// 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/host/kernel_launch.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/fmha.hpp"

#include "bias.hpp"
#include "mask.hpp"
#include "rotary.hpp"

#include <array>
#include <type_traits>
#include <utility>
#include <variant>

struct FmhaFwdFp16
{
};

struct FmhaFwdBf16
{
};

struct FmhaFwdFp8
{
};

struct FmhaFwdBf8
{
};

struct FmhaFwdFp8Fp16
{
};

struct FmhaFwdFp8Bf16
{
};

template <typename DataType>
struct FmhaFwdTypeConfig;

template <>
struct FmhaFwdTypeConfig<FmhaFwdFp16>
{
    using QDataType             = ck_tile::half_t;
    using KDataType             = ck_tile::half_t;
    using VDataType             = ck_tile::half_t;
    using BiasDataType          = ck_tile::half_t;
    using RandValOutputDataType = uint8_t;
    using LSEDataType           = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
    using SaccDataType          = float; // data type for first gemm accumulation
    using SMPLComputeDataType   = float; // data type for reduction, softmax
    using PDataType             = ck_tile::half_t; // data type for A matrix of second gemm
    using OaccDataType          = float;           // data type for second gemm accumulation
    using ODataType             = ck_tile::half_t;
};

template <>
struct FmhaFwdTypeConfig<FmhaFwdBf16>
{
    using QDataType             = ck_tile::bf16_t;
    using KDataType             = ck_tile::bf16_t;
    using VDataType             = ck_tile::bf16_t;
    using BiasDataType          = ck_tile::bf16_t;
    using RandValOutputDataType = uint8_t;
    using LSEDataType           = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
    using SaccDataType          = float; // data type for first gemm accumulation
    using SMPLComputeDataType   = float; // data type for reduction, softmax
    using PDataType             = ck_tile::bf16_t; // data type for A matrix of second gemm
    using OaccDataType          = float;           // data type for second gemm accumulation
    using ODataType             = ck_tile::bf16_t;
};

template <>
struct FmhaFwdTypeConfig<FmhaFwdFp8>
{
    using QDataType             = ck_tile::fp8_t;
    using KDataType             = ck_tile::fp8_t;
    using VDataType             = ck_tile::fp8_t;
    using BiasDataType          = float;
    using RandValOutputDataType = uint8_t;
    using LSEDataType           = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
    using SaccDataType          = float; // data type for first gemm accumulation
    using SMPLComputeDataType   = float; // data type for reduction, softmax
    using PDataType             = ck_tile::fp8_t; // data type for A matrix of second gemm
    using OaccDataType          = float;          // data type for second gemm accumulation
    using ODataType             = ck_tile::fp8_t;
};

template <>
struct FmhaFwdTypeConfig<FmhaFwdBf8>
{
    using QDataType             = ck_tile::bf8_t;
    using KDataType             = ck_tile::bf8_t;
    using VDataType             = ck_tile::bf8_t;
    using BiasDataType          = ck_tile::bf8_t;
    using RandValOutputDataType = uint8_t;
    using LSEDataType           = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
    using SaccDataType          = float; // data type for first gemm accumulation
    using SMPLComputeDataType   = float; // data type for reduction, softmax
    using PDataType             = ck_tile::bf8_t; // data type for A matrix of second gemm
    using OaccDataType          = float;          // data type for second gemm accumulation
    using ODataType             = ck_tile::bf8_t;
};

struct FmhaMasks
{
    using NoMask      = ck_tile::GenericAttentionMask<false>;
    using GenericMask = ck_tile::GenericAttentionMask<true, true>;
    using CausalMask  = ck_tile::GenericAttentionMask<true, false>;
};

