// 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/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/ops/gemm/kernel/grouped_gemm_kernel.hpp"
#include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"

template <typename Tuple>
class TestCkTileGroupedGemm : 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>;
    using DsLayout    = ck_tile::tuple<>;
    using DsDataType  = ck_tile::tuple<>;

    // Get the persistent value from ck_tile::bool_constant
    using PersistentType             = std::tuple_element_t<7, Tuple>;
    static constexpr bool Persistent = PersistentType::value;

    struct GroupedGemKernelParam
    {
        static const bool kPadM = false;
        static const bool kPadN = false;
        static const bool kPadK = false;

        static const int kBlockPerCu         = 1;
        static const ck_tile::index_t M_Tile = 256;
        static const ck_tile::index_t N_Tile = 256;
        static const ck_tile::index_t K_Tile = 64;

        static const ck_tile::index_t M_Warp = 2;
        static const ck_tile::index_t N_Warp = 2;
        static const ck_tile::index_t K_Warp = 1;

        static const ck_tile::index_t M_Warp_Tile = 32;
        static const ck_tile::index_t N_Warp_Tile = 32;
        static const ck_tile::index_t K_Warp_Tile = 16;
    };

    using grouped_gemm_kargs = ck_tile::GemmHostArgs</*NumDTensor = 0*/>;
    std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs)
    {
        return gemm_descs.size() * sizeof(ck_tile::GemmTransKernelArg);
    }

    template <typename ALayout, typename BLayout, typename CLayout>
    void invoke_grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
                             const ck_tile::stream_config& s,
                             void* kargs_ptr)
    {
        constexpr bool DoubleSmemBuffer = false;
        constexpr bool TransposeC       = false;

        constexpr ck_tile::index_t TileParitionerGroupNum = 8;
        constexpr ck_tile::index_t TileParitionerM01      = 4;

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

        using Traits              = ck_tile::TileGemmTraits<GroupedGemKernelParam::kPadM,
                                               GroupedGemKernelParam::kPadN,
                                               GroupedGemKernelParam::kPadK,
                                               ALayout,
                                               BLayout,
                                               CLayout>;
        using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<GroupedGemKernelParam::kPadM,
                                                                     GroupedGemKernelParam::kPadN,
                                                                     GroupedGemKernelParam::kPadK,
                                                                     DoubleSmemBuffer,
                                                                     ALayout,
                                                                     BLayout,
                                                                     CLayout,
                                                                     TransposeC>;
        using GemmPipelineProblem =
            ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;

        using BaseGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrCompV3<GemmPipelineProblem>;

        const ck_tile::index_t k_grain = gemm_descs[0].k_batch * GroupedGemKernelParam::K_Tile;
        const ck_tile::index_t K_split =
            (gemm_descs[0].K + k_grain - 1) / k_grain * GroupedGemKernelParam::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);

        float ave_time{0};

        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 scheduler        = ck_tile::GemmPipelineScheduler::Intrawave;
            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 = ck_tile::GemmPipelineAgBgCrCompV3<UniversalGemmProblem>;
            using GemmEpilogue = ck_tile::CShuffleEpilogue<
                ck_tile::CShuffleEpilogueProblem<ADataType,
                                                 BDataType,
                                                 DsDataType,
                                                 AccDataType,
                                                 CDataType,
                                                 DsLayout,
                                                 CLayout,
                                                 ck_tile::element_wise::PassThrough,
                                                 GemmPipelineProblem::kBlockSize,
                                                 TilePartitioner::MPerBlock,
                                                 TilePartitioner::NPerBlock,
                                                 GroupedGemKernelParam::M_Warp,
                                                 GroupedGemKernelParam::N_Warp,
                                                 GroupedGemKernelParam::M_Warp_Tile,
                                                 GroupedGemKernelParam::N_Warp_Tile,
                                                 GroupedGemKernelParam::K_Warp_Tile,
                                                 UniversalGemmProblem::TransposeC,
                                                 memory_operation>>;
            using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
            auto kargs   = Kernel::MakeKargs(gemm_descs);
            EXPECT_TRUE(Kernel::IsSupportedArgument(kargs));

