#pragma once

#include "common.h"

#include "../utils.cuh"
#include "../dispatch_utils.h"
#include "gemm_utils.cuh"


#pragma nv_diag_suppress 177

#ifdef _MSC_VER
#define ALWAYSINLINE [[msvc::forceinline]]
#else
#define ALWAYSINLINE __attribute__((always_inline))
#endif

// #define ENABLE_NAN_CHECK 1
#if ENABLE_NAN_CHECK
#define STRINGIZE(x) STRINGIZE2(x)
#define STRINGIZE2(x) #x
#define CHECK_NAN(data, name) checkNan(data, name " at " STRINGIZE(__LINE__))
#else
#define CHECK_NAN(data, name)
#endif

namespace nunchaku::kernels {

template<bool bf16>
class GEMMConfig_W4A4 {
public:
    // BE CAREFUL: weights need to be repacked when the tiling size changes

    static constexpr int BLOCK_M = 256;
    static constexpr int BLOCK_N = 128;
    static constexpr int WARP_SIZE = 32;
    static constexpr int NUM_WARPS = 8;

    static constexpr int INSN_M = 16;
    static constexpr int INSN_N = 16;
    static constexpr int INSN_K = 64;

    using half_t  = typename std::conditional_t<bf16, __nv_bfloat16,  half>;
    using half2_t = typename std::conditional_t<bf16, __nv_bfloat162, half2>;
};

using GEMMConfig_W4A4_FP16 = GEMMConfig_W4A4<false>;
using GEMMConfig_W4A4_BF16 = GEMMConfig_W4A4<true>;


class GEMMConfig_W8A8 {
public:
    static constexpr int BLOCK_M = 256;
    static constexpr int BLOCK_N = 128;
    static constexpr int WARP_SIZE = 32;
    static constexpr int NUM_WARPS = 8;

    static constexpr int INSN_M = 16;
    static constexpr int INSN_N = 16;
    static constexpr int INSN_K = 32;

#if 0
    using half_t  = half;
    using half2_t = half2;
#else
    using half_t  = __nv_bfloat16;
    using half2_t = __nv_bfloat162;
#endif
};

template<class Config>
class GEMMBase : public Config {
public:
    using Config::BLOCK_M;
    using Config::BLOCK_N;
    using Config::WARP_SIZE;
    using Config::NUM_WARPS;
    using Config::INSN_M;
    using Config::INSN_N;
    using Config::INSN_K;

    using typename Config::half_t;
    using typename Config::half2_t;

    static constexpr int WARP_M = BLOCK_M / NUM_WARPS;
    static constexpr int WARP_N = BLOCK_N;
    static constexpr int WARP_K = INSN_K;

    static constexpr int WARP_M_TILES = WARP_M / INSN_M;
    static constexpr int WARP_N_TILES = WARP_N / INSN_N;
    static constexpr int WARP_K_TILES = WARP_K / INSN_K;

    /**
     * refer to https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#mma-16864-c
     * 
     * wscales store order: (pack = 4)
     *  0   1   8   9   <-- load by lane 0, broadcast to lane {0, 4, 8, ..., 28} (8x)
     *  2   3   10  11  <-- load by lane 1, broadcast to lane {1, 5, 9, ..., 29} (8x)
     *  4   5   12  13  <-- load by lane 2, broadcast to lane {2, 6, 10, ..., 30} (8x)
     *  6   7   14  15  <-- load by lane 3, broadcast to lane {3, 7, 11, ..., 31} (8x)
     * 
     *  16  17  24  25  <-- load by lane 4, broadcast to lane {0, 4, 8, ..., 28} (8x)
     *  ...
     *  22  23  30  31  <-- load by lane 7, broadcast to lane {3, 7, 11, ..., 31} (8x)
     *  ... ...
     *  112 113 120 121 <-- load by lane 28, broadcast to lane {0, 4, 8, ..., 28} (8x)
     *  ...
     *  118 119 126 127 <-- load by lane 31, broadcast to lane {3, 7, 11, ..., 31} (8x)
     *  
     * wscales store order: (pack = 8)
     *  0   1   8   9   16  17  24  25  <-- load by lane 0, broadcast to lane {0, 4, 8, ..., 28} (8x)
     *  2   3   10  11  18  19  26  27  <-- load by lane 1, broadcast to lane {1, 5, 9, ..., 29} (8x)
     *  4   5   12  13  20  21  28  29  <-- load by lane 2, broadcast to lane {2, 6, 10, ..., 30} (8x)
     *  6   7   14  15  22  23  30  31  <-- load by lane 3, broadcast to lane {3, 7, 11, ..., 31} (8x)
     * 
     *  224 225 232 233 240 241 248 249 <-- load by lane 28, broadcast to lane {0, 4, 8, ..., 28} (8x)
     *  ...
     *  230 231 238 239 246 247 254 255 <-- load by lane 31, broadcast to lane {3, 7, 11, ..., 31} (8x)
     * 
     * {k}-th wscale used by lane {i} => {k // (WSCALES_PACK_SIZE * WARP_SIZE)}-th pack, in lane {4*(k // WSCALES_PACK_SIZE) + i % 4}, element {k % WSCALES_PACK_SIZE}
     * 
     * max pack size set to 8 since max load size is 16 bytes / lane
     * min pack size set to 2 since shuffle granularity is 32b 2*half
     * */ 
    static constexpr int WSCALES_PACK_SIZE = clamp(WARP_N / WARP_SIZE, 4 / sizeof(half), 16 / sizeof(half));
    static constexpr int WSCALES_NUM_PACKS = ceilDiv(WARP_N, (WSCALES_PACK_SIZE * WARP_SIZE));
    static constexpr int WSCALES_VALID_LANES = std::min(WARP_SIZE, WARP_N / WSCALES_PACK_SIZE);