// runtime args, some will passed to karg, some will used to compute grids/blocks
struct fmha_fwd_args
{
    const void* q_ptr;
    const void* k_ptr;
    const void* v_ptr;
    const void* bias_ptr; // bias or alibi_slope pointer
    void* rand_val_ptr;
    void* lse_ptr;
    void* o_ptr;

    const void* seqstart_q_ptr;
    const void* seqstart_k_ptr;
    const void*
        seqlen_k_ptr; // only used if both 'seqstart_q_ptr' & 'seqstart_k_ptr' are not nullptr

    ck_tile::index_t seqlen_q;
    ck_tile::index_t seqlen_k;
    ck_tile::index_t batch;
    ck_tile::index_t max_seqlen_q;
    ck_tile::index_t hdim_q;
    ck_tile::index_t hdim_v;
    ck_tile::index_t nhead_q;
    ck_tile::index_t nhead_k;

    float scale_s;
    float scale_p;
    float scale_o;

    ck_tile::index_t stride_q;
    ck_tile::index_t stride_k;
    ck_tile::index_t stride_v;
    ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
    ck_tile::index_t stride_randval;
    ck_tile::index_t stride_o;
    ck_tile::index_t nhead_stride_q;
    ck_tile::index_t nhead_stride_k;
    ck_tile::index_t nhead_stride_v;
    ck_tile::index_t nhead_stride_bias;
    ck_tile::index_t nhead_stride_randval;
    ck_tile::index_t nhead_stride_lse;
    ck_tile::index_t nhead_stride_o;
    ck_tile::index_t batch_stride_q;
    ck_tile::index_t batch_stride_k;
    ck_tile::index_t batch_stride_v;
    ck_tile::index_t batch_stride_bias;
    ck_tile::index_t batch_stride_randval;
    ck_tile::index_t batch_stride_lse;
    ck_tile::index_t batch_stride_o;

    ck_tile::index_t window_size_left;
    ck_tile::index_t window_size_right;
    ck_tile::index_t mask_type;

    float p_drop;
    bool s_randval;

    std::variant<std::pair<uint64_t, uint64_t>, std::pair<const void*, const void*>>
        drop_seed_offset;
};

struct fmha_fwd_splitkv_args
{
    const void* q_ptr;
    const void* k_ptr;
    const void* v_ptr;
    const void* bias_ptr; // bias or alibi_slope pointer
    void* lse_acc_ptr;
    void* o_acc_ptr;
    void* lse_ptr;
    void* o_ptr;

    void* block_table_ptr;
    ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
    ck_tile::index_t page_block_size;          // only used if 'block_table_ptr' is not nullptr
    bool is_gappy; // differentiate seqstart_k_ptr usage. only used if 'block_table_ptr' is not
                   // nullptr.

    const void* cache_batch_idx;

    // the real seqlen_q & seqlen_k are decided by following:
    // batch mode: seqlen_q = kargs.seqlen_q
    //             seqlen_k = kargs.seqlen_k
    // group mode: seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
    //             seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
    //                      or kargs.seqlen_k_ptr[b]
    //
    // batch mode (kvcache):
    //             seqlen_q = kargs.seqlen_q
    //             seqlen_k = kargs.seqlen_k_ptr[b]
    // group mode (kvcache):
    //             seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
    //
    //     when is_gappy=true:
    //             seqlen_k = kargs.seqlen_k_ptr[b]
    //             seqstart_k_ptr[b] now store local offset of each batch
    //
    //     when is_gappy=false:
    //             seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
    //                      or kargs.seqlen_k_ptr[b]
    const void* seqstart_q_ptr;
    const void* seqstart_k_ptr;
    const void* seqlen_k_ptr;

    ck_tile::index_t seqlen_q;
    ck_tile::index_t seqlen_k;
    ck_tile::index_t batch;
    ck_tile::index_t max_seqlen_q;
    ck_tile::index_t hdim_q;
    ck_tile::index_t hdim_v;
    ck_tile::index_t nhead_q;
    ck_tile::index_t nhead_k;
    ck_tile::index_t num_splits;

    float scale_s;
    float scale_p;
    float scale_o;

