// SPDX-License-Identifier: MIT
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once

#include <sstream>
#include <gtest/gtest.h>

#include "ck_tile/core.hpp"
#include "ck_tile/host.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/host/permute_pk_int4.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"

template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
auto calculate_rtol_atol(const ck_tile::index_t K,
                         const ck_tile::index_t kbatch,
                         const float max_accumulated_value)
{
    using ComputeType =
        std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
    // Calculate thresholds
    const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
        ck_tile::integer_divide_ceil(K, kbatch));
    const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
        max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
    // Calculate error due to split_k accumulation
    const auto rtol_split_k =
        ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
    const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
        max_accumulated_value, kbatch);
    // Use higher threshold
    return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
}

enum struct GemmPipelineType
{
    WeightPreshuffleV1,
    WeightPreshuffleV2
};

template <GemmPipelineType PT, typename Problem>
struct GemmPipelineTypeSelector;

template <typename Problem>
struct GemmPipelineTypeSelector<GemmPipelineType::WeightPreshuffleV1, Problem>
{
    using base_pipeline = ck_tile::BaseWeightPreshufflePipelineAGmemBGmemCRegV1<Problem>;
    using pipeline      = ck_tile::WeightPreshufflePipelineAGmemBGmemCRegV1<Problem>;

    static constexpr auto GetName() { return "GemmPipelineAgBgCrWeightPreshuffleV1"; }
};

template <typename Problem>
struct GemmPipelineTypeSelector<GemmPipelineType::WeightPreshuffleV2, Problem>
{
    using base_pipeline = ck_tile::BaseWeightPreshufflePipelineAGmemBGmemCRegV2<Problem>;
    using pipeline      = ck_tile::WeightPreshufflePipelineAGmemBGmemCRegV2<Problem>;

    static constexpr auto GetName() { return "GemmPipelineAgBgCrWeightPreshuffleV2"; }
};

template <typename Datatype>
struct config
{
    static constexpr ck_tile::index_t M_Tile = 128;
    static constexpr ck_tile::index_t N_Tile = 128;
    static constexpr ck_tile::index_t K_Tile = 128 / sizeof(Datatype);

    static constexpr ck_tile::index_t M_Warp = 1;
    static constexpr ck_tile::index_t N_Warp = 4;
    static constexpr ck_tile::index_t K_Warp = 1;

    static constexpr ck_tile::index_t M_Warp_Tile = 32;
    static constexpr ck_tile::index_t N_Warp_Tile = 32;
    static constexpr ck_tile::index_t K_Warp_Tile = sizeof(Datatype) == 2 ? 16 : 32;
};

template <typename Datatype>
struct config_wmma
{
    static constexpr ck_tile::index_t M_Tile = 128;
    static constexpr ck_tile::index_t N_Tile = 128;
    static constexpr ck_tile::index_t K_Tile = 128 / sizeof(Datatype);

    static constexpr ck_tile::index_t M_Warp = 1;
    static constexpr ck_tile::index_t N_Warp = 4;
    static constexpr ck_tile::index_t K_Warp = 1;

    static constexpr ck_tile::index_t M_Warp_Tile = 16;
    static constexpr ck_tile::index_t N_Warp_Tile = 16;
    static constexpr ck_tile::index_t K_Warp_Tile = 16;
};

template <typename Tuple>
class TestCkTileGemmPipeline : public ::testing::Test
{
    protected:
    using ALayout                      = std::tuple_element_t<0, Tuple>;
    using BLayout                      = std::tuple_element_t<1, Tuple>;
    using CLayout                      = std::tuple_element_t<2, Tuple>;
    using ADataType                    = std::tuple_element_t<3, Tuple>;
    using BDataType                    = std::tuple_element_t<4, Tuple>;
    using AccDataType                  = std::tuple_element_t<5, Tuple>;
    using CDataType                    = std::tuple_element_t<6, Tuple>;
    static constexpr auto Scheduler    = std::tuple_element_t<7, Tuple>::value;
    static constexpr auto PipelineType = std::tuple_element_t<8, Tuple>::value;

    using DsLayout   = ck_tile::tuple<>;
    using DsDataType = ck_tile::tuple<>;

    static constexpr bool Persistent =
        ck_tile::tuple_element_or_default_t<Tuple, 9, std::false_type>::value;
    // TODO: expose tile size through test t-param ?

