#pragma once

#include "common.cuh"
#include "convert.cuh"
#include "vecdotq.cuh"

#include <cstdint>

#define FATTN_KQ_STRIDE       256
#define HALF_MAX_HALF         __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
#define SOFTMAX_FTZ_THRESHOLD -20.0f                   // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.

typedef void (* fattn_kernel_t)(
        const char * __restrict__ Q,
        const char * __restrict__ K,
        const char * __restrict__ V,
        const char * __restrict__ mask,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
        const float scale,
        const float max_bias,
        const float m0,
        const float m1,
        const uint32_t n_head_log2,
        const float logit_softcap,
        const int ne00,
        const int ne01,
        const int ne02,
        const int ne03,
        const int ne10,
        const int ne11,
        const int ne12,
        const int ne13,
        const int ne31,
        const int nb31,
        const int nb01,
        const int nb02,
        const int nb03,
        const int nb11,
        const int nb12,
        const int nb13,
        const int nb21,
        const int nb22,
        const int nb23,
        const int ne0,
        const int ne1,
        const int ne2,
        const int ne3);

typedef half (*vec_dot_KQ_f16_t)(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);
typedef float (*vec_dot_KQ_f32_t)(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);

template<typename T, int D, int warp_size>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c;
    GGML_UNUSED(Q_v);

    T sum = 0.0f;

#pragma unroll
    for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) {
        const int k_KQ = k_KQ_0 + threadIdx.x;

        const int ib    = k_KQ /  QI8_1;
        const int iqs4  = k_KQ %  QI4_0;
        const int shift = k_KQ & (QI8_1/2);

        const int v = (get_int_b2(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
        const int u = Q_q8[k_KQ_0/warp_size];

        const int sumi = ggml_cuda_dp4a(v, u, 0);

#ifdef FP16_AVAILABLE
        if (std::is_same<T, half>::value) {
            const half2  * Q_ds = (const half2  *) Q_ds_v;

            const half2 sum2 = __half2half2(K_q4_0[ib].d) * Q_ds[k_KQ_0/warp_size];
            sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2) /* *8/QI8_1 == 1 */);
        } else
#endif // FP16_AVAILABLE
        {
            const float2 * Q_ds = (const float2 *) Q_ds_v;

            sum += (T) (__half2float(K_q4_0[ib].d) * (sumi*Q_ds[k_KQ_0/warp_size].x - (8/QI8_1)*Q_ds[k_KQ_0/warp_size].y));
        }
    }

    return sum;
}

template<typename T, int D, int warp_size>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c;
    GGML_UNUSED(Q_v);

    T sum = 0.0f;

#pragma unroll
    for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) {
        const int k_KQ = k_KQ_0 + threadIdx.x;

        const int ib    = k_KQ /  QI8_1;
        const int iqs4  = k_KQ %  QI4_1;
        const int shift = k_KQ & (QI8_1/2);

        const int v = (get_int_b4(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
        const int u = Q_q8[k_KQ_0/warp_size];

        const int sumi = ggml_cuda_dp4a(v, u, 0);

#ifdef FP16_AVAILABLE
        if (std::is_same<T, half>::value) {
            const half2  * Q_ds = (const half2  *) Q_ds_v;

            const half2 d4d8_m4s8 = K_q4_1[ib].dm * Q_ds[k_KQ_0/warp_size];
            const half2 sumid4d8_m4s8scaled = d4d8_m4s8 * make_half2(sumi, 1.0f/QI8_1);
            sum += (T) (__low2half(sumid4d8_m4s8scaled) + __high2half(sumid4d8_m4s8scaled));
        } else
#endif // FP16_AVAILABLE
        {
            const float2 * Q_ds = (const float2 *) Q_ds_v;

            const float sumid4d8   =  __low2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/warp_size].x * sumi;
            const float m4s8scaled = __high2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/warp_size].y / QI8_1;

            sum += (T) (sumid4d8 + m4s8scaled);
        }
    }

    return sum;
}

template<typename T, int D, int warp_size>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c;
    GGML_UNUSED(Q_v);