    ck_tile::index_t stride_q;
    ck_tile::index_t stride_k;
    ck_tile::index_t stride_v;
    ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
    ck_tile::index_t stride_o_acc;
    ck_tile::index_t stride_o;
    ck_tile::index_t nhead_stride_q;
    ck_tile::index_t nhead_stride_k;
    ck_tile::index_t nhead_stride_v;
    ck_tile::index_t nhead_stride_bias;
    ck_tile::index_t nhead_stride_lse;
    ck_tile::index_t nhead_stride_lse_acc;
    ck_tile::index_t nhead_stride_o_acc;
    ck_tile::index_t nhead_stride_o;
    ck_tile::index_t batch_stride_q;
    ck_tile::index_t batch_stride_k;
    ck_tile::index_t batch_stride_v;
    ck_tile::index_t batch_stride_bias;
    ck_tile::index_t batch_stride_lse;
    ck_tile::index_t batch_stride_lse_acc;
    ck_tile::index_t batch_stride_o_acc;
    ck_tile::index_t batch_stride_o;
    ck_tile::index_t split_stride_lse_acc;
    ck_tile::index_t split_stride_o_acc;

    ck_tile::index_t window_size_left;
    ck_tile::index_t window_size_right;
    ck_tile::index_t mask_type;
};

struct fmha_fwd_appendkv_args
{
    void* q_ptr;
    void* k_ptr;
    const void* knew_ptr;
    void* v_ptr;
    const void* vnew_ptr;

    const void* seqlen_k_ptr;

    ck_tile::index_t seqlen_q;
    ck_tile::index_t seqlen_knew;
    ck_tile::index_t batch;
    ck_tile::index_t hdim_q;
    ck_tile::index_t hdim_v;
    ck_tile::index_t nhead_q;
    ck_tile::index_t nhead_k;

    const void* rotary_cos_ptr; // only used if 'rotary_dim' > 0
    const void* rotary_sin_ptr; // only used if 'rotary_dim' > 0
    ck_tile::index_t rotary_dim;
    bool has_mask;

    void* block_table_ptr;
    ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
    ck_tile::index_t page_block_size;          // only used if 'block_table_ptr' is not nullptr

    const void* cache_batch_idx; // only used if block_table_ptr is nullptr -> batch mode (kvcache)

    ck_tile::index_t stride_q;
    ck_tile::index_t stride_k;
    ck_tile::index_t stride_knew;
    ck_tile::index_t stride_v;
    ck_tile::index_t stride_vnew;
    ck_tile::index_t nhead_stride_q;
    ck_tile::index_t nhead_stride_k;
    ck_tile::index_t nhead_stride_knew;
    ck_tile::index_t nhead_stride_v;
    ck_tile::index_t nhead_stride_vnew;
    ck_tile::index_t batch_stride_q;
    ck_tile::index_t batch_stride_k;
    ck_tile::index_t batch_stride_knew;
    ck_tile::index_t batch_stride_v;
    ck_tile::index_t batch_stride_vnew;
};

