatMulKernelInfo(false, false, 7, 3, 4, true), false },  // N0 not in {4, 8, 16}
        { MatMulKernelInfo(false, false, 7, 1, 4, true), false },  // N0 not in {4, 8, 16}
        { MatMulKernelInfo(false, false, 7, 12, 4, true), false }, // N0 not in {4, 8, 16}
        { MatMulKernelInfo(false, false, 7, 4, 4, true), true },
        { MatMulKernelInfo(false, false, 7, 8, 4, true), true },
        { MatMulKernelInfo(false, false, 7, 16, 4, true), true },

        // Lhs not-transposed, Rhs transposed
        { MatMulKernelInfo(false, true, 0, 1, 1), false },  // M0 should be > 0
        { MatMulKernelInfo(false, true, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(false, true, 3, 7, 1), false },  // N0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(false, true, 3, 3, 12), false }, // K0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(false, true, 3, 3, 6), false },  // K0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(false, true, 5, 1, 2), true },
        { MatMulKernelInfo(false, true, 3, 3, 3), true },
        { MatMulKernelInfo(false, true, 2, 4, 8), true },
        { MatMulKernelInfo(false, true, 2, 4, 5, true), false }, // K0 not in {4, 8, 16}
        { MatMulKernelInfo(false, true, 2, 4, 9, true), false }, // K0 not in {4, 8, 16}
        { MatMulKernelInfo(false, true, 2, 4, 3, true), false }, // K0 not in {4, 8, 16}
        { MatMulKernelInfo(false, true, 2, 4, 4, true), true },
        { MatMulKernelInfo(false, true, 2, 4, 8, true), true },
        { MatMulKernelInfo(false, true, 2, 8, 16, true), true },

        // Lhs transposed, Rhs-not-transposed
        { MatMulKernelInfo(true, false, 1, 1, 0), false },  // K0 should be > 0
        { MatMulKernelInfo(true, false, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, false, 3, 7, 1), false },  // N0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, false, 6, 3, 12), false }, // M0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, false, 5, 3, 6), false },  // M0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, false, 4, 1, 22), true },
        { MatMulKernelInfo(true, false, 3, 3, 3), true },
        { MatMulKernelInfo(true, false, 2, 4, 8), true },
        { MatMulKernelInfo(true, false, 2, 3, 8, true), false }, // N0 not in {4, 8, 16}
        { MatMulKernelInfo(true, false, 2, 7, 8, true), false }, // N0 not in {4, 8, 16}
        { MatMulKernelInfo(true, false, 2, 5, 8, true), false }, // N0 not in {4, 8, 16}
        { MatMulKernelInfo(true, false, 2, 4, 8, true), true },
        { MatMulKernelInfo(true, false, 2, 8, 8, true), true },
        { MatMulKernelInfo(true, false, 2, 16, 8, true), true },

        // Lhs transposed, Rhs-transposed
        { MatMulKernelInfo(true, true, 2, 1, 5), false },  // K0 should in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, true, 1, 8, 7), false },  // K0 should in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, true, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, true, 3, 7, 1), false },  // N0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, true, 6, 3, 12), false }, // M0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, true, 5, 3, 6), false },  // M0 not in {1, 2, 3, 4, 8, 16}
        { MatMulKernelInfo(true, true, 4, 8, 16), true },
        { MatMulKernelInfo(true, true, 3, 3, 4), true },
        { MatMulKernelInfo(true, true, 16, 4, 8), true },
        { MatMulKernelInfo(true, true, 2, 2, 1, true), false }, // K0 not in {4, 8, 16}
        { MatMulKernelInfo(true, true, 2, 2, 5, true), false }, // K0 not in {4, 8, 16}
        { MatMulKernelInfo(true, true, 2, 4, 7, true), false }, // K0 not in {4, 8, 16}
        { MatMulKernelInfo(true, true, 2, 4, 4, true), true },
        { MatMulKernelInfo(true, true, 2, 8, 8, true), true },
        { MatMulKernelInfo(true, true, 2, 8, 16, true), true },
    };