    T sum = 0.0f;

#pragma unroll
    for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) {
        const int k_KQ = k_KQ_0 + threadIdx.x;

        const int ib    = k_KQ /  QI8_1;
        const int iqs4  = k_KQ %  QI5_0;
        const int iqs8  = k_KQ %  QI8_1;
        const int shift = k_KQ & (QI8_1/2);

        int v = (get_int_b2(K_q5_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
        const int vh = get_int_b2(K_q5_0[ib].qh, 0) >> (iqs8 * QI5_0);
        v |= (vh <<  4) & 0x00000010; // 0 ->  4
        v |= (vh << 11) & 0x00001000; // 1 -> 12
        v |= (vh << 18) & 0x00100000; // 2 -> 20
        v |= (vh << 25) & 0x10000000; // 3 -> 28

        const int u = Q_q8[k_KQ_0/warp_size];

        const int sumi = ggml_cuda_dp4a(v, u, 0);

#ifdef FP16_AVAILABLE
        if (std::is_same<T, half>::value) {
            const half2  * Q_ds = (const half2  *) Q_ds_v;

            const half2 sum2 = __half2half2(K_q5_0[ib].d) * Q_ds[k_KQ_0/warp_size];
            sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2)*__float2half(2.0f)) /* *16/QI8_1 == 2 */;
        } else
#endif // FP16_AVAILABLE
        {
            const float2 * Q_ds = (const float2 *) Q_ds_v;

            sum += (T) (__half2float(K_q5_0[ib].d) * (sumi*Q_ds[k_KQ_0/warp_size].x - (16/QI8_1)*Q_ds[k_KQ_0/warp_size].y));
        }
    }

    return sum;
}

template<typename T, int D, int warp_size>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c;
    GGML_UNUSED(Q_v);

    T sum = 0.0f;

#pragma unroll
    for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) {
        const int k_KQ = k_KQ_0 + threadIdx.x;

        const int ib    = k_KQ /  QI8_1;
        const int iqs4  = k_KQ %  QI5_1;
        const int iqs8  = k_KQ %  QI8_1;
        const int shift = k_KQ & (QI8_1/2);

        int v = (get_int_b2(K_q5_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
        const int vh = get_int_b2(K_q5_1[ib].qh, 0) >> (iqs8 * QI5_1);
        v |= (vh <<  4) & 0x00000010; // 0 ->  4
        v |= (vh << 11) & 0x00001000; // 1 -> 12
        v |= (vh << 18) & 0x00100000; // 2 -> 20
        v |= (vh << 25) & 0x10000000; // 3 -> 28

        const int u = Q_q8[k_KQ_0/warp_size];

        const int sumi = ggml_cuda_dp4a(v, u, 0);

#ifdef FP16_AVAILABLE
        if (std::is_same<T, half>::value) {
            const half2  * Q_ds = (const half2  *) Q_ds_v;

            const half2 d5d8_m5s8 = K_q5_1[ib].dm * Q_ds[k_KQ_0/warp_size];
            const half2 sumid5d8_m5s8scaled = d5d8_m5s8 * make_half2(sumi, 1.0f/QI8_1);
            sum += (T) (__low2half(sumid5d8_m5s8scaled) + __high2half(sumid5d8_m5s8scaled));
        } else
#endif // FP16_AVAILABLE
        {
            const float2 * Q_ds = (const float2 *) Q_ds_v;

            const float sumid5d8   =  __low2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/warp_size].x * sumi;
            const float m5s8scaled = __high2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/warp_size].y / QI8_1;

            sum += (T) (sumid5d8 + m5s8scaled);
        }
    }

    return sum;
}

template <typename T, int D, int warp_size>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c;
    GGML_UNUSED(Q_v);

    T sum = 0.0f;

#pragma unroll
    for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) {
        const int k_KQ = k_KQ_0 + threadIdx.x;

        const int ib  = k_KQ / QI8_0;
        const int iqs = k_KQ % QI8_0;

        const int v = get_int_b2(K_q8_0[ib].qs, iqs);

