// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include "ck_tile/core.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_policy.hpp"

namespace ck_tile {

/*
This pipeline deal with a gemm(actually 2 gemm) with one very small(token), one very big(weight)
we need to design the pipeline such that all waves along gemm-N dim (gemm-m only 1 wave)

    <----- gemm-N ------>
    +----+----+----+----+
    | w0 | w1 | w2 | w3 | gemm-m
    +----+----+----+----+
*/
template <typename Problem_, typename Policy_ = FusedMoeGemmPipelineFlatmmPolicy>
struct FusedMoeGemmPipeline_FlatmmUk_int8
{
    using Problem = remove_cvref_t<Problem_>;
    using Policy  = remove_cvref_t<Policy_>;

    using BlockShape = typename Problem::BlockShape; // this is FusedMoeGemmShape

    using ADataType            = typename Problem::ADataType;
    using GDataType            = typename Problem::GDataType;
    using DDataType            = typename Problem::DDataType;
    using AccDataType          = typename Problem::AccDataType;
    using ODataType            = typename Problem::ODataType;
    using AScaleDataType       = typename Problem::AScaleDataType;
    using GScaleDataType       = typename Problem::GScaleDataType;
    using DScaleDataType       = typename Problem::DScaleDataType;
    using YSmoothScaleDataType = typename Problem::YSmoothScaleDataType;
    using TopkWeightDataType   = typename Problem::TopkWeightDataType;
    using IndexDataType        = typename Problem::IndexDataType;
    using YDataType            = typename Problem::YDataType;

    using Traits = typename Problem::Traits;

    static constexpr bool IsGateOnly          = Traits::IsGateOnly;
    static constexpr bool UseSmoothQuant      = Traits::UseSmoothQuant;
    static constexpr bool PadHiddenSize       = Traits::PadHiddenSize;
    static constexpr bool PadIntermediateSize = Traits::PadIntermediateSize;

    static constexpr index_t kAlignmentA = Policy::template GetAlignment_A<Problem>();
    static constexpr index_t kAlignmentG = Policy::template GetAlignment_G<Problem>();
    static constexpr index_t kAlignmentD = Policy::template GetAlignment_D<Problem>();
    static constexpr index_t kAlignmentO = Policy::template GetAlignment_O<Problem>();

    static constexpr index_t SLD_A = static_cast<index_t>(FusedMoeGemmPipelineSequencerEnum::SLD_A);
    static constexpr index_t GLD_A = static_cast<index_t>(FusedMoeGemmPipelineSequencerEnum::GLD_A);
    static constexpr index_t GLD_B = static_cast<index_t>(FusedMoeGemmPipelineSequencerEnum::GLD_B);
    static constexpr index_t GST_O = static_cast<index_t>(FusedMoeGemmPipelineSequencerEnum::GST_O);

    static constexpr index_t kBlockPerCu = []() {
        if constexpr(Problem::kBlockPerCu != -1)
            return Problem::kBlockPerCu;
        else
        {
            // minimize occupancy
            return 2;
        }
    }();

    static constexpr const char* name = "flatmm_uk";

    CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
    {
        constexpr index_t smem_0 = Policy::template GetUK_0<Problem>().GetSmemSize();
        constexpr index_t smem_1 = Policy::template GetUK_1<Problem>().GetSmemSize();
        constexpr index_t smem_bridge =
            BlockShape::Block_M0 * BlockShape::Block_N0 * sizeof(YDataType);
        return max(smem_0, max(smem_1, smem_bridge));
    }

    // this is the thread-offset along row/col
    CK_TILE_HOST_DEVICE static auto GetACoord()
    {
        constexpr auto a_dist = Policy::template MakeGlobalTileDistribution_A<Problem>();
        const auto a_coord    = a_dist.calculate_index();
        return a_coord;
    }

    // this is the thread-offset along row/col
    CK_TILE_HOST_DEVICE static auto GetOCoord()
    {
        constexpr auto o_dist = Policy::template MakeOGlobalTileDistribution<Problem>();
        const auto o_coord    = o_dist.calculate_index();
        return o_coord;
    }