    /**
     * ascales store order: (pack = 2)
     *  0   8   <-- load by lane 0, broadcast to lane {0, 1, 2, 3} (4x)
     *  1   9   <-- load by lane 1, broadcast to lane {4, 5, 6, 7} (4x)
     *  2   10
     *  ...
     *  6   14
     *  7   15  <-- load by lane 7, broadcast to lane {28, 29, 30, 31} (4x)
     *  ... ...
     *  48  56  <-- load by lane 24, broadcast to lane {0, 1, 2, 3} (4x)
     *  49  57
     *  ...
     *  54  62
     *  55  63  <-- load by lane 31, broadcast to lane {28, 29, 30, 31}  (4x)
     * 
     * {k}-th wscale used by lane {i} => {k // (ASCALES_PACK_SIZE * WARP_SIZE)}-th pack, in lane {8*(k // ASCALES_PACK_SIZE) + i // 4}, element {k % ASCALES_PACK_SIZE}
     */
    static constexpr int ASCALES_PACK_SIZE = clamp(WARP_M / WARP_SIZE, 4 / sizeof(half), 16 / sizeof(half));
    static constexpr int ASCALES_NUM_PACKS = ceilDiv(WARP_M, (ASCALES_PACK_SIZE * WARP_SIZE));
    static constexpr int ASCALES_VALID_LANES = std::min(WARP_SIZE, WARP_M / ASCALES_PACK_SIZE);

    using packed_act_t = uint4;
    using packed_wgt_t = uint4;
    
    struct alignas(32) packed_psum_t {
        int data[8];
    };
    struct alignas(16) packed_fpsum_t {
        half2_t data[4];
    };
    struct alignas(8) packed_gated_fpsum_t {
        half_t data[4];
    };
    // 16 * 16 matrix
    struct alignas(32) packed_f32psum_t {
        float data[8];

        static constexpr packed_f32psum_t zeros() {
            packed_f32psum_t result;
            for (int i = 0; i < 8; i++) {
                result.data[i] = 0;
            }
            return result;
        };
    };

    struct packed_wscale_t {
        half2_t data[WSCALES_PACK_SIZE / 2];
    };
    struct packed_ascale_t {
        half2_t data[ASCALES_PACK_SIZE / 2];
    };

    using act_warp = std::array<packed_act_t, WARP_M_TILES>;
    using wgt_warp = std::array<packed_wgt_t, WARP_N_TILES>;
    using ascale_warp = std::array<packed_ascale_t, ASCALES_NUM_PACKS>;
    using wscale_warp = std::array<packed_wscale_t, WSCALES_NUM_PACKS>;
    using fpsum_warp = std::array<packed_fpsum_t, WARP_M_TILES * WARP_N_TILES>;
    using f32psum_warp = std::array<packed_f32psum_t, WARP_M_TILES * WARP_N_TILES>;
    using gated_fpsum_warp = std::array<packed_gated_fpsum_t, WARP_M_TILES * WARP_N_TILES>;


    struct BlockInfo {
        int bm;
        int bn;
        int numBlocksM;
        int numBlocksN;
    };

    __device__ __forceinline__
    static packed_f32psum_t mma_f16xf16_f32(packed_fpsum_t a, packed_fpsum_t b, packed_f32psum_t psum) {
        static_assert(std::is_same_v<half_t, half> || std::is_same_v<half_t, __nv_bfloat16>);

        if constexpr (std::is_same_v<half_t, half>) {
            asm volatile(
                "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 "
                "{%0,  %1,  %2,  %3},"
                "{%4,  %5,  %6,  %7},"
                "{%8,  %9},"
                "{%10,  %11,  %12,  %13};\n"
                : 
                "=f"(psum.data[0]), "=f"(psum.data[1]), "=f"(psum.data[2]), "=f"(psum.data[3])
                : 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[0])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[1])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[2])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[3])),
                "r"(*reinterpret_cast<unsigned int *>(&b.data[0])), 
                "r"(*reinterpret_cast<unsigned int *>(&b.data[1])),