        T Q_d;
        if (std::is_same<T, half>::value) {
            const half2  * Q_ds = (const half2  *) Q_ds_v;
            Q_d = __low2half(Q_ds[k_KQ_0/warp_size]);
        } else {
            const float2 * Q_ds = (const float2 *) Q_ds_v;
            Q_d = Q_ds[k_KQ_0/warp_size].x;
        }

        sum += vec_dot_q8_0_q8_1_impl<T, 1>(&v, &Q_q8[k_KQ_0/warp_size], K_q8_0[ib].d, Q_d);
    }

    return sum;
}

template <typename T, int D, int warp_size>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds_v) {

    const half2 * K_h2 = (const half2 *) K_c;
    GGML_UNUSED(Q_q8);
    GGML_UNUSED(Q_ds_v);

#ifdef FP16_AVAILABLE
    if (std::is_same<T, half>::value) {
        const half2 * Q_h2 = (const half2 *) Q_v;

        half2 sum2 = make_half2(0.0f, 0.0f);

#pragma unroll
        for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += warp_size) {
            const int k_KQ = k_KQ_0 + threadIdx.x;

            const half2 K_ik = K_h2[k_KQ];
            sum2 += K_ik * Q_h2[k_KQ_0/warp_size];
        }

        return __low2half(sum2) + __high2half(sum2);
    }
#endif // FP16_AVAILABLE

    const float2 * Q_f2 = (const float2 *) Q_v;

    float sum = 0.0f;

#pragma unroll
    for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += warp_size) {
        const int k_KQ = k_KQ_0 + threadIdx.x;

        const half2 K_ik = K_h2[k_KQ];
        sum +=  __low2float(K_ik) * Q_f2[k_KQ_0/warp_size].x;
        sum += __high2float(K_ik) * Q_f2[k_KQ_0/warp_size].y;
    }

    return sum;
}

template <typename Tds>
static __device__ __forceinline__ void quantize_q8_1_to_shared(
    const float * __restrict__ x, const float scale, int * __restrict__ yq32, void * __restrict__ yds) {

    float vals[sizeof(int)] = {0.0f};
#pragma unroll
    for (int l = 0; l < int(sizeof(int)); ++l) {
        vals[l] = scale * x[4*threadIdx.x + l];
    }

    float amax = fabsf(vals[0]);
    float sum  = vals[0];
#pragma unroll
    for (int l = 1; l < int(sizeof(int)); ++l) {
        amax = fmaxf(amax, fabsf(vals[l]));
        sum += vals[l];
    }
#pragma unroll
    for (int mask = QI8_1/2; mask > 0; mask >>= 1) {
        amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, 32));
        sum +=             __shfl_xor_sync(0xFFFFFFFF, sum,  mask, 32);
    }

    const float d = amax / 127;
    int q32 = 0;
    int8_t * q8 = (int8_t *) &q32;

    if (d != 0.0f) {
#pragma unroll
        for (int l = 0; l < int(sizeof(int)); ++l) {
            q8[l] = roundf(vals[l] / d);
        }
    }

    yq32[threadIdx.x] = q32;
    if (threadIdx.x % QI8_1 == 0) {
        if (std::is_same<Tds, half2>::value) {
            ((half2  *) yds)[threadIdx.x/QI8_1] =  make_half2(d, sum);
        } else {
            ((float2 *) yds)[threadIdx.x/QI8_1] = make_float2(d, sum);
        }
    }
}

typedef half  (*dequantize_1_f16_t)(const void *, const int64_t);
typedef float (*dequantize_1_f32_t)(const void *, const int64_t);

template <typename T>
static __device__ __forceinline__ T dequantize_1_q4_0(const void * __restrict__ vx, const int64_t i) {
    const block_q4_0 * x = (const block_q4_0 *) vx;

    const int64_t ib    =  i          /  QK4_0;
    const int     iqs   =  i          % (QK4_0/2);
    const int     shift = (i % QK4_0) / (QK4_0/2);

    const T   d  = x[ib].d;
    const int q0 = x[ib].qs[iqs];
    const int q  = ((q0 >> (4*shift)) & 0x0F) - 8;