    // Set big enough shapes so that block sizes are not truncated. Also, set all dimensions equal
    // so that it doesn't fail for different NT/T configurations. We aim to test the block sizes here,
    // not the shapes themselves.
    const TensorInfo lhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32);
    const TensorInfo rhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32);

    const bool export_to_cl_image_supported = image2d_from_buffer_supported(CLKernelLibrary::get().get_device());
    for(auto &pair : supported_block_sizes)
    {
        TensorInfo output_info;
        Status     status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first);

        if(!pair.first.export_rhs_to_cl_image || export_to_cl_image_supported)
        {
            ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
        }
    }
}

TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL)
{
    // We skip this test if the hardware does not support exporting to CL Image
    if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device()))
    {
        constexpr size_t pixel_size  = 4;
        const size_t     max_image_w = pixel_size * CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>();
        const size_t     max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>();

        using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool, bool, bool>;
        const std::vector<ShapeConfigurationTuple> shape_configurations =
        {
            // lhs_shape, rhs_shape, adj_lhs, adj_rhs, expected
            // Lhs t/Nt, Rhs Nt
            // Transposition of Lhs doesn't add any value to the tests, therefore always assumed false below
            { TensorShape(5U, 1U), TensorShape(3U, 5U), false, false, false },  // N should be multiple of 4
            { TensorShape(5U, 1U), TensorShape(14U, 5U), false, false, false }, // N should be multiple of 4
            { TensorShape(5U, 1U), TensorShape(12U, 5U), false, false, true },
            { TensorShape(5U, 1U), TensorShape(8U, 5U), false, false, true },
            { TensorShape(5U, 1U), TensorShape(4U, 5U), false, false, true },
            { TensorShape(max_image_h + 1, 1U), TensorShape(4U, max_image_h + 1), false, false, false }, // Cannot fit into CL Image memory's height
            { TensorShape(5U, 1U), TensorShape(max_image_w + 1, 5U), false, false, false },              // Cannot fit into CL Image memory's width
            { TensorShape(max_image_h, 1U), TensorShape(4U, max_image_h), false, false, true },          // Barely fits into CL Image memory's height
            { TensorShape(5U, 1U), TensorShape(max_image_w, 5U), false, false, true },                   // Barely fits into CL Image memory's width

            // Lhs Nt/T , Rhs T
            { TensorShape(5U, 1U), TensorShape(5U, 3U), false, true, false },  // K should be multiple of 4
            { TensorShape(5U, 1U), TensorShape(5U, 14U), false, true, false }, // K should be multiple of 4
            { TensorShape(4U, 1U), TensorShape(4U, 10U), false, true, true },
            { TensorShape(8U, 1U), TensorShape(8U, 9U), false, true, true },
            { TensorShape(12U, 1U), TensorShape(12U, 6U), false, true, true },
        };

        for(auto &tuple : shape_configurations)
        {
            TensorShape lhs_shape = std::get<0>(tuple);
            TensorShape rhs_shape = std::get<1>(tuple);

            const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32);
            const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32);

            const bool adj_lhs = std::get<2>(tuple);
            const bool adj_rhs = std::get<3>(tuple);

            // We choose M0, N0, K0 equal to 4 so that they're always valid for CLImage in any combination
            const MatMulKernelInfo matmul_kernel_info
            {
                adj_lhs, adj_rhs, 4, 4, 4, true /* export_rhs_to_cl_image */
            };

            TensorInfo output_info;
            Status     status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, matmul_kernel_info);

            const bool expected = std::get<4>(tuple);
            ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
        }
    }
}

TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
{
    // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
    using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>;
    const std::vector<ShapeConfigurationTuple> shape_configurations =
    {
        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U), true },
        { TensorShape(10U, 12U), TensorShape(3U, 10U), TensorShape(3U), true },
        { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true },
        { TensorShape(8U, 4U), TensorShape(2U, 5U), TensorShape(2U), false }, // Mismatch in the K dimension
        { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension
        { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), true },
        { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // no batch broadcasting
        { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // mismatch in batch dimension
        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(1U), false },                                 // Unsupported bias broadcasting.
        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U, 3U), false },                             // 2D bias is unsupported.
        { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(6U), false },                                 // bias first dimension != dst first dimension
    };