                // "r"(0), "r"(0), "r"(0), "r"(0)
                "f"(psum.data[0]), "f"(psum.data[1]), "f"(psum.data[2]), "f"(psum.data[3])
            );
            asm volatile(
                "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 "
                "{%0,  %1,  %2,  %3},"
                "{%4,  %5,  %6,  %7},"
                "{%8,  %9},"
                "{%10,  %11,  %12,  %13};\n"
                : 
                "=f"(psum.data[4]), "=f"(psum.data[5]), "=f"(psum.data[6]), "=f"(psum.data[7])
                : 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[0])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[1])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[2])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[3])),
                "r"(*reinterpret_cast<unsigned int *>(&b.data[2])), 
                "r"(*reinterpret_cast<unsigned int *>(&b.data[3])),
                // "r"(0), "r"(0), "r"(0), "r"(0)
                "f"(psum.data[4]), "f"(psum.data[5]), "f"(psum.data[6]), "f"(psum.data[7])
            );
        }

        if constexpr (std::is_same_v<half_t, __nv_bfloat16>) {
            asm volatile(
                "mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 "
                "{%0,  %1,  %2,  %3},"
                "{%4,  %5,  %6,  %7},"
                "{%8,  %9},"
                "{%10,  %11,  %12,  %13};\n"
                : 
                "=f"(psum.data[0]), "=f"(psum.data[1]), "=f"(psum.data[2]), "=f"(psum.data[3])
                : 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[0])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[1])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[2])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[3])),
                "r"(*reinterpret_cast<unsigned int *>(&b.data[0])), 
                "r"(*reinterpret_cast<unsigned int *>(&b.data[1])),

                // "r"(0), "r"(0), "r"(0), "r"(0)
                "f"(psum.data[0]), "f"(psum.data[1]), "f"(psum.data[2]), "f"(psum.data[3])
            );
            asm volatile(
                "mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 "
                "{%0,  %1,  %2,  %3},"
                "{%4,  %5,  %6,  %7},"
                "{%8,  %9},"
                "{%10,  %11,  %12,  %13};\n"
                : 
                "=f"(psum.data[4]), "=f"(psum.data[5]), "=f"(psum.data[6]), "=f"(psum.data[7])
                : 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[0])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[1])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[2])), 
                "r"(*reinterpret_cast<unsigned int *>(&a.data[3])),
                "r"(*reinterpret_cast<unsigned int *>(&b.data[2])), 
                "r"(*reinterpret_cast<unsigned int *>(&b.data[3])),
                // "r"(0), "r"(0), "r"(0), "r"(0)
                "f"(psum.data[4]), "f"(psum.data[5]), "f"(psum.data[6]), "f"(psum.data[7])
            );
        }
        
        return psum;
    }

    __device__ __forceinline__
    static packed_fpsum_t packed_fp32_to_fp16(packed_f32psum_t input) {
        packed_fpsum_t results;
        for (int i = 0; i < 4; i++) {
            results.data[i] = float22half2<half2_t>(float2(input.data[i * 2], input.data[i * 2 + 1]));
        }
        return results;
    }

    __device__ __forceinline__
    static packed_f32psum_t packed_fp16_to_fp32(packed_fpsum_t input) {
        packed_f32psum_t results;
        for (int i = 0; i < 4; i++) {
            float2 tmp = half22float2(input.data[i]);
            results.data[i * 2] = tmp.x;
            results.data[i * 2 + 1] = tmp.y;
        }
        return results;
    }

    __device__ __forceinline__
    static fpsum_warp packed_fp32_to_fp16(f32psum_warp input) {
        fpsum_warp results;
    #pragma unroll
        for (int i = 0; i < results.size(); i++) {
            results[i] = packed_fp32_to_fp16(input[i]);
        }
        return results;
    }

    // activation: row major, [M / BLOCK_M, K / WARP_K, NUM_WARPS, WARP_M_TILES, WARP_SIZE] of packed_act_t
    __device__ __forceinline__ 
    static void load_act(const packed_act_t *act, int k, int K, act_warp &out, bool pred) {
        int laneId = threadIdx.x % WARP_SIZE;
        int warpId = threadIdx.x / WARP_SIZE;
    #pragma unroll
        for (int i = 0; i < WARP_M_TILES; i++) {
            //if (pred) {
                // out[i] = load(&act[((warpId * WARP_M_TILES + i) * K / WARP_K + k) * WARP_SIZE + laneId]);
                out[i] = load_pred(&act[((k * NUM_WARPS + warpId) * WARP_M_TILES + i) * WARP_SIZE + laneId], pred);
            //}
        }
    }

    // weight: column major: [N / BLOCK_N, 1, K / WARP_K, WARP_N_TILES, WARP_SIZE] of packed_wgt_t
    __device__ __forceinline__ 
    static void load_wgt(const packed_wgt_t *wgt, int k, int K, wgt_warp &out, bool pred) {
        int laneId = threadIdx.x % WARP_SIZE;
        
        // const packed_wgt_t *ptr = &wgt[(0 * K / WARP_K + k) * WARP_SIZE + laneId];
        const packed_wgt_t *ptr = &wgt[(0 + k * WARP_N_TILES) * WARP_SIZE + laneId];
        // int offset = K / WARP_K * WARP_SIZE;
    #pragma unroll
        for (int i = 0; i < WARP_N_TILES; i++) {
            //if (pred) {
                // out[i] = load(&wgt[(i * K / WARP_K + k) * WARP_SIZE + laneId]);
                // out[i] = load(&wgt[(i + k * WARP_N_TILES) * WARP_SIZE + laneId]);
                out[i] = load_pred(&ptr[i * WARP_SIZE], pred);
                // ptr += offset;
            //}
        }
    }