#ifdef FP16_AVAILABLE
    if (std::is_same<T, half>::value) {
        return ((half) d)*((half) q);
    }
#endif // FP16_AVAILABLE

    return ((float) d)*((float) q);
}

template <typename T>
static __device__ __forceinline__ T dequantize_1_q4_1(const void * __restrict__ vx, const int64_t i) {
    const block_q4_1 * x = (const block_q4_1 *) vx;

    const int64_t ib    =  i          /  QK4_1;
    const int     iqs   =  i          % (QK4_1/2);
    const int     shift = (i % QK4_1) / (QK4_1/2);

    const half2 dm = x[ib].dm;
    const int   q0 = x[ib].qs[iqs];
    const int   q  = ((q0 >> (4*shift)) & 0x0F);

#ifdef FP16_AVAILABLE
    if (std::is_same<T, half>::value) {
        return __low2half(dm)*((half) q) + __high2half(dm);
    }
#endif // FP16_AVAILABLE

    return __low2float(dm)*((float) q) + __high2float(dm);
}

template <typename T>
static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__ vx, const int64_t i) {
    const block_q5_0 * x = (const block_q5_0 *) vx;

    const int64_t ib    =  i          /  QK5_0;
    const int     idq   =  i          %  QK5_0;
    const int     iqs   =  i          % (QK5_0/2);
    const int     shift = (i % QK5_0) / (QK5_0/2);

    const T   d   = x[ib].d;
    const int ql0 = x[ib].qs[iqs];
    const int qh0 = get_int_b2(x[ib].qh, 0);
    const int ql  = ((ql0 >> (4*shift)) & 0x0F);
    const int qh  = ((qh0 >> idq) << 4) & 0x10;
    const int q   = (ql | qh) - 16;

#ifdef FP16_AVAILABLE
    if (std::is_same<T, half>::value) {
        return ((half) d)*((half) q);
    }
#endif // FP16_AVAILABLE

    return ((float) d)*((float) q);
}

template <typename T>
static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__ vx, const int64_t i) {
    const block_q5_1 * x = (const block_q5_1 *) vx;

    const int64_t ib    =  i          /  QK5_1;
    const int     idq   =  i          %  QK5_1;
    const int     iqs   =  i          % (QK5_1/2);
    const int     shift = (i % QK5_1) / (QK5_1/2);

    const half2 dm  = x[ib].dm;
    const int   ql0 = x[ib].qs[iqs];
    const int   qh0 = get_int_b4(x[ib].qh, 0);
    const int   ql  = ((ql0 >> (4*shift)) & 0x0F);
    const int   qh  = ((qh0 >> idq) << 4) & 0x10;
    const int   q   = (ql | qh);

#ifdef FP16_AVAILABLE
    if (std::is_same<T, half>::value) {
        return __low2half(dm)*((half) q) + __high2half(dm);
    }
#endif // FP16_AVAILABLE

    return __low2float(dm)*((float) q) + __high2float(dm);
}

template <typename T>
static __device__ __forceinline__ T dequantize_1_q8_0(const void * __restrict__ vx, const int64_t i) {
    const block_q8_0 * x = (const block_q8_0 *) vx;

    const int64_t ib  = i / QK8_0;
    const int     iqs = i % QK8_0;

    const T   d = x[ib].d;
    const int q = x[ib].qs[iqs];

#ifdef FP16_AVAILABLE
    if (std::is_same<T, half>::value) {
        return ((half) d)*((half) q);
    }
#endif // FP16_AVAILABLE

    return ((float) d)*((float) q);
}

template <typename T>
static __device__ __forceinline__ T dequantize_1_f16(const void * __restrict__ vx, const int64_t i) {
    const half * x = (const half *) vx;

    return x[i];
}

template <int D, int warp_size = WARP_SIZE>
constexpr __device__ vec_dot_KQ_f16_t get_vec_dot_KQ_f16(ggml_type type_K) {
    return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<half, D, warp_size> :
        type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<half, D, warp_size> :
        type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<half, D, warp_size> :
        type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<half, D, warp_size> :
        type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<half, D, warp_size> :
        type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<half, D, warp_size> :
        nullptr;
}

