ik_llama.cpp/ggml/include
Kawrakow 3b94f0a73e
AVX512+AVXVNNI GEMM implementation for quants using Q8_K for activations (#710)
* q8_k_r16: basics

* q8_k_r16: iq4_xs now uses q8_k_r16 on Zen4+

PP performance is about the same as using q8_k_r8 on the Ryzen-7950X,
so we expect nice gains on Zen5, and we don't need to wory about
using 2 different q8_k_r8 implementations for fancy SIMD.

* q8_k_r16: iq2_xxs now uses q8_k_r16 on Zen4+

* q8_k_r16: iq2_xs now uses q8_k_r16 on Zen4+

* q8_k_r16: iq2_s now uses q8_k_r16 on Zen4+

* q8_k_r16: iq3_xxs now uses q8_k_r16 on Zen4+

* q8_k_r16: iq3_s now uses q8_k_r16 on Zen4+

* q8_k_r16: iq1_s and iq1_m now uses q8_k_r16 on Zen4+

* q8_k_r16: q2_K and q3_K now uses q8_k_r16 on Zen4+

* q8_k_r16: iq2_ks and iq2_k now uses q8_k_r16 on Zen4+

* q8_k_r16: iq2_kl now uses q8_k_r16 on Zen4+

* q8_k_r16: iq3_ks and iq3_k now uses q8_k_r16 on Zen4+

* q8_k_r16: iq4_kss, iq4_ks, and iq4_k now use q8_k_r16 on Zen4+

* q8_k_r16: iq5_ks, iq5_k, and iq6_k now use q8_k_r16 on Zen4+

* Fix AVX2

* Just always set num_rows to 16

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-08-22 06:27:07 +03:00
..
ggml-alloc.h Merge mainline llama.cpp (#3) 2024-07-27 07:55:01 +02:00
ggml-backend.h Bug fixes from mainline (#439) 2025-05-20 17:03:14 +03:00
ggml-blas.h Merge mainline llama.cpp (#3) 2024-07-27 07:55:01 +02:00
ggml-cann.h Merge mainline llama.cpp (#3) 2024-07-27 07:55:01 +02:00
ggml-cuda.h Merge mainline - Aug 12 2024 (#17) 2024-08-12 15:14:32 +02:00
ggml-kompute.h Merge mainline llama.cpp (#3) 2024-07-27 07:55:01 +02:00
ggml-metal.h Merge mainline - Aug 12 2024 (#17) 2024-08-12 15:14:32 +02:00
ggml-rpc.h Fix non rpc build error (#506) 2025-06-08 17:27:00 +03:00
ggml-sycl.h Merge mainline llama.cpp (#3) 2024-07-27 07:55:01 +02:00
ggml-vulkan.h Vulkan: a fresh start (#608) 2025-07-15 08:03:13 +02:00
ggml.h AVX512+AVXVNNI GEMM implementation for quants using Q8_K for activations (#710) 2025-08-22 06:27:07 +03:00