    // ascales: row major [M / BLOCK_M, K / group size, NUM_WARPS, ASCALES_NUM_PACKS, ASCALES_VALID_LANES] of packed_ascale_t
    __device__ __forceinline__ 
    static void load_ascale(const packed_ascale_t *ascales, int group, int M, ascale_warp &out, bool pred) {
        int laneId = threadIdx.x % WARP_SIZE;
        int warpId = threadIdx.x / WARP_SIZE;
    #pragma unroll
        for (int i = 0; i < ASCALES_NUM_PACKS; i++) {
            // if (pred && laneId < ASCALES_VALID_LANES) {
                // out[i] = ascales[(group * M / WARP_M + warpId) * ASCALES_VALID_LANES * ASCALES_NUM_PACKS + i * ASCALES_VALID_LANES + laneId];
                out[i] = load_pred(&ascales[(group * NUM_WARPS + warpId) * ASCALES_NUM_PACKS * ASCALES_VALID_LANES + i * ASCALES_VALID_LANES + laneId], pred && laneId < ASCALES_VALID_LANES);

            // }
        }
    }

    // wscales: column major [N / BLOCK_N, K / group size, 1, WSCALES_NUM_PACKS, WSCALES_VALID_LANES] of packed_wscale_t </del>
    __device__ __forceinline__
    static void load_wscale(const packed_wscale_t *wscales, int group, int N, wscale_warp &out, bool pred) {
        int laneId = threadIdx.x % WARP_SIZE;

        // static_assert(WSCALES_NUM_PACKS * WSCALES_VALID_LANES == 32);
        // static_assert(sizeof(packed_wscale_t) == 8);

        // const packed_wscale_t *ptr = &wscales[(group * WSCALES_NUM_PACKS + 0) * WSCALES_VALID_LANES + laneId];
        // // const packed_wscale_t *ptr = (const packed_wscale_t *)((const char *)wscales) + ((group * WSCALES_NUM_PACKS + 0) * WSCALES_VALID_LANES + laneId) * sizeof(packed_wscale_t);

    #pragma unroll
        for (int i = 0; i < WSCALES_NUM_PACKS; i++) {
            // if (pred && laneId < WSCALES_VALID_LANES) {
                
                // out[i] = wscales[group * N / WARP_N * WSCALES_VALID_LANES * WSCALES_NUM_PACKS + i * WSCALES_VALID_LANES + laneId];
                // out[i] = load(&wscales[group * N / WARP_N * WSCALES_VALID_LANES * WSCALES_NUM_PACKS + i * WSCALES_VALID_LANES + laneId]);
                out[i] = load_pred(&wscales[(group * WSCALES_NUM_PACKS + i) * WSCALES_VALID_LANES + laneId], pred && laneId < WSCALES_VALID_LANES);
                // out[i] = load(&ptr[i * WSCALES_VALID_LANES]);
            // }
        }
    }

    // get {k}-th and {k+1}-th wscale from the block, k must be multiples of 2, k must be uniform across all lanes
    __device__ __forceinline__
    static half2_t broadcast_wscale(wscale_warp block, int k, int laneId) {
        const int packIdx = k / (WSCALES_PACK_SIZE * WARP_SIZE);
        const int srcLane = 4 * (k / WSCALES_PACK_SIZE) + laneId % 4;
        const int elementIdx = k % WSCALES_PACK_SIZE / 2;
        return __shfl_sync(~0, block[packIdx].data[elementIdx], srcLane);
    }
    // get {k}-th and {k+1}-th ascale from the block, k must be multiples of 2, k must be uniform across all lanes
    __device__ __forceinline__
    static half2_t broadcast_ascale(ascale_warp block, int k, int laneId) {
        const int packIdx = k / (ASCALES_PACK_SIZE * WARP_SIZE);
        const int srcLane = 8 * (k / ASCALES_PACK_SIZE) + laneId / 4;
        const int elementIdx = k % ASCALES_PACK_SIZE / 2;
        return __shfl_sync(~0, block[packIdx].data[elementIdx], srcLane);
    }

    template<bool FAST_I2F = false, typename F>
    __device__ __forceinline__
    static void apply_scales(F &&getpsum, ascale_warp ascale, wscale_warp wscale, fpsum_warp &fpsum) {
        const int laneId = threadIdx.x % WARP_SIZE;
        const int warpId = threadIdx.x / WARP_SIZE;

        half2_t asx[WARP_M_TILES];
        half2_t asy[WARP_M_TILES];

        for (int i = 0; i < WARP_M_TILES; i++) {
            half2_t as = broadcast_ascale(ascale, i * 2, laneId);
            asx[i] = half2_t(as.x, as.x);
            asy[i] = half2_t(as.y, as.y);
        }

        for (int j = 0; j < WARP_N_TILES; j++) {
            half2_t ws1 = broadcast_wscale(wscale, j * 4, laneId);
            half2_t ws2 = broadcast_wscale(wscale, j * 4 + 2, laneId);

            for (int i = 0; i < WARP_M_TILES; i++) {
                auto &fsum = fpsum[i * WARP_N_TILES + j];

                packed_psum_t psum = getpsum(i, j);

