b10704

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.

Website:

• https://llama.app

Attestations:

• https://github.com/ggml-org/llama.cpp/attestations/44013780

macOS/iOS:

• macOS Apple Silicon (arm64)
• macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED
• macOS Intel (x64)
• iOS XCFramework

Linux:

• Ubuntu x64 (CPU)
• Ubuntu arm64 (CPU)
• Ubuntu s390x (CPU)
• Ubuntu x64 (Vulkan)
• Ubuntu arm64 (Vulkan)
• Ubuntu x64 (ROCm 7.14)
• Ubuntu x64 (OpenVINO)
• Ubuntu x64 (SYCL FP32)
• Ubuntu x64 (SYCL FP16)

Android:

• Android arm64 (CPU)

Windows:

• Windows x64 (CPU)
• Windows arm64 (CPU)
• Windows arm64 (OpenCL Adreno)
• Windows x64 (CUDA 12) - CUDA 12.4 DLLs
• Windows x64 (CUDA 13) - CUDA 13.3 DLLs
• Windows arm64 (CUDA 13) (preview) - CUDA 13.4 DLLs
• Windows x64 (Vulkan)
• Windows x64 (OpenVINO)
• Windows x64 (SYCL)
• Windows x64 (ROCm 7.14)

openEuler:

• DISABLED
• openEuler x86 (310p)
• openEuler x86 (910b, ACL Graph)
• openEuler aarch64 (310p)
• openEuler aarch64 (910b, ACL Graph)

UI:

• UI