template <typename FmhaKernel>
auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
{
    assert(args.nhead_q % args.nhead_k == 0);
    auto kargs = [&] {
        // create group mode kernel arguments
        if constexpr(FmhaKernel::kIsGroupMode)
        {
            return FmhaKernel::MakeKargsImpl(args.q_ptr,
                                             args.k_ptr,
                                             args.v_ptr,
                                             args.bias_ptr,
                                             args.rand_val_ptr,
                                             args.lse_ptr,
                                             args.o_ptr,
                                             args.seqstart_q_ptr,
                                             args.seqstart_k_ptr,
                                             args.seqlen_k_ptr,
                                             args.hdim_q,
                                             args.hdim_v,
                                             args.nhead_q,
                                             args.nhead_q / args.nhead_k,
                                             args.scale_s,
                                             args.scale_p,
                                             args.scale_o,
                                             args.stride_q,
                                             args.stride_k,
                                             args.stride_v,
                                             args.stride_bias,
                                             args.stride_randval,
                                             args.stride_o,
                                             args.nhead_stride_q,
                                             args.nhead_stride_k,
                                             args.nhead_stride_v,
                                             args.nhead_stride_bias,
                                             args.nhead_stride_randval,
                                             args.nhead_stride_lse,
                                             args.nhead_stride_o,
                                             args.window_size_left,
                                             args.window_size_right,
                                             args.mask_type,
                                             args.p_drop,
                                             args.s_randval,
                                             args.drop_seed_offset);
        }
        else
        { // create batch mode kernel arguments
            return FmhaKernel::MakeKargsImpl(args.q_ptr,
                                             args.k_ptr,
                                             args.v_ptr,
                                             args.bias_ptr,
                                             args.rand_val_ptr,
                                             args.lse_ptr,
                                             args.o_ptr,
                                             args.seqlen_q,
                                             args.seqlen_k,
                                             args.hdim_q,
                                             args.hdim_v,
                                             args.nhead_q,
                                             args.nhead_q / args.nhead_k,
                                             args.scale_s,
                                             args.scale_p,
                                             args.scale_o,
                                             args.stride_q,
                                             args.stride_k,
                                             args.stride_v,
                                             args.stride_bias,
                                             args.stride_randval,
                                             args.stride_o,
                                             args.nhead_stride_q,
                                             args.nhead_stride_k,
                                             args.nhead_stride_v,
                                             args.nhead_stride_bias,
                                             args.nhead_stride_randval,
                                             args.nhead_stride_lse,
                                             args.nhead_stride_o,
                                             args.batch_stride_q,
                                             args.batch_stride_k,
                                             args.batch_stride_v,
                                             args.batch_stride_bias,
                                             args.batch_stride_randval,
                                             args.batch_stride_lse,
                                             args.batch_stride_o,
                                             args.window_size_left,
                                             args.window_size_right,
                                             args.mask_type,
                                             args.p_drop,
                                             args.s_randval,
                                             args.drop_seed_offset);
        }
    }();

    if constexpr(FmhaKernel::kIsGroupMode)
    {
        dim3 grids = FmhaKernel::GridSize(
            args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, args.seqlen_k_ptr != nullptr);
        return ck_tile::make_tuple(kargs, grids);
    }
    else
    {
        dim3 grids =
            FmhaKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, false);
        return ck_tile::make_tuple(kargs, grids);
    }
}

