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| author | Kawrakow <iwankawrakow@gmail.com> | 2024-10-25 13:08:43 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2024-10-25 13:08:43 +0200 |
| commit | 6b968f38946117552ffed300771c44ba9b39d3e4 (patch) | |
| tree | dc6b0df69f31ea77d9941d6798a4ef411c688080 /examples | |
| parent | 9114078959b404899fd67e1af45f0dcbee51b47f (diff) | |
Bitnet changes (#106)
* 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 f2d315b46f7aacc7df4b86bd8acba387b30e11ca.
As far as I can tell, the commit breaks Metal TG.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/quantize/quantize.cpp | 2 |
1 files changed, 0 insertions, 2 deletions
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index c88033b6..b5907e2b 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -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", }, |