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* Slight MLA TG performance improvement on CUDA The low MLA performance on CUDA is dues to the wk_b * q_nope operation. It turns into n_head matrix multiplications with n_head separate quantization and GEMV steps. The associated overhead is just too much for TG where each GEMV is very fast (512 x 128 = 131 KFLOP for DeepSeek-Lite, 4X that for DeepSeekV3/R1). The way it was done there was also a copy of each q_nope row before quantization, which I have now eliminated. This results in a ~2.5% speedup. What needs to happen instead is to launch a single computation that quantizes all heads, and then have a kernel that does the GEMV for all heads instead of n_head sequential GEMVs. * Slightly better * CUDA: Quantize non-contiguous tensors * Much better MLA It is a total hack, but it works. * Cleanup Remove duplicated gemv's. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>