Enable fused SDPA vector kernel for asymmetric Q/V head dims (192, 128)

[yohann-bearzi]

The sdpa_vector and sdpa_vector_2pass_1 kernels are already templated on a separate value head dim (template <typename T, int D, int V = D>), but no (D, V) pairs with D != V were instantiated, and use_fallback required query_head_dim == value_head_dim. Models with asymmetric head dims therefore fall back to a compiled-graph attention decomposition (multiple GatherAxis dispatches per layer) instead of the fused kernel.

This adds instantiate_sdpa_vector(type, 192, 128) and relaxes use_fallback to allow this specific asymmetric case. All other head dims are unchanged.

Motivation: MiMo-V2.5 uses head_dim=192 for Q/K and v_head_dim=128 for V. On current main it hits the fallback path.

Testing: Verified on MiMo-V2.5 (Metal, M3 Ultra): decode throughput 28.7 → 29.8 tok/s (+4%), generated output (top-1) unchanged. The kernel templates already supported V != D; this only instantiates and enables the existing code path. Incremental build on current main is clean.

Scope: Minimal — 2 files, +5/-3. No new kernel code, just one instantiation + the fallback guard.

[yohann-bearzi]

For models using block_fp8 quantization (e.g. MiMo-V2.5, which motivated this asymmetric-head-dim fix), the block_fp8 matmul and MoE kernels are available as a standalone MLX extension: https://github.com/yohann-bearzi/mlx-block-fp8 — it builds against stock upstream MLX (kernels vendored, no fork) and pairs with this SDPA change for full MiMo-V2.5 decode throughput.

The MiMo-V2.5 MLX weights (block_fp8) are on Hugging Face: https://huggingface.co/bearzi/MiMo-V2.5-MLX

[yohann-bearzi]

@zcbenz would you please be able to review?