template <int D, int warp_size = WARP_SIZE>
constexpr __device__ vec_dot_KQ_f32_t get_vec_dot_KQ_f32(ggml_type type_K) {
    return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<float, D, warp_size> :
        type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<float, D, warp_size> :
        type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<float, D, warp_size> :
        type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<float, D, warp_size> :
        type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<float, D, warp_size> :
        type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<float, D, warp_size> :
        nullptr;
}

constexpr __device__ dequantize_1_f16_t get_dequantize_1_f16(ggml_type type_V) {
    return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0<half> :
        type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1<half> :
        type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0<half> :
        type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1<half> :
        type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0<half> :
        type_V == GGML_TYPE_F16 ? dequantize_1_f16<half> :
        nullptr;
}

constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
    return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0<float> :
        type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1<float> :
        type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0<float> :
        type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1<float> :
        type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0<float> :
        type_V == GGML_TYPE_F16 ? dequantize_1_f16<float> :
        nullptr;
}

template<int D, int ncols1, int ncols2> // D == head size
__launch_bounds__(D, 1)
static __global__ void flash_attn_stream_k_fixup(
        float * __restrict__ dst, const float2 * __restrict__ dst_fixup, const int ne01, const int ne02, const int ne11) {
    constexpr int ncols = ncols1*ncols2;

    const int bidx0 = blockIdx.x;
    const int j     = blockIdx.y;
    const int c     = blockIdx.z;
    const int jc    = j*ncols2 + c;
    const int tid   = threadIdx.x;

    const float * dst_fixup_data = ((const float *) dst_fixup) + gridDim.x*(2*2*ncols);

    const int iter_k = ne11 / FATTN_KQ_STRIDE;
    const int iter_j = (ne01 + (ncols1 - 1)) / ncols1;

    const int kbc0      = (bidx0 + 0)*iter_k*iter_j*(ne02/ncols2) / gridDim.x;
    const int kbc0_stop = (bidx0 + 1)*iter_k*iter_j*(ne02/ncols2) / gridDim.x;

    const bool did_not_have_any_data   = kbc0 == kbc0_stop;
    const bool wrote_beginning_of_tile = kbc0 % iter_k == 0;
    const bool did_not_write_last      = kbc0/iter_k == kbc0_stop/iter_k && kbc0_stop % iter_k != 0;
    if (did_not_have_any_data || wrote_beginning_of_tile || did_not_write_last) {
        return;
    }

    const int channel = kbc0 / (iter_k*iter_j);
    const int jt      = (kbc0 - channel*iter_k*iter_j) / iter_k;

    if (jt*ncols1 + j >= ne01) {
        return;
    }

    dst += jt*ne02*(ncols1*D) + channel*(ncols2*D) + (j*ne02 + c)*D + tid;

    // Load the partial result that needs a fixup:
    float dst_val = 0.0f;
    float max_val = 0.0f;
    float rowsum  = 0.0f;
    {
        dst_val = *dst;

        const float2 tmp = dst_fixup[bidx0*ncols + jc];
        max_val = tmp.x;
        rowsum  = tmp.y;
    }

    // Iterate over previous blocks and compute the combined results.
    // All CUDA blocks that get here must have a previous block that needs a fixup.
    int bidx = bidx0 - 1;
    int kbc_stop = kbc0;
    while(true) {
        const int kbc = bidx*iter_k*iter_j*(ne02/ncols2) / gridDim.x;
        if (kbc == kbc_stop) { // Did not have any data.
            bidx--;
            kbc_stop = kbc;
            continue;
        }

        const float dst_add = dst_fixup_data[bidx*ncols*D + jc*D + tid];

        const float2 tmp = dst_fixup[(gridDim.x + bidx)*ncols + jc];

