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* Adding q8_KV - Basics + AVX2 gemm/gemv * q8_KV: Better AVX2 gemm * q8_KV: Better Zen4 gemm We get 225.7 t/s for L3-8B. In comparison q8_0 without run-tinme-repacking is at 169 t/s. * q8_KV: AVX2 gemm/gemv We get 254 t/s for L3-8B vs 194 t/s for q8_0 without rtr. * q8_KV: be able to use it for K cache This required quite a few fixes in ggml and llama.cpp: * ggml: do not calculate row size as n/block_size*type_size. I had removed most of it when implementing the quants with per row scale, bit it was stull lurking in ggml_copy. Not sure if these were the last remnants of ggmil-style row sizes, or if there are still places left * llama.cpp: get rid of the the 1d K cache assumption. Create and manage the K-cache as a 2D tensor so we can have per row meta data as needed by q8_KV. Using q8_KV for K-cache results in non-negligible performance gains. More details to follow, but for DeepSeek-Lite with MLA, we get 18% speedup for PP-8192 compared to q8_0 K-cache. * q8_KV: be able to use it for K cache in FA * q8_KV: repack it for K*Q in FA * q8_KV: slightly faster gemv on Zen4 * q8_KV: slightly faster gemv on Zen4 * q8_KV: ARM_NEON We get PP-512 = 167 t/s for L3-8B without interleaving! We do the interleaving on the fly, so I wonder if this could be done for other quants as well. * q8_KV: use it in FA on NEON * q8_KV_r8 - repacked q8_KV On Zen4 it is slower than q8_k_r8 (292 vs 370 t/s) This makes no sense whatsoever as the q8_KV_r8 GEMM is basically the q8_k_r8 GEMM with the unnecessary block stuff removed (so, one would think that it would be faster). * q8_KV_r8: don't use nrc_y = 16 on Zen4 This is faster - 350 t/s. Why? Much better than the 290 t/s we had before, but still slower than the 370 t/s for q8_k_r8. * q8_KV: nrc_y = 16 also doesn't pay off in FA * Minor --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>