                // constexpr int target = 0;
                // if (threadIdx.x == 3 && j == 1 && i == 0) {
                    
                //     printf("before ws2 = %f %f fsum.data[%d] = %f %f\n", (float)ws2.x, (float)ws2.y, target, (float)fsum.data[target].x, (float)fsum.data[target].y);
                // }

                auto scale_fma_normal = [&]() ALWAYSINLINE {
                    fsum.data[0] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[0]), __int2float_rn(psum.data[1]))), __hmul2(asx[i], ws1), fsum.data[0]);
                    fsum.data[1] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[2]), __int2float_rn(psum.data[3]))), __hmul2(asy[i], ws1), fsum.data[1]);
                    fsum.data[2] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[4]), __int2float_rn(psum.data[5]))), __hmul2(asx[i], ws2), fsum.data[2]);
                    fsum.data[3] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[6]), __int2float_rn(psum.data[7]))), __hmul2(asy[i], ws2), fsum.data[3]);
                };

                // should be faster on sm_80
                auto scale_fma_fast = [&]() ALWAYSINLINE {
                    fsum.data[0] = __hfma2(float22half2<half2_t>(make_float2(int2float_fast(psum.data[0]), int2float_fast(psum.data[1]))), __hmul2(asx[i], ws1), fsum.data[0]);
                    fsum.data[1] = __hfma2(float22half2<half2_t>(make_float2(int2float_fast(psum.data[2]), int2float_fast(psum.data[3]))), __hmul2(asy[i], ws1), fsum.data[1]);
                    fsum.data[2] = __hfma2(float22half2<half2_t>(make_float2(int2float_fast(psum.data[4]), int2float_fast(psum.data[5]))), __hmul2(asx[i], ws2), fsum.data[2]);
                    fsum.data[3] = __hfma2(float22half2<half2_t>(make_float2(int2float_fast(psum.data[6]), int2float_fast(psum.data[7]))), __hmul2(asy[i], ws2), fsum.data[3]);
                };
            
            #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ <= 800
                if constexpr (FAST_I2F) {
                    scale_fma_fast();
                } else {
                    scale_fma_normal();
                }
            #else
                scale_fma_normal();
            #endif
                // if (threadIdx.x == 3 && j == 1 && i == 0) {
                //     printf("before ws2 = %f %f fsum.data[%d] = %f %f\n", (float)ws2.x, (float)ws2.y, target, (float)fsum.data[target].x, (float)fsum.data[target].y);
                // }
            }
        }
    }

    template<typename F>
    __device__ __forceinline__
    static void apply_scales(F &&getpsum, ascale_warp ascale, wscale_warp wscale, f32psum_warp &fpsum) {
        const int laneId = threadIdx.x % WARP_SIZE;
        const int warpId = threadIdx.x / WARP_SIZE;

        float2 asx[WARP_M_TILES];
        float2 asy[WARP_M_TILES];

        for (int i = 0; i < WARP_M_TILES; i++) {
            half2_t as = broadcast_ascale(ascale, i * 2, laneId);
            asx[i] = half22float2(half2_t(as.x, as.x));
            asy[i] = half22float2(half2_t(as.y, as.y));
        }

        auto fma2 = [](float2 a, float2 b, float &cx, float &cy) ALWAYSINLINE {
            cx += a.x * b.x;
            cy += a.y * b.y;
        };

        for (int j = 0; j < WARP_N_TILES; j++) {
            float2 ws1 = half22float2(broadcast_wscale(wscale, j * 4, laneId));
            float2 ws2 = half22float2(broadcast_wscale(wscale, j * 4 + 2, laneId));

            for (int i = 0; i < WARP_M_TILES; i++) {
                auto &fsum = fpsum[i * WARP_N_TILES + j];

                packed_psum_t psum = getpsum(i, j);

                fma2(make_float2(__int2float_rn(psum.data[0]), __int2float_rn(psum.data[1])), asx[i] * ws1, fsum.data[0], fsum.data[1]);
                fma2(make_float2(__int2float_rn(psum.data[2]), __int2float_rn(psum.data[3])), asy[i] * ws1, fsum.data[2], fsum.data[3]);
                fma2(make_float2(__int2float_rn(psum.data[4]), __int2float_rn(psum.data[5])), asx[i] * ws2, fsum.data[4], fsum.data[5]);
                fma2(make_float2(__int2float_rn(psum.data[6]), __int2float_rn(psum.data[7])), asy[i] * ws2, fsum.data[6], fsum.data[7]);
            }
        }
    }

    /**
     * input: WARP_M of half (in shared memory, per-warp)
     * output: [..., ASCALES_NUM_PACKS, ASCALES_VALID_LANES] in global memory, per-warp
     */
    __device__ __forceinline__
    static void pack_ascales(const half_t *input, packed_ascale_t *output) {
        const int laneId = threadIdx.x % WARP_SIZE;
    #pragma unroll
        for (int j = 0; j < ASCALES_NUM_PACKS; j++) {
            if (laneId < ASCALES_VALID_LANES) {
                packed_ascale_t tmp;
    #pragma unroll
                for (int i = 0; i < ASCALES_PACK_SIZE; i += 2) {
                    tmp.data[i / 2].x = input[j * ASCALES_PACK_SIZE * WARP_SIZE + laneId / 8 * 8 * ASCALES_PACK_SIZE + laneId % 8 + i * 8];
                    tmp.data[i / 2].y = input[j * ASCALES_PACK_SIZE * WARP_SIZE + laneId / 8 * 8 * ASCALES_PACK_SIZE + laneId % 8 + (i + 1) * 8];
                }
                output[j * ASCALES_VALID_LANES + laneId] = tmp;
            }
        }
    }