    CK_TILE_DEVICE constexpr auto GetNumRowCoords_A()
    {
        constexpr index_t KLans   = BlockShape::Block_K0 / kAlignmentA;
        constexpr index_t MLans   = BlockShape::BlockSize / KLans;
        constexpr index_t MRepeat = BlockShape::Block_M0 / MLans;

        return MRepeat;
    }

    // TODO: properlly support scatter/gather for load only
    CK_TILE_DEVICE auto GetRowCoords_A(index_t base_offset)
    {
        constexpr index_t KLans   = BlockShape::Block_K0 / kAlignmentA;
        constexpr index_t MLans   = BlockShape::BlockSize / KLans;
        constexpr index_t MRepeat = BlockShape::Block_M0 / MLans;

        auto base_coord = threadIdx.x / KLans + base_offset;

        array<index_t, MRepeat> coords;
        static_for<0, MRepeat, 1>{}([&](auto i) { coords.at(i) = base_coord + i * MLans; });

        return coords;
    }
    //for mma and A scale
        CK_TILE_DEVICE auto GetRowCoords_A_mma(index_t base_offset)
    {
        // constexpr index_t KLans   = 2;
        constexpr index_t MLans   = 16;
        constexpr index_t MRepeat = BlockShape::Repeat_M1;

        auto base_coord = threadIdx.x % MLans + base_offset;

        array<index_t, MRepeat> coords;
        static_for<0, MRepeat, 1>{}([&](auto i) { coords.at(i) = base_coord + i * MLans; });

        return coords;
    }
    template <typename ROW_COORDS>
    CK_TILE_DEVICE auto GetRowID(const ROW_COORDS coords, const IndexDataType* sorted_token_ids_ptr)
    {
        constexpr index_t n_size = coords.size();

        array<index_t, n_size> row_ids;
        static_for<0, n_size, 1>{}([&](auto i) {
            row_ids.at(i) = sorted_token_ids_ptr[coords[i]]; // base_coord + i * MLans;
        });

        return row_ids;
    }

    template <typename ROW_COORDS>
    CK_TILE_DEVICE auto GetWeightScale(const ROW_COORDS coords,
                                       const TopkWeightDataType* sorted_weight_ptr)
    {
        constexpr index_t n_size = coords.size();

        array<TopkWeightDataType, n_size> w;
        static_for<0, n_size, 1>{}([&](auto i) {
            w.at(i) = sorted_weight_ptr[coords[i]]; // base_coord + i * MLans;
        });

        return w;
    }

    template <typename ROW_IDS>
    CK_TILE_DEVICE auto GetAScale(const ROW_IDS row_ids_mma,
                                       const AScaleDataType* a_scale_ptr)
    {
        constexpr index_t n_size = row_ids_mma.size();

        array<TopkWeightDataType, n_size> w;
        static_for<0, n_size, 1>{}([&](auto i) {
            auto row_id = row_ids_mma[i] & 0xffffff;
            auto itp_k = row_ids_mma[i] >> 24;
            w.at(i) = a_scale_ptr[row_id * 5+itp_k];
        });

        return w;
    }

    // TODO: this row id is before shuffle atomic, need use acc distribution
    CK_TILE_DEVICE auto GetRowCoords_O(index_t base_offset)
    {
        constexpr index_t MLanes   = BlockShape::Warp_M1;
        constexpr index_t Repeat_M = BlockShape::Repeat_M1;

        auto base_coord = threadIdx.x % MLanes + base_offset;

        array<index_t, Repeat_M> coords;
        static_for<0, Repeat_M, 1>{}([&](auto i) { coords.at(i) = base_coord + i * MLanes; });

        return coords;
    }
        // TODO: this row id is before shuffle atomic, need use acc distribution
    //this calculation shared by G and SMQ
    CK_TILE_DEVICE auto GetColCoords_GQSMQ(index_t base_offset)
    {
        constexpr index_t MLanes   = BlockShape::Warp_M1;
        constexpr index_t Repeat_N = 2;//different,this load is partitioned along N