    template <typename GemmConfig, bool PadM, bool PadN, bool PadK, bool Preshuffle>
    void invoke_gemm(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s)
    {
        constexpr bool kPadM      = PadM;
        constexpr bool kPadN      = PadN;
        constexpr bool kPadK      = PadK;
        constexpr bool preshuffle = Preshuffle;

        constexpr bool DoubleSmemBuffer =
            (PipelineType == GemmPipelineType::WeightPreshuffleV2) ? true : false;

        // TODO: For now - but this should also be a test parameter
        constexpr bool TransposeC = false;

        constexpr int kBlockPerCu                         = 2;
        constexpr ck_tile::index_t TileParitionerGroupNum = 8;
        constexpr ck_tile::index_t TileParitionerM01      = 4;

        // ===============================================

        using GemmShape = ck_tile::TileGemmShape<
            ck_tile::sequence<GemmConfig::M_Tile, GemmConfig::N_Tile, GemmConfig::K_Tile>,
            ck_tile::sequence<GemmConfig::M_Warp, GemmConfig::N_Warp, GemmConfig::K_Warp>,
            ck_tile::sequence<GemmConfig::M_Warp_Tile,
                              GemmConfig::N_Warp_Tile,
                              GemmConfig::K_Warp_Tile>>;
        using TilePartitioner = ck_tile::
            GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;

        using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
        static constexpr bool StructuredSparsity = false;
        static constexpr bool NumWaveGroup       = 1;

        using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<kPadM,
                                                                     kPadN,
                                                                     kPadK,
                                                                     DoubleSmemBuffer,
                                                                     ALayout,
                                                                     BLayout,
                                                                     CLayout,
                                                                     TransposeC,
                                                                     StructuredSparsity,
                                                                     Persistent,
                                                                     NumWaveGroup,
                                                                     preshuffle>;

        using GemmPipelineProblem =
            ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;

        using BaseGemmPipeline =
            typename GemmPipelineTypeSelector<PipelineType, GemmPipelineProblem>::base_pipeline;

        const ck_tile::index_t k_grain     = args.k_batch * GemmConfig::K_Tile;
        const ck_tile::index_t K_split     = (args.K + k_grain - 1) / k_grain * GemmConfig::K_Tile;
        const ck_tile::index_t num_loop    = TilePartitioner::GetLoopNum(K_split);
        const bool has_hot_loop            = BaseGemmPipeline::BlockHasHotloop(num_loop);
        const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);

        const auto Run = [&](const auto has_hot_loop_,
                             const auto tail_number_,
                             const auto memory_operation_) {
            constexpr bool has_hot_loop_v   = has_hot_loop_.value;
            constexpr auto tail_number_v    = tail_number_.value;
            constexpr auto memory_operation = memory_operation_.value;

            using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
                                                                               BDataType,
                                                                               AccDataType,
                                                                               GemmShape,
                                                                               GemmUniversalTraits,
                                                                               Scheduler,
                                                                               has_hot_loop_v,
                                                                               tail_number_v>;

            using GemmPipeline =
                typename GemmPipelineTypeSelector<PipelineType, UniversalGemmProblem>::pipeline;

            using GemmEpilogue = ck_tile::CShuffleEpilogue<
                ck_tile::CShuffleEpilogueProblem<ADataType,
                                                 BDataType,
                                                 DsDataType,
                                                 AccDataType,
                                                 CDataType,
                                                 DsLayout,
                                                 CLayout,
                                                 ck_tile::element_wise::PassThrough,
                                                 TilePartitioner::MPerBlock,
                                                 TilePartitioner::NPerBlock,
                                                 GemmConfig::M_Warp,
                                                 GemmConfig::N_Warp,
                                                 GemmConfig::M_Warp_Tile,
                                                 GemmConfig::N_Warp_Tile,
                                                 GemmConfig::K_Warp_Tile,
                                                 UniversalGemmProblem::TransposeC,
                                                 memory_operation>>;