    /**
     * input: WARP_N of half (in shared memory, per-warp)
     * output: [..., WSCALES_NUM_PACKS, WSCALES_VALID_LANES] in global memory, per-warp
     */
    __device__ __forceinline__
    static void pack_wscales(const half_t *input, packed_wscale_t *output) {
        const int laneId = threadIdx.x % WARP_SIZE;
    #pragma unroll
        for (int j = 0; j < WSCALES_NUM_PACKS; j++) {
            if (laneId < WSCALES_VALID_LANES) {
                packed_wscale_t tmp;
    #pragma unroll
                for (int i = 0; i < WSCALES_PACK_SIZE; i += 2) {
                    tmp.data[i / 2] = *reinterpret_cast<const half2_t *>(&input[j * WSCALES_PACK_SIZE * WARP_SIZE + laneId / 4 * 4 * WSCALES_PACK_SIZE + laneId % 4 * 2 + i * 4]);
                }
                store(&output[j * WSCALES_VALID_LANES + laneId], tmp);
            }
        }
    }

    struct unpack_fpsum {
        // +8 to prevent bank conflicts
        using matrix_t = half_t[8][WARP_N + 8];

        static constexpr int SHMEM_SIZE = sizeof(matrix_t);
        static constexpr int PACK_SIZE = WARP_N / WARP_SIZE;
        using pack_t = std::array<half_t, PACK_SIZE>;

        // F (int rowId, pack_t &pack)
        template<typename ...F>
        __device__ __forceinline__
        void operator()(fpsum_warp fpsum, half_t *output, int stride, int maxRows, int maxCols, void *shmem, F &&...plugins) {
            const int laneId = threadIdx.x % WARP_SIZE;
            
            matrix_t &mat = *reinterpret_cast<matrix_t *>(shmem);


            // pack_t reduce_tmp;
            // constexpr bool enableReduce = !std::is_void_v<FuncReduce>;

            // if constexpr (enableReduce) {
            //     reduce_tmp.fill(reduce_initval);
            //     // reduce_tmp = load<true>(reinterpret_cast<pack_t *>(&reduce_result[laneId * PACK_SIZE]));
            // }
            // auto doReduce = [&reduce_tmp](pack_t pack) {
            //     if constexpr (enableReduce) {
            //         for (int i = 0; i < PACK_SIZE; i++) {
            //             reduce_tmp[i] = FuncReduce()(reduce_tmp[i], pack[i]);
            //         }
            //     }
            // };

            #pragma unroll
            for (int i = 0; i < WARP_M_TILES; i++) {
            #pragma unroll
                for (int j = 0; j < WARP_N_TILES; j++) {
                    packed_fpsum_t &fsum = fpsum[i * WARP_N_TILES + j];
                    int row = laneId / 4;
                    int col = laneId % 4 * 2 + j * INSN_N;
                    *reinterpret_cast<half2_t *>(&mat[row][col + 0]) = fsum.data[0];
                    *reinterpret_cast<half2_t *>(&mat[row][col + 8]) = fsum.data[2];
                }
                __syncwarp();

            #pragma unroll
                for (int row = 0; row < 8; row++) {
                    pack_t pack = *reinterpret_cast<pack_t *>(&mat[row][laneId * PACK_SIZE]);

                    // if constexpr (enableReduce) {
                    //     doReduce(pack);
                    // }

                    (plugins(i * INSN_M + row, pack), ...);

                    bool pred = i * INSN_M + row < maxRows && laneId * PACK_SIZE < maxCols;
                    // if (pred) {
                    store_pred(reinterpret_cast<pack_t *>(&output[(i * INSN_M + row) * stride + laneId * PACK_SIZE]), pack, pred);
                    // }
                }
                __syncwarp();

            #pragma unroll
                for (int j = 0; j < WARP_N_TILES; j++) {
                    packed_fpsum_t &fsum = fpsum[i * WARP_N_TILES + j];
                    int row = laneId / 4;
                    int col = laneId % 4 * 2 + j * INSN_N;
                    *reinterpret_cast<half2_t *>(&mat[row][col + 0]) = fsum.data[1];
                    *reinterpret_cast<half2_t *>(&mat[row][col + 8]) = fsum.data[3];
                }
                __syncwarp();

            #pragma unroll
                for (int row = 0; row < 8; row++) {
                    pack_t pack = *reinterpret_cast<pack_t *>(&mat[row][laneId * PACK_SIZE]);

                    // if constexpr (enableReduce) {
                    //     doReduce(pack);
                    // }

