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 <iwan.kawrakow@gmail.com>

1 files changed, 1 insertions, 0 deletions

diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index a845eaf5..68ec6126 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -15197,6 +15197,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
         case GGML_TYPE_IQ2_TN: break;
         case GGML_TYPE_IQ1_TN: break;
         case GGML_TYPE_IQ4_KS: break;
+        case GGML_TYPE_IQ4_KSS: break;
         case GGML_TYPE_Q4_0_4_4:
         case GGML_TYPE_Q4_0_4_8:
             {