the logic of mul_mat_vec_q in llama.cpp

[liuyao0]

I'm trying to replace the mixed-precision quantization GEMM CUDA kernel in llama.cpp with my implementation. For this, I must understand the data arrangement and calculation logic in kernel mul_mat_vec_q.

I tried to understand the code by reading it but failed, for a large number of unknown variables and complex parallel calculations.

I want to know how I can understand the code. What do qk, qi, vdr mean？and how the kernel works?

template <ggml_type type, int ncols_y>
// tell the compiler to use as many registers as it wants, see nwarps definition below
__launch_bounds__(calc_nwarps(ncols_y, get_device_table_id())*ggml_cuda_get_physical_warp_size(), 1)
static __global__ void mul_mat_vec_q(
    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) {

    constexpr int qk  = ggml_cuda_type_traits<type>::qk;
    constexpr int qi  = ggml_cuda_type_traits<type>::qi;
    constexpr int vdr = get_vdr_mmvq(type);
    constexpr mmvq_parameter_table_id table_id = get_device_table_id();
    constexpr int nwarps = calc_nwarps(ncols_y, table_id);
    constexpr int rows_per_cuda_block = calc_rows_per_block(ncols_y, table_id);
    constexpr int warp_size = ggml_cuda_get_physical_warp_size();

    constexpr vec_dot_q_cuda_t vec_dot_q_cuda = get_vec_dot_q_cuda(type);

    const     int tid = warp_size*threadIdx.y + threadIdx.x;
    const     int row0 = rows_per_cuda_block*blockIdx.x;
    const     int blocks_per_row_x = ncols_x / qk;
    const     int blocks_per_col_y = nrows_y / QK8_1;
    constexpr int blocks_per_iter = vdr * nwarps * warp_size / qi;

    // partial sum for each thread
    float tmp[ncols_y][rows_per_cuda_block] = {{0.0f}};

    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
        const int kby = kbx * (qk/QK8_1); // y block index that aligns with kbx

        // x block quant index when casting the quants to int
        const int kqs = vdr * (tid % (qi/vdr));

#pragma unroll
        for (int j = 0; j < ncols_y; ++j) {
#pragma unroll
            for (int i = 0; i < rows_per_cuda_block; ++i) {
                tmp[j][i] += vec_dot_q_cuda(vx, &y[j*blocks_per_col_y + kby], (row0 + i)*blocks_per_row_x + kbx, kqs);
            }
        }
    }

    __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y][rows_per_cuda_block][warp_size];
    if (threadIdx.y > 0) {
#pragma unroll
        for (int j = 0; j < ncols_y; ++j) {
#pragma unroll
            for (int i = 0; i < rows_per_cuda_block; ++i) {
                tmp_shared[threadIdx.y-1][j][i][threadIdx.x] = tmp[j][i];
            }
        }
    }
    __syncthreads();
    if (threadIdx.y > 0) {
        return;
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int j = 0; j < ncols_y; ++j) {
#pragma unroll
        for (int i = 0; i < rows_per_cuda_block; ++i) {
#pragma unroll
            for (int l = 0; l < nwarps-1; ++l) {
                tmp[j][i] += tmp_shared[l][j][i][threadIdx.x];
            }
            tmp[j][i] = warp_reduce_sum<warp_size>(tmp[j][i]);
        }

        if (threadIdx.x < rows_per_cuda_block && (rows_per_cuda_block == 1 || row0 + threadIdx.x < (unsigned)nrows_dst)) {
            dst[j*nrows_dst + row0 + threadIdx.x] = tmp[j][threadIdx.x];
        }
    }

    GGML_UNUSED(nrows_x);
}

I really feel terrible. Who can help me.