ikawrakow/ik_llama.cpp
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Bitnet changes (#106)

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* Adapting iq2_bn to work without separate scale tensors

Why? It is becoming burdensome to maintain the special Bitnet
conversion in convert_hf_to_gguf.py, so I thnk it is better
to make iq1_bn and iq2_bn just work with the mainline
conversion script (which does not generate scales).

* Adapting iq1_bn to work without separate scale tensors

* Adapting iq2_bn: CUDA dequantize

* Adapting iq2_bn: CUDA works

* Adapting iq1_bn: CUDA works

* Adapting iq1_bn, iq2_bn: NEON

* Adapting iq1_bn, iq2_bn: Metal

Dequantize works, but there is still something wrong
with the dot products.

* WIP

Absoolutely don't see what is wrong with the iq1_bn and iq2_bn
vector dot product kernels.

* Remove iq1_tn and iq2_tn - Part 1

Now that iq1_bn and iq2_bn have per row scales, there is no
reason to also have iq1_tn and iq2_tn.

* Remove iq1_tn and iq2_tn - Part 2

* Bitnet: use the standard llm_build_kv to build self attention

My main motivation was to enable FA. But FA does not work anyway
because head size is 100 for the Botnet ternary models
(and I had forgotten this little detail).

* Revert "Avoid rebuild of GGML graph for each token (#98)"

This reverts commit f2d315b46f.
As far as I can tell, the commit breaks Metal TG.

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2024-10-25 13:08:43 +02:00
committed by GitHub
parent b535dcd416
commit 4b35340f45
23 changed files with 281 additions and 1622 deletions
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2
examples/quantize/quantize.cpp
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@@ -29,8 +29,6 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", },
{ "IQ1_BN", LLAMA_FTYPE_MOSTLY_IQ1_BN, " 1.62 bpw quantization (Bitnet)", },
{ "IQ2_BN", LLAMA_FTYPE_MOSTLY_IQ2_BN, " 2.00 bpw quantization (Bitnet)", },
{ "IQ1_TN", LLAMA_FTYPE_MOSTLY_IQ1_TN, " 1.63 bpw quantization (TriLM)", },
{ "IQ2_TN", LLAMA_FTYPE_MOSTLY_IQ2_TN, " 2.00 bpw quantization (TriLM)", },
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
{ "IQ3_XXS", LLAMA_FTYPE_MOSTLY_IQ3_XXS, " 3.06 bpw quantization", },