root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-26 11:24:35 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Better perplexity for 2- and 3-bit quantization for LLaMA-v2-70B (#2807)

Browse Source 
* Better perplexity for 2- and 3-bit quantization for the 70B model

* PR comment

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow 2023-08-26 17:27:49 +03:00 committed by GitHub
parent 771551a793
commit 7592375403
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 14 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
llama.cpp
View File

@ -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)); 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; const size_t align = GGUF_DEFAULT_ALIGNMENT;
struct gguf_context * ctx_out = gguf_init_empty(); 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; ++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_attention_wv = 0;
int i_feed_forward_w2 = 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 (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) && 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; (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; ++i_attention_wv;
} else if (name.find("ffn_down.weight") != std::string::npos) { } else if (name.find("ffn_down.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;