From 7592375403a0bd0456d5ec2cdf8350e591f04fb0 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sat, 26 Aug 2023 17:27:49 +0300 Subject: [PATCH] Better perplexity for 2- and 3-bit quantization for LLaMA-v2-70B (#2807) * Better perplexity for 2- and 3-bit quantization for the 70B model * PR comment --------- Co-authored-by: Iwan Kawrakow --- llama.cpp | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/llama.cpp b/llama.cpp index b0a3b5768..52fcaceff 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4653,6 +4653,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::unique_ptr ml(new llama_model_loader(fname_inp, /*use_mmap*/ false)); + llama_model model; + llm_load_arch(*ml, model); + llm_load_hparams(*ml, model, 0, 0, 0); + const size_t align = GGUF_DEFAULT_ALIGNMENT; struct gguf_context * ctx_out = gguf_init_empty(); @@ -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) { + LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n", + __func__, n_attention_wv, n_feed_forward_w2, model.hparams.n_layer); + } int i_attention_wv = 0; int i_feed_forward_w2 = 0; @@ -4769,6 +4777,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_attention_wv < 4) new_type = GGML_TYPE_Q5_K; else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) && (i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K; + if (model.type == MODEL_70B) { + // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is + // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with + // nearly negligible increase in model size by quantizing this tensor with more bits: + if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K; + } ++i_attention_wv; } else if (name.find("ffn_down.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;