        // Scale the current and new value accumulators depending on the max. values.
        const float max_val_new = fmaxf(max_val, tmp.x);

        const float diff_val = max_val - max_val_new;
        const float diff_add = tmp.x   - max_val_new;

        const float scale_val = diff_val >= SOFTMAX_FTZ_THRESHOLD ? expf(diff_val) : 0.0f;
        const float scale_add = diff_add >= SOFTMAX_FTZ_THRESHOLD ? expf(diff_add) : 0.0f;

        dst_val = scale_val*dst_val + scale_add*dst_add;
        rowsum  = scale_val*rowsum  + scale_add*tmp.y;

        max_val = max_val_new;

        // If this block started in a previous tile we are done and don't need to combine additional partial results.
        if (kbc % iter_k == 0 || kbc/iter_k < kbc0/iter_k) {
            break;
        }
        bidx--;
        kbc_stop = kbc;
    }

    // Write back final result:
    *dst = dst_val / rowsum;
}

template<int D> // D == head size
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
static __global__ void flash_attn_combine_results(
        const float  * __restrict__ VKQ_parts,
        const float2 * __restrict__ VKQ_meta,
        float * __restrict__ dst,
        const int parallel_blocks) {
    VKQ_parts += parallel_blocks*D * gridDim.z*blockIdx.x;
    VKQ_meta  += parallel_blocks   * gridDim.z*blockIdx.x;
    dst       +=                 D * gridDim.z*blockIdx.x;

    const int tid = threadIdx.x;
    __builtin_assume(tid < D);

    extern __shared__ float2 meta[];
    if (tid < 2*parallel_blocks) {
        ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.z*(2*parallel_blocks) + tid];
    }

    __syncthreads();

    float kqmax = meta[0].x;
    for (int l = 1; l < parallel_blocks; ++l) {
        kqmax = max(kqmax, meta[l].x);
    }

    float VKQ_numerator   = 0.0f;
    float VKQ_denominator = 0.0f;
    for (int l = 0; l < parallel_blocks; ++l) {
        const float diff = meta[l].x - kqmax;
        float KQ_max_scale = expf(diff);
        const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
        *((uint32_t *) &KQ_max_scale) &= ftz_mask;

        VKQ_numerator   += KQ_max_scale * VKQ_parts[l*gridDim.z*D + blockIdx.z*D + tid];
        VKQ_denominator += KQ_max_scale * meta[l].y;
    }

    dst[blockIdx.z*D + tid] = VKQ_numerator / VKQ_denominator;
}

[[noreturn]]
static void on_no_fattn_vec_case(const int D) {
    if (D == 64) {
        fprintf(stderr, "Unsupported KV type combination for head_size 64.\n");
        fprintf(stderr, "By default only f16 KV cache is supported.\n");
        fprintf(stderr, "Compile with GGML_CUDA_FA_ALL_QUANTS for V cache quantization support.\n");
        GGML_ABORT("fatal error");
    } else if (D == 128) {
        fprintf(stderr, "Unsupported KV type combination for head_size 128.\n");
        fprintf(stderr, "Supported combinations:\n");
        fprintf(stderr, "  - K == q4_0, V == q4_0,  4.50 BPV\n");
        fprintf(stderr, "  - K == q8_0, V == q8_0,  8.50 BPV\n");
        fprintf(stderr, "  - K == f16,  V == f16,  16.00 BPV\n");
        fprintf(stderr, "Compile with GGML_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n");
        GGML_ABORT("fatal error");
    } else {
        fprintf(stderr, "Unsupported KV type combination for head_size %d.\n", D);
        fprintf(stderr, "Only f16 is supported.\n");
        GGML_ABORT("fatal error");
    }
}

template <int DV, int ncols1, int ncols2>
void launch_fattn(
    ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, const int nwarps, const size_t nbytes_shared,
    const int KQ_row_granularity, const bool need_f16_K, const bool need_f16_V, const bool stream_k, const int warp_size = WARP_SIZE
) {
    constexpr int ncols = ncols1 * ncols2;

    const ggml_tensor * Q = dst->src[0];
    const ggml_tensor * K = dst->src[1];
    const ggml_tensor * V = dst->src[2];

    const ggml_tensor * mask = dst->src[3];

    ggml_tensor * KQV = dst;

