// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include "ck_tile/core.hpp"
#include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qr_ks_vs_default_policy.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"

namespace ck_tile {

// This pipeline is qkv all located in LDS
template <typename Problem_, typename Policy_ = BlockFmhaPipelineQRKSVSDefaultPolicy>
struct [[deprecated]] BlockFmhaPipelineQRKSVSFp8
{
    using Problem             = remove_cvref_t<Problem_>;
    using Policy              = remove_cvref_t<Policy_>;
    using QDataType           = remove_cvref_t<typename Problem::QDataType>;
    using KDataType           = remove_cvref_t<typename Problem::KDataType>;
    using VDataType           = remove_cvref_t<typename Problem::VDataType>;
    using SaccDataType        = remove_cvref_t<typename Problem::SaccDataType>;
    using SMPLComputeDataType = remove_cvref_t<typename Problem::SMPLComputeDataType>;
    using BiasDataType        = remove_cvref_t<typename Problem::BiasDataType>;
    using LSEDataType         = remove_cvref_t<typename Problem::LSEDataType>;
    using PDataType           = remove_cvref_t<typename Problem::PDataType>;
    using OaccDataType        = remove_cvref_t<typename Problem::OaccDataType>;
    using ODataType           = remove_cvref_t<typename Problem::ODataType>;
    using FmhaMask            = remove_cvref_t<typename Problem::FmhaMask>;

    using BlockFmhaShape             = remove_cvref_t<typename Problem::BlockFmhaShape>;
    using VLayout                    = remove_cvref_t<typename BlockFmhaShape::VLayout>;
    static constexpr bool kQLoadOnce = true; // if q_tile load whole block length (hdim) at once
    static_assert(kQLoadOnce == Policy::QLoadOnce);

    static constexpr index_t kBlockSize = Problem::kBlockSize;

    static constexpr index_t kM0            = BlockFmhaShape::kM0;
    static constexpr index_t kN0            = BlockFmhaShape::kN0;
    static constexpr index_t kK0            = BlockFmhaShape::kK0;
    static constexpr index_t kN1            = BlockFmhaShape::kN1;
    static constexpr index_t kK1            = BlockFmhaShape::kK1;
    static constexpr index_t kK0BlockLength = BlockFmhaShape::kK0BlockLength;

    static constexpr bool kIsGroupMode = Problem::kIsGroupMode;
    static constexpr bool kPadSeqLenQ  = Problem::kPadSeqLenQ;
    static constexpr bool kPadSeqLenK  = Problem::kPadSeqLenK;
    static constexpr bool kPadHeadDimQ = Problem::kPadHeadDimQ;
    static constexpr bool kPadHeadDimV = Problem::kPadHeadDimV;
    static constexpr bool kHasBias     = Problem::kHasBias;
    static constexpr bool kStoreLSE    = Problem::kStoreLSE;

    // last dimension vector length used to create tensor view(and decide buffer_load vector length)
    // ... together with tensor distribution. tensor dist should able to overwrite this
    static constexpr index_t kAlignmentQ =
        kPadHeadDimQ ? 1 : Policy::template GetAlignmentQ<Problem>();
    static constexpr index_t kAlignmentK =
        kPadHeadDimQ ? 1 : Policy::template GetAlignmentK<Problem>();
    static constexpr index_t kAlignmentV = []() {
        if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
            return kPadHeadDimV ? 1 : Policy::template GetAlignmentV<Problem>();
        else
            return kPadSeqLenK ? 1 : Policy::template GetAlignmentV<Problem>();
    }();

    static constexpr index_t kAlignmentO =
        kPadHeadDimV ? 1 : Policy::template GetAlignmentO<Problem>();
    static constexpr index_t kAlignmentBias =
        kPadSeqLenK ? 1 : Policy::template GetAlignmentBias<Problem>();

    static constexpr index_t kBlockPerCu = []() {
        if constexpr(Problem::kBlockPerCu != -1)
            return Problem::kBlockPerCu;
        else
        {
            if constexpr(kK0BlockLength <= 32)
            {
                return 2;
            }
            else if constexpr(kK0BlockLength <= 64)
            {
                return 3;
            }
            else if constexpr(kK0BlockLength <= 128)
            {
                if constexpr(kHasBias)
                    return 1;
                else
                    return 2;
            }
            else if constexpr(kK0BlockLength <= 256)
            {
                return 1;
            }
        }
    }();

    static constexpr const char* name = "qr_fp8";

    CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
    {
        return Policy::template GetSmemSize<Problem>();
    }

    template <typename QDramBlockWindowTmp,
              typename KDramBlockWindowTmp,
              typename VDramBlockWindowTmp,
              typename BiasDramBlockWindowTmp,
              typename LSEDramBlockWindowTmp>
    CK_TILE_HOST_DEVICE auto
    operator()(const QDramBlockWindowTmp& q_dram_block_window_tmp,       // M0*K0 tile
               const KDramBlockWindowTmp& k_dram_block_window_tmp,       // N0*K0 tile
               const VDramBlockWindowTmp& v_dram_block_window_tmp,       // N1*K1 tile
               const BiasDramBlockWindowTmp& bias_dram_block_window_tmp, // M0*N0 tile
               LSEDramBlockWindowTmp& /*lse_dram_window_tmp*/,           // not supported
               FmhaMask mask,
               float scale_s,
               float descale_qk,
               float descale_sv,
               void* smem_ptr) const
    {
        static_assert(
            std::is_same_v<QDataType, remove_cvref_t<typename QDramBlockWindowTmp::DataType>> &&
                std::is_same_v<KDataType, remove_cvref_t<typename KDramBlockWindowTmp::DataType>> &&
                std::is_same_v<VDataType, remove_cvref_t<typename VDramBlockWindowTmp::DataType>>,
            "wrong!");

        static_assert(kM0 == QDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
                          kN0 == KDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
                          kK0 == KDramBlockWindowTmp{}.get_window_lengths()[number<1>{}] &&
                          kN1 == VDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
                          kK1 == VDramBlockWindowTmp{}.get_window_lengths()[number<1>{}] &&
                          kM0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<0>{}] &&
                          kN0 == BiasDramBlockWindowTmp{}.get_window_lengths()[number<1>{}],
                      "wrong!");

        // K tile in LDS
        KDataType* k_lds_ptr = static_cast<KDataType*>(static_cast<void*>(
            static_cast<char*>(smem_ptr) + Policy::template GetSmemSizeQ<Problem>()));
        auto k_lds           = make_tensor_view<address_space_enum::lds>(
            k_lds_ptr, Policy::template MakeKLdsBlockDescriptor<Problem>());
        auto k_lds_window =
            make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});

        // V tile in LDS
        auto v_lds = make_tensor_view<address_space_enum::lds>(
            reinterpret_cast<VDataType*>(smem_ptr),
            Policy::template MakeVLdsBlockDescriptor<Problem>());
        auto v_lds_window = make_tile_window(
            v_lds, Policy::template MakeVLdsBlockDescriptor<Problem>().get_lengths(), {0, 0});

        // Block GEMM
        constexpr auto gemm_0 = Policy::template GetQKBlockGemm<Problem>();
        constexpr auto gemm_1 = Policy::template GetKVBlockGemm<Problem>();

        auto q_dram_window = make_tile_window(
            q_dram_block_window_tmp.get_bottom_tensor_view(),
            q_dram_block_window_tmp.get_window_lengths(),
            q_dram_block_window_tmp.get_window_origin(),
            Policy::template MakeQDramTileDistribution<Problem, decltype(gemm_0)>());

        auto q = load_tile(q_dram_window);

        using SaccBlockTileType = decltype(gemm_0.MakeCBlockTile());
        auto s_acc              = SaccBlockTileType{};

        // reduction function for softmax
        const auto f_max = [](auto e0, auto e1) { return max(e0, e1); };
        const auto f_sum = [](auto e0, auto e1) { return e0 + e1; };

        // infer Sacc, S, P, M, L, Oacc type
        using SBlockTileType = decltype(cast_tile<SMPLComputeDataType>(s_acc));

        using MLBlockTileType = decltype(block_tile_reduce<SMPLComputeDataType>(
            SBlockTileType{}, sequence<1>{}, f_max, SMPLComputeDataType{0}));

        using OaccBlockTileType = decltype(gemm_1.MakeCBlockTile());

