Kawrakow 8c86231f93 Adding IQ1_TN - 1.6875 bpw for TriLM ternary models (#44) ...

* Adding iq1_tn - 1.6875 bpw for TriLM ternary models

* iq1_tn: NEON

* iq1_tn: faster NEON

* iq2_bn: improve performance on NEON

We now get TG-128 = 100 t/s for Bitnet-3B-1.58b!

* iq1_tn: improve AVX2

PP-512 goes to 533 t/s up from 455.
TG-128 @ 2 threads goes to 16.6 t/s up from 14.2.
However, we seem to have a bottleneck somewhere as
TG saturates at 8 threads.

* iq1_tn: improve Zen4

PP-512 goes to 485 t/s up from 352. With FA we get 545 t/s up from 380.
TG-128 @ 1 thread goes to 12.4 t/s up from 10.4.
However, we seem to have a bottleneck somewhere as
TG saturates at 8 threads.

* iq2_bn: improve on Zen4

We now get PP-512 = 614 t/s up from 542 t/s

* iq2_bn: improve AVX2 implementation

We now get PP-512 = 753 t/s up from 680 t/s.

* Remove unnecessary barrier in ggml_compute_forward_mul_mat

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

2024-09-09 14:56:34 +03:00

..

llama.h

Adding IQ1_TN - 1.6875 bpw for TriLM ternary models (#44)

2024-09-09 14:56:34 +03:00