    GGML_ASSERT(Q->type == GGML_TYPE_F32);
    GGML_ASSERT(KQV->type == GGML_TYPE_F32);

    GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
    GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
        "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");

    GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");

    GGML_ASSERT(Q->ne[3] == 1);

    ggml_cuda_pool & pool = ctx.pool();
    cudaStream_t main_stream = ctx.stream();
    const int id  = ggml_cuda_get_device();
    const int cc  = ggml_cuda_info().devices[id].cc;
    const int nsm = ggml_cuda_info().devices[id].nsm;

    ggml_cuda_pool_alloc<half>   K_f16(pool);
    ggml_cuda_pool_alloc<half>   V_f16(pool);
    ggml_cuda_pool_alloc<float>  dst_tmp(pool);
    ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);

    const char * K_data = (const char *) K->data;
    size_t nb11 = K->nb[1];
    size_t nb12 = K->nb[2];
    size_t nb13 = K->nb[3];

    const char * V_data = (const char *) V->data;
    size_t nb21 = V->nb[1];
    size_t nb22 = V->nb[2];
    size_t nb23 = V->nb[3];

    if (need_f16_K && K->type != GGML_TYPE_F16) {
        GGML_ASSERT(ggml_is_contiguously_allocated(K));
        K_f16.alloc(ggml_nelements(K));
        to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(K->type);
        to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream);
        K_data = (char *) K_f16.ptr;

        const size_t bs = ggml_blck_size(K->type);
        const size_t ts = ggml_type_size(K->type);

        nb11 = nb11*bs*sizeof(half)/ts;
        nb12 = nb12*bs*sizeof(half)/ts;
        nb13 = nb13*bs*sizeof(half)/ts;
    }

    if (need_f16_V && V->type != GGML_TYPE_F16) {
        GGML_ASSERT(ggml_is_contiguously_allocated(V));
        V_f16.alloc(ggml_nelements(V));
        to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type);
        to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream);
        V_data = (char *) V_f16.ptr;

        const size_t bs = ggml_blck_size(V->type);
        const size_t ts = ggml_type_size(V->type);

        nb21 = nb21*bs*sizeof(half)/ts;
        nb22 = nb22*bs*sizeof(half)/ts;
        nb23 = nb23*bs*sizeof(half)/ts;
    }

    int parallel_blocks = 1;

    const int ntiles_x = ((Q->ne[1] + ncols1 - 1) / ncols1);
    const int ntiles_total = ntiles_x * (Q->ne[2] / ncols2) * Q->ne[3];

    const dim3 block_dim(warp_size, nwarps, 1);
    int max_blocks_per_sm = 1; // Max. number of active blocks limited by occupancy.
    CUDA_CHECK(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_blocks_per_sm, fattn_kernel, block_dim.x * block_dim.y * block_dim.z, nbytes_shared));

    dim3 blocks_num;
    if (stream_k) {
        // For short contexts it can be faster to have the SMs work on whole tiles because this lets us skip the fixup.
        const int max_blocks = max_blocks_per_sm*nsm;
        const int tiles_nwaves = (ntiles_total + max_blocks - 1) / max_blocks;
        const int tiles_efficiency_percent = 100 * ntiles_total / (max_blocks*tiles_nwaves);

        const int nblocks_stream_k = max_blocks;

        const bool use_stream_k = cc >= GGML_CUDA_CC_ADA_LOVELACE || tiles_efficiency_percent < 75;

        blocks_num.x = use_stream_k ? nblocks_stream_k : ntiles_total;
        blocks_num.y = 1;
        blocks_num.z = 1;

        dst_tmp_meta.alloc(blocks_num.x*ncols * (2*2 + DV) * sizeof(float));
    } else {
        GGML_ASSERT(K->ne[1] % KQ_row_granularity == 0);
        const int ntiles_KQ = K->ne[1] / KQ_row_granularity; // Max. number of parallel blocks limited by tensor size.

