llama.cpp b9622 boosts Vulkan with non-contiguous ops and fastdiv

llama.cpp's latest release, b9622, brings significant optimizations to its Vulkan GPU backend. The update overhauls how unary and GLU (gated linear unit) operations handle non-contiguous memory layouts—a common scenario when running large language models on GPU. Instead of assuming contiguous tensors, the new code passes all stride information and uses fast division (fastdiv) for index calculations, which reduces computational overhead and improves throughput. Additionally, the team added the expm1 activation function (exp(x)-1) after conducting a careful precision analysis, ensuring numerical stability across hardware. These changes required refactoring the unary operation code into a single file and packing fastdiv constants to work around compiler quirks.

The release was extensively validated across a wide range of platforms: macOS Apple Silicon (both with and without KleidiAI), Intel x64, iOS, Linux on x64, arm64, s390x, Ubuntu with Vulkan and ROCm 7.2, OpenVINO, SYCL, Android arm64, Windows x64/arm64 with CUDA 12/13 and Vulkan, and even openEuler variants. This broad testing guarantees that the performance gains are reliable whether you're running local models on a gaming PC, a MacBook, or a server-grade AMD GPU. For developers self-hosting models like Llama, Mistral, or Gemma, b9622 means faster inference and lower latency for operations involving GLU activations (common in modern transformers) without breaking compatibility across diverse hardware setups.