            const dim3 grids      = Kernel::GridSize(gemm_descs);
            constexpr dim3 blocks = Kernel::BlockSize();

            ck_tile::hip_check_error(hipMemcpyWithStream(kargs_ptr,
                                                         kargs.data(),
                                                         get_workspace_size(gemm_descs),
                                                         hipMemcpyHostToDevice,
                                                         s.stream_id_));

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

            ave_time = ck_tile::launch_kernel(
                s,
                ck_tile::make_kernel<blocks.x, GroupedGemKernelParam::kBlockPerCu>(
                    Kernel{},
                    grids,
                    blocks,
                    0,
                    ck_tile::cast_pointer_to_constant_address_space(kargs_ptr),
                    gemm_descs.size()));
            return ave_time;
        };

        const auto RunSplitk = [&](const auto has_hot_loop_, const auto tail_number_) {
            if(gemm_descs[0].k_batch == 1)
            {
                std::cout << "Run without SplitK" << std::endl;
                Run(has_hot_loop_,
                    tail_number_,
                    ck_tile::integral_constant<ck_tile::memory_operation_enum,
                                               ck_tile::memory_operation_enum::set>{});
            }
            else
            {
                std::cout << "Run using SplitK" << std::endl;
                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);
    }

    template <typename ALayout, typename BLayout, typename CLayout>
    void invoke_grouped_gemm_persistent(const ck_tile::stream_config& s,
                                        const ck_tile::index_t num_groups,
                                        void* kargs_ptr,
                                        bool splitk)
    {
        constexpr bool TransposeC       = false;
        constexpr bool DoubleSmemBuffer = false;

        constexpr int kBlockPerCu                         = 1;
        constexpr ck_tile::index_t TileParitionerGroupNum = 8;
        constexpr ck_tile::index_t TileParitionerM01      = 4;

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

        using Traits = ck_tile::TileGemmTraits<GroupedGemKernelParam::kPadM,
                                               GroupedGemKernelParam::kPadN,
                                               GroupedGemKernelParam::kPadK,
                                               ALayout,
                                               BLayout,
                                               CLayout>;
        using GemmUniversalTraits =
            ck_tile::PersistentTileGemmUniversalTraits<GroupedGemKernelParam::kPadM,
                                                       GroupedGemKernelParam::kPadN,
                                                       GroupedGemKernelParam::kPadK,
                                                       DoubleSmemBuffer,
                                                       ALayout,
                                                       BLayout,
                                                       CLayout,
                                                       TransposeC>;
        using GemmPipelineProblem =
            ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;

        const auto Run = [&](const auto memory_operation_) {
            constexpr auto scheduler        = ck_tile::GemmPipelineScheduler::Intrawave;
            constexpr auto memory_operation = memory_operation_.value;

            // We create the GEMM pipeline without specifying hotloop or tailnumber.
            // These are automatically run inside the kernel based on the given input data.
            using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
                                                                               BDataType,
                                                                               AccDataType,
                                                                               GemmShape,
                                                                               GemmUniversalTraits,
                                                                               scheduler>;

            using GemmPipeline = ck_tile::GemmPipelineAgBgCrCompV3<UniversalGemmProblem>;
            using GemmEpilogue = ck_tile::CShuffleEpilogue<
                ck_tile::CShuffleEpilogueProblem<ADataType,
                                                 BDataType,
                                                 DsDataType,
                                                 AccDataType,
                                                 CDataType,
                                                 DsLayout,
                                                 CLayout,
                                                 ck_tile::element_wise::PassThrough,
                                                 GemmPipelineProblem::kBlockSize,
                                                 TilePartitioner::MPerBlock,
                                                 TilePartitioner::NPerBlock,
                                                 GroupedGemKernelParam::M_Warp,
                                                 GroupedGemKernelParam::N_Warp,
                                                 GroupedGemKernelParam::M_Warp_Tile,
                                                 GroupedGemKernelParam::N_Warp_Tile,
                                                 GroupedGemKernelParam::K_Warp_Tile,
                                                 UniversalGemmProblem::TransposeC,
                                                 memory_operation>>;
            using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
            constexpr dim3 blocks = Kernel::BlockSize();
            const dim3 grids      = Kernel::MaxOccupancyGridSize(s);