        // parallel_blocks should be at least large enough to achieve max. occupancy for a single wave:
        parallel_blocks = std::max((nsm * max_blocks_per_sm) / ntiles_total, 1);

        // parallel_blocks must not be larger than what the tensor size allows:
        parallel_blocks = std::min(parallel_blocks, ntiles_KQ);

        // If ntiles_total % blocks_per_wave != 0 then some efficiency is lost due to tail effects.
        // Test whether parallel_blocks can be set to a higher value for better efficiency.
        const int blocks_per_wave = nsm * max_blocks_per_sm;
        int nwaves_best = 0;
        int efficiency_percent_best = 0;
        for (int parallel_blocks_test = parallel_blocks; parallel_blocks_test <= ntiles_KQ; ++parallel_blocks_test) {
            const int nblocks_total = ntiles_total * parallel_blocks_test;
            const int nwaves = (nblocks_total + blocks_per_wave - 1) / blocks_per_wave;
            const int efficiency_percent = 100 * nblocks_total / (nwaves*blocks_per_wave);

            // Stop trying configurations with more waves if we already have good efficiency to avoid excessive overhead.
            if (efficiency_percent_best >= 90 && nwaves > nwaves_best) {
                break;
            }

            if (efficiency_percent > efficiency_percent_best) {
                nwaves_best = nwaves;
                efficiency_percent_best = efficiency_percent;
                parallel_blocks = parallel_blocks_test;
            }
        }

        blocks_num.x = ntiles_x;
        blocks_num.y = parallel_blocks;
        blocks_num.z = Q->ne[2]*Q->ne[3];

        if (parallel_blocks > 1) {
            dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
            dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
        }
    }

    float scale         = 1.0f;
    float max_bias      = 0.0f;
    float logit_softcap = 0.0f;

    memcpy(&scale,         (const float *) KQV->op_params + 0, sizeof(float));
    memcpy(&max_bias,      (const float *) KQV->op_params + 1, sizeof(float));
    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));

    if (logit_softcap != 0.0f) {
        scale /= logit_softcap;
    }

    const uint32_t n_head      = Q->ne[2];
    const uint32_t n_head_log2 = 1u << uint32_t(floorf(log2f(float(n_head))));

    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);

    GGML_ASSERT(block_dim.x % warp_size == 0);
    fattn_kernel<<<blocks_num, block_dim, nbytes_shared, main_stream>>>(
        (const char *) Q->data,
        K_data,
        V_data,
        mask ? ((const char *) mask->data) : nullptr,
        !stream_k && parallel_blocks > 1 ? dst_tmp.ptr : (float *) KQV->data, dst_tmp_meta.ptr,
        scale, max_bias, m0, m1, n_head_log2, logit_softcap,
        Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
        K->ne[0], K->ne[1], K->ne[2], K->ne[3],
        mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
        Q->nb[1], Q->nb[2], Q->nb[3],
        nb11, nb12, nb13,
        nb21, nb22, nb23,
        KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
    );
    CUDA_CHECK(cudaGetLastError());

    if (stream_k) {
        if (ntiles_total % blocks_num.x != 0) { // Fixup is only needed if the SMs work on fractional tiles.
            const dim3 block_dim_combine(DV, 1, 1);
            const dim3 blocks_num_combine = {blocks_num.x, ncols1, ncols2};

            flash_attn_stream_k_fixup<DV, ncols1, ncols2>
                <<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
                ((float *) KQV->data, dst_tmp_meta.ptr, Q->ne[1], Q->ne[2], K->ne[1]);
        }
    } else if (parallel_blocks > 1) {
        const dim3 block_dim_combine(DV, 1, 1);
        const dim3 blocks_num_combine(Q->ne[1], 1, blocks_num.z);
        const size_t nbytes_shared_combine = parallel_blocks*sizeof(float2);

        flash_attn_combine_results<DV>
            <<<blocks_num_combine, block_dim_combine, nbytes_shared_combine, main_stream>>>
            (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data, parallel_blocks);
    }
    CUDA_CHECK(cudaGetLastError());
}