        // init Oacc, M, L
        auto o_acc = OaccBlockTileType{};
        auto m     = MLBlockTileType{};
        auto l     = MLBlockTileType{};

        clear_tile(o_acc);
        set_tile(m, -numeric<SMPLComputeDataType>::infinity());
        clear_tile(l);

        const auto q_origin = q_dram_window.get_window_origin();
        const auto [seqlen_k_start, seqlen_k_end] =
            mask.GetTileRangeAlongX(q_origin.at(number<0>{}), number<kM0>{}, number<kN0>{});

        const auto num_total_loop = integer_divide_ceil(seqlen_k_end - seqlen_k_start, kN0);

        // check early exit if masked and no work to do.
        if constexpr(FmhaMask::IsMasking)
        {
            if(num_total_loop <= 0)
            {
                // Note: here occ are all cleard, return it
                // Note: q loaded but no fence, ignore it.
                return o_acc;
            }
        }

        auto k_dram_block_window =
            make_tile_window(k_dram_block_window_tmp.get_bottom_tensor_view(),
                             k_dram_block_window_tmp.get_window_lengths(),
                             {seqlen_k_start, 0});

        const auto bias_origin = bias_dram_block_window_tmp.get_window_origin();
        auto bias_dram_window  = make_tile_window(
            bias_dram_block_window_tmp.get_bottom_tensor_view(),
            bias_dram_block_window_tmp.get_window_lengths(),
            {bias_origin.at(number<0>{}), seqlen_k_start}, // M/N
            Policy::template MakeBiasDramTileDistribution<Problem, decltype(gemm_0)>());

        auto v_dram_window =
            make_tile_window(v_dram_block_window_tmp.get_bottom_tensor_view(),
                             v_dram_block_window_tmp.get_window_lengths(),
                             {0, seqlen_k_start}, // TODO: hdim split?
                             Policy::template MakeVDramTileDistribution<Problem>());

        // auto q_tile = tile_elementwise_in(q_element_func, q);
        auto q_tile = q;

        // prefetch K tile
        index_t i_total_loops      = 0;
        constexpr index_t k0_loops = kK0BlockLength / kK0;
        constexpr index_t k1_loops = kN0 / kK1;

        static_assert(2 <= k0_loops);
        static_assert(1 <= k1_loops);

        scale_s = scale_s * descale_qk;
        do
        {
            // STAGE 1, QK gemm
            auto k_dram_window = make_tile_window(
                k_dram_block_window.get_bottom_tensor_view(),
                k_dram_block_window.get_window_lengths(),
                k_dram_block_window.get_window_origin(),
                Policy::template MakeKDramTileDistribution<Problem>()); // K DRAM tile window for
                                                                        // load

            auto k_block_tile = load_tile(k_dram_window);
            {
                move_tile_window(k_dram_window, {0, kK0});
                clear_tile(s_acc); // initialize C
                store_tile(k_lds_window, k_block_tile);
                k_block_tile = load_tile(k_dram_window);
            }

            if constexpr(kHasBias)
            {
                __builtin_amdgcn_sched_barrier(
                    0); // prevent from messing up the order of global loads
            }
            const auto bias_tile = load_tile(bias_dram_window); // load bias tile
            if constexpr(kHasBias)
            {
                __builtin_amdgcn_sched_barrier(
                    0); // prevent from messing up the order of global loads
            }

            if constexpr(k0_loops > 2)
            {
                static_for<0, k0_loops - 2, 1>{}([&](auto i_k0) {
                    block_sync_lds();
                    gemm_0(s_acc,
                           get_slice_tile(q_tile,
                                          sequence<0, i_k0 * kK0>{},
                                          sequence<kM0, (i_k0 + 1) * kK0>{}),
                           k_lds_window);
                    block_sync_lds();
                    move_tile_window(k_dram_window, {0, kK0});

                    store_tile(k_lds_window,
                               k_block_tile);                // LDS write i + 1
                    k_block_tile = load_tile(k_dram_window); // global read i + 2
                });
            }

            const auto v_prefetch = load_tile(v_dram_window); // prefetch load v tile
            {                                                 // tail
                block_sync_lds();
                gemm_0(s_acc,
                       get_slice_tile(q_tile,
                                      sequence<0, (k0_loops - 2) * kK0>{},
                                      sequence<kM0, (k0_loops - 1) * kK0>{}),
                       k_lds_window);
                block_sync_lds();

                store_tile(k_lds_window, k_block_tile);
                block_sync_lds();

                gemm_0(s_acc,
                       get_slice_tile(q_tile,
                                      sequence<0, (k0_loops - 1) * kK0>{},
                                      sequence<kM0, k0_loops * kK0>{}),
                       k_lds_window);
            }