                    (plugins(i * INSN_M + 8 + row, pack), ...);

                    bool pred = i * INSN_M + 8 + row < maxRows && laneId * PACK_SIZE < maxCols;
                    // if (pred) {
                    store_pred(reinterpret_cast<pack_t *>(&output[(i * INSN_M + 8 + row) * stride + laneId * PACK_SIZE]), pack, pred);
                    // }
                }
                __syncwarp();
            }
            // if (enableReduce) {
            //     store<true>(reinterpret_cast<pack_t *>(&reduce_result[laneId * PACK_SIZE]), reduce_tmp);
            // }
        }
    };

    
    template<typename F>
    __device__ __forceinline__
    static fpsum_warp apply_act(fpsum_warp fpsum, F func) {
        fpsum_warp result;
    #pragma unroll
        for (int i = 0; i < WARP_M_TILES; i++) {
    #pragma unroll
            for (int j = 0; j < WARP_N_TILES; j++) {
    #pragma unroll
                for (int k = 0; k < 4; k++) {
                    half2_t &dst = result[i * WARP_N_TILES + j].data[k];
                    half2_t src = fpsum[i * WARP_N_TILES + j].data[k];
                    dst.x = func(src.x);
                    dst.y = func(src.y);
                }
            }
        }
        return result;
    }
    

    struct EpilogueDefault {
        struct Arguments {
            half_t *out;
            int actualM, actualN;
        };

        __device__ __forceinline__
        void operator()(const BlockInfo binfo, fpsum_warp fpsum, int M, int N, int K, Arguments args) {
            const int warpId = threadIdx.x / WARP_SIZE;
            
            __shared__ alignas(128) uint8_t shmem[NUM_WARPS][ceilDiv(unpack_fpsum::SHMEM_SIZE, 128) * 128];

            const int m_offset = binfo.bm * BLOCK_M + warpId * WARP_M;
            const int n_offset = binfo.bn * BLOCK_N;

            unpack_fpsum()(
                fpsum, 
                args.out + m_offset * args.actualN + n_offset, 
                args.actualN, 
                args.actualM - m_offset,
                args.actualN - n_offset,
                shmem[warpId],
                [&](int rowId, unpack_fpsum::pack_t &pack) ALWAYSINLINE {
                    if constexpr (std::is_same_v<half_t, half>) {
                    #pragma unroll
                        for (int i = 0; i < pack.size(); i++) {
                            pack[i] = __hmin(pack[i], (half)65504);
                            pack[i] = __hmax(pack[i], (half)-65504);
                        }
                    }
                }
            );
        }
    };

    struct EpilogueNop {
        // workaround for layout mismatch between host and device code
        struct Arguments { size_t unused; };

        __device__ __forceinline__
        void operator()(const BlockInfo binfo, fpsum_warp fpsum, int M, int N, int K, Arguments args) {
        }
    };

    template<bool USE_BIAS = true, bool USE_SCALE = false>
    struct EpilogueBias {
        struct Arguments {
            const packed_wscale_t *bias;  // [N / BLOCK_N, WSCALES_NUM_PACKS, WSCALES_VALID_LANES] of packed_wscale_t
            const packed_wscale_t *scale;
        };

        __device__ __forceinline__
        void apply_bias(fpsum_warp &fpsum, int M, int N, int K, const packed_wscale_t *bias, const packed_wscale_t *scale) {
            const int laneId = threadIdx.x % WARP_SIZE;

            // if (laneId == 0) {
            //     printf("block.x=%d block.y=%d warpId=%d bias=%p\n", blockIdx.x, blockIdx.y, threadIdx.x / WARP_SIZE, bias);
            // }

            wscale_warp b, s;
            if constexpr (USE_BIAS) {
                load_wscale(bias, 0, N, b, true);
            }
            if constexpr (USE_SCALE) {
                load_wscale(scale, 0, N, s, true);
            }

            for (int j = 0; j < WARP_N_TILES; j++) {
                half2_t b1, b2;
                half2_t s1, s2;
                if constexpr (USE_BIAS) {
                    b1 = broadcast_wscale(b, j * 4, laneId);
                    b2 = broadcast_wscale(b, j * 4 + 2, laneId);
                }
                if constexpr (USE_SCALE) {
                    s1 = broadcast_wscale(s, j * 4, laneId);
                    s2 = broadcast_wscale(s, j * 4 + 2, laneId);
                }
                

                for (int i = 0; i < WARP_M_TILES; i++) {
                    auto &fsum = fpsum[i * WARP_N_TILES + j];

                    if constexpr (USE_SCALE && USE_BIAS) {
                        fsum.data[0] = __hfma2(fsum.data[0], s1, b1);
                        fsum.data[1] = __hfma2(fsum.data[1], s1, b1);
                        fsum.data[2] = __hfma2(fsum.data[2], s2, b2);
                        fsum.data[3] = __hfma2(fsum.data[3], s2, b2);
                    } else if constexpr (USE_SCALE) {
                        fsum.data[0] = __hmul2(fsum.data[0], s1);
                        fsum.data[1] = __hmul2(fsum.data[1], s1);
                        fsum.data[2] = __hmul2(fsum.data[2], s2);
                        fsum.data[3] = __hmul2(fsum.data[3], s2);
                    } else if constexpr (USE_BIAS) {
                        fsum.data[0] = __hadd2(fsum.data[0], b1);
                        fsum.data[1] = __hadd2(fsum.data[1], b1);
                        fsum.data[2] = __hadd2(fsum.data[2], b2);
                        fsum.data[3] = __hadd2(fsum.data[3], b2);
                    }
                }
            }
        }

