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CUDA: some micro-optimizations in mmf.cuh for mul_mat_id #15926

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Merged
merged 4 commits into from
Sep 15, 2025
+23 −35
Merged

CUDA: some micro-optimizations in mmf.cuh for mul_mat_id #15926

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82dd2c7
CUDA: MUL_MAT_ID optimizations for mmf
am17an Sep 10, 2025
bb831b2
unroll n_expert_used loop + remove warp syncs
am17an Sep 10, 2025
bf08ea5
Remove tempalte from n_expert_used
am17an Sep 12, 2025
5016ca5
Update k inside the loop as it's not a candidate for unrolling
am17an Sep 15, 2025
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58 changes: 23 additions & 35 deletions ggml/src/ggml-cuda/mmf.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -57,31 +57,33 @@ static __global__ void mul_mat_f(
T * tile_xy = (T *) compute_base + threadIdx.y*(tile_A::I * tile_k_padded);

if constexpr (has_ids) {
__shared__ int has_any;
if (threadIdx.y == 0) {
int local_has_any = 0;
for (int j = threadIdx.x; j < cols_per_block; j += warp_size) {
int slot = -1;
for (int k = 0; k < nchannels_dst; ++k) {
const int idv = ids[j*stride_row_id + k*stride_col_id];
if (idv == expert_idx) {
slot = k;
break;
}
}
if (j < cols_per_block) {
local_has_any |= (slot >= 0);
slot_map[j] = slot;
int found = 0;

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;
}

for (int k = threadIdx.x; k < nchannels_dst; k += warp_size) {
int match = id_row[k*stride_col_id] == expert_idx;

if (match) {
slot_map[j] = k;
found = 1;
break;
}
}
has_any = warp_reduce_any(local_has_any);
}
__syncthreads();
if (has_any == 0) {

if (!__syncthreads_or(found)) {
return;
}
}


for (int col = threadIdx.y*warp_size + threadIdx.x; col < ncols; col += nwarps*warp_size) {
tile_A A[ntA][warp_size / tile_A::J];
#pragma unroll
Expand All @@ -106,14 +108,7 @@ 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 {
float val = 0.0f;
if (j < cols_per_block) {
const int slot = slot_map[j];
if (slot >= 0) {
val = y[slot*stride_channel_y + j*stride_col_y + col];
}
}
tile_xy[j0*tile_k_padded + threadIdx.x] = val;
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;
}
}
} else if constexpr (std::is_same_v<T, half2> || std::is_same_v<T, nv_bfloat162>) {
Expand All @@ -125,14 +120,7 @@ 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 = make_float2(0.0f, 0.0f);
if (j < cols_per_block) {
const int slot = slot_map[j];
if (slot >= 0) {
const float2 * y2_slot = (const float2 *)(y + slot*stride_channel_y);
tmp = y2_slot[j*stride_col_y + col];
}
}
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);
tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y};
}
}
Expand Down Expand Up @@ -221,7 +209,7 @@ static inline void mul_mat_f_switch_ids(
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,
(x, y, ids, dst, ncols_x, 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 {
Expand Down
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