Files
llama.cpp/ggml
Pascal f1793c1c4e CUDA: use the fast mm_ids_helper path for any n_expert_used (#27978)
The optimized path grouped warp lanes by token and required
warp_size % n_expert_used == 0, with a single hardcoded exception
padding 6 up to 8. Every other count fell back to the generic path,
which walks the tokens one at a time with a warp reduction per token,
for each of the n_expert blocks.

The lane group only has to divide the warp, and the loop body already
guards the padded lanes with iex < n_expert_used, so the padding
generalizes to the next power of two. The 6 -> 8 case and every count
already dispatched keep the exact same padding as before.

n_expert_used = 10 now reaches the fast path. Measured on
Qwen3.8-Flash-Next (512 experts, 10 used) at 55k context on an
RTX PRO 6000, warm runs with the first one discarded:

  prompt processing   2334 -> 2600 t/s

Token generation is unaffected, since a single token leaves nothing to
walk. Other expert counts reach the fast path by adding their case to
the dispatch.
2026-08-30 16:06:32 +02:00
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