template <typename Kernel>
auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
{
    assert(args.nhead_q % args.nhead_k == 0);
    auto kargs = [&] {
        // create group mode kernel arguments
        if constexpr(Kernel::kIsGroupMode)
        {
            return Kernel::MakeKargs(
                args.q_ptr,
                args.k_ptr,
                args.v_ptr,
                args.bias_ptr,
                (1 < args.num_splits ? args.lse_acc_ptr : args.lse_ptr),
                (1 < args.num_splits ? args.o_acc_ptr : args.o_ptr),
                args.batch,
                args.seqstart_q_ptr,
                args.seqstart_k_ptr,
                args.seqlen_k_ptr,
                args.hdim_q,
                args.hdim_v,
                args.nhead_q,
                args.nhead_q / args.nhead_k,
                args.num_splits,
                args.block_table_ptr,
                args.batch_stride_block_table,
                args.page_block_size,
                args.is_gappy,
                args.scale_s,
                args.scale_p,
                args.stride_q,
                args.stride_k,
                args.stride_v,
                args.stride_bias,
                (1 < args.num_splits ? args.stride_o_acc : args.stride_o),
                args.nhead_stride_q,
                args.nhead_stride_k,
                args.nhead_stride_v,
                args.nhead_stride_bias,
                (1 < args.num_splits ? args.nhead_stride_lse_acc : args.nhead_stride_lse),
                (1 < args.num_splits ? args.nhead_stride_o_acc : args.nhead_stride_o),
                args.batch_stride_k, // only used for paged-kvcache
                args.batch_stride_v, // only used for paged-kvcache
                (1 < args.num_splits ? args.split_stride_lse_acc : 0),
                (1 < args.num_splits ? args.split_stride_o_acc : 0),
                args.window_size_left,
                args.window_size_right,
                args.mask_type);
        }
        else
        { // create batch mode kernel arguments
            return Kernel::MakeKargs(
                args.q_ptr,
                args.k_ptr,
                args.v_ptr,
                args.bias_ptr,
                (1 < args.num_splits ? args.lse_acc_ptr : args.lse_ptr),
                (1 < args.num_splits ? args.o_acc_ptr : args.o_ptr),
                args.batch,
                args.seqlen_q,
                args.seqlen_k,
                args.seqlen_k_ptr,
                args.hdim_q,
                args.hdim_v,
                args.nhead_q,
                args.nhead_q / args.nhead_k,
                args.num_splits,
                args.block_table_ptr,
                args.batch_stride_block_table,
                args.page_block_size,
                args.cache_batch_idx,
                args.scale_s,
                args.scale_p,
                args.stride_q,
                args.stride_k,
                args.stride_v,
                args.stride_bias,
                (1 < args.num_splits ? args.stride_o_acc : args.stride_o),
                args.nhead_stride_q,
                args.nhead_stride_k,
                args.nhead_stride_v,
                args.nhead_stride_bias,
                (1 < args.num_splits ? args.nhead_stride_lse_acc : args.nhead_stride_lse),
                (1 < args.num_splits ? args.nhead_stride_o_acc : args.nhead_stride_o),
                args.batch_stride_q,
                args.batch_stride_k,
                args.batch_stride_v,
                args.batch_stride_bias,
                (1 < args.num_splits ? args.batch_stride_lse_acc : args.batch_stride_lse),
                (1 < args.num_splits ? args.batch_stride_o_acc : args.batch_stride_o),
                (1 < args.num_splits ? args.split_stride_lse_acc : 0),
                (1 < args.num_splits ? args.split_stride_o_acc : 0),
                args.window_size_left,
                args.window_size_right,
                args.mask_type);
        }
    }();

    dim3 grids =
        Kernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, args.num_splits);

    return ck_tile::make_tuple(kargs, grids);
}

template <typename Kernel>
auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_splitkv_args args)
{
    assert(args.nhead_q % args.nhead_k == 0);
    auto kargs = [&] {
        // create group mode kernel argumentszs
        if constexpr(Kernel::kIsGroupMode)
        {
            return Kernel::MakeKargs(args.lse_acc_ptr,
                                     args.o_acc_ptr,
                                     args.lse_ptr,
                                     args.o_ptr,
                                     args.batch,
                                     args.seqstart_q_ptr,
                                     args.hdim_v,
                                     args.num_splits,
                                     args.scale_o,
                                     args.stride_o_acc,
                                     args.stride_o,
                                     args.nhead_stride_lse_acc,
                                     args.nhead_stride_o_acc,
                                     args.nhead_stride_lse,
                                     args.nhead_stride_o,
                                     args.split_stride_lse_acc,
                                     args.split_stride_o_acc);
        }
        else
        { // create batch mode kernel arguments
            return Kernel::MakeKargs(args.lse_acc_ptr,
                                     args.o_acc_ptr,
                                     args.lse_ptr,
                                     args.o_ptr,
                                     args.batch,
                                     args.seqlen_q,
                                     args.hdim_v,
                                     args.num_splits,
                                     args.scale_o,
                                     args.stride_o_acc,
                                     args.stride_o,
                                     args.nhead_stride_lse_acc,
                                     args.nhead_stride_o_acc,
                                     args.nhead_stride_lse,
                                     args.nhead_stride_o,
                                     args.batch_stride_lse_acc,
                                     args.batch_stride_o_acc,
                                     args.batch_stride_lse,
                                     args.batch_stride_o,
                                     args.split_stride_lse_acc,
                                     args.split_stride_o_acc);
        }
    }();

    dim3 grids = Kernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v);