    for(auto &tuple : shape_configurations)
    {
        const bool expected = std::get<3>(tuple);

        for(bool adj_lhs :
            {
                false, true
            })
        {
            for(bool adj_rhs :
                {
                    false, true
                })
            {
                TensorShape lhs_shape = std::get<0>(tuple);
                TensorShape rhs_shape = std::get<1>(tuple);
                TensorShape bia_shape = std::get<2>(tuple);

                if(adj_lhs)
                {
                    permute(lhs_shape, PermutationVector(1U, 0U));
                }

                if(adj_rhs)
                {
                    permute(rhs_shape, PermutationVector(1U, 0U));
                }

                const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32);
                const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32);
                const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::F32);
                TensorInfo       output_info;

                MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };

                Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
                ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
            }
        }
    }
}

TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
{
    // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
    using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>;
    const std::vector<DataTypeConfigurationTuple> data_type_configurations =
    {
        { DataType::F32, DataType::F32, DataType::F32, true },
        { DataType::F16, DataType::F16, DataType::F16, true },
        { DataType::F16, DataType::F32, DataType::F32, false },                                              // no mixed precision
        { DataType::F64, DataType::F64, DataType::F64, false },                                              // no double precision
        { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false },                                  // no quantized types
        { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false },             // no quantized types
        { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types
        { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false },                               // no quantized types
        { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false },                                  // no quantized types
        { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false },                                     // no quantized types
        { DataType::S64, DataType::S64, DataType::S64, false },                                              // no integral types
        { DataType::S32, DataType::S32, DataType::S32, false },                                              // no integral types
        { DataType::S16, DataType::S16, DataType::S16, false },                                              // no integral types
        { DataType::S8, DataType::S8, DataType::S8, false },                                                 // no integral types
        { DataType::U64, DataType::U64, DataType::U64, false },                                              // no integral types
        { DataType::U32, DataType::U32, DataType::U32, false },                                              // no integral types
        { DataType::U16, DataType::U16, DataType::U16, false },                                              // no integral types
        { DataType::U8, DataType::U8, DataType::U8, false },                                                 // no integral types
    };

    const TensorShape      shape = TensorShape(10U, 10U);
    const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false };
    for(auto &tuple : data_type_configurations)
    {
        const bool expected = std::get<3>(tuple);

        const TensorInfo lhs_info(shape, 1, std::get<0>(tuple));
        const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
        TensorInfo       output_info(shape, 1, std::get<2>(tuple));

        Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, matmul_kernel_info);
        ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
    }
}

TEST_SUITE_END() // Validate

TEST_SUITE(Float)
TEST_SUITE(FP32)
TEST_SUITE(Buffer)
FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(),
                                                                                                                   framework::dataset::make("TransposeA", { false, true })),
                                                                                                                   framework::dataset::make("TransposeB", { false, true })),
                                                                                                                   m0_values_precommit),
                                                                                                                   n0_values_precommit),
                                                                                                                   k0_values_precommit),
                                                                                                           framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                   framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
                                                                                                                    framework::dataset::make("TransposeA", { false, true })),
                                                                                                                    framework::dataset::make("TransposeB", { false, true })),
                                                                                                                    m0_values_precommit),
                                                                                                                    n0_values_precommit),
                                                                                                                    k0_values_precommit),
                                                                                                            framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                    framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulKernelBiasFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
                                                                                                                   framework::dataset::make("TransposeA", { false, true })),
                                                                                                                   framework::dataset::make("TransposeB", { false, true })),
                                                                                                                   m0_values_precommit),
                                                                                                                   n0_values_precommit),
                                                                                                                   k0_values_precommit),
                                                                                                                   framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                           framework::dataset::make("DataType", DataType::F32)))

