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CUDA: use mmvq for mul-mat-id for small batch sizes (#18958)

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* CUDA: use mmvq for mul-mat-id for small batch sizes

* add mmvq too

* Fix perf issue on ampere. Use mmvf mm-id only for non-nvidia GPUs

* templatize multi_token_path
This commit is contained in:
Aman Gupta
2026-02-03 23:31:23 +08:00
committed by GitHub
parent a6fd8ca1fe
commit 8bece2eb20
4 changed files with 224 additions and 121 deletions
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ggml/src/ggml-cuda/mmvq.cu
+85 -50
View File
@@ -137,15 +137,15 @@ static constexpr __host__ __device__ int calc_rows_per_block(int ncols_dst, int
return 1;
}
// tell the compiler to use as many registers as it wants, see nwarps definition below
template <ggml_type type, int ncols_dst, bool has_fusion>
template <ggml_type type, int ncols_dst, bool has_fusion, bool is_multi_token_id = false>
__launch_bounds__(calc_nwarps(ncols_dst, 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, const int32_t * __restrict__ ids, const ggml_cuda_mm_fusion_args_device fusion, float * __restrict__ dst,
const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t stride_row_x, const uint32_t stride_col_y,
const uint32_t stride_col_dst, const uint3 channel_ratio, const uint32_t stride_channel_x,
const uint32_t stride_channel_y, const uint32_t stride_channel_dst, const uint3 sample_ratio,
const uint32_t stride_sample_x, const uint32_t stride_sample_y, const uint32_t stride_sample_dst) {
const uint32_t stride_sample_x, const uint32_t stride_sample_y, const uint32_t stride_sample_dst,
const uint32_t ids_stride) {
constexpr int qk = ggml_cuda_type_traits<type>::qk;
constexpr int qi = ggml_cuda_type_traits<type>::qi;
@@ -162,11 +162,25 @@ static __global__ void mul_mat_vec_q(
const int blocks_per_row_x = ncols_x / qk;
constexpr int blocks_per_iter = vdr * nwarps*warp_size / qi;
// The MUL_MAT_ID code path with ids != nullptr is only implemented for ncols_dst == 1.
const uint32_t channel_dst = blockIdx.y;
const uint32_t channel_x = ncols_dst == 1 && ids ? ids[channel_dst] : fastdiv(channel_dst, channel_ratio);
const uint32_t channel_y = ncols_dst == 1 && ids ? fastmodulo(channel_dst, nchannels_y) : channel_dst;
const uint32_t sample_dst = blockIdx.z;
uint32_t token_idx = 0;
uint32_t channel_x;
uint32_t channel_y;
uint32_t sample_dst;
if constexpr (is_multi_token_id) {
// Multi-token MUL_MAT_ID path, adding these in the normal path causes a perf regression for n_tokens=1 case
token_idx = blockIdx.z;
channel_x = ids[channel_dst + token_idx * ids_stride];
channel_y = fastmodulo(channel_dst, nchannels_y);
sample_dst = 0;
} else {
channel_x = ncols_dst == 1 && ids ? ids[channel_dst] : fastdiv(channel_dst, channel_ratio);
channel_y = ncols_dst == 1 && ids ? fastmodulo(channel_dst, nchannels_y) : channel_dst;
sample_dst = blockIdx.z;
}
const uint32_t sample_x = fastdiv(sample_dst, sample_ratio);
const uint32_t sample_y = sample_dst;
@@ -188,11 +202,11 @@ static __global__ void mul_mat_vec_q(
active_glu = fusion.glu_op;
}
const uint32_t channel_bias = ids ? channel_x : channel_dst;
float x_biases[ncols_dst] = { 0.0f };
float gate_biases[ncols_dst] = { 0.0f };
if constexpr (has_fusion) {
const uint32_t channel_bias = ids ? channel_x : channel_dst;
if (use_bias) {
x_bias = x_bias + sample_dst*stride_sample_dst + channel_bias*stride_channel_dst + row0;
// 1. Hide latency by prefetching bias and gate here
@@ -222,6 +236,9 @@ static __global__ void mul_mat_vec_q(
float tmp_gate[ncols_dst][rows_per_cuda_block] = {{0.0f}};
const block_q8_1 * y = ((const block_q8_1 *) vy) + sample_y*stride_sample_y + channel_y*stride_channel_y;
if constexpr (is_multi_token_id) {
y += token_idx*stride_col_y;
}
const int kbx_offset = sample_x*stride_sample_x + channel_x*stride_channel_x + row0*stride_row_x;
for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
@@ -275,6 +292,10 @@ static __global__ void mul_mat_vec_q(
dst += sample_dst*stride_sample_dst + channel_dst*stride_channel_dst + row0;
if constexpr (is_multi_token_id) {
dst += token_idx*stride_col_dst;
}
// sum up partial sums and write back result
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
@@ -335,40 +356,41 @@ static __global__ void mul_mat_vec_q(
}
static std::pair<dim3, dim3> calc_launch_params(
const int ncols_dst, const int nrows_x, const int nchannels_y, const int nsamples_y,
const int ncols_dst, const int nrows_x, const int nchannels_dst, const int nsamples_or_ntokens,
const int warp_size, const mmvq_parameter_table_id table_id) {
const int64_t nblocks = (nrows_x + calc_rows_per_block(ncols_dst, table_id) - 1) / calc_rows_per_block(ncols_dst, table_id);
const dim3 block_nums(nblocks, nchannels_y, nsamples_y);
const dim3 block_nums(nblocks, nchannels_dst, nsamples_or_ntokens);
const dim3 block_dims(warp_size, calc_nwarps(ncols_dst, table_id), 1);
return {block_nums, block_dims};
}
template<ggml_type type, int c_ncols_dst>
template<ggml_type type, int c_ncols_dst, bool is_multi_token_id = false>
static void mul_mat_vec_q_switch_fusion(
const void * vx, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t stride_row_x, const uint32_t stride_col_y,
const uint32_t stride_col_dst, const uint3 channel_ratio, const uint32_t stride_channel_x,
const uint32_t stride_channel_y, const uint32_t stride_channel_dst, const uint3 sample_ratio,
const uint32_t stride_sample_x, const uint32_t stride_sample_y, const uint32_t stride_sample_dst,
const dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared, cudaStream_t stream) {
const dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared,
const uint32_t ids_stride, cudaStream_t stream) {
const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
if constexpr (c_ncols_dst == 1) {
if (has_fusion) {
mul_mat_vec_q<type, c_ncols_dst, true><<<block_nums, block_dims, nbytes_shared, stream>>>
mul_mat_vec_q<type, c_ncols_dst, true, is_multi_token_id><<<block_nums, block_dims, nbytes_shared, stream>>>
(vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
return;
}
}
GGML_ASSERT(!has_fusion && "fusion only supported for ncols_dst=1");
mul_mat_vec_q<type, c_ncols_dst, false><<<block_nums, block_dims, nbytes_shared, stream>>>
mul_mat_vec_q<type, c_ncols_dst, false, is_multi_token_id><<<block_nums, block_dims, nbytes_shared, stream>>>
(vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
}
template <ggml_type type>
@@ -379,7 +401,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
const int nchannels_x, const int nchannels_y, const int nchannels_dst,
const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
const int nsamples_x, const int nsamples_dst, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
cudaStream_t stream) {
const int ids_stride, cudaStream_t stream) {
GGML_ASSERT(ncols_x % ggml_blck_size(type) == 0);
GGML_ASSERT(ncols_dst <= MMVQ_MAX_BATCH_SIZE);
@@ -393,8 +415,19 @@ static void mul_mat_vec_q_switch_ncols_dst(
const mmvq_parameter_table_id table_id = get_device_table_id(ggml_cuda_info().devices[device].cc);
const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
const bool has_ids = ids != nullptr;
if (has_ids && ncols_dst > 1) {
// Multi-token MUL_MAT_ID path only - single-token goes through regular path below
constexpr int c_ncols_dst = 1;
std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, ncols_dst, warp_size, table_id);
mul_mat_vec_q_switch_fusion<type, c_ncols_dst, true>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
dims.first, dims.second, 0, ids_stride, stream);
return;
}
GGML_ASSERT(!ids || ncols_dst == 1);
switch (ncols_dst) {
case 1: {
constexpr int c_ncols_dst = 1;
@@ -402,7 +435,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
dims.first, dims.second, 0, stream);
dims.first, dims.second, 0, ids_stride, stream);
} break;
case 2: {
constexpr int c_ncols_dst = 2;
@@ -410,7 +443,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
dims.first, dims.second, 0, stream);
dims.first, dims.second, 0, ids_stride, stream);
} break;
case 3: {
constexpr int c_ncols_dst = 3;
@@ -418,7 +451,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
dims.first, dims.second, 0, stream);
dims.first, dims.second, 0, ids_stride, stream);
} break;
case 4: {
constexpr int c_ncols_dst = 4;
@@ -426,7 +459,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
dims.first, dims.second, 0, stream);
dims.first, dims.second, 0, ids_stride, stream);
} break;
case 5: {
constexpr int c_ncols_dst = 5;
@@ -434,7 +467,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
dims.first, dims.second, 0, stream);
dims.first, dims.second, 0, ids_stride, stream);
} break;
case 6: {
constexpr int c_ncols_dst = 6;
@@ -442,7 +475,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
dims.first, dims.second, 0, stream);
dims.first, dims.second, 0, ids_stride, stream);
} break;
case 7: {
constexpr int c_ncols_dst = 7;
@@ -450,7 +483,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
dims.first, dims.second, 0, stream);
dims.first, dims.second, 0, ids_stride, stream);
} break;
case 8: {
constexpr int c_ncols_dst = 8;
@@ -458,7 +491,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
dims.first, dims.second, 0, stream);
dims.first, dims.second, 0, ids_stride, stream);
} break;
default:
GGML_ABORT("fatal error");
@@ -474,127 +507,127 @@ static void mul_mat_vec_q_switch_type(
const int nchannels_x, const int nchannels_y, const int nchannels_dst,
const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
const int nsamples_x, const int nsamples_dst, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
cudaStream_t stream) {
const int ids_stride, cudaStream_t stream) {
switch (type_x) {
case GGML_TYPE_Q4_0:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q4_0>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q4_1:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q4_1>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q5_0:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q5_0>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q5_1:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q5_1>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q8_0:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q8_0>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_MXFP4:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_MXFP4>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q2_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q2_K>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q3_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q3_K>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q4_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q4_K>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q5_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q5_K>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q6_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q6_K>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_IQ2_XXS:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ2_XXS>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_IQ2_XS:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ2_XS>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_IQ2_S:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ2_S>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_IQ3_XXS:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ3_XXS>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_IQ1_S:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ1_S>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_IQ1_M:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ1_M>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_IQ4_NL:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ4_NL>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_IQ4_XS:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ4_XS>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_IQ3_S:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ3_S>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
default:
GGML_ABORT("fatal error");
@@ -622,7 +655,7 @@ void ggml_cuda_mul_mat_vec_q(
GGML_ASSERT( nb0 == ts_dst);
GGML_ASSERT(!ids || ids->nb[0] == ggml_type_size(ids->type));
GGML_ASSERT(!ids || ne12 == 1); // Implementation is only correct for batch size 1.
GGML_ASSERT(!ids || ne12 <= MMVQ_MAX_BATCH_SIZE);
const float * src1_d = (const float *) src1->data;
const int32_t * ids_d = ids ? (const int32_t *) ids->data : nullptr;
@@ -693,11 +726,13 @@ void ggml_cuda_mul_mat_vec_q(
const int64_t stride_channel_dst = ids ? s1 : s2;
const int64_t stride_channel_y = ids ? s11 : s12;
const int64_t ids_stride = ids ? ids->nb[1] / ggml_type_size(ids->type) : 0;
mul_mat_vec_q_switch_type(
src0->data, src0->type, src1_q8_1.get(), ids_d, fusion_local, dst_d, ne00,
ne01, ncols_dst, s01, stride_col_y, stride_col_dst,
ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
ne03, ne3, s03, s13, s3, stream);
ne03, ne3, s03, s13, s3, ids_stride, stream);
}
void ggml_cuda_op_mul_mat_vec_q(
@@ -726,7 +761,7 @@ void ggml_cuda_op_mul_mat_vec_q(
ggml_cuda_mm_fusion_args_device fusion_local{};
mul_mat_vec_q_switch_type(
src0_dd_i, src0->type, src1_ddq_i, nullptr, fusion_local, dst_dd_i, ne00, row_diff, src1_ncols, stride_row_x, stride_col_y, nrows_dst,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, stream);
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, stream);
GGML_UNUSED_VARS(src1, dst, src1_ddf_i, src1_ncols, src1_padded_row_size);
}