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	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 <iwan.kawrakow@gmail.com>
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							| @@ -4653,6 +4653,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s | ||||
|  | ||||
|     std::unique_ptr<llama_model_loader> 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; | ||||
|   | ||||
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