{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                                                                   framework::dataset::make("TransposeA", { false })),
                                                                                                                   framework::dataset::make("TransposeB", { false })),
                                                                                                                   m0_values_nightly_lhs_nt),
                                                                                                                   n0_values_nightly_rhs_nt),
                                                                                                                   k0_values_nightly_lhs_nt_rhs_nt),
                                                                                                                   framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                                   framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                                                                     framework::dataset::make("TransposeA", { false })),
                                                                                                                     framework::dataset::make("TransposeB", { true })),
                                                                                                                     m0_values_nightly_lhs_nt),
                                                                                                                     n0_values_nightly_rhs_t),
                                                                                                                     k0_values_nightly_rhs_t),
                                                                                                                     framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                                     framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                                                                     framework::dataset::make("TransposeA", { true })),
                                                                                                                     framework::dataset::make("TransposeB", { false })),
                                                                                                                     m0_values_nightly_lhs_t),
                                                                                                                     n0_values_nightly_rhs_nt),
                                                                                                                     k0_values_nightly_lhs_t_rhs_nt),
                                                                                                                     framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                                     framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY,
                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                               framework::dataset::make("TransposeA", { true })),
                                                                       framework::dataset::make("TransposeB", { true })),
                                                               m0_values_nightly_lhs_t),
                                                       n0_values_nightly_rhs_t),
                                               k0_values_nightly_rhs_t),
                                       framework::dataset::make("ExportRhsToCLImage", { false })),
                               framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
// Running High Dimensional test is enough for FP32, because we're stressing the number of dimensions, not data type or M0/N0/K0
// It's a good idea to test for each Lhs/Rhs T/NT combinations because they're different CL kernels
FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(),
                                                                                                                      framework::dataset::make("TransposeA", { false, true })),
                                                                                                                      framework::dataset::make("TransposeB", { false, true })),
                                                                                                                      framework::dataset::make("M0", { 2 })),
                                                                                                                      framework::dataset::make("N0", { 2 })),
                                                                                                                      framework::dataset::make("K0", { 2 })),
                                                                                                                      framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                              framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
TEST_SUITE_END() // Buffer

TEST_SUITE(ExportRhsToCLImage)
FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL,
                       combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(),
                                                                               framework::dataset::make("TransposeA", { true, false })),
                                                                       framework::dataset::make("TransposeB", { false })),
                                                               framework::dataset::make("M0", { 2 })),
                                                       framework::dataset::make("N0", { 4, 8, 16 })),
                                               framework::dataset::make("K0", { 2, 4 })),
                                       framework::dataset::make("ExportRhsToCLImage", { true })),
                               framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    if(_device_supports_export_to_cl_image)
    {
        validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
    }
}
FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY,
                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(),
                                                                               framework::dataset::make("TransposeA", { true, false })),
                                                                       framework::dataset::make("TransposeB", { false })),
                                                               framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
                                                       framework::dataset::make("N0", { 4, 8, 16 })),
                                               framework::dataset::make("K0", { 1, 2, 3, 4 })),
                                       framework::dataset::make("ExportRhsToCLImage", { true })),
                               framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    if(_device_supports_export_to_cl_image)
    {
        validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
    }
}
FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL,
                       combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsT(),
                                                                               framework::dataset::make("TransposeA", { true, false })),
                                                                       framework::dataset::make("TransposeB", { true })),
                                                               framework::dataset::make("M0", { 2 })),
                                                       framework::dataset::make("N0", { 2, 4 })),
                                               framework::dataset::make("K0", { 4, 8, 16 })),
                                       framework::dataset::make("ExportRhsToCLImage", { true })),
                               framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    if(_device_supports_export_to_cl_image)
    {
        validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
    }
}
FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY,
                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsT(),
                                                                               framework::dataset::make("TransposeA", { true, false })),
                                                                       framework::dataset::make("TransposeB", { true })),
                                                               framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
                                                       framework::dataset::make("N0", { 1, 2, 3, 4 })),
                                               framework::dataset::make("K0", { 4, 8, 16 })),
                                       framework::dataset::make("ExportRhsToCLImage", { true })),
                               framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    if(_device_supports_export_to_cl_image)
    {
        validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
    }
}
TEST_SUITE_END() // ExportRhsToCLImage
TEST_SUITE_END() // FP32