    return ck_tile::make_tuple(kargs, grids);
}

template <typename Kernel>
auto fmha_fwd_appendkv_create_kargs_and_grids(fmha_fwd_appendkv_args args)
{
    assert(args.nhead_q % args.nhead_k == 0);
    auto kargs = Kernel::MakeKargs(args.q_ptr,
                                   args.k_ptr,
                                   args.knew_ptr,
                                   args.v_ptr,
                                   args.vnew_ptr,
                                   args.seqlen_q,
                                   args.seqlen_k_ptr,
                                   args.seqlen_knew,
                                   args.hdim_q,
                                   args.hdim_v,
                                   args.nhead_q,
                                   args.nhead_q / args.nhead_k,
                                   args.rotary_cos_ptr,
                                   args.rotary_sin_ptr,
                                   args.rotary_dim,
                                   args.has_mask,
                                   args.block_table_ptr,
                                   args.batch_stride_block_table,
                                   args.page_block_size,
                                   args.cache_batch_idx,
                                   args.stride_q,
                                   args.stride_k,
                                   args.stride_knew,
                                   args.stride_v,
                                   args.stride_vnew,
                                   args.nhead_stride_q,
                                   args.nhead_stride_k,
                                   args.nhead_stride_knew,
                                   args.nhead_stride_v,
                                   args.nhead_stride_vnew,
                                   args.batch_stride_q,
                                   args.batch_stride_k,
                                   args.batch_stride_knew,
                                   args.batch_stride_v,
                                   args.batch_stride_vnew);

    dim3 grids = Kernel::GridSize(args.batch, args.nhead_q, args.seqlen_q, args.seqlen_knew);

    return ck_tile::make_tuple(kargs, grids);
}

// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <ck_tile::index_t HDim_,
          typename DataType_,
          bool kIsGroupMode_,
          ck_tile::index_t kM0_,
          ck_tile::index_t kN0_,
          ck_tile::index_t kK0_,
          ck_tile::index_t kN1_,
          ck_tile::index_t kK1_,
          ck_tile::index_t kK0BlockLength_,
          bool kIsVLayoutRowMajor_,
          ck_tile::BlockFmhaPipelineEnum FmhaPipelineEnum_,
          typename FmhaMask_,
          ck_tile::BlockAttentionBiasEnum BiasEnum_,
          bool kStoreLse_,
          bool kHasDropout_,
          bool kDoFp8StaticQuant_,
          bool kPadS_,
          bool kPadSK_,
          bool kPadD_,
          bool kPadDv_>
struct fmha_fwd_traits_
{
    static constexpr ck_tile::index_t HDim           = HDim_;
    using DataType                                   = ck_tile::remove_cvref_t<DataType_>;
    static constexpr bool kIsGroupMode               = kIsGroupMode_;
    static constexpr ck_tile::index_t kM0            = kM0_;
    static constexpr ck_tile::index_t kN0            = kN0_;
    static constexpr ck_tile::index_t kK0            = kK0_;
    static constexpr ck_tile::index_t kN1            = kN1_;
    static constexpr ck_tile::index_t kK1            = kK1_;
    static constexpr ck_tile::index_t kK0BlockLength = kK0BlockLength_;
    static constexpr bool kIsVLayoutRowMajor         = kIsVLayoutRowMajor_;
    static constexpr auto FmhaPipelineEnum           = FmhaPipelineEnum_;
    using FmhaMask                                   = ck_tile::remove_cvref_t<FmhaMask_>;
    static constexpr auto BiasEnum                   = BiasEnum_;
    static constexpr bool kStoreLse                  = kStoreLse_;
    static constexpr bool kHasDropout                = kHasDropout_;
    static constexpr bool kDoFp8StaticQuant          = kDoFp8StaticQuant_;
    static constexpr bool kPadS                      = kPadS_;
    static constexpr bool kPadSK                     = kPadSK_;
    static constexpr bool kPadD                      = kPadD_;
    static constexpr bool kPadDv                     = kPadDv_;
};