        // auto h_id = threadIdx.x / MLanes ;
        // auto r_id = threadIdx.x & 0xffff;
        // auto p_id = r_id/4;
        // auto q_is = threadIdx.x & 0x3;

        array<index_t, Repeat_N> coords;
        static_for<0, Repeat_N, 1>{}([&](auto i) { coords.at(i) = base_offset + (threadIdx.x / MLanes) * 4 + 
                                                                            (threadIdx.x & 0xffff)/4 * 64  +
                                                                             threadIdx.x & 0x3 + 
                                                                            i * 256 ; });
        return coords;
    }
    //this calculation shared by G and SMQ
    template <typename COL_IDS>
    CK_TILE_DEVICE auto GetGQScale(const COL_IDS coords,
                                       const GScaleDataType* g_scale_ptr)
    {
        constexpr index_t n_size = coords.size();

        array<GScaleDataType, n_size> g_scale_value;
        static_for<0, n_size, 1>{}([&](auto i) {
            g_scale_value.at(i) = g_scale_ptr[coords[i]];
        });

        return g_scale_value;
    }
    template <typename COL_IDS>
    CK_TILE_DEVICE auto GetSMQScale(const COL_IDS coords,
                                       const YSmoothScaleDataType * y_scale_ptr)
    {
        constexpr index_t n_size = coords.size();
        array<YSmoothScaleDataType, n_size> y_scale_value;
        static_for<0, n_size, 1>{}([&](auto i) {
            y_scale_value.at(i) = y_scale_ptr[coords[i]];
        });

        return  y_scale_value;
    }


    template <typename Karg>
    CK_TILE_DEVICE auto operator()(const Karg& kargs,
                                   CK_TILE_LDS_ADDR void* smem,
                                   index_t sorted_tile_id,
                                   index_t intermediate_tile_id)
    {
        constexpr index_t hidden_radio_0            = IsGateOnly ? 1 : 2;
        ck_tile::index_t shared_intermediate_size_0 = kargs.intermediate_size;
        ck_tile::index_t shared_intermediate_size_1 = kargs.intermediate_size / hidden_radio_0;

        index_t nr_0 = shared_intermediate_size_0 / BlockShape::Warp_N0; // divide N in W
        index_t kr_0 = kargs.hidden_size / BlockShape::Warp_K0;          // divide K in W
        index_t nr_1 = kargs.hidden_size / BlockShape::Warp_N1;
        index_t kr_1 = shared_intermediate_size_1 / BlockShape::Warp_K1;

        const IndexDataType expert_id = __builtin_amdgcn_readfirstlane(
            reinterpret_cast<const IndexDataType*>(kargs.sorted_expert_ids_ptr)[sorted_tile_id]);
        index_t expert_stride_0 = shared_intermediate_size_0 * kargs.hidden_size;
        index_t expert_stride_1 = shared_intermediate_size_1 * kargs.hidden_size;
        /////////////
        index_t d_scale_expert_stride_1 = kargs.hidden_size;
        // nr*kr*w
        index_t interm_idx_nr0 = __builtin_amdgcn_readfirstlane(
            intermediate_tile_id *
            BlockShape::Block_Nr0); // intermediate_tile_id * Block_N / (N in W)

        index_t interm_idx_kr1 = __builtin_amdgcn_readfirstlane(
            intermediate_tile_id *
            BlockShape::Block_Kr1); // intermediate_tile_id * Block_N / (N in W)

        auto row_coords_a = GetRowCoords_A(sorted_tile_id * BlockShape::Block_M0);
        auto row_coords_a_mma = GetRowCoords_O(sorted_tile_id * BlockShape::Block_M0);
        auto row_ids_a    = GetRowID(
            row_coords_a, reinterpret_cast<const IndexDataType*>(kargs.sorted_token_ids_ptr));
        auto row_ids_a_mma    = GetRowID(
            row_coords_a_mma, reinterpret_cast<const IndexDataType*>(kargs.sorted_token_ids_ptr));
        auto token_id = generate_tuple(
            [&](auto i) {
                return (row_ids_a[i]) &0xffffff;
            },
            number<row_ids_a.size()>{});
        auto a_coords = generate_tuple(
            [&](auto i) {
                return (token_id[i]) * kargs.stride_token +
                       threadIdx.x % (BlockShape::Block_K0 / kAlignmentA) * kAlignmentA;
            },
            number<token_id.size()>{});
        auto a_res =
            make_wave_buffer_resource(reinterpret_cast<const ADataType*>(kargs.a_ptr),
                                      kargs.num_tokens * kargs.stride_token * sizeof(ADataType));
        ////////
        auto g_win = [&]() {
            const GDataType* g_ptr = reinterpret_cast<const GDataType*>(kargs.g_ptr) +
                                     static_cast<long_index_t>(expert_id) * expert_stride_0 +
                                     interm_idx_nr0 * kr_0 * BlockShape::Block_W0;
            auto g_view_ = make_naive_tensor_view<address_space_enum::global>(
                g_ptr,
                make_tuple(nr_0, kr_0, number<BlockShape::Block_W0>{}),
                make_tuple(kr_0 * BlockShape::Block_W0, number<BlockShape::Block_W0>{}, 1),
                number<kAlignmentG>{},
                number<1>{});

