Better perplexity for 2- and 3-bit quantization for LLaMA-v2-70B #2807
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I'd assume the same should apply to 34B? |
ggerganov
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Aug 26, 2023
llama.cpp
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| @@ -4678,6 +4682,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s | |||
| ++n_feed_forward_w2; | |||
| } | |||
| } | |||
| if (n_attention_wv != n_feed_forward_w2 || (uint32_t)n_attention_wv != model.hparams.n_layer) { | |||
| fprintf(stderr, "============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n", | |||
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Use LLAMA_LOG_WARN with __func__ prefix as all other logs
mattgauf
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* master: (773 commits) server : add `/detokenize` endpoint (ggml-org#2802) convert.py : advanced option (ggml-org#2753) llama : use Unicode Escape Sequence to replace encoded characters (ggml-org#2814) flake.nix : add rocm support and cleanup (ggml-org#2808) llama : move #includes out of _GNU_SOURCE conditional (ggml-org#2817) main : fix bug (penalize_nl=false doesn't work) + suppress warning on mingw (ggml-org#1528) llama : use std::abs in llama_sample_tail_free (ggml-org#2800) k-quants : remove unnecessary tensor shape restrictions (ggml-org#2811) Better perplexity for 2- and 3-bit quantization for LLaMA-v2-70B (ggml-org#2807) Fix HellaSwag (ggml-org#2805) flake : build llama.cpp on Intel with nix (ggml-org#2795) Handle null rope scaling value (ggml-org#2793) Fix spm whitespaces (ggml-org#2806) examples : skip unnecessary external lib in server README.md how-to (ggml-org#2804) llama : fix struct decl (ggml-org#2790) Faster perplexity computation (ggml-org#2786) llama : add llama_beam_search() (ggml-org#2267) convert.py : Get rope scale from HuggingFace models (ggml-org#2772) llama-bench : add model sizes (ggml-org#2771) convert.py : export rope freq_base when converting CodeLlama from an HF model (ggml-org#2773) ...
akawrykow
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Aug 29, 2023
…l-org#2807) * Better perplexity for 2- and 3-bit quantization for the 70B model * PR comment --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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How long is the context for the perplexity values in the table, @ikawrakow? |
512 tokens |
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In LLaMA-v2-70B eight heads share the same
KandVattention tensors, and as a result they are 8X smaller than the attentionQtensor. The attentionVtensor is quite important for generation quality, so it is often quantized with more bits when using k_quants. Given this, we can get a nice improvement in perplexity score (as a measure of generation quality) with negligible increase in quantized model size by quantizing the entire attentionVtensor with 5 bits when the k_quants logic has decided to quantize it with 3 or 4 bits. The table shows the PPL change for a subset of the k_quants: