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CUDA: mul_mat_id for mmf for bs <= 64 for f16 and bs <= 32 for f32 (#16277)

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* CUDA: mul_mat_id for mmf for bs <= 64 for f16 and bs <= 32 for f32

This commit adds mul_mat_id support for ncols_dst >= 16. It does this by
packing ncols_dst tiles into the blockDim.y.

My tests on a RTX 3090 show that this is faster than the cuBLAS fallback
for f16 till bs=64, and for f32 till bs=32

* Review: refactor if statement
This commit is contained in:
Aman Gupta
2025-09-28 00:49:32 +08:00
committed by GitHub
parent 75a3a6c2cd
commit c0bfc57af4
4 changed files with 92 additions and 47 deletions
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6
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@@ -2031,7 +2031,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
const int cc = ggml_cuda_info().devices[id].cc;
const int warp_size = ggml_cuda_info().devices[id].warp_size;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1]);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1], /*mul_mat_id=*/false);
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc);
}
@@ -2039,7 +2039,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
const int cc = ggml_cuda_info().devices[ctx.device].cc;
const int warp_size = ggml_cuda_info().devices[ctx.device].warp_size;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1]);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1], /*mul_mat_id=*/false);
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc);
}
@@ -2111,7 +2111,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
return;
}
if (ggml_cuda_should_use_mmf(src0->type, cc, WARP_SIZE, src0->ne, src1->ne[2])) {
if (ggml_cuda_should_use_mmf(src0->type, cc, WARP_SIZE, src0->ne, src1->ne[2], /*mul_mat_id=*/true)) {
ggml_cuda_mul_mat_f(ctx, src0, src1, ids, dst);
return;
}

16
ggml/src/ggml-cuda/mmf.cu
View File

@@ -84,7 +84,7 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr
}
}
bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * src0_ne, const int src1_ncols) {
bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * src0_ne, const int src1_ncols, bool mul_mat_id) {
if (ggml_is_quantized(type)) {
return false;
@@ -96,8 +96,18 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const
if (src0_ne[1] % MMF_ROWS_PER_BLOCK != 0) {
return false;
}
if (src1_ncols > 16) {
return false;
if (mul_mat_id) {
if (type == GGML_TYPE_F32 && src1_ncols > 32) {
return false;
}
if ((type == GGML_TYPE_F16 || type == GGML_TYPE_BF16) && src1_ncols > 64) {
return false;
}
} else {
if (src1_ncols > 16) {
return false;
}
}
switch (type) {

113
ggml/src/ggml-cuda/mmf.cuh
View File

@@ -9,13 +9,13 @@ using namespace ggml_cuda_mma;
void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * scr0_ne, const int src1_ncols);
bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * scr0_ne, const int src1_ncols, bool mul_mat_id);
template <typename T, int rows_per_block, int cols_per_block, int nwarps, bool has_ids>
__launch_bounds__(ggml_cuda_get_physical_warp_size()*nwarps, 1)
static __global__ void mul_mat_f(
const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst,
const int ncols, const int nchannels_dst, const int stride_row, const int stride_col_y, const int stride_col_dst,
const int ncols, const int ncols_dst_total, const int nchannels_dst, const int stride_row, const int stride_col_y, const int stride_col_dst,
const int stride_col_id, const int stride_row_id,
const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) {
@@ -31,9 +31,20 @@ static __global__ void mul_mat_f(
const int row0 = blockIdx.x * rows_per_block;
const int expert_idx = has_ids ? blockIdx.y : 0;
int expert_idx = 0;
int col_base = 0;
const int channel_dst = has_ids ? 0 : blockIdx.y;
if constexpr (has_ids) {
// experts + tiles of ncols_dst are packed in the y dimension
int col_tiles = (ncols_dst_total + cols_per_block - 1) / cols_per_block;
const int nchannels_x = gridDim.y / col_tiles;
const int tile_idx = blockIdx.y / nchannels_x;
expert_idx = blockIdx.y - tile_idx * nchannels_x;
col_base = tile_idx * cols_per_block;
}
const int channel_x = has_ids ? expert_idx : (channel_dst / channel_ratio);
const int channel_y = channel_dst;
const int sample_dst = blockIdx.z;
@@ -44,6 +55,14 @@ static __global__ void mul_mat_f(
y += int64_t(sample_y) *stride_sample_y + (has_ids ? 0 : channel_y *stride_channel_y);
dst += int64_t(sample_dst)*stride_sample_dst + (has_ids ? 0 : channel_dst*stride_channel_dst);
if constexpr (has_ids) {
constexpr int y_stride_scale = std::is_same_v<T, float> ? 1 : 2;
const int64_t col_offset = col_base;
y += col_offset * stride_col_y * y_stride_scale;
dst += col_offset * stride_col_dst;
ids += col_offset * stride_row_id;
}
const float2 * y2 = (const float2 *) y;
extern __shared__ char data_mmv[];
@@ -61,12 +80,17 @@ static __global__ void mul_mat_f(
for (int j0 = 0; j0 < cols_per_block; j0 += nwarps) {
const int j = j0 + threadIdx.y;
const int32_t * __restrict__ id_row = ids + j*stride_row_id;
if (threadIdx.x == 0) {
slot_map[j] = -1;
}
if (col_base + j >= ncols_dst_total) {
continue;
}
const int32_t * __restrict__ id_row = ids + j*stride_row_id;
for (int k = threadIdx.x; k < nchannels_dst; k += warp_size) {
int match = id_row[k*stride_col_id] == expert_idx;
@@ -108,7 +132,8 @@ static __global__ void mul_mat_f(
if constexpr (!has_ids) {
tile_xy[j0*tile_k_padded + threadIdx.x] = j < cols_per_block ? y[j*stride_col_y + col] : 0.0f;
} else {
tile_xy[j0*tile_k_padded + threadIdx.x] = j < cols_per_block ? y[slot_map[j]*stride_channel_y + j*stride_col_y + col] : 0.0f;
const bool valid = j < cols_per_block && (col_base + j) < ncols_dst_total && slot_map[j] >= 0;
tile_xy[j0*tile_k_padded + threadIdx.x] = valid ? y[slot_map[j]*stride_channel_y + j*stride_col_y + col] : 0.0f;
}
}
} else if constexpr (std::is_same_v<T, half2> || std::is_same_v<T, nv_bfloat162>) {
@@ -120,7 +145,8 @@ static __global__ void mul_mat_f(
const float2 tmp = j < cols_per_block ? y2[j*stride_col_y + col] : make_float2(0.0f, 0.0f);
tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y};
} else {
float2 tmp = j < cols_per_block && slot_map[j] >= 0 ? *(const float2*) &y[slot_map[j]*stride_channel_y + 2*(j*stride_col_y + col)] : make_float2(0.0f, 0.0f);
const bool valid = j < cols_per_block && (col_base + j) < ncols_dst_total && slot_map[j] >= 0;
float2 tmp = valid ? *(const float2*) &y[slot_map[j]*stride_channel_y + 2*(j*stride_col_y + col)] : make_float2(0.0f, 0.0f);
tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y};
}
}
@@ -183,14 +209,14 @@ static __global__ void mul_mat_f(
dst[j*stride_col_dst + row0 + threadIdx.x] = sum;
} else {
const int slot = (j < cols_per_block) ? slot_map[j] : -1;
if (slot >= 0) {
if (slot >= 0 && (col_base + j) < ncols_dst_total) {
dst[slot*stride_channel_dst + j*stride_col_dst + row0 + threadIdx.x] = sum;
}
}
}
#else
GGML_UNUSED_VARS(x, y, ids, dst,
ncols, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
ncols, ncols_dst_total, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
@@ -201,20 +227,23 @@ static __global__ void mul_mat_f(
template<typename T, int cols_per_block, int nwarps>
static inline void mul_mat_f_switch_ids(
const T * x, const float * y, const int32_t * ids, float * dst,
const int64_t ncols_x, const int64_t nchannels_dst,
const int64_t ncols_x, const int64_t ncols_dst, const int64_t nchannels_dst,
const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
const int64_t stride_col_id, const int64_t stride_row_id,
const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
const int64_t sample_ratio, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
const dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared_total, cudaStream_t stream) {
if (ids) {
mul_mat_f<T, MMF_ROWS_PER_BLOCK, cols_per_block, nwarps, true><<<block_nums, block_dims, nbytes_shared_total, stream>>>
(x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
const int64_t col_tiles = (ncols_dst + cols_per_block - 1) / cols_per_block;
dim3 block_nums_ids = block_nums;
block_nums_ids.y *= col_tiles;
mul_mat_f<T, MMF_ROWS_PER_BLOCK, cols_per_block, nwarps, true><<<block_nums_ids, block_dims, nbytes_shared_total, stream>>>
(x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} else {
mul_mat_f<T, MMF_ROWS_PER_BLOCK, cols_per_block, nwarps, false><<<block_nums, block_dims, nbytes_shared_total, stream>>>
(x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
(x, y, ids, dst, ncols_x, cols_per_block, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
}
@@ -223,7 +252,8 @@ static inline void mul_mat_f_switch_ids(
template <typename T, int cols_per_block>
void mul_mat_f_cuda(
const T * x, const float * y, const int32_t * ids, float * dst,
const int64_t ncols_x, const int64_t nrows_x, const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
const int64_t ncols_x, const int64_t nrows_x, const int64_t ncols_dst,
const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
const int64_t stride_col_id, const int64_t stride_row_id,
const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
@@ -268,49 +298,49 @@ void mul_mat_f_cuda(
switch (nwarps_best) {
case 1: {
mul_mat_f_switch_ids<T, cols_per_block, 1>(
x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream);
} break;
case 2: {
mul_mat_f_switch_ids<T, cols_per_block, 2>(
x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream);
} break;
case 3: {
mul_mat_f_switch_ids<T, cols_per_block, 3>(
x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream);
} break;
case 4: {
mul_mat_f_switch_ids<T, cols_per_block, 4>(
x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream);
} break;
case 5: {
mul_mat_f_switch_ids<T, cols_per_block, 5>(
x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream);
} break;
case 6: {
mul_mat_f_switch_ids<T, cols_per_block, 6>(
x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream);
} break;
case 7: {
mul_mat_f_switch_ids<T, cols_per_block, 7>(
x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream);
} break;
case 8: {
mul_mat_f_switch_ids<T, cols_per_block, 8>(
x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream);
} break;
@@ -332,84 +362,89 @@ static void mul_mat_f_switch_cols_per_block(
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
cudaStream_t stream) {
switch (ncols_dst) {
const int ncols_case = (ids && ncols_dst > 16) ? 16 : ncols_dst;
GGML_ASSERT(ids || ncols_dst <= 16);
switch (ncols_case) {
case 1: {
mul_mat_f_cuda<T, 1>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 1>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 2: {
mul_mat_f_cuda<T, 2>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 2>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 3: {
mul_mat_f_cuda<T, 3>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 3>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 4: {
mul_mat_f_cuda<T, 4>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 4>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 5: {
mul_mat_f_cuda<T, 5>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 5>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 6: {
mul_mat_f_cuda<T, 6>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 6>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 7: {
mul_mat_f_cuda<T, 7>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 7>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 8: {
mul_mat_f_cuda<T, 8>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 8>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 9: {
mul_mat_f_cuda<T, 9>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 9>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 10: {
mul_mat_f_cuda<T, 10>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 10>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 11: {
mul_mat_f_cuda<T, 11>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 11>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 12: {
mul_mat_f_cuda<T, 12>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 12>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 13: {
mul_mat_f_cuda<T, 13>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 13>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 14: {
mul_mat_f_cuda<T, 14>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 14>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 15: {
mul_mat_f_cuda<T, 15>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 15>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
case 16: {
mul_mat_f_cuda<T, 16>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
mul_mat_f_cuda<T, 16>(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst,
stride_col_id, stride_row_id, 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);
} break;
@@ -422,7 +457,7 @@ static void mul_mat_f_switch_cols_per_block(
#define DECL_MMF_CASE_HELPER(T, ncols_dst) \
template void mul_mat_f_cuda<T, ncols_dst>( \
const T * x, const float * y, const int32_t * ids, float * dst, \
const int64_t ncols_x, const int64_t nrows_x, const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst, \
const int64_t ncols_x, const int64_t nrows_x, int64_t ncols_dst_total, const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst, \
const int64_t stride_col_id, const int64_t stride_row_id, \
const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst, \
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,\