Adding Q6_0 (#77)

* Adding q6_0 - basics + AVX2/Zen4 working

* Adding q6_0: CUDA dequantize works, but not mmvq

* Adding q6_0: CUDA mmvq works

* Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache

* Add q6_0 to CPU flash attention

Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache
gives about the same PPL as q8_0 K-cache and q4_0 V-cache,
while needing the exact same RAM.
I.e., what was the point?

* q6_0: slightly better kv-cache result

Better than q8_0+q4_0, but not as good as q8_0+iq4_nl

* q6_0: works on ARM_NEON

* q6_0: dequantize works on Metal, but not vector dot product

* q6_0: it now works on Metal

Outperforms q5_0 by a significant margin. E.g.
| model                          |       size |     params | backend    | ngl | threads |          test |              t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: |
| llama 8B Q6_0                  |   6.08 GiB |     8.03 B | Metal      | 100 |       4 |         tg128 |     44.02 ± 0.08 |
| llama 8B Q5_0                  |   5.21 GiB |     8.03 B | Metal      | 100 |       4 |         tg128 |     40.13 ± 0.12 |
| llama 8B Q6_0                  |   6.08 GiB |     8.03 B | Metal      | 100 |       4 |         pp512 |    500.55 ± 0.32 |
| llama 8B Q5_0                  |   5.21 GiB |     8.03 B | Metal      | 100 |       4 |         pp512 |    448.02 ± 0.27 |

* q6_0: can now be used for kv-cache on Metal

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2024-10-02 15:22:13 +03:00
committed by GitHub
parent 6dec4112a1
commit 104e7e26c4
19 changed files with 678 additions and 13 deletions

View File

@@ -20,6 +20,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", },
{ "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", },
{ "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
{ "Q6_0", LLAMA_FTYPE_MOSTLY_Q6_0, " 6.5 bpw quantization", },
{ "IQ2_XXS", LLAMA_FTYPE_MOSTLY_IQ2_XXS, " 2.06 bpw quantization", },
{ "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", },
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },