Commit Graph

7 Commits

Author SHA1 Message Date
Kawrakow
785cac7ee5 bitnet: put the scale in a separate tensor
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.
2024-06-22 12:02:52 +03:00
Kawrakow
1f9541172f Bitnet(1.75 bpw): higher precision fp8 scale
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).
2024-06-22 12:02:52 +03:00
Kawrakow
39982764d7 Bitnet: 2.25 bpw version
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.
2024-06-22 12:02:52 +03:00
Kawrakow
318899c8b7 bitnet: add 2 bpw quantization
The scalar dot product already chieves 37 t/s for TG!
2024-06-22 12:02:51 +03:00
Kawrakow
f9ba085ef7 Move Q8_K64 quantization to iqk-quantize.cpp and add copyright notice 2024-06-22 12:02:51 +03:00
Kawrakow
b0967ffa79 bitnet: fix scalar dot product
I had forgotten to adjust for the change to q8_K64.
On the M2 I'm getting 10.8 t/s with the scalar version!
2024-06-22 12:02:51 +03:00
Kawrakow
81576cdcac bitnet: python + llama 2024-06-22 12:02:51 +03:00