diff options
Diffstat (limited to 'llama.cpp')
| -rw-r--r-- | llama.cpp | 85 |
1 files changed, 78 insertions, 7 deletions
@@ -515,6 +515,11 @@ struct llama_file_loader { case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: break; default: { throw format("unrecognized tensor type %u\n", shard.type); @@ -590,6 +595,11 @@ struct llama_file_saver { case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: break; default: LLAMA_ASSERT(false); } @@ -906,6 +916,16 @@ static const char *llama_ftype_name(enum llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0"; case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1"; case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0"; + // K-quants + case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K"; + case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small"; + case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium"; + case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large"; + case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small"; + case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium"; + case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small"; + case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium"; + case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K"; default: return "unknown, may not work"; } } @@ -2113,8 +2133,18 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break; case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break; case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break; + // K-quants + case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break; + case LLAMA_FTYPE_MOSTLY_Q3_K_S: + case LLAMA_FTYPE_MOSTLY_Q3_K_M: + case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break; + case LLAMA_FTYPE_MOSTLY_Q4_K_S: + case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break; + case LLAMA_FTYPE_MOSTLY_Q5_K_S: + case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break; + case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break; default: throw format("invalid output file type %d\n", ftype); - }; + } if (nthread <= 0) { nthread = std::thread::hardware_concurrency(); @@ -2124,6 +2154,20 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s /*vocab_only*/ false)); llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype); + int n_attention_wv = 0; + int n_feed_forward_w2 = 0; + for (auto& tensor : model_loader->tensors_map.tensors) { + if (tensor.name.find("attention.wv.weight") != std::string::npos) { + ++n_attention_wv; + } + else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) { + ++n_feed_forward_w2; + } + } + + int i_attention_wv = 0; + int i_feed_forward_w2 = 0; + size_t total_size_org = 0; size_t total_size_new = 0; std::vector<int64_t> hist_all(1 << 4, 0); @@ -2166,6 +2210,27 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0); } else { new_type = quantized_type; + if (tensor.name == "output.weight") new_type = GGML_TYPE_Q6_K; + else if (tensor.name.find("attention.wv.weight") != std::string::npos) { + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && + (i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8 || + (i_attention_wv - n_attention_wv/8)%3 == 2)) new_type = GGML_TYPE_Q6_K; + ++i_attention_wv; + } + else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) { + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && + (i_feed_forward_w2 < n_feed_forward_w2/8 || i_feed_forward_w2 >= 7*n_feed_forward_w2/8 || + (i_feed_forward_w2 - n_feed_forward_w2/8)%3 == 2)) new_type = GGML_TYPE_Q6_K; + ++i_feed_forward_w2; + } + else if (tensor.name.find("attention.wo.weight") != std::string::npos) { + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + } float * f32_data; size_t nelements = tensor.ne.at(0) * tensor.ne.at(1); llama_buffer f32_conv_buf; @@ -2233,12 +2298,16 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0); + int64_t tot_count = 0; for (size_t i = 0; i < hist_cur.size(); i++) { hist_all[i] += hist_cur[i]; + tot_count += hist_cur[i]; } - for (size_t i = 0; i < hist_cur.size(); i++) { - printf("%5.3f ", hist_cur[i] / float(nelements)); + if (tot_count > 0) { + for (size_t i = 0; i < hist_cur.size(); i++) { + printf("%5.3f ", hist_cur[i] / float(nelements)); + } } printf("\n"); } @@ -2256,11 +2325,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s sum_all += hist_all[i]; } - printf("%s: hist: ", __func__); - for (size_t i = 0; i < hist_all.size(); i++) { - printf("%5.3f ", hist_all[i] / float(sum_all)); + if (sum_all > 0) { + printf("%s: hist: ", __func__); + for (size_t i = 0; i < hist_all.size(); i++) { + printf("%5.3f ", hist_all[i] / float(sum_all)); + } + printf("\n"); } - printf("\n"); } } |