TEST_SUITE(FP16)
TEST_SUITE(Buffer)
FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
                                                                                                                   framework::dataset::make("TransposeA", { false, true })),
                                                                                                                   framework::dataset::make("TransposeB", { false, true })),
                                                                                                                   m0_values_precommit),
                                                                                                                   n0_values_precommit),
                                                                                                                   k0_values_precommit),
                                                                                                           framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                   framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                                                                  framework::dataset::make("TransposeA", { false })),
                                                                                                                  framework::dataset::make("TransposeB", { false })),
                                                                                                                  m0_values_nightly_lhs_nt),
                                                                                                                  n0_values_nightly_rhs_nt),
                                                                                                                  k0_values_nightly_lhs_nt_rhs_nt),
                                                                                                                  framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                                  framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                                                                    framework::dataset::make("TransposeA", { false })),
                                                                                                                    framework::dataset::make("TransposeB", { true })),
                                                                                                                    m0_values_nightly_lhs_nt),
                                                                                                                    n0_values_nightly_rhs_t),
                                                                                                                    k0_values_nightly_rhs_t),
                                                                                                                    framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                                    framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                                                                    framework::dataset::make("TransposeA", { true })),
                                                                                                                    framework::dataset::make("TransposeB", { false })),
                                                                                                                    m0_values_nightly_lhs_t),
                                                                                                                    n0_values_nightly_rhs_nt),
                                                                                                                    k0_values_nightly_lhs_t_rhs_nt),
                                                                                                                    framework::dataset::make("ExportRhsToCLImage", { false })),
                                                                                                                    framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY,
                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
                                                                               framework::dataset::make("TransposeA", { true })),
                                                                       framework::dataset::make("TransposeB", { true })),
                                                               m0_values_nightly_lhs_t),
                                                       n0_values_nightly_rhs_t),
                                               k0_values_nightly_rhs_t),
                                       framework::dataset::make("ExportRhsToCLImage", { false })),
                               framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
TEST_SUITE_END() // Buffer

TEST_SUITE(ExportRhsToCLImage)
FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL,
                       combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(),
                                                                               framework::dataset::make("TransposeA", { true, false })),
                                                                       framework::dataset::make("TransposeB", { false })),
                                                               framework::dataset::make("M0", { 2 })),
                                                       framework::dataset::make("N0", { 4, 8, 16 })),
                                               framework::dataset::make("K0", { 2, 4 })),
                                       framework::dataset::make("ExportRhsToCLImage", { true })),
                               framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    if(_device_supports_export_to_cl_image)
    {
        validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
    }
}
FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY,
                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(),
                                                                               framework::dataset::make("TransposeA", { true, false })),
                                                                       framework::dataset::make("TransposeB", { false })),
                                                               framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
                                                       framework::dataset::make("N0", { 4, 8, 16 })),
                                               framework::dataset::make("K0", { 1, 2, 3, 4 })),
                                       framework::dataset::make("ExportRhsToCLImage", { true })),
                               framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    if(_device_supports_export_to_cl_image)
    {
        validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
    }
}
FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL,
                       combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsT(),
                                                                               framework::dataset::make("TransposeA", { true, false })),
                                                                       framework::dataset::make("TransposeB", { true })),
                                                               framework::dataset::make("M0", { 2 })),
                                                       framework::dataset::make("N0", { 2, 4 })),
                                               framework::dataset::make("K0", { 4, 8, 16 })),
                                       framework::dataset::make("ExportRhsToCLImage", { true })),
                               framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    if(_device_supports_export_to_cl_image)
    {
        validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
    }
}
FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY,
                       combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsT(),
                                                                               framework::dataset::make("TransposeA", { true, false })),
                                                                       framework::dataset::make("TransposeB", { true })),
                                                               framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
                                                       framework::dataset::make("N0", { 1, 2, 3, 4 })),
                                               framework::dataset::make("K0", { 4, 8, 16 })),
                                       framework::dataset::make("ExportRhsToCLImage", { true })),
                               framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    if(_device_supports_export_to_cl_image)
    {
        validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
    }
}
TEST_SUITE_END() // ExportRhsToCLImage
TEST_SUITE_END() // FP16
TEST_SUITE_END() // Float
TEST_SUITE_END() // MatMulKernel
TEST_SUITE_END() // CL
} // namespace validation
} // namespace test
} // namespace arm_compute