It seems it is enough to have 4 scales per row for Q8.
I get PPL = 8.5470 with this, which is slightly higher than
the 8.5430 we get with 1 scale per 128 activations, but still
OK, I think.
With this, we get the following performance:
Systema | quant | PP-512 | TG-128a | quant | PP-512 | TG-12s |
M2 Max | iq2bn 229.02 ± 0.37 78.75 ± 0.61 | iq1bn | 146.67 ± 2.85 33.12 ± 0.03
Ryzen7950| iq2bn 379.36 ± 1.03 49.08 ± 0.18 | iq1bn | 247.12 ± 1.53 32.80 ± 0.02
Ryzen5975| iq2bn 465.28 ± 0.57 39.17 ± 0.02 | iq1bn | 325.86 ± 0.46 26.60 ± 0.10
and correspondingly add an extra ggml_mul_mat operation.
As per @ggerganov, this is how things should be done.
It seems to be working, but as far as I can tell this
results in a ~15% performance penalty for prompt processing.
Commiting so I can go and test on othe platforms.
Use 3 bits for the exponent and 5 bits for the mantissa.
This makes PPL to be the same as fp16 (but the previous
version with 4 bits for the exponent and mantissa was
good enough for any practical purposes).
Just scaler and AVX2 for now.
PP-512 is even faster (325 t/s on the Ryzn-7950X, 404 t/s on
Ryzen-5975WX). We lose ~6-7% for TG due to being memory bound and
the model being 10% larger.
