From 76b97c80645362ac65a2e33043fd8d46bdaf8c56 Mon Sep 17 00:00:00 2001 From: Kawrakow Date: Wed, 16 Oct 2024 15:18:26 +0300 Subject: Adding IQ4_KSS: 4.0 bpw quants (#89) * iq4_kss: WIP * iq4_kss: CUDA dequantize works So we can run perplexity. Sadly, the result does not look good on the bpw vs quantization error plot. * iq4_kss: slightly better quantization * iq4_kss: another small quantization improvement * iq4_kss: CUDA works TG-128 performance is very decent with 131 t/s for LLaMA-3.1-8B. In comparison, we have 123 t/s for q4_0 and 128 t/s for iq4_ks. I.e., the reduced model size more than offsets the additional bit fiddling required for iq4_kss. * iq4_kss: new bit arrangement - CUDA and Zen4 work Did not lose performance on CUDA. Zen4 is decent, but not great: PP-512(LLaMA-3.1-8B) = 163 t/s. TG-128 is of course better than other 4-bit quants due to smaller model size. We get 14.5 t/s @ 8 threads. * iq4_kss: ARM_NEON. Predictably very slow * iq4_kss: Metal PP is not too bad - just 10% slower than q4_0. But TG is 30% slower, i.e., predictably bad. * iq4_kss: somewhat faster Metal dot product 45.75 t/s -> 48.75 t/s. Still 22% slower than q4_0 * iq4_kss: AVX2 Bad, but better than I expected. PP-512(LLaMA-3.1-8B) = 167 t/s on the Ryzen-5950X. I.e., with 32 AVX2 threads we get the performance of 16 Zen4 threads. * iq4_kss: very slightly faster Metal dot product 48.7 t/s -> 49.3 t/s --------- Co-authored-by: Iwan Kawrakow --- examples/quantize/quantize.cpp | 1 + 1 file changed, 1 insertion(+) (limited to 'examples/quantize') diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 1ace5720..8e0d0969 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -44,6 +44,7 @@ static const std::vector QUANT_OPTIONS = { { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", }, { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", }, { "IQ4_KS", LLAMA_FTYPE_MOSTLY_IQ4_KS, " 4.25 bpw non-linear quantization", }, + { "IQ4_KSS", LLAMA_FTYPE_MOSTLY_IQ4_KSS, " 4.0 bpw non-linear quantization", }, { "IQ2_K", LLAMA_FTYPE_MOSTLY_IQ2_K, " 2.375 bpw non-linear quantization",}, { "IQ2_KS", LLAMA_FTYPE_MOSTLY_IQ2_KS, " 2.1875 bpw non-linear quantization",}, { "IQ3_K", LLAMA_FTYPE_MOSTLY_IQ3_K, " 3.44 bpw non-linear quantization", }, -- cgit v1.2.3