mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-11-07 16:44:13 +01:00
1565 lines
58 KiB
Plaintext
1565 lines
58 KiB
Plaintext
#include "mmq.cuh"
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#include "vecdotq.cuh"
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typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
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typedef void (*load_tiles_cuda_t)(
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
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typedef float (*vec_dot_q_mul_mat_cuda_t)(
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const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
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const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
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typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
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typedef void (mul_mat_q_t)(
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const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
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const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst);
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struct mmq_arch_config_t {
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int x;
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int y;
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int nwarps;
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};
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struct mmq_config_t {
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mmq_arch_config_t rdna2;
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mmq_arch_config_t rdna1;
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mmq_arch_config_t ampere;
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mmq_arch_config_t pascal;
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};
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constexpr mmq_config_t MMQ_CONFIG_Q4_0 = {
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// x y nwarps
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{ 64, 128, 8},
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{ 64, 64, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{ 64, 128, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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constexpr mmq_config_t MMQ_CONFIG_Q4_1 = {
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// x y nwarps
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{ 64, 128, 8},
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{ 64, 64, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{ 64, 128, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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constexpr mmq_config_t MMQ_CONFIG_Q5_0 = {
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// x y nwarps
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{ 64, 128, 8},
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{ 64, 64, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{128, 64, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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constexpr mmq_config_t MMQ_CONFIG_Q5_1 = {
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// x y nwarps
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{ 64, 128, 8},
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{ 64, 64, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{128, 64, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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constexpr mmq_config_t MMQ_CONFIG_Q8_0 = {
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// x y nwarps
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{ 64, 128, 8},
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{ 64, 64, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{128, 64, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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constexpr mmq_config_t MMQ_CONFIG_Q2_K = {
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// x y nwarps
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{ 64, 128, 8},
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{128, 32, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{ 64, 128, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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constexpr mmq_config_t MMQ_CONFIG_Q3_K = {
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// x y nwarps
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{128, 64, 8},
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{ 32, 128, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{128, 128, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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constexpr mmq_config_t MMQ_CONFIG_Q4_K = {
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// x y nwarps
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{ 64, 128, 8},
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{ 32, 64, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{ 64, 128, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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constexpr mmq_config_t MMQ_CONFIG_Q5_K = {
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// x y nwarps
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{ 64, 128, 8},
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{ 32, 64, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{ 64, 128, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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constexpr mmq_config_t MMQ_CONFIG_Q6_K = {
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// x y nwarps
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{ 64, 128, 8},
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{ 32, 64, 8},
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#ifdef CUDA_USE_TENSOR_CORES
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{ 4, 32, 4},
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#else
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{ 64, 64, 4},
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#endif // CUDA_USE_TENSOR_CORES
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{ 64, 64, 8},
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};
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// ------------------------------------------------------------
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template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
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GGML_UNUSED(x_qh);
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GGML_UNUSED(x_sc);
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__shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y];
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__shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0];
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*x_ql = tile_x_qs;
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*x_dm = (half2 *) tile_x_d;
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}
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template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI4_0;
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const int kqsx = k % QI4_0;
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const block_q4_0 * bx0 = (const block_q4_0 *) vx;
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float * x_dmf = (float *) x_dm;
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#pragma unroll
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for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
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int i = i0 + i_offset;
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if (need_check) {
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i = min(i, i_max);
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}
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const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
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x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
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// x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
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}
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const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
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const int kbxd = k % blocks_per_tile_x_row;
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#pragma unroll
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for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
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int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
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if (need_check) {
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i = min(i, i_max);
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}
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const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
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x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
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}
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}
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static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
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const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
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const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
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const float * x_dmf = (const float *) x_dm;
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int u[2*VDR_Q4_0_Q8_1_MMQ];
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#pragma unroll
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for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
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u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
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u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
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}
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return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
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(&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
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y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
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}
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template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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__shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + + mmq_y];
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__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1];
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*x_ql = tile_x_qs;
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*x_dm = tile_x_dm;
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}
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template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI4_1;
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const int kqsx = k % QI4_1;
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const block_q4_1 * bx0 = (const block_q4_1 *) vx;
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#pragma unroll
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for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
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int i = i0 + i_offset;
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if (need_check) {
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i = min(i, i_max);
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}
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const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
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x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
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}
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const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
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const int kbxd = k % blocks_per_tile_x_row;
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#pragma unroll
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for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
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int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
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if (need_check) {
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i = min(i, i_max);
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}
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const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
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x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
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}
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}
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static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
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const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
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const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
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int u[2*VDR_Q4_1_Q8_1_MMQ];
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#pragma unroll
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for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
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u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
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u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
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}
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return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
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(&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
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y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
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}
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template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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__shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y];
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__shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0];
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*x_ql = tile_x_ql;
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*x_dm = (half2 *) tile_x_d;
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}
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template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI5_0;
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const int kqsx = k % QI5_0;
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const block_q5_0 * bx0 = (const block_q5_0 *) vx;
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#pragma unroll
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for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
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int i = i0 + i_offset;
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if (need_check) {
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i = min(i, i_max);
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}
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const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
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const int ql = get_int_from_uint8(bxi->qs, kqsx);
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const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
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int qs0 = (ql >> 0) & 0x0F0F0F0F;
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qs0 |= (qh << 4) & 0x00000010; // 0 -> 4
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qs0 |= (qh << 11) & 0x00001000; // 1 -> 12
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qs0 |= (qh << 18) & 0x00100000; // 2 -> 20
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qs0 |= (qh << 25) & 0x10000000; // 3 -> 28
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qs0 = __vsubss4(qs0, 0x10101010); // subtract 16
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x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
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int qs1 = (ql >> 4) & 0x0F0F0F0F;
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qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4
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qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12
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qs1 |= (qh << 2) & 0x00100000; // 18 -> 20
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qs1 |= (qh << 9) & 0x10000000; // 19 -> 28
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qs1 = __vsubss4(qs1, 0x10101010); // subtract 16
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x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
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}
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const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
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const int kbxd = k % blocks_per_tile_x_row;
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float * x_dmf = (float *) x_dm;
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#pragma unroll
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for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
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int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
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if (need_check) {
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i = min(i, i_max);
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}
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const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
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x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
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}
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}
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static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
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const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
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const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
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const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
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const float * x_dmf = (const float *) x_dm;
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const float * y_df = (const float *) y_ds;
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int u[2*VDR_Q5_0_Q8_1_MMQ];
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#pragma unroll
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for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
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u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
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u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
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}
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return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
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|
(&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
|
|
}
|
|
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI5_1;
|
|
const int kqsx = k % QI5_1;
|
|
|
|
const block_q5_1 * bx0 = (const block_q5_1 *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
|
const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
|
|
|
|
int qs0 = (ql >> 0) & 0x0F0F0F0F;
|
|
qs0 |= (qh << 4) & 0x00000010; // 0 -> 4
|
|
qs0 |= (qh << 11) & 0x00001000; // 1 -> 12
|
|
qs0 |= (qh << 18) & 0x00100000; // 2 -> 20
|
|
qs0 |= (qh << 25) & 0x10000000; // 3 -> 28
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
|
|
|
|
int qs1 = (ql >> 4) & 0x0F0F0F0F;
|
|
qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4
|
|
qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12
|
|
qs1 |= (qh << 2) & 0x00100000; // 18 -> 20
|
|
qs1 |= (qh << 9) & 0x10000000; // 19 -> 28
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
|
|
int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
|
|
|
|
const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
|
|
const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
|
|
|
|
int u[2*VDR_Q5_1_Q8_1_MMQ];
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
|
|
u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
|
|
u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
|
|
}
|
|
|
|
return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
|
|
(&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
|
|
|
|
__shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y];
|
|
__shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0];
|
|
|
|
*x_ql = tile_x_qs;
|
|
*x_dm = (half2 *) tile_x_d;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI8_0;
|
|
const int kqsx = k % QI8_0;
|
|
float * x_dmf = (float *) x_dm;
|
|
|
|
const block_q8_0 * bx0 = (const block_q8_0 *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
|
|
int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
|
|
|
|
const float * x_dmf = (const float *) x_dm;
|
|
const float * y_df = (const float *) y_ds;
|
|
|
|
return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
|
|
(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
|
|
y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI2_K;
|
|
const int kqsx = k % QI2_K;
|
|
|
|
const block_q2_K * bx0 = (const block_q2_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
|
|
int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
|
|
int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
|
|
|
|
x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
const int kbx = k / QI2_K;
|
|
const int ky = (k % QI2_K) * QR2_K;
|
|
const float * y_df = (const float *) y_ds;
|
|
|
|
int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
|
|
|
|
const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
|
|
const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
|
|
v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
|
|
}
|
|
|
|
const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
|
|
|
|
const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
|
|
return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K];
|
|
__shared__ int tile_x_qh[mmq_y * (WARP_SIZE/2) + mmq_y/2];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_qh = tile_x_qh;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI3_K;
|
|
const int kqsx = k % QI3_K;
|
|
|
|
const block_q3_K * bx0 = (const block_q3_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
float * x_dmf = (float *) x_dm;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
|
|
int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
|
|
int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
|
|
|
|
// invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
|
|
x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
|
|
int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
|
|
|
|
const int ksc = k % (QI3_K/4);
|
|
|
|
const int ksc_low = ksc % (QI3_K/8);
|
|
const int shift_low = 4 * (ksc / (QI3_K/8));
|
|
const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
|
|
|
|
const int ksc_high = QI3_K/8;
|
|
const int shift_high = 2 * ksc;
|
|
const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
|
|
|
|
const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
|
|
|
|
x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
|
|
const int kbx = k / QI3_K;
|
|
const int ky = (k % QI3_K) * QR3_K;
|
|
const float * x_dmf = (const float *) x_dm;
|
|
const float * y_df = (const float *) y_ds;
|
|
|
|
const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
|
|
|
|
int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
|
|
const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
|
|
const int shift = 2 * ((ky % 32) / 8);
|
|
const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
|
|
|
|
const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
|
|
const int vlh = (vh << 2) & 0x04040404;
|
|
|
|
v[l] = __vsubss4(vll, vlh);
|
|
}
|
|
|
|
const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
|
|
return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI4_K; // == 0 if QK_K == 256
|
|
const int kqsx = k % QI4_K; // == k if QK_K == 256
|
|
|
|
const block_q4_K * bx0 = (const block_q4_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
|
|
const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
|
|
int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
|
|
int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
|
|
|
|
const int * scales = (const int *) bxi->scales;
|
|
|
|
const int ksc = k % (WARP_SIZE/8);
|
|
|
|
// scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
|
|
int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
|
|
scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits
|
|
|
|
x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
|
|
|
|
const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
|
|
return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
|
|
x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI5_K; // == 0 if QK_K == 256
|
|
const int kqsx = k % QI5_K; // == k if QK_K == 256
|
|
|
|
const block_q5_K * bx0 = (const block_q5_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
const int ky = QR5_K*kqsx;
|
|
|
|
const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
|
const int ql0 = (ql >> 0) & 0x0F0F0F0F;
|
|
const int ql1 = (ql >> 4) & 0x0F0F0F0F;
|
|
|
|
const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
|
|
const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
|
|
const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
|
|
|
|
const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
|
|
const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
|
|
x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
|
|
const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
|
|
int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
|
|
int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
|
|
|
|
const int * scales = (const int *) bxi->scales;
|
|
|
|
const int ksc = k % (WARP_SIZE/8);
|
|
|
|
// scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
|
|
int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
|
|
scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits
|
|
|
|
x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
|
|
|
|
const int index_x = i * (QR5_K*WARP_SIZE + 1) + QR5_K*k;
|
|
const int index_y = j * WARP_SIZE + (QR5_K*k) % WARP_SIZE;
|
|
return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
|
|
x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI6_K; // == 0 if QK_K == 256
|
|
const int kqsx = k % QI6_K; // == k if QK_K == 256
|
|
|
|
const block_q6_K * bx0 = (const block_q6_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
const int ky = QR6_K*kqsx;
|
|
|
|
const int ql = get_int_from_uint8(bxi->ql, kqsx);
|
|
const int ql0 = (ql >> 0) & 0x0F0F0F0F;
|
|
const int ql1 = (ql >> 4) & 0x0F0F0F0F;
|
|
|
|
const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
|
|
const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
|
|
const int qh1 = (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) & 0x30303030;
|
|
|
|
const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
|
|
const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
|
|
x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
|
|
const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256
|
|
float * x_dmf = (float *) x_dm;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
|
|
int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
|
|
int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
|
|
|
|
x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
GGML_UNUSED(x_qh);
|
|
|
|
const float * x_dmf = (const float *) x_dm;
|
|
const float * y_df = (const float *) y_ds;
|
|
|
|
const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
|
|
|
|
const int index_x = i * (QR6_K*WARP_SIZE + 1) + QR6_K*k;
|
|
const int index_y = j * WARP_SIZE + (QR6_K*k) % WARP_SIZE;
|
|
return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
|
|
}
|
|
|
|
template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
|
|
allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
|
|
static __device__ __forceinline__ void mul_mat_q(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
const block_q_t * x = (const block_q_t *) vx;
|
|
const block_q8_1 * y = (const block_q8_1 *) vy;
|
|
|
|
const int blocks_per_row_x = ncols_x / qk;
|
|
const int blocks_per_col_y = nrows_y / QK8_1;
|
|
const int blocks_per_warp = WARP_SIZE / qi;
|
|
|
|
const int & ncols_dst = ncols_y;
|
|
|
|
const int row_dst_0 = blockIdx.x*mmq_y;
|
|
const int & row_x_0 = row_dst_0;
|
|
|
|
const int col_dst_0 = blockIdx.y*mmq_x;
|
|
const int & col_y_0 = col_dst_0;
|
|
|
|
int * tile_x_ql = nullptr;
|
|
half2 * tile_x_dm = nullptr;
|
|
int * tile_x_qh = nullptr;
|
|
int * tile_x_sc = nullptr;
|
|
|
|
allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
|
|
|
|
__shared__ int tile_y_qs[mmq_x * WARP_SIZE];
|
|
__shared__ half2 tile_y_ds[mmq_x * WARP_SIZE/QI8_1];
|
|
|
|
float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
|
|
|
|
for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
|
|
|
|
load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
|
|
threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);
|
|
|
|
#pragma unroll
|
|
for (int ir = 0; ir < qr; ++ir) {
|
|
const int kqs = ir*WARP_SIZE + threadIdx.x;
|
|
const int kbxd = kqs / QI8_1;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < mmq_x; i += nwarps) {
|
|
const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
|
|
|
|
const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
|
|
|
|
const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE;
|
|
tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
|
|
const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
|
|
const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
|
|
const int col_y_eff = min(col_y_0 + ids, ncols_y-1);
|
|
|
|
// if the sum is not needed it's faster to transform the scale to f32 ahead of time
|
|
const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds;
|
|
half2 * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
|
|
if (need_sum) {
|
|
*dsi_dst = *dsi_src;
|
|
} else {
|
|
float * dfi_dst = (float *) dsi_dst;
|
|
*dfi_dst = __low2float(*dsi_src);
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
// #pragma unroll // unrolling this loop causes too much register pressure
|
|
for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
|
|
#pragma unroll
|
|
for (int j = 0; j < mmq_x; j += nwarps) {
|
|
#pragma unroll
|
|
for (int i = 0; i < mmq_y; i += WARP_SIZE) {
|
|
sum[i/WARP_SIZE][j/nwarps] += vec_dot(
|
|
tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
|
|
threadIdx.x + i, threadIdx.y + j, k);
|
|
}
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
}
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int j = 0; j < mmq_x; j += nwarps) {
|
|
const int col_dst = col_dst_0 + j + threadIdx.y;
|
|
|
|
if (col_dst >= ncols_dst) {
|
|
return;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < mmq_y; i += WARP_SIZE) {
|
|
const int row_dst = row_dst_0 + threadIdx.x + i;
|
|
|
|
if (row_dst >= nrows_dst) {
|
|
continue;
|
|
}
|
|
|
|
dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
|
|
}
|
|
}
|
|
}
|
|
|
|
static constexpr __device__ mmq_arch_config_t get_arch_config_device(mmq_config_t mmq_config) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
return mmq_config.rdna2;
|
|
#else
|
|
return mmq_config.rdna1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
#else
|
|
|
|
#if __CUDA_ARCH__ >= CC_VOLTA
|
|
return mmq_config.ampere;
|
|
#else
|
|
return mmq_config.pascal;
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_0.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q4_0(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_0);
|
|
|
|
mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q4_0<arch_config.y>,
|
|
load_tiles_q4_0<arch_config.y, arch_config.nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q4_0_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_1.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#elif __CUDA_ARCH__ < CC_VOLTA
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_1.pascal.nwarps, 2)
|
|
#endif // __CUDA_ARCH__ < CC_VOLTA
|
|
mul_mat_q4_1(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_1);
|
|
|
|
mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q4_1<arch_config.y>,
|
|
load_tiles_q4_1<arch_config.y, arch_config.nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q4_1_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_0.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q5_0(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_0);
|
|
|
|
mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q5_0<arch_config.y>,
|
|
load_tiles_q5_0<arch_config.y, arch_config.nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q5_0_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_1.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q5_1(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_1);
|
|
|
|
mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q5_1<arch_config.y>,
|
|
load_tiles_q5_1<arch_config.y, arch_config.nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q5_1_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q8_0.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q8_0(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q8_0);
|
|
|
|
mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q8_0<arch_config.y>,
|
|
load_tiles_q8_0<arch_config.y, arch_config.nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q8_0_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q2_K.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q2_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q2_K);
|
|
|
|
mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q2_K<arch_config.y>,
|
|
load_tiles_q2_K<arch_config.y, arch_config.nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q2_K_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q3_K.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#elif __CUDA_ARCH__ < CC_VOLTA
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q3_K.pascal.nwarps, 2)
|
|
#endif // __CUDA_ARCH__ < CC_VOLTA
|
|
mul_mat_q3_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q3_K);
|
|
|
|
mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q3_K<arch_config.y>,
|
|
load_tiles_q3_K<arch_config.y, arch_config.nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q3_K_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#elif __CUDA_ARCH__ < CC_VOLTA
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.pascal.nwarps, 2)
|
|
#endif // __CUDA_ARCH__ < CC_VOLTA
|
|
mul_mat_q4_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_K);
|
|
|
|
mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q4_K<arch_config.y>,
|
|
load_tiles_q4_K<arch_config.y, arch_config.nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q4_K_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_K.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q5_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_K);
|
|
|
|
mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q5_K<arch_config.y>,
|
|
load_tiles_q5_K<arch_config.y, arch_config.nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q5_K_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q6_K.rdna2.nwarps, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#elif __CUDA_ARCH__ < CC_VOLTA
|
|
__launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.pascal.nwarps, 2)
|
|
#endif // __CUDA_ARCH__ < CC_VOLTA
|
|
mul_mat_q6_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q6_K);
|
|
|
|
mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, arch_config.x, arch_config.y, arch_config.nwarps, allocate_tiles_q6_K<arch_config.y>,
|
|
load_tiles_q6_K<arch_config.y, arch_config.nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
GGML_UNUSED(get_arch_config_device);
|
|
GGML_UNUSED(vec_dot_q6_K_q8_1_mul_mat);
|
|
NO_DEVICE_CODE;
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define MMQ_SWITCH_CASE(type_suffix) \
|
|
case GGML_TYPE_Q##type_suffix: if (row_diff % arch_config.y == 0) { \
|
|
const bool need_check = false; \
|
|
mul_mat_q##type_suffix<need_check><<<block_nums, block_dims, 0, stream>>> \
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|
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst); \
|
|
} else { \
|
|
const bool need_check = true; \
|
|
mul_mat_q##type_suffix<need_check><<<block_nums, block_dims, 0, stream>>> \
|
|
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst); \
|
|
} break; \
|
|
|
|
void ggml_cuda_op_mul_mat_q(
|
|
ggml_backend_cuda_context & ctx,
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
|
|
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
|
|
const int64_t src1_padded_row_size, cudaStream_t stream) {
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
GGML_ASSERT(ne10 % QK8_1 == 0);
|
|
|
|
const int64_t ne0 = dst->ne[0];
|
|
|
|
const int64_t row_diff = row_high - row_low;
|
|
|
|
int id = ggml_cuda_get_device();
|
|
const int compute_capability = ggml_cuda_info().devices[id].cc;
|
|
|
|
// the main device has a larger memory buffer to hold the results from all GPUs
|
|
// nrows_dst == nrows of the matrix that the kernel writes into
|
|
const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
|
|
|
|
mmq_config_t mmq_config;
|
|
|
|
switch (src0->type) {
|
|
case GGML_TYPE_Q4_0:
|
|
mmq_config = MMQ_CONFIG_Q4_0;
|
|
break;
|
|
case GGML_TYPE_Q4_1:
|
|
mmq_config = MMQ_CONFIG_Q4_1;
|
|
break;
|
|
case GGML_TYPE_Q5_0:
|
|
mmq_config = MMQ_CONFIG_Q5_0;
|
|
break;
|
|
case GGML_TYPE_Q5_1:
|
|
mmq_config = MMQ_CONFIG_Q5_1;
|
|
break;
|
|
case GGML_TYPE_Q8_0:
|
|
mmq_config = MMQ_CONFIG_Q8_0;
|
|
break;
|
|
case GGML_TYPE_Q2_K:
|
|
mmq_config = MMQ_CONFIG_Q2_K;
|
|
break;
|
|
case GGML_TYPE_Q3_K:
|
|
mmq_config = MMQ_CONFIG_Q3_K;
|
|
break;
|
|
case GGML_TYPE_Q4_K:
|
|
mmq_config = MMQ_CONFIG_Q4_K;
|
|
break;
|
|
case GGML_TYPE_Q5_K:
|
|
mmq_config = MMQ_CONFIG_Q5_K;
|
|
break;
|
|
case GGML_TYPE_Q6_K:
|
|
mmq_config = MMQ_CONFIG_Q6_K;
|
|
break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
break;
|
|
}
|
|
|
|
mmq_arch_config_t arch_config;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
arch_config = mmq_config.rdna2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
arch_config = mmq_config.rdna1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
arch_config = mmq_config.ampere;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
arch_config = mmq_config.pascal;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (row_diff + arch_config.y - 1) / arch_config.y;
|
|
const int block_num_y = (src1_ncols + arch_config.x - 1) / arch_config.x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, arch_config.nwarps, 1);
|
|
|
|
switch (src0->type) {
|
|
MMQ_SWITCH_CASE(4_0)
|
|
MMQ_SWITCH_CASE(4_1)
|
|
MMQ_SWITCH_CASE(5_0)
|
|
MMQ_SWITCH_CASE(5_1)
|
|
MMQ_SWITCH_CASE(8_0)
|
|
MMQ_SWITCH_CASE(2_K)
|
|
MMQ_SWITCH_CASE(3_K)
|
|
MMQ_SWITCH_CASE(4_K)
|
|
MMQ_SWITCH_CASE(5_K)
|
|
MMQ_SWITCH_CASE(6_K)
|
|
default:
|
|
GGML_ASSERT(false);
|
|
break;
|
|
}
|
|
|
|
GGML_UNUSED(src1);
|
|
GGML_UNUSED(dst);
|
|
GGML_UNUSED(src1_ddf_i);
|
|
}
|
|
|
|
bool ggml_cuda_supports_mmq(enum ggml_type type) {
|
|
switch (type) {
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_Q5_0:
|
|
case GGML_TYPE_Q5_1:
|
|
case GGML_TYPE_Q8_0:
|
|
case GGML_TYPE_Q2_K:
|
|
case GGML_TYPE_Q3_K:
|
|
case GGML_TYPE_Q4_K:
|
|
case GGML_TYPE_Q5_K:
|
|
case GGML_TYPE_Q6_K:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
}
|