        __device__ __forceinline__
        void operator()(const BlockInfo binfo, fpsum_warp &fpsum, int M, int N, int K, Arguments args) {
            const int bn = binfo.bn;
            if constexpr (USE_BIAS || USE_SCALE) {
                apply_bias(
                    fpsum, M, N, K,
                    args.bias + bn * WSCALES_NUM_PACKS * WSCALES_VALID_LANES,
                    args.scale + bn * WSCALES_NUM_PACKS * WSCALES_VALID_LANES
                );
            }
        }
    };

    struct EpilogueSilu {
        struct Arguments { size_t unused; };

        __device__ __forceinline__
        void operator()(const BlockInfo binfo, fpsum_warp &fpsum, int M, int N, int K, Arguments args) {
            fpsum = apply_act(fpsum, [](half_t x) { return silu(x); });
        }
    };

    template<typename ...Epilogues>
    struct EpilogueCombination {
        using Arguments = std::tuple<typename Epilogues::Arguments...>;

        __device__ __forceinline__
        void operator()(const BlockInfo binfo, fpsum_warp &fpsum, int M, int N, int K, Arguments args) {
            // this function makes intellisense crashes :(
    #if __INTELLISENSE__
            __trap();   // should not happen when actually compiling
    #else
            std::tuple<Epilogues...> epilogues;
            auto run = [&]<size_t idx>() {
                std::get<idx>(epilogues).operator()(binfo, fpsum, M, N, K, std::get<idx>(args));
            };
            auto foreach = [&]<size_t ...Is>(std::index_sequence<Is...>) {
                (run.template operator()<Is>(), ...);
            };
            foreach(std::make_index_sequence<sizeof...(Epilogues)>());
    #endif
        }
    };


};

#define IMPORT_GEMM_BASE(config) \
    using Base = GEMMBase<config>;  \
    using Base::BLOCK_M;    \
    using Base::BLOCK_N;    \
    using Base::WARP_SIZE;  \
    using Base::NUM_WARPS;  \
    using Base::INSN_M; \
    using Base::INSN_N; \
    using Base::INSN_K; \
    using typename Base::half_t;    \
    using typename Base::half2_t;   \
    using Base::WARP_M; \
    using Base::WARP_N; \
    using Base::WARP_K; \
    using Base::WARP_M_TILES;   \
    using Base::WARP_N_TILES;   \
    using Base::WARP_K_TILES;   \
    using Base::WSCALES_PACK_SIZE;  \
    using Base::WSCALES_NUM_PACKS;  \
    using Base::WSCALES_VALID_LANES;    \
    using Base::ASCALES_PACK_SIZE;  \
    using Base::ASCALES_NUM_PACKS;  \
    using Base::ASCALES_VALID_LANES;    \
    using typename Base::packed_act_t;  \
    using typename Base::packed_wgt_t;  \
    using typename Base::packed_psum_t; \
    using typename Base::packed_fpsum_t;    \
    using typename Base::packed_gated_fpsum_t;  \
    using typename Base::packed_f32psum_t;  \
    using typename Base::packed_wscale_t;   \
    using typename Base::packed_ascale_t;   \
    using typename Base::act_warp;  \
    using typename Base::wgt_warp;  \
    using typename Base::ascale_warp;   \
    using typename Base::wscale_warp;   \
    using typename Base::fpsum_warp;    \
    using typename Base::f32psum_warp;  \
    using typename Base::gated_fpsum_warp;  \
    using typename Base::BlockInfo; \
    using typename Base::unpack_fpsum;  \
    using typename Base::EpilogueDefault;   \
    using typename Base::EpilogueNop;   \
    template<bool USE_BIAS, bool USE_SCALE> \
    using EpilogueBias = typename Base::EpilogueBias<USE_BIAS, USE_SCALE>; \
    using Base::mma_f16xf16_f32; \
    using Base::packed_fp32_to_fp16; \
    using Base::packed_fp16_to_fp32; \
    using Base::load_act;   \
    using Base::load_wgt;   \
    using Base::load_ascale; \
    using Base::load_wscale; \
    using Base::broadcast_wscale; \
    using Base::broadcast_ascale; \
    using Base::apply_scales; \
    using Base::pack_ascales; \
    using Base::pack_wscales; \
    using Base::apply_act;


template<typename kernel, typename ...T>
__global__
static void invoke_kernel(T ...args) {
    kernel()(args...);
}

template<typename T>
__global__
static void test_sizeof_device() {
    printf("sizeof on device = %d\n", (int)sizeof(T));
}

template<typename T>
static void test_sizeof_host() {
    printf("sizeof on host = %d\n", (int)sizeof(T));
}

template<typename T>
static void test_sizeof() {
    printf("typeid = %s\n", typeid(T).name());
    test_sizeof_host<T>();
    test_sizeof_device<T><<<1, 1>>>();
    checkCUDA(cudaDeviceSynchronize());
}

};  // namespace nunchaku::kernels