char-fork/llama.cpp

git clone https://git.t4t.associates/char-fork/llama.cpp

fairydreamingcuda : CUDA GGML_OP_LIGHTNING_INDEXER implementation (generic vector kernel + wmma kernel) (#25545)3b53219362026-07-15

#include "common.cuh"
#include "lightning-indexer.cuh"
#include "fattn-common.cuh"
#include "convert.cuh"

#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
#if defined(TURING_MMA_AVAILABLE)

typedef union {
    int2 i2;
    half2 h2[2];
} half4;

// TODO add support for AMD cards via rocWMMA
#include <mma.h>
namespace wmma = nvcuda::wmma;

template <int WARPS_PER_BLOCK, int K_VECS_PER_BLOCK, int64_t N_EMBD, int64_t N_HEAD, ggml_type TYPE_K>
static __global__ void lightning_indexer_kernel_wmma(
        const float * Q, const char * K, const float * W, const half * M, float * dst,
        int64_t n_stream, int64_t n_batch, int64_t n_kv,
        size_t nb1, size_t nb2, size_t nb3,
        size_t nbq1, size_t nbq2, size_t nbq3,
        size_t nbk1, size_t nbk2, size_t nbk3,
        size_t nbw1, size_t nbw2, size_t nbw3,
        size_t nbm1, size_t nbm2, size_t nbm3,
        int64_t nem3
    ) {

    constexpr int THREADS_PER_BLOCK = WARPS_PER_BLOCK * WARP_SIZE;
    constexpr int HEADS_PER_INNER_LOOP = 8;
    constexpr int K_EMBD_PER_INNER_LOOP = 16;
    constexpr int N_EMBD_PADDED = N_EMBD + 8;

    const int i_batch  = blockIdx.y;
    const int i_stream = blockIdx.z;
    const int i_warp   = threadIdx.y;
    const int i_lane   = threadIdx.x;
    const int tid      = i_warp * WARP_SIZE + i_lane;

    // each block processes K_VECS_PER_BLOCK K vectors
    const int start_kv = blockIdx.x * K_VECS_PER_BLOCK;

    const char  * q_base = (const char  *)                 Q + i_batch*nbq2 + i_stream*nbq3;
    const float * w_base = (const float *) ((const char *) W + i_batch*nbw1 + i_stream*nbw3);

    // phase 1 - load weights and first Q tile to shared memory

    __shared__ float w_shared[N_HEAD];
    __shared__ int2  q_shared_h[HEADS_PER_INNER_LOOP][N_EMBD_PADDED / 4];

    if (tid < N_HEAD) {
        w_shared[tid] = w_base[tid];
    }

    // total number of half4 elements in HEADS_PER_INNER_LOOP x N_EMBD Q tile
    constexpr int N_Q_TILE = HEADS_PER_INNER_LOOP * (N_EMBD / 4);
    // number of registers needed in each thread to store Q tile in thread block
    constexpr int N_Q_NEXT = (N_Q_TILE + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK;

#pragma unroll
    for (int i_q = tid; i_q < N_Q_TILE; i_q += THREADS_PER_BLOCK) {
        const int i_head = i_q / (N_EMBD / 4);
        const int i_embd = i_q % (N_EMBD / 4);
        const float4 q = *(const float4 *) (q_base + i_head*nbq1 + i_embd*sizeof(float4));
        half4 q_packed;
        q_packed.h2[0] = __float22half2_rn(make_float2(q.x, q.y));
        q_packed.h2[1] = __float22half2_rn(make_float2(q.z, q.w));
        q_shared_h[i_head][i_embd] = q_packed.i2;
    }

    // phase 2 - load (and dequantize if needed) K to shared mem

    __shared__ half2 k_shared_h[K_VECS_PER_BLOCK][N_EMBD_PADDED / 4][2];

    constexpr int n_k = K_VECS_PER_BLOCK * (N_EMBD / 4);