            using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
            auto kargs   = Kernel::MakeKernelArgs(args);

            dim3 grids;
            if constexpr(Persistent)
            {
                grids = Kernel::MaxOccupancyGridSize(s);
            }
            else
            {
                grids = Kernel::GridSize(args.M, args.N, args.k_batch);
            }
            const dim3 blocks = Kernel::BlockSize();

            if(!Kernel::IsSupportedArgument(kargs))
            {
                throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
            }

            if(s.log_level_ > 0)
            {
                std::cout << "Launching kernel with args:" << " grid: {" << grids.x << ", "
                          << grids.y << ", " << grids.z << "}" << ", blocks: {" << blocks.x << ", "
                          << blocks.y << ", " << blocks.z << "}" << std::endl;
            }

            ck_tile::launch_kernel(
                s, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
        };

        const auto RunSplitk = [&](const auto has_hot_loop_, const auto tail_number_) {
            if(args.k_batch == 1)
            {
                Run(has_hot_loop_,
                    tail_number_,
                    ck_tile::integral_constant<ck_tile::memory_operation_enum,
                                               ck_tile::memory_operation_enum::set>{});
            }
            else
            {
                Run(has_hot_loop_,
                    tail_number_,
                    ck_tile::integral_constant<ck_tile::memory_operation_enum,
                                               ck_tile::memory_operation_enum::atomic_add>{});
            }
        };

        BaseGemmPipeline::TailHandler(RunSplitk, has_hot_loop, tail_num);
    }

    public:
    std::vector<int> k_batches_;

    void SetUp() override
    {

        // Otherwise, use k_batch = 1 and 2
        k_batches_ = {1};
    }

    template <typename GemmConfig, typename T>
    auto shuffle_b(const ck_tile::HostTensor<T>& t)
    {
        assert(t.get_lengths().size() == 2);
        int n_ = t.get_lengths()[1];
        int k_ = t.get_lengths()[0];

        if(ck_tile::is_gfx12_supported())
        {
            constexpr int divisor      = 2;
            constexpr int kABK1PerLane = 8;
            constexpr int kABK0PerLane = GemmConfig::K_Warp_Tile / divisor / kABK1PerLane;
            ck_tile::HostTensor<T> t_view({n_ / GemmConfig::N_Warp_Tile,
                                           GemmConfig::N_Warp_Tile,
                                           k_ / GemmConfig::K_Warp_Tile,
                                           kABK0PerLane,
                                           divisor,
                                           kABK1PerLane});
            std::copy(t.begin(), t.end(), t_view.begin());
            return ck_tile::reference_permute(t_view, {0, 2, 4, 1, 3, 5});
        }
        else
        {
            int divisor = 1;
            if(ck_tile::is_gfx11_supported())
            {
                divisor = 1;
            }
            else
            {
                assert(is_wave32() == false);
                divisor = GemmConfig::N_Warp_Tile == 32 ? 2 : 4;
            }
            ck_tile::HostTensor<T> t_view({n_ / GemmConfig::N_Warp_Tile,
                                           GemmConfig::N_Warp_Tile,
                                           k_ / GemmConfig::K_Warp_Tile,
                                           divisor,
                                           GemmConfig::K_Warp_Tile / divisor});
            std::copy(t.begin(), t.end(), t_view.begin());
            return ck_tile::reference_permute(t_view, {0, 2, 3, 1, 4});
        }
    }
    template <bool PadM = true, bool PadN = true, bool PadK = true, bool Preshuffle = false>
    void Run(const int M,
             const int N,
             const int K,
             const int StrideA = 0,
             const int StrideB = 0,
             const int StrideC = 0)
    {
        for(auto kb : k_batches_)
        {
#if CK_TILE_USE_WMMA
            RunSingle<config_wmma<ADataType>, PadM, PadN, PadK, Preshuffle>(
                M, N, K, StrideA, StrideB, StrideC, kb);
#else
            RunSingle<config<ADataType>, PadM, PadN, PadK, Preshuffle>(
                M, N, K, StrideA, StrideB, StrideC, kb);
#endif
        }
    }

