/*
 * 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.
 */
#ifdef ACL_INTERNAL_TEST_CKW_IN_DF
#include "tests/AssetsLibrary.h"
#include "tests/CL/CLAccessor.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
#include "tests/datasets/LargeMatMulDataset.h"
#include "tests/datasets/SmallMatMulDataset.h"
#include "tests/validation/Validation.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/GEMM.h"

#include "tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.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(0.02)); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
}

/** 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", { 1, 2, 4 });

/** K0 values to test --precommit*/
const auto k0_values_precommit = framework::dataset::make("K0", { 1, 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 });

TEST_SUITE(CL)
TEST_SUITE(DYNAMIC_FUSION)

TEST_SUITE(MatMul)

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 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 },

    };

    // Create a new workload sketch
    auto              cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
    auto              context        = GpuWorkloadContext{ &cl_compile_ctx };
    GpuWorkloadSketch sketch{ &context };

    // 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    = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32));
    const TensorInfo rhs_info    = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32));

    for(auto &pair : supported_block_sizes)
    {
        MatMulAttributes matmul_attr {};
        matmul_attr.adj_lhs(pair.first.adj_lhs);
        matmul_attr.adj_rhs(pair.first.adj_rhs);

        GpuMatMulSettings matmul_settings {};
        matmul_settings.m0(pair.first.m0);
        matmul_settings.n0(pair.first.n0);
        matmul_settings.k0(pair.first.k0);

        Status status = GpuMatMul::validate_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings);
        ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
    }
}

TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
{
    // Create a sketch
    auto              cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
    auto              context        = GpuWorkloadContext{ &cl_compile_ctx };
    GpuWorkloadSketch sketch{ &context };

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

    for(auto &tuple : shape_configurations)
    {
        const bool expected = std::get<2>(tuple);

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

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

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

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

                MatMulAttributes matmul_attr {};
                matmul_attr.adj_lhs(adj_lhs);
                matmul_attr.adj_rhs(adj_rhs);

                GpuMatMulSettings matmul_settings {};
                matmul_settings.m0(1);
                matmul_settings.n0(1);
                matmul_settings.k0(1);

                Status status = GpuMatMul::validate_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings);
                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
    };
    // Create a sketch
    auto              cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
    auto              context        = GpuWorkloadContext{ &cl_compile_ctx };
    GpuWorkloadSketch sketch{ &context };

    const TensorShape shape = TensorShape(10U, 10U);
    MatMulAttributes  matmul_attr {};
    matmul_attr.adj_lhs(false);
    matmul_attr.adj_rhs(false);
    GpuMatMulSettings matmul_settings {};
    matmul_settings.m0(1);
    matmul_settings.n0(1);
    matmul_settings.k0(1);

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

        const TensorInfo lhs_info    = context.create_tensor_info(TensorInfo(shape, 1, std::get<0>(tuple)));
        const TensorInfo rhs_info    = context.create_tensor_info(TensorInfo(shape, 1, std::get<1>(tuple)));

        Status status = GpuMatMul::validate_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings);
        ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
    }
}

TEST_SUITE_END() // Validate

template <typename T>
using DynamicFusionGpuMatmulFixture = DynamicFusionGpuMatMulValidationFixture<CLTensor, CLAccessor,GpuMatMul, T>;

TEST_SUITE(Float)
TEST_SUITE(FP32)

FIXTURE_DATA_TEST_CASE(RunTiny, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(),
                                                                                                                   framework::dataset::make("TransposeA", { false })),
                                                                                                                   framework::dataset::make("TransposeB", { 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, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
                                                                                                                   framework::dataset::make("TransposeA", { false })),
                                                                                                                   framework::dataset::make("TransposeB", { 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(RunLargeRhsTransposed, DynamicFusionGpuMatmulFixture<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);
}

// Running High Dimensional test is enough for FP32, because we're stressing the number of dimensions, not data type or M0/N0/K0
FIXTURE_DATA_TEST_CASE(RunHighDimensional, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(),
                                                                                                                      framework::dataset::make("TransposeA", { false })),
                                                                                                                      framework::dataset::make("TransposeB", { 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() // FP32

TEST_SUITE(FP16)

FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuMatmulFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
                                                                                                                   framework::dataset::make("TransposeA", { false })),
                                                                                                                   framework::dataset::make("TransposeB", { 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(RunLargeRhsTransposed, DynamicFusionGpuMatmulFixture<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);
}

TEST_SUITE_END() // FP16

TEST_SUITE_END() // Float
TEST_SUITE_END() // MatMul
TEST_SUITE_END() // DYNAMIC_FUSION
TEST_SUITE_END() // CL
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif // ACL_INTERNAL_TEST_CKW_IN_DF