    if constexpr (TYPE_K == GGML_TYPE_F16) {
#pragma unroll
        for (int i_k = tid; i_k < n_k; i_k += THREADS_PER_BLOCK) {
            const int i_k_vec = i_k / (N_EMBD / 4);
            const int i_embd = i_k % (N_EMBD / 4);
            const int i_kv = start_kv + i_k_vec;
            if (i_kv < n_kv) {
                const int2 * k_base = (const int2 *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3);
                *(int2*) &k_shared_h[i_k_vec][i_embd] = k_base[i_embd];
            } else {
                *(int2*) &k_shared_h[i_k_vec][i_embd] = make_int2(0, 0);
            }
        }
    } else {
        constexpr dequantize_V_t dequantize_k = get_dequantize_V<TYPE_K, half, 4>();
#pragma unroll
        for (int i_k = tid; i_k < n_k; i_k += THREADS_PER_BLOCK) {
            const int i_k_vec = i_k / (N_EMBD / 4);
            const int i_embd = i_k % (N_EMBD / 4);
            const int i_kv = start_kv + i_k_vec;
            if (i_kv < n_kv) {
                const void * k_base = (const void *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3);
                dequantize_k(k_base, &k_shared_h[i_k_vec][i_embd][0], i_embd * 4);
            } else {
                *(int2*) &k_shared_h[i_k_vec][i_embd] = make_int2(0, 0);
            }
        }
    }

    __syncthreads();

    // phase 3 - calculate lightning indexer scores

    __shared__ float qk_shared[WARPS_PER_BLOCK][HEADS_PER_INNER_LOOP][K_VECS_PER_BLOCK];

    // load K fragment
    wmma::fragment<wmma::matrix_b, HEADS_PER_INNER_LOOP, K_VECS_PER_BLOCK, K_EMBD_PER_INNER_LOOP, half, wmma::col_major> frag_k;
    wmma::load_matrix_sync(frag_k, (half*) &k_shared_h[0][i_warp * K_EMBD_PER_INNER_LOOP / 4], N_EMBD_PADDED);

    float score_k = 0.0f;

    for (int i_head_0 = 0; i_head_0 < N_HEAD; i_head_0 += HEADS_PER_INNER_LOOP) {
        const int i_head_next = i_head_0 + HEADS_PER_INNER_LOOP;

        // we don't use accumulator for anything, fill it with zeros
        wmma::fragment<wmma::accumulator, HEADS_PER_INNER_LOOP, K_VECS_PER_BLOCK, K_EMBD_PER_INNER_LOOP, float> frag_acc;
        wmma::fill_fragment(frag_acc, 0.0f);

        // load Q fragment
        wmma::fragment<wmma::matrix_a, HEADS_PER_INNER_LOOP, K_VECS_PER_BLOCK, K_EMBD_PER_INNER_LOOP, half, wmma::row_major> frag_q;
        wmma::load_matrix_sync(frag_q, (half*) &q_shared_h[0][i_warp * K_EMBD_PER_INNER_LOOP / 4], N_EMBD_PADDED);

        // preload next Q tile to registers during matrix multiplication
        float4 q_next[N_Q_NEXT];

        if (i_head_next < N_HEAD) {
#pragma unroll
            for (int i_q = tid, i_q_next = 0; i_q < N_Q_TILE; i_q += THREADS_PER_BLOCK) {
                const int i_head = i_head_next + i_q / (N_EMBD / 4);
                const int i_embd =               i_q % (N_EMBD / 4);
                q_next[i_q_next++] = *(const float4 *) (q_base + i_head*nbq1 + i_embd*sizeof(float4));
            }
        }

        // perform matrix multiplication
        wmma::mma_sync(frag_acc, frag_q, frag_k, frag_acc);
        wmma::store_matrix_sync((float*) &qk_shared[i_warp][0][0], frag_acc, K_VECS_PER_BLOCK, wmma::mem_row_major);

        // make sure all threads finished using q_shared_h so we can store next tile
        __syncthreads();

        // write preloaded Q tile to shared memory
        if (i_head_next < N_HEAD) {
#pragma unroll
            for (int i_q = tid, i_q_next = 0; i_q < N_Q_TILE; i_q += THREADS_PER_BLOCK) {
                const int i_head = i_q / (N_EMBD / 4);
                const int i_embd = i_q % (N_EMBD / 4);
                half4 q_packed;
                q_packed.h2[0] = __float22half2_rn(make_float2(q_next[i_q_next].x, q_next[i_q_next].y));
                q_packed.h2[1] = __float22half2_rn(make_float2(q_next[i_q_next].z, q_next[i_q_next].w));
                q_shared_h[i_head][i_embd] = q_packed.i2;
                ++i_q_next;
            }
        }

        // accumulate QK multiplication results from all block warps
        // (there are 256 threads in block and 256 matmul outputs)
        // TODO it will break if WARP_SIZE is not 32
        const int h = tid / K_VECS_PER_BLOCK;
        const int k = tid % K_VECS_PER_BLOCK;
        const float w_val = w_shared[i_head_0 + h];

        float sum = 0.0f;
#pragma unroll
        for (int w = 0; w < WARPS_PER_BLOCK; ++w) {
            sum += qk_shared[w][h][k];
        }

        // ReLU, weight
        sum = sum > 0.0f ? sum : 0.0f;
        sum *= w_val;

        // wait until qk_shared[0] is no longer used
        __syncthreads();

        // reuse qk_shared[0] for storing partial results
        qk_shared[0][h][k] = sum;

        // wait until all threads write their results
        __syncthreads();

        // accumulate result over heads
        if (tid < K_VECS_PER_BLOCK) {
#pragma unroll
            for (int i_head = 0; i_head < HEADS_PER_INNER_LOOP; ++i_head) {
                score_k += qk_shared[0][i_head][tid];
            }
        }

        // make sure all threads finished using qk_shared
        __syncthreads();
    }

    // phase 4 - store output to VRAM

    if (tid < K_VECS_PER_BLOCK) {
        const int i_kv = start_kv + tid;
        if (i_kv < n_kv) {
            const half * m_base = (const half *) ((const char *) M + i_batch*nbm1 + (i_stream%nem3)*nbm3);
            float * dst_base = (float *) ((char *) dst + i_batch*nb1 + i_stream*nb3);
            dst_base[i_kv] = score_k + __half2float(m_base[i_kv]);
        }
    }
}

#else // defined(TURING_MMA_AVAILABLE)

template <int WARPS_PER_BLOCK, int K_VECS_PER_BLOCK, int64_t N_EMBD, int64_t N_HEAD, ggml_type TYPE_K>
static __global__ void lightning_indexer_kernel_wmma(
        const float * Q, const char * K, const float * W, const half * M, float * dst,
        int64_t n_stream, int64_t n_batch, int64_t n_kv,
        size_t nb1, size_t nb2, size_t nb3,
        size_t nbq1, size_t nbq2, size_t nbq3,
        size_t nbk1, size_t nbk2, size_t nbk3,
        size_t nbw1, size_t nbw2, size_t nbw3,
        size_t nbm1, size_t nbm2, size_t nbm3,
        int64_t nem3
    ) {
    GGML_UNUSED_VARS(Q, K, W, M, dst,
        n_stream, n_batch, n_kv,
        nb1, nb2, nb3,
        nbq1, nbq2, nbq3,
        nbk1, nbk2, nbk3,
        nbw1, nbw2, nbw3,
        nem3);
    NO_DEVICE_CODE;
}

#endif // defined(TURING_MMA_AVAILABLE)
#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)

// TODO there is one ugly assumption used in this kernel - that WARP_SIZE is equal to 32
// thanks to that one warp operating on float4 processes whole indexer K/Q vectors
// 32 * 4 = 128 (N_EMBD)

template <int WARPS_PER_BLOCK, int K_VECS_PER_BLOCK, int64_t N_EMBD, int64_t N_HEAD, ggml_type TYPE_K>
static __global__ void lightning_indexer_kernel_vec(
        const float * Q, const char * K, const float * W, const half * M, float * dst,
        int64_t n_stream, int64_t n_batch, int64_t n_kv,
        size_t nb1, size_t nb2, size_t nb3,
        size_t nbq1, size_t nbq2, size_t nbq3,
        size_t nbk1, size_t nbk2, size_t nbk3,
        size_t nbw1, size_t nbw2, size_t nbw3,
        size_t nbm1, size_t nbm2, size_t nbm3,
        int64_t nem3
    ) {

    constexpr int K_VECS_PER_WARP = K_VECS_PER_BLOCK / WARPS_PER_BLOCK;
    constexpr int THREADS_PER_BLOCK = WARPS_PER_BLOCK * WARP_SIZE;

    const int i_batch  = blockIdx.y;
    const int i_stream = blockIdx.z;
    const int i_warp   = threadIdx.y;
    const int i_lane   = threadIdx.x;
    const int tid      = i_warp * WARP_SIZE + i_lane;

    // each warp processes K_VECS_PER_WARP K vectors
    const int start_kv_block = blockIdx.x * K_VECS_PER_BLOCK;
    const int start_kv = start_kv_block + i_warp * K_VECS_PER_WARP;

    const char  * q_base = (const char  *)                 Q + i_batch*nbq2 + i_stream*nbq3;
    const float * w_base = (const float *) ((const char *) W + i_batch*nbw1 + i_stream*nbw3);

    // phase 1 - load (and dequantize if needed) K to registers

    float4 k_reg_f[K_VECS_PER_WARP];

    if constexpr (TYPE_K == GGML_TYPE_F32) {
        // direct copy of float4
#pragma unroll
        for (int k = 0; k < K_VECS_PER_WARP; ++k) {
            int i_kv = start_kv + k;
            if (i_kv < n_kv) {
                const float4 * k_base = (const float4 *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3);
                k_reg_f[k] = k_base[i_lane];
            } else {
                k_reg_f[k] = make_float4(0, 0, 0, 0);
            }
        }
    } else {
        // dequantize remaining types to float
        constexpr dequantize_V_t dequantize_k = get_dequantize_V<TYPE_K, float, 4>();
#pragma unroll
        for (int k = 0; k < K_VECS_PER_WARP; ++k) {
            int i_kv = start_kv + k;
            if (i_kv < n_kv) {
                const void * k_base = (const void *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3);
                dequantize_k(k_base, &k_reg_f[k], i_lane * 4);
            } else {
                k_reg_f[k] = make_float4(0, 0, 0, 0);
            }
        }
    }

    float score_k[K_VECS_PER_WARP] = { 0.0f };

    // load weights and Q only for N_HEAD_INNER heads at once to reduce shared memory usage
    constexpr int N_HEAD_INNER = N_HEAD / 4;

    for (int i_head_0 = 0; i_head_0 < N_HEAD; i_head_0 += N_HEAD_INNER) {
        // phase 2 - load weights and Q to shared memory

        __shared__ float  w_shared[N_HEAD_INNER];
        __shared__ float4 q_shared_f[N_HEAD_INNER][N_EMBD / 4];

        if (tid < N_HEAD_INNER) {
            w_shared[tid] = w_base[i_head_0 + tid];
        }

        constexpr int n_q = N_HEAD_INNER * (N_EMBD / 4);
#pragma unroll
        for (int i_q = tid; i_q < n_q; i_q += THREADS_PER_BLOCK) {
            const int i_head_inner = i_q / (N_EMBD / 4);
            const int i_head = i_head_0 + i_head_inner;
            const int i_embd = i_q % (N_EMBD / 4);
            q_shared_f[i_head_inner][i_embd] = *(const float4 *) (q_base + i_head*nbq1 + i_embd*sizeof(float4));
        }

        __syncthreads();

        // phase 3 - calculate lightning indexer scores

        for (int i_head_inner = 0; i_head_inner < N_HEAD_INNER; ++i_head_inner) {
            const float w_val = w_shared[i_head_inner];
            float qk[K_VECS_PER_WARP] = { 0.0f };

            // dot product of floats
            const float4 q_vec = q_shared_f[i_head_inner][i_lane];

#pragma unroll
            for (int k = 0; k < K_VECS_PER_WARP; ++k) {
                ggml_cuda_mad(qk[k], q_vec.x, k_reg_f[k].x);
                ggml_cuda_mad(qk[k], q_vec.y, k_reg_f[k].y);
                ggml_cuda_mad(qk[k], q_vec.z, k_reg_f[k].z);
                ggml_cuda_mad(qk[k], q_vec.w, k_reg_f[k].w);
            }

#pragma unroll
            for (int k = 0; k < K_VECS_PER_WARP; ++k) {
                float sum = warp_reduce_sum(qk[k]);

                // ReLU, weight
                if (i_lane == 0) {
                    sum = (sum > 0.0f) ? sum : 0.0f;
                    score_k[k] += sum * w_val;
                }
            }
        }

        __syncthreads();
    }

    // phase 4 - store outputs to shared memory

    __shared__ float dst_shared[K_VECS_PER_BLOCK];

    if (i_lane == 0) {
#pragma unroll
        for (int k = 0; k < K_VECS_PER_WARP; ++k) {
            dst_shared[i_warp * K_VECS_PER_WARP + k] = score_k[k];
        }
    }

    __syncthreads();

    // phase 5 - write from shared memory to VRAM in coalesced manner

    if (tid < K_VECS_PER_BLOCK) {
        int i_kv = start_kv_block + tid;
        if (i_kv < n_kv) {
            const half * m_base = (const half *) ((const char *) M + i_batch*nbm1 + (i_stream%nem3)*nbm3);
            float * dst_base = (float *) ((char *) dst + i_batch*nb1 + i_stream*nb3);
            dst_base[i_kv] = dst_shared[tid] + __half2float(m_base[i_kv]);
        }
    }
}

#define LIGHTNING_INDEXER_CASE(lightning_indexer_kernel, n_embd, n_head, K, type_K)         \
    if (K->type == (type_K)) {                                                              \
        lightning_indexer_kernel<WARPS_PER_BLOCK, K_VECS_PER_BLOCK, n_embd, n_head, type_K> \
            <<<grid, block, 0, ctx.stream()>>>(                                             \
            q_d, k_d, w_d, m_d, dst_d,                                                      \
            n_stream, n_batch, n_kv,                                                        \
            nb1, nb2, nb3,                                                                  \
            nbq1, nbq2, nbq3,                                                               \
            nbk1, nbk2, nbk3,                                                               \
            nbw1, nbw2, nbw3,                                                               \
            nbm1, nbm2, nbm3,                                                               \
            nem3                                                                            \
        );                                                                                  \
    } else

void ggml_cuda_lightning_indexer(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const ggml_tensor * q = dst->src[0];
    const ggml_tensor * k = dst->src[1];
    const ggml_tensor * w = dst->src[2]; // weights
    const ggml_tensor * m = dst->src[3]; // mask

    GGML_ASSERT(dst->type == GGML_TYPE_F32);
    GGML_ASSERT(  q->type == GGML_TYPE_F32);
    GGML_ASSERT(  w->type == GGML_TYPE_F32);
    GGML_ASSERT(  m->type == GGML_TYPE_F16);

    GGML_TENSOR_LOCALS(int64_t, neq,  q, ne)
    GGML_TENSOR_LOCALS(size_t,  nbq,  q, nb)
    GGML_TENSOR_LOCALS(int64_t, nek,  k, ne)
    GGML_TENSOR_LOCALS(size_t,  nbk,  k, nb)
    GGML_TENSOR_LOCALS(int64_t, new,  w, ne)
    GGML_TENSOR_LOCALS(size_t,  nbw,  w, nb)
    GGML_TENSOR_LOCALS(int64_t, nem,  m, ne)
    GGML_TENSOR_LOCALS(size_t,  nbm,  m, nb)
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne)
    GGML_TENSOR_LOCALS(size_t,  nb, dst, nb)

    // input tensor rows must be contiguous
    GGML_ASSERT(nbq0 == ggml_type_size(q->type));
    GGML_ASSERT(nbk0 == ggml_type_size(k->type));
    GGML_ASSERT(nbw0 == ggml_type_size(w->type));
    GGML_ASSERT(nbm0 == ggml_type_size(m->type));

    // dst cannot be transposed or permuted
    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb0 <= nb1);
    GGML_ASSERT(nb1 <= nb2);
    GGML_ASSERT(nb2 <= nb3);

    const int n_embd   = q->ne[0];
    const int n_head   = q->ne[1];
    const int n_batch  = q->ne[2];
    const int n_stream = q->ne[3];
    const int n_kv     = k->ne[2];

    const float *   q_d = (const float *)   q->data;
    const char  *   k_d = (const char  *)   k->data;
    const float *   w_d = (const float *)   w->data;
    const half  *   m_d = (const half  *)   m->data;
    float       * dst_d = (      float *) dst->data;

    const int device = ggml_cuda_get_device();
    const int cc     = ggml_cuda_info().devices[device].cc;

    if (n_embd == 128 && n_head == 64) {
#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
        if (GGML_CUDA_CC_IS_NVIDIA(cc) && turing_mma_available(cc) && k->type != GGML_TYPE_F32 && k->type != GGML_TYPE_BF16) {
            // use wmma kernel
            constexpr int K_VECS_PER_BLOCK = 32;
            constexpr int WARPS_PER_BLOCK = 8;

            dim3 block(32, WARPS_PER_BLOCK);
            int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK);
            dim3 grid(num_kv_blocks, n_batch, n_stream);

