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2024-03-12ggml : reuse quantum structs across backends (#5943)Georgi Gerganov
* ggml : reuse quant blocks across backends ggml-ci * ggml : define helper constants only for CUDA and SYCL ggml-ci * ggml : define helper quantum constants for SYCL ggml-ci
2024-03-111.5 bit: we can do even better (#5999)Kawrakow
* iq1_s: we can do even better Spent one of the 4 scale bits on a signs of a 0.125 shift. I.e., quants are now -1 + delta, delta, 1 + delta, where delta is +/- 0.125. CUDA works, same performance as before. PPL(LLaMA-v2-7B) is now 11.85! * iq1_s: make scalar and AVX2 work with the new version * iq1_s: make Neon work with new version. ~10% drop in performance, so will need some more work. * iq1_s: make Metal work with new version * iq1_s: very slightly faster dequantize on Metal * iq1_s: fix dequantize on the CPU --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-03-11Better 1.5 bit quantization (#5971)Kawrakow
* Trying blocvks of 16 for IQ1_S - seems slightly better * iq1s_blocks16: Adjust scale fudge factor to 1.125 * iq1s_blocks16: going to blocks of 32 with 2048 lattice points, so same bpw. This is even better than blocks of 16. Should I try blocks of 64? But to keep the same bpw, when I go to 4096 lattice points, I need to remove blocks alltogether and just have superblocks of 256 weights. * iq1s_blocks16: Use 2*<x^2> as sigma2 in weight adjustment * iq1s_blocks16: scalar and AVX2 dot products * iq1s_blocks16: CUDA dot product * iq1s_blocks16: Metal works, Neon does not Metal works but TG is dog slow (35 t/s). PP is OKish (493 t/s). Not seeing the bug in the Neon implementation for now. * iq1s_blocks16: fixed Neon * iq1s_blocks16: very slightly faster TG on Metal Still pathetic at 37 t/s * iq1s_blocks16: speedup Metal by packing codebook into uint32_t's * Formatting * iq1s_blocks16: uint32_t codebook is also better in CUDA TG-128 is now 204 t/s up from 194 t/s. PP-512 is 5890 t/s, so significantly better than other quants * iq1s_blocks16: slightly faster Neon dot product * iq1s_blocks16: faster AVX2 dot product * iq1s_blocks16: adjust to ggml-common.h --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-03-10ggml : remove __constant__ specifier for CUDA tables (#5940)Georgi Gerganov
2024-03-09ggml : add ggml-common.h to deduplicate shared code (#5940)Georgi Gerganov
* ggml : add ggml-common.h to shared code ggml-ci * scripts : update sync scripts * sycl : reuse quantum tables ggml-ci * ggml : minor * ggml : minor * sycl : try to fix build