            if(s.log_level_ > 0)
            {
                std::cout << "Launching kernel: " << Kernel::GetName() << " 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<blocks.x, kBlockPerCu>(
                                       Kernel{},
                                       grids,
                                       blocks,
                                       0,
                                       ck_tile::cast_pointer_to_constant_address_space(kargs_ptr),
                                       num_groups));
        };

        if(splitk)
        {
            Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
                                           ck_tile::memory_operation_enum::atomic_add>{});
        }
        else
        {

            Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
                                           ck_tile::memory_operation_enum::set>{});
        }
    }

    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));
    }

    public:
    void Run(const std::vector<int>& Ms,
             const std::vector<int>& Ns,
             const std::vector<int>& Ks,
             std::vector<int>& stride_As,
             std::vector<int>& stride_Bs,
             std::vector<int>& stride_Cs,
             const int kbatch      = 1,
             const int group_count = 16)
    {
        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)
                {
                    if constexpr(std::is_same_v<decltype(layout),
                                                ck_tile::tensor_layout::gemm::RowMajor>)
                    {
                        return col;
                    }
                    else
                    {
                        return row;
                    }
                }
                else
                    return stride;
            };

        std::vector<ck_tile::HostTensor<ADataType>> a_m_k_tensors;
        std::vector<ck_tile::HostTensor<BDataType>> b_k_n_tensors;
        std::vector<ck_tile::HostTensor<CDataType>> c_m_n_tensors;

        a_m_k_tensors.reserve(group_count);
        b_k_n_tensors.reserve(group_count);
        c_m_n_tensors.reserve(group_count);

        std::vector<std::unique_ptr<ck_tile::DeviceMem>> a_m_k_dev_buf;
        std::vector<std::unique_ptr<ck_tile::DeviceMem>> b_k_n_dev_buf;
        std::vector<std::unique_ptr<ck_tile::DeviceMem>> c_m_n_dev_buf;

        a_m_k_dev_buf.reserve(group_count);
        b_k_n_dev_buf.reserve(group_count);
        c_m_n_dev_buf.reserve(group_count);

        std::vector<grouped_gemm_kargs> gemm_descs;
        gemm_descs.reserve(group_count);

        for(int i = 0; i < group_count; ++i)
        {
            const ck_tile::index_t M = Ms[i];
            const ck_tile::index_t N = Ns[i];
            const ck_tile::index_t K = Ks[i];

            stride_As[i] = f_get_default_stride(M, N, stride_As[i], ALayout{});
            stride_Bs[i] = f_get_default_stride(K, N, stride_Bs[i], BLayout{});
            stride_Cs[i] = f_get_default_stride(M, N, stride_Cs[i], CLayout{});

            a_m_k_tensors.push_back(ck_tile::HostTensor<ADataType>(
                f_host_tensor_descriptor(M, K, stride_As[i], ALayout{})));
            b_k_n_tensors.push_back(ck_tile::HostTensor<BDataType>(
                f_host_tensor_descriptor(K, N, stride_Bs[i], BLayout{})));
            c_m_n_tensors.push_back(ck_tile::HostTensor<CDataType>(
                f_host_tensor_descriptor(M, N, stride_Cs[i], CLayout{})));