            auto g_window_ = make_tile_window_linear_raw(
                g_view_,
                make_tuple(number<BlockShape::Block_Nr0>{},
                           number<BlockShape::Block_Kr0>{},
                           number<BlockShape::Block_W0>{}),
                {0, 0, 0},
                Policy::template MakeGlobalTileDistribution_G<Problem>(),
                sequence<0, 1, 1>{});
            return g_window_;
        }();

        auto g_res    = g_win.get_bottom_tensor_view().get_buffer_view().cached_buf_res_;
        auto g_coords = generate_tuple([&](auto i) { return g_win.cached_coords_[i].get_offset(); },
                                       number<decltype(g_win)::NumAccess_NonLinear>{});
        /////////////////////
        const auto d_win = [&]() {
            const DDataType* d_ptr = reinterpret_cast<const DDataType*>(kargs.d_ptr) +
                                     static_cast<long_index_t>(expert_id) * expert_stride_1 +
                                     interm_idx_kr1 * BlockShape::Block_W1;
            // note interm_idx_nr0 is along the gemm-k dim of 2nd gemm

            const auto d_view_ = make_naive_tensor_view<address_space_enum::global>(
                d_ptr,
                make_tuple(nr_1, kr_1, BlockShape::Block_W1),
                make_tuple(kr_1 * BlockShape::Block_W1, BlockShape::Block_W1, 1),
                number<kAlignmentD>{},
                number<1>{});

            const auto d_window_ = make_tile_window_linear_raw(
                d_view_,
                make_tuple(number<BlockShape::Block_Nr1>{},
                           number<BlockShape::Block_Kr1>{},
                           number<BlockShape::Block_W1>{}),
                {0, 0, 0},
                Policy::template MakeGlobalTileDistribution_D<Problem>(),
                sequence<0, 1, 1>{});
            return d_window_;
        }();
        auto d_res = d_win.get_bottom_tensor_view().get_buffer_view().cached_buf_res_;
        //////gq
        auto dq_win = [&]() {
            const DScaleDataType* dq_ptr = reinterpret_cast<const DScaleDataType*>(kargs.d_scale_ptr) + 
                                            static_cast<long_index_t>(expert_id) * d_scale_expert_stride_1;
         //   const GDataType* g_ptr = reinterpret_cast<const GScaleDataType*>(kargs.d_scale_ptr)//remember to add expert_id as expert_idx
            auto dq_view_ = make_naive_tensor_view_packed<address_space_enum::global>(
                dq_ptr,
                make_tuple(kargs.hidden_size),
                number<1>{});

            return dq_view_;
        }();

        auto dq_res = dq_win.get_buffer_view().cached_buf_res_;
        ////

        // TODO: load D order is N0.K0...127, N64.K0...127, N0.K128...255, N64.K128...255
        //      block-k=512, block-n=128
        //                    wg                     |<----- W_   ----->|
        //       Nr(2)*Nw(4)* Kr *Kr0(4)*Kr1(4) * [Kl(4)*Nl(16)*Kv(8)]->one issue
        //          y   p          y     y         p     p       y
        //          1              2     0(imm)
        auto d_coords = [&]() {
            constexpr index_t Nr_          = 4;
            constexpr index_t Nw_          = 4;
            // constexpr index_t Kr0_         = 2;//no more need in int8, method changed, this will be handed in res_s
            // constexpr index_t Kr1_         = 4;
            // constexpr index_t Kl_          = 4;
            constexpr index_t Nl_          = 16;
            constexpr index_t Kv_          = 16;
            // constexpr index_t W_           = Kl_ * Nl_ * Kv_;
            //constexpr index_t num_offsets_ = Nr_ * Kr0_;
            constexpr index_t num_offsets_ = Nr_ ;
            index_t base_os_               = (threadIdx.x % 64) * Kv_ + (threadIdx.x / 64) *
                                                              shared_intermediate_size_1 *
                                                              Nl_; // Kr0_ * Kr1_ * W_;
            return generate_tuple(
                [&](auto i) {
                    constexpr auto i_nr_  = number<i % Nr_>{};
                    return i_nr_ * shared_intermediate_size_1 * Nw_ * Nl_ +
                           base_os_;
                },
                number<num_offsets_>{});
        }();