            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_F16)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q4_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q4_1)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q5_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q5_1)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q8_0)
            GGML_ABORT("fatal error");
        } else {
#else // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
        {
#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
            // use vector kernel
            constexpr int K_VECS_PER_WARP = 8;
            constexpr int WARPS_PER_BLOCK = 8;
            constexpr int K_VECS_PER_BLOCK = K_VECS_PER_WARP * WARPS_PER_BLOCK;

            dim3 block(32, WARPS_PER_BLOCK);
            int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK);
            dim3 grid(num_kv_blocks, n_batch, n_stream);

            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_F16)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q4_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q4_1)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q5_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q5_1)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q8_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_BF16)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_F32)
            GGML_ABORT("fatal error");
        }
    } else if (n_embd == 128 && n_head == 32) {
#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
        if (GGML_CUDA_CC_IS_NVIDIA(cc) && turing_mma_available(cc) && k->type != GGML_TYPE_F32 && k->type != GGML_TYPE_BF16) {
            // use wmma kernel
            constexpr int K_VECS_PER_BLOCK = 32;
            constexpr int WARPS_PER_BLOCK = 8;

            dim3 block(32, WARPS_PER_BLOCK);
            int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK);
            dim3 grid(num_kv_blocks, n_batch, n_stream);

            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_F16)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q4_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q4_1)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q5_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q5_1)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q8_0)
            GGML_ABORT("fatal error");
        } else {
#else // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
        {
#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
            // use vector kernel
            constexpr int K_VECS_PER_WARP = 8;
            constexpr int WARPS_PER_BLOCK = 8;
            constexpr int K_VECS_PER_BLOCK = K_VECS_PER_WARP * WARPS_PER_BLOCK;

            dim3 block(32, WARPS_PER_BLOCK);
            int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK);
            dim3 grid(num_kv_blocks, n_batch, n_stream);

            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_F16)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q4_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q4_1)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q5_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q5_1)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q8_0)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_BF16)
            LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_F32)
            GGML_ABORT("fatal error");
        }
    } else {
        GGML_ABORT("fatal error");
    }
}

bool ggml_cuda_lightning_indexer_supported(int device, const ggml_tensor * dst) {
    GGML_UNUSED(device);

    const ggml_tensor * q = dst->src[0];
    const ggml_tensor * k = dst->src[1];
    const ggml_tensor * w = dst->src[2]; // weights
    const ggml_tensor * m = dst->src[3]; // mask

    GGML_TENSOR_LOCALS(int64_t, neq,  q, ne)
    GGML_TENSOR_LOCALS(size_t,  nbq,  q, nb)
    GGML_TENSOR_LOCALS(int64_t, nek,  k, ne)
    GGML_TENSOR_LOCALS(size_t,  nbk,  k, nb)
    GGML_TENSOR_LOCALS(int64_t, new,  w, ne)
    GGML_TENSOR_LOCALS(size_t,  nbw,  w, nb)
    GGML_TENSOR_LOCALS(int64_t, nem,  m, ne)
    GGML_TENSOR_LOCALS(size_t,  nbm,  m, nb)
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne)
    GGML_TENSOR_LOCALS(size_t,  nb, dst, nb)

    if (neq0 != 128) {
        return false;
    }

    if (neq1 != 64 && neq1 != 32) {
        return false;
    }

    // alignment checks
    for (const ggml_tensor * t : {q, k}) {
        if (ggml_is_quantized(t->type)) {
            continue;
        }
        for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
            if (t->nb[i] % 16 != 0) {
                return false;
            }
        }
    }

    switch(k->type) {
        case GGML_TYPE_F32:
        case GGML_TYPE_BF16:
        case GGML_TYPE_F16:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_Q5_1:
        case GGML_TYPE_Q5_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q4_0:
            return true;
        default:
            return false;
    }
}