template <typename Traits_>
float fmha_fwd_(const ck_tile::stream_config&, fmha_fwd_args);

template <ck_tile::index_t HDim_,
          typename DataType_,
          bool kIsGroupMode_,
          ck_tile::index_t kM0_,
          ck_tile::index_t kN0_,
          ck_tile::index_t kK0_,
          ck_tile::index_t kN1_,
          ck_tile::index_t kK1_,
          ck_tile::index_t kK0BlockLength_,
          bool kIsVLayoutRowMajor_,
          ck_tile::BlockFmhaPipelineEnum FmhaPipelineEnum_,
          typename FmhaMask_,
          ck_tile::BlockAttentionBiasEnum BiasEnum_,
          bool kStoreLse_,
          bool kDoFp8StaticQuant_,
          bool kIsPagedKV_,
          bool kPadS_,
          bool kPadSK_,
          bool kPadD_,
          bool kPadDv_>
struct fmha_fwd_splitkv_traits_
{
    static constexpr ck_tile::index_t HDim           = HDim_;
    using DataType                                   = ck_tile::remove_cvref_t<DataType_>;
    static constexpr bool kIsGroupMode               = kIsGroupMode_;
    static constexpr ck_tile::index_t kM0            = kM0_;
    static constexpr ck_tile::index_t kN0            = kN0_;
    static constexpr ck_tile::index_t kK0            = kK0_;
    static constexpr ck_tile::index_t kN1            = kN1_;
    static constexpr ck_tile::index_t kK1            = kK1_;
    static constexpr ck_tile::index_t kK0BlockLength = kK0BlockLength_;
    static constexpr bool kIsVLayoutRowMajor         = kIsVLayoutRowMajor_;
    static constexpr auto FmhaPipelineEnum           = FmhaPipelineEnum_;
    using FmhaMask                                   = ck_tile::remove_cvref_t<FmhaMask_>;
    static constexpr auto BiasEnum                   = BiasEnum_;
    static constexpr bool kStoreLse                  = kStoreLse_;
    static constexpr bool kDoFp8StaticQuant          = kDoFp8StaticQuant_;
    static constexpr bool kPadS                      = kPadS_;
    static constexpr bool kPadSK                     = kPadSK_;
    static constexpr bool kPadD                      = kPadD_;
    static constexpr bool kPadDv                     = kPadDv_;
    static constexpr bool kIsPagedKV                 = kIsPagedKV_;
};

template <typename Traits_>
void fmha_fwd_splitkv_oneshot_(const ck_tile::stream_config&, fmha_fwd_splitkv_args);

template <typename Traits_>
std::string fmha_fwd_splitkv_get_name_();

template <ck_tile::index_t HDim_,
          typename DataType_,
          bool kIsGroupMode_,
          ck_tile::index_t kN1_,
          bool kStoreLse_,
          bool kDoFp8StaticQuant_,
          bool kPadS_,
          bool kPadDv_>
struct fmha_fwd_splitkv_combine_traits_
{
    static constexpr ck_tile::index_t HDim  = HDim_;
    using DataType                          = ck_tile::remove_cvref_t<DataType_>;
    static constexpr bool kIsGroupMode      = kIsGroupMode_;
    static constexpr ck_tile::index_t kN1   = kN1_;
    static constexpr bool kStoreLse         = kStoreLse_;
    static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
    static constexpr bool kPadS             = kPadS_;
    static constexpr bool kPadDv            = kPadDv_;
};

template <typename Traits_>
void fmha_fwd_splitkv_combine_oneshot_(const ck_tile::stream_config&, fmha_fwd_splitkv_args);

template <typename Traits_>
std::string fmha_fwd_splitkv_combine_get_name_();

// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <ck_tile::index_t HDim_,
          typename DataType_,
          ck_tile::index_t kTileSizeS_,
          ck_tile::index_t kTileSizeSk_,
          ck_tile::index_t kTileSizeD_,
          ck_tile::index_t kTileSizeDv_,
          bool kIsVLayoutRowMajor_,
          bool kPadS_,
          bool kPadSk_,
          bool kPadD_,
          bool kPadDv_,
          ck_tile::RotaryEmbeddingEnum RotaryEnum_,
          bool kIsPagedKV_>
struct fmha_fwd_appendkv_traits_
{
    static constexpr ck_tile::index_t HDim        = HDim_;
    using DataType                                = ck_tile::remove_cvref_t<DataType_>;
    static constexpr ck_tile::index_t kTileSizeS  = kTileSizeS_;
    static constexpr ck_tile::index_t kTileSizeSk = kTileSizeSk_;
    static constexpr ck_tile::index_t kTileSizeD  = kTileSizeD_;
    static constexpr ck_tile::index_t kTileSizeDv = kTileSizeDv_;
    static constexpr bool kIsVLayoutRowMajor      = kIsVLayoutRowMajor_;
    static constexpr bool kPadS                   = kPadS_;
    static constexpr bool kPadSk                  = kPadSk_;
    static constexpr bool kPadD                   = kPadD_;
    static constexpr bool kPadDv                  = kPadDv_;
    static constexpr auto RotaryEnum              = RotaryEnum_;
    static constexpr bool kIsPagedKV              = kIsPagedKV_;
};

template <typename Traits_>
float fmha_fwd_appendkv_(const ck_tile::stream_config&, fmha_fwd_appendkv_args);

// This is the public API, will be generated by script
struct fmha_fwd_traits
{
    int hdim_q;
    int hdim_v;
    std::string data_type;
    bool is_group_mode;
    bool is_v_rowmajor;
    mask_enum mask_type;
    bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
    bool has_lse;
    bool has_dropout;
    bool do_fp8_static_quant;
    // TODO: padding check is inside this api
};
float fmha_fwd(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);

struct fmha_fwd_splitkv_traits
{
    int hdim_q;
    int hdim_v;
    std::string data_type;
    bool is_group_mode;
    bool is_v_rowmajor;
    mask_enum mask_type;
    bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
    bool has_lse;
    bool do_fp8_static_quant;
    // TODO: padding check is inside this api
};
float fmha_fwd_splitkv(fmha_fwd_splitkv_traits,
                       fmha_fwd_splitkv_args,
                       const ck_tile::stream_config&);

struct fmha_fwd_appendkv_traits
{
    int hdim_q;
    int hdim_v;
    std::string data_type;
    bool is_v_rowmajor;
    rope_enum rope_type;
};
float fmha_fwd_appendkv(fmha_fwd_appendkv_traits,
                        fmha_fwd_appendkv_args,
                        const ck_tile::stream_config&);

template <typename Int = int>
Int num_splits_heuristic(Int batch_nhead_mblocks, Int num_SMs, Int max_splits)
{
    // If we have enough to almost fill the SMs, then just use 1 split
    if(batch_nhead_mblocks >= 0.8f * num_SMs)
    {
        return 1;
    }

    max_splits = std::min({max_splits, num_SMs});

    constexpr std::array<Int, 5> num_splits_array = {1, 2, 4, 8, 16};

    float max_efficiency = 0.f;
    std::array<float, num_splits_array.size()> efficiency;

    for(size_t idx = 0; idx < num_splits_array.size() && num_splits_array[idx] <= max_splits; ++idx)
    {
        float n_blocks = float(batch_nhead_mblocks * num_splits_array[idx]) / num_SMs;
        float eff      = n_blocks / std::ceil(n_blocks);

        if(eff > max_efficiency)
        {
            max_efficiency = eff;
        }
        efficiency[idx] = eff;
    }
    for(size_t idx = 0; idx < num_splits_array.size() && num_splits_array[idx] <= max_splits; ++idx)
    {
        if(efficiency[idx] >= 0.85 * max_efficiency)
        {
            return num_splits_array[idx];
        }
    }
    return 1;
}