        auto o_coords = generate_tuple(
            [&](auto i) {
                return token_id[i] * kargs.stride_token +
                       threadIdx.x % (BlockShape::Block_N1 / kAlignmentO) * kAlignmentO;
            },
            number<row_ids_a.size()>{});

        auto o_flags =
            generate_tuple([&](auto i) { return cmp_lt_to_exec(token_id[i], kargs.num_tokens); },
                           number<row_ids_a.size()>{});

        auto o_res =
            make_wave_buffer_resource(reinterpret_cast<const ODataType*>(kargs.o_ptr),
                                      kargs.num_tokens * kargs.stride_token * sizeof(ODataType));

        auto row_coords_o = GetRowCoords_O(sorted_tile_id * BlockShape::Block_M0);
        auto w_scale      = GetWeightScale(
            row_coords_o, reinterpret_cast<const TopkWeightDataType*>(kargs.sorted_weight_ptr));
        auto a_scale      = GetAScale(
            row_ids_a_mma, reinterpret_cast<const AScaleDataType*>(kargs.a_scale_ptr));
        auto gqsmq_coords = GetColCoords_GQSMQ(intermediate_tile_id * BlockShape::Block_K1);
        auto dq_coords = gqsmq_coords[0];//only one for this tiling
        auto gq_scale = GetGQScale(
            gqsmq_coords, (reinterpret_cast<const GScaleDataType*>(kargs.g_scale_ptr) + static_cast<long_index_t>(expert_id) * shared_intermediate_size_0));
        auto smq_scale = GetSMQScale(
            gqsmq_coords, (reinterpret_cast<const YSmoothScaleDataType*>(kargs.y_smooth_scale_ptr) + static_cast<long_index_t>(expert_id) * shared_intermediate_size_0));
        auto uk_0  = Policy::template GetUK_0<Problem>();
       // auto acc_0= uk_0(
                    uk_0(a_scale,
                        gq_scale,
                        d_res,
                        dq_res,                     
                        a_res,
                        a_coords,
                        g_res,
                        g_coords,
                          smem,
                          kargs.hidden_size,
                          BlockShape::Block_K0, // tile offset for B matrix each unroll
                          BlockShape::Block_Kr0 *
                          BlockShape::Block_W0); // tile offset for B matrix each unroll
        if(hipBlockIdx_x == 0 && hipBlockIdx_y == 0 && hipBlockIdx_z == 0 &&
    hipThreadIdx_x  == 5)
{
    printf("\ngemm0 done\n");

}
        // sweep_tile(
        //     acc_0,
        //     [&](auto idx0, auto idx1) {
        //         fp32x2_t v_{acc_0(idx0), acc_0(idx1)};
        //         typename Problem::GateActivation{}(v_, v_);
        //         acc_0(idx0) = v_.x;
        //         acc_0(idx1) = v_.y;
        //     },
        //     sequence<1, 2>{});

        // auto y_pre = cast_tile<YDataType>(acc_0);

        // block_sync_lds();

        // store_tile(bridge_sst_win, y_pre);
        // block_sync_lds();

        auto uk_1 = Policy::template GetUK_1<Problem>();
        uk_1(
            // dq_res,
            //  d_res,
             dq_coords,
             d_coords,
             o_res,
             o_coords,
             o_flags,
             smem,
             kargs.hidden_size, // total n number
             w_scale,
             smq_scale,
             BlockShape::Block_N1,
             shared_intermediate_size_1 * BlockShape::Block_N1 - kr_1 * BlockShape::Block_W1, // along N
             kr_1 * BlockShape::Block_W1,
             BlockShape::Block_N1);                               // along N
    }
};

} // namespace ck_tile