    template <typename GemmConfig, bool PadM, bool PadN, bool PadK, bool Preshuffle>
    void RunSingle(const int M,
                   const int N,
                   const int K,
                   const int StrideA,
                   const int StrideB,
                   const int StrideC,
                   int kbatch = 1)
    {
        using namespace ck_tile::literals;

        auto f_host_tensor_descriptor = [](std::size_t row,
                                           std::size_t col,
                                           std::size_t stride,
                                           auto layout) {
            if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
            {
                return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
            }
            else
            {
                return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
            }
        };

        auto f_get_default_stride =
            [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
                if(stride == 0)
                {
                    // give a chance if stride is zero, return a default packed stride
                    if constexpr(std::is_same_v<decltype(layout),
                                                ck_tile::tensor_layout::gemm::RowMajor>)
                    {
                        return col;
                    }
                    else
                    {
                        return row;
                    }
                }
                else
                    return stride;
            };

        ck_tile::index_t stride_A = f_get_default_stride(M, K, StrideA, ALayout{});
        ck_tile::index_t stride_B = f_get_default_stride(K, N, StrideB, BLayout{});
        ck_tile::index_t stride_C = f_get_default_stride(M, N, StrideC, CLayout{});

        ck_tile::HostTensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, stride_A, ALayout{}));
        ck_tile::HostTensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, stride_B, BLayout{}));
        ck_tile::HostTensor<CDataType> c_m_n_dev_result(
            f_host_tensor_descriptor(M, N, stride_C, CLayout{}));

        ck_tile::FillUniformDistributionIntegerValue<ADataType>{-5, 5}(a_m_k);
        ck_tile::FillUniformDistributionIntegerValue<BDataType>{-5, 5}(b_k_n);

        ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
        ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
        ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());

        a_m_k_dev_buf.ToDevice(a_m_k.data());
        ck_tile::HostTensor<BDataType> b_shuffle_host = shuffle_b<GemmConfig>(b_k_n);
        if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
        {
            // Permute vector pk_i4x4 data for device implementation
            ck_tile::HostTensor<BDataType> b_shuffle_host_dev = b_shuffle_host;
            ck_tile::permute_vectors_i4x4_b(b_shuffle_host_dev);
            b_k_n_dev_buf.ToDevice(b_shuffle_host_dev.data());
        }
        else
        {
            b_k_n_dev_buf.ToDevice(b_shuffle_host.data());
        }
        c_m_n_dev_buf.SetZero();
        c_m_n_dev_result.SetZero();

        ck_tile::GemmHostArgs args{a_m_k_dev_buf.GetDeviceBuffer(),
                                   b_k_n_dev_buf.GetDeviceBuffer(),
                                   c_m_n_dev_buf.GetDeviceBuffer(),
                                   kbatch,
                                   M,
                                   N,
                                   K,
                                   stride_A,
                                   stride_B,
                                   stride_C};

        invoke_gemm<GemmConfig, PadM, PadN, PadK, Preshuffle>(
            args, ck_tile::stream_config{nullptr, false});

        c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
        bool pass = true;

        ck_tile::HostTensor<CDataType> c_m_n_host_ref(
            f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
        c_m_n_host_ref.SetZero();

        ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
            a_m_k, b_k_n, c_m_n_host_ref);

        const float max_accumulated_value =
            *std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
        const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
            K, kbatch, max_accumulated_value);
        pass = ck_tile::check_err(c_m_n_dev_result,
                                  c_m_n_host_ref,
                                  "Error: Incorrect results!",
                                  rtol_atol.at(ck_tile::number<0>{}),
                                  rtol_atol.at(ck_tile::number<1>{}));
        EXPECT_TRUE(pass);
    }
};