            // STAGE 2, scale_s, add bias, mask, softmax
            if constexpr(kHasBias)
            {
                tile_elementwise_inout(
                    [&](auto& x, const auto& y) {
#if !CK_TILE_FMHA_FWD_FAST_EXP2
                        x = scale_s * x + type_convert<SaccDataType>((y));
#else
                        x = scale_s * x + log2e_v<SaccDataType> * type_convert<SaccDataType>((y));
#endif
                    },
                    s_acc,
                    bias_tile);
            }
            else
            {
#if !CK_TILE_FMHA_FWD_FAST_EXP2
                tile_elementwise_inout([&scale_s](auto& x) { x = x * scale_s; }, s_acc);
#endif
            }
            move_tile_window(bias_dram_window, {0, kN0});
            if constexpr(kPadSeqLenK || FmhaMask::IsMasking)
            {
                const auto k_origin      = k_dram_block_window.get_window_origin();
                bool need_perpixel_check = mask.IsEdgeTile(q_origin.at(number<0>{}),
                                                           k_origin.at(number<0>{}),
                                                           number<kM0>{},
                                                           number<kN0>{});
                if(need_perpixel_check)
                {
                    set_tile_if(
                        s_acc, -numeric<SMPLComputeDataType>::infinity(), [&](auto tile_idx) {
                            const auto row = q_origin.at(number<0>{}) + tile_idx.at(number<0>{});
                            const auto col = k_origin.at(number<0>{}) + tile_idx.at(number<1>{});
                            return mask.IsOutOfBound(row, col);
                        });
                }
            }

            const auto s = cast_tile<SMPLComputeDataType>(s_acc); // S{j}
            auto m_local = block_tile_reduce<SMPLComputeDataType>(
                s,
                sequence<1>{},
                f_max,
                -numeric<SMPLComputeDataType>::infinity()); // m_local = rowmax(S{j})
            block_tile_reduce_sync(m_local, f_max, bool_constant<false>{});

            const auto m_old = m; // m{j-1}
            tile_elementwise_inout(
                [](auto& e0, auto e1, auto e2) { e0 = max(e1, e2); }, m, m_old, m_local); // m{j}

            auto p_compute = make_static_distributed_tensor<SMPLComputeDataType>(
                s.get_tile_distribution()); // Pcompute{j}

            static const auto get_validated_m = [](SMPLComputeDataType raw_m) {
                /// NOTICE: bias might be materialized mask including -inf values, need
                /// consideration
                if constexpr(kHasBias || FmhaMask::IsMasking)
                {
                    return raw_m == -numeric<SMPLComputeDataType>::infinity()
                               ? type_convert<SMPLComputeDataType>(0.f)
                               : raw_m;
                }
                else
                {
                    return raw_m;
                }
            };

            constexpr auto p_spans = decltype(p_compute)::get_distributed_spans();
            sweep_tile_span(p_spans[number<0>{}], [&](auto idx0) {
                constexpr auto i_idx = make_tuple(idx0);
#if CK_TILE_FMHA_FWD_FAST_EXP2
                auto row_max = scale_s * get_validated_m(m[i_idx]);
#endif
                sweep_tile_span(p_spans[number<1>{}], [&](auto idx1) {
                    constexpr auto i_j_idx = make_tuple(idx0, idx1);
#if CK_TILE_FMHA_FWD_FAST_EXP2
                    if constexpr(kHasBias)
                    {
                        p_compute(i_j_idx) = exp2(s[i_j_idx] - get_validated_m(m[i_idx]));
                    }
                    else
                    {
                        p_compute(i_j_idx) = exp2(scale_s * s[i_j_idx] - row_max);
                    }
#else
                    p_compute(i_j_idx)     = exp(s[i_j_idx] - get_validated_m(m[i_idx]));
#endif
                });
            });