            std::cout << "gemm[" << i << "]"
                      << " a_m_k: " << a_m_k_tensors[i].mDesc
                      << " b_k_n: " << b_k_n_tensors[i].mDesc
                      << " c_m_n: " << c_m_n_tensors[i].mDesc << " KBatch: " << kbatch << std::endl;

            ck_tile::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k_tensors[i]);
            ck_tile::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n_tensors[i]);

            a_m_k_dev_buf.push_back(std::make_unique<ck_tile::DeviceMem>(
                a_m_k_tensors[i].get_element_space_size_in_bytes()));
            b_k_n_dev_buf.push_back(std::make_unique<ck_tile::DeviceMem>(
                b_k_n_tensors[i].get_element_space_size_in_bytes()));
            c_m_n_dev_buf.push_back(std::make_unique<ck_tile::DeviceMem>(
                c_m_n_tensors[i].get_element_space_size_in_bytes()));

            a_m_k_dev_buf[i]->ToDevice(a_m_k_tensors[i].data());
            b_k_n_dev_buf[i]->ToDevice(b_k_n_tensors[i].data());
            c_m_n_dev_buf[i]->SetZero();
            c_m_n_tensors[i].SetZero();

            const void* p_a = a_m_k_dev_buf[i]->GetDeviceBuffer();
            const void* p_b = b_k_n_dev_buf[i]->GetDeviceBuffer();
            void* p_c       = c_m_n_dev_buf[i]->GetDeviceBuffer();

            gemm_descs.push_back(
                {p_a, p_b, {}, p_c, kbatch, M, N, K, stride_As[i], stride_Bs[i], {}, stride_Cs[i]});
        }

        ck_tile::DeviceMem gemm_workspace;
        gemm_workspace.Realloc(get_workspace_size(gemm_descs));

        if constexpr(Persistent)
        {
            // Generate kernel arguments
            std::vector<ck_tile::GemmTransKernelArg> kargs;
            void* kargs_ptr   = gemm_workspace.GetDeviceBuffer();
            const bool splitk = gemm_descs[0].k_batch > 1;
            for(const auto& arg : gemm_descs)
            {
                kargs.emplace_back(ck_tile::GemmKernelArgs<>{arg.a_ptr,
                                                             arg.b_ptr,
                                                             {},
                                                             arg.e_ptr,
                                                             arg.M,
                                                             arg.N,
                                                             arg.K,
                                                             arg.stride_A,
                                                             arg.stride_B,
                                                             {},
                                                             arg.stride_E,
                                                             arg.k_batch});
            }
            const auto stream = ck_tile::stream_config{nullptr, false, 1};
            ck_tile::hip_check_error(
                hipMemcpyWithStream(kargs_ptr,
                                    kargs.data(),
                                    kargs.size() * sizeof(ck_tile::GemmTransKernelArg),
                                    hipMemcpyHostToDevice,
                                    stream.stream_id_));
            invoke_grouped_gemm_persistent<ALayout, BLayout, CLayout>(
                stream, group_count, kargs_ptr, splitk);
        }
        else
        {
            invoke_grouped_gemm<ALayout, BLayout, CLayout>(
                gemm_descs,
                ck_tile::stream_config{nullptr, false, 1},
                gemm_workspace.GetDeviceBuffer());
        }

        // Copy results back to host for validation
        for(int i = 0; i < group_count; i++)
        {
            c_m_n_dev_buf[i]->FromDevice(c_m_n_tensors[i].data());
        }

        bool pass{true};
        for(int i = 0; i < group_count; ++i)
        {
            ck_tile::HostTensor<CDataType> c_m_n_host_ref(
                f_host_tensor_descriptor(Ms[i], Ns[i], stride_Cs[i], CLayout{}));
            c_m_n_host_ref.SetZero();
            ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
                a_m_k_tensors[i], b_k_n_tensors[i], 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(Ks[i], kbatch, max_accumulated_value);
            pass &= ck_tile::check_err(c_m_n_tensors[i],
                                       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);
    }
};