            auto rowsum_p = block_tile_reduce<SMPLComputeDataType>(
                p_compute, sequence<1>{}, f_sum, SMPLComputeDataType{0}); // rowsum(Pcompute{j})

            block_tile_reduce_sync(rowsum_p, f_sum, bool_constant<false>{});
            // l{j}, Oacc{j}
            constexpr auto o_spans = decltype(o_acc)::get_distributed_spans();
            sweep_tile_span(o_spans[number<0>{}], [&](auto idx0) {
                constexpr auto i_idx = make_tuple(idx0);
#if CK_TILE_FMHA_FWD_FAST_EXP2
                const auto tmp = [&]() {
                    if constexpr(kHasBias)
                    {
                        return exp2(m_old[i_idx] - get_validated_m(m[i_idx]));
                    }
                    else
                    {
                        auto row_max = scale_s * get_validated_m(m[i_idx]);
                        return exp2(scale_s * m_old[i_idx] - row_max);
                    }
                }();
#else
                const auto tmp       = exp(m_old[i_idx] - get_validated_m(m[i_idx]));
#endif
                l(i_idx) = tmp * l[i_idx] + rowsum_p[i_idx];
                sweep_tile_span(o_spans[number<1>{}], [&](auto idx1) {
                    constexpr auto i_j_idx = make_tuple(idx0, idx1);
                    // FIXME: this use different equation from FA v2 paper,
                    // but produce correc result.
                    // Is the equation wrong?
                    o_acc(i_j_idx) *= tmp;
                });
            });

            block_sync_lds();
            if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
            {
                auto v_shuffle_tmp = make_static_distributed_tensor<VDataType>(
                    Policy::template MakeShuffledVRegBlockDescriptor<Problem>());
                shuffle_tile(v_shuffle_tmp, v_prefetch);
                store_tile(v_lds_window,
                           v_shuffle_tmp); // store the prefetch
            }
            else
            {
                store_tile(v_lds_window,
                           v_prefetch); // store the prefetch
            }
            move_tile_window(v_dram_window, {0, kK1});

            const auto p = cast_tile<PDataType>(p_compute);

            // STAGE 3, KV gemm
            if constexpr(k1_loops > 1)
            {
                static_for<0, k1_loops - 1, 1>{}([&](auto i_k1) {
                    const auto v = load_tile(v_dram_window); // load next v
                    block_sync_lds();
                    gemm_1(o_acc,
                           get_slice_tile(
                               p, sequence<0, i_k1 * kK1>{}, sequence<kM0, (i_k1 + 1) * kK1>{}),
                           v_lds_window);
                    block_sync_lds();
                    if constexpr(std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor>)
                    {
                        auto v_shuffle_tmp = make_static_distributed_tensor<VDataType>(
                            Policy::template MakeShuffledVRegBlockDescriptor<Problem>());
                        shuffle_tile(v_shuffle_tmp, v);
                        store_tile(v_lds_window, v_shuffle_tmp);
                    }
                    else
                    {
                        store_tile(v_lds_window, v);
                    }
                    move_tile_window(v_dram_window, {0, kK1});
                });
            }
            // move K tile windows
            move_tile_window(k_dram_block_window, {kN0, 0});
            // tail
            {
                block_sync_lds();
                gemm_1(o_acc,
                       get_slice_tile(p, sequence<0, (k1_loops - 1) * kK1>{}, sequence<kM0, kN0>{}),
                       v_lds_window);
                block_sync_lds();
            }
        } while(++i_total_loops < num_total_loop);

        // finally, O
        constexpr auto o_spans = decltype(o_acc)::get_distributed_spans();

        sweep_tile_span(o_spans[number<0>{}], [&](auto idx0) {
            constexpr auto i_idx = make_tuple(idx0);
            auto tmp             = [&]() {
                if constexpr(FmhaMask::IsMasking)
                {
                    return l[i_idx] == 0.f ? 0.f : 1 / l[i_idx];
                }
                else
                    return 1 / l[i_idx];
            }();
            tmp = tmp * descale_sv;
            sweep_tile_span(o_spans[number<1>{}], [&](auto idx1) {
                constexpr auto i_j_idx = make_tuple(idx0, idx1);
                o_acc(i_j_idx) *= tmp;
            });
        });

        return o_acc;
    }
};

} // namespace ck_tile
