diff --git a/include/llama.h b/include/llama.h index 20375bf04..c9d7cb8a1 100644 --- a/include/llama.h +++ b/include/llama.h @@ -169,7 +169,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_IQ2_XL = 36, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_XL = 37, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q2_K_L = 38, // except 1d tensors - + LLAMA_FTYPE_MOSTLY_IQ1_XS = 39, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/include/quantize.cpp b/include/quantize.cpp new file mode 100644 index 000000000..db04bf64e --- /dev/null +++ b/include/quantize.cpp @@ -0,0 +1,457 @@ +#include "common.h" +#include "llama.h" + +#include +#include +#include +#include +#include +#include +#include + +struct quant_option { + std::string name; + llama_ftype ftype; + std::string desc; +}; + +static const std::vector QUANT_OPTIONS = { + { "Q4_0", LLAMA_FTYPE_MOSTLY_Q4_0, " 4.34G, +0.4685 ppl @ Llama-3-8B", }, + { "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 4.78G, +0.4511 ppl @ Llama-3-8B", }, + { "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 5.21G, +0.1316 ppl @ Llama-3-8B", }, + { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 5.65G, +0.1062 ppl @ Llama-3-8B", }, + { "IQ2_XXS", LLAMA_FTYPE_MOSTLY_IQ2_XXS, " 2.06 bpw quantization", }, + { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", }, + { "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", }, + { "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", }, + { "IQ2_XL", LLAMA_FTYPE_MOSTLY_IQ2_XL, " 2.85 bpw quantization mix", }, + { "IQ1_XS", LLAMA_FTYPE_MOSTLY_IQ1_XS, " 1.6-1.7 bpw quantization mix", }, + { "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", }, + { "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", }, + { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.96G, +3.5199 ppl @ Llama-3-8B", }, + { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.96G, +3.1836 ppl @ Llama-3-8B", }, + { "Q2_K_L", LLAMA_FTYPE_MOSTLY_Q2_K_L, " 2.96G, +3.1836 ppl @ Llama-3-8B", }, + { "IQ3_XXS", LLAMA_FTYPE_MOSTLY_IQ3_XXS, " 3.06 bpw quantization", }, + { "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", }, + { "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", }, + { "IQ3_XL", LLAMA_FTYPE_MOSTLY_IQ3_XL, " 3.85 bpw quantization mix", }, + { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, + { "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization", }, + { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 3.41G, +1.6321 ppl @ Llama-3-8B", }, + { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.74G, +0.6569 ppl @ Llama-3-8B", }, + { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 4.03G, +0.5562 ppl @ Llama-3-8B", }, + { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", }, + { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", }, + { "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", }, + { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 4.37G, +0.2689 ppl @ Llama-3-8B", }, + { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 4.58G, +0.1754 ppl @ Llama-3-8B", }, + { "Q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", }, + { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 5.21G, +0.1049 ppl @ Llama-3-8B", }, + { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 5.33G, +0.0569 ppl @ Llama-3-8B", }, + { "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 6.14G, +0.0217 ppl @ Llama-3-8B", }, + { "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 7.96G, +0.0026 ppl @ Llama-3-8B", }, + { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B", }, + { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", }, + { "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", }, + { "F16", LLAMA_FTYPE_MOSTLY_F16, "14.00G, +0.0020 ppl @ Mistral-7B", }, + { "BF16", LLAMA_FTYPE_MOSTLY_BF16, "14.00G, -0.0050 ppl @ Mistral-7B", }, + { "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", }, + // Note: Ensure COPY comes after F32 to avoid ftype 0 from matching. + { "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", }, +}; + +static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE = "quantize.imatrix.file"; +static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix.dataset"; +static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix.entries_count"; +static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS = "quantize.imatrix.chunks_count"; + +static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) { + std::string ftype_str; + + for (auto ch : ftype_str_in) { + ftype_str.push_back(std::toupper(ch)); + } + for (auto & it : QUANT_OPTIONS) { + if (it.name == ftype_str) { + ftype = it.ftype; + ftype_str_out = it.name; + return true; + } + } + try { + int ftype_int = std::stoi(ftype_str); + for (auto & it : QUANT_OPTIONS) { + if (it.ftype == ftype_int) { + ftype = it.ftype; + ftype_str_out = it.name; + return true; + } + } + } + catch (...) { + // stoi failed + } + return false; +} + +// usage: +// ./llama-quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads] +// +[[noreturn]] +static void usage(const char * executable) { + printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); + printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); + printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); + printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n"); + printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n"); + printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n"); + printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n"); + printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n"); + printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n"); + printf(" --keep-split: will generate quatized model in the same shards as input"); + printf(" --override-kv KEY=TYPE:VALUE\n"); + printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n"); + printf("Note: --include-weights and --exclude-weights cannot be used together\n"); + printf("\nAllowed quantization types:\n"); + for (auto & it : QUANT_OPTIONS) { + if (it.name != "COPY") { + printf(" %2d or ", it.ftype); + } else { + printf(" "); + } + printf("%-7s : %s\n", it.name.c_str(), it.desc.c_str()); + } + exit(1); +} + +static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map> & imatrix_data) { + std::ifstream in(imatrix_file.c_str(), std::ios::binary); + if (!in) { + printf("%s: failed to open %s\n",__func__, imatrix_file.c_str()); + exit(1); + } + int n_entries; + in.read((char *)&n_entries, sizeof(n_entries)); + if (in.fail() || n_entries < 1) { + printf("%s: no data in file %s\n", __func__, imatrix_file.c_str()); + exit(1); + } + for (int i = 0; i < n_entries; ++i) { + int len; in.read((char *)&len, sizeof(len)); + std::vector name_as_vec(len+1); + in.read((char *)name_as_vec.data(), len); + if (in.fail()) { + printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str()); + exit(1); + } + name_as_vec[len] = 0; + std::string name{name_as_vec.data()}; + auto & e = imatrix_data[name]; + int ncall; + in.read((char *)&ncall, sizeof(ncall)); + int nval; + in.read((char *)&nval, sizeof(nval)); + if (in.fail() || nval < 1) { + printf("%s: failed reading number of values for entry %d\n", __func__, i); + imatrix_data = {}; + exit(1); + } + e.resize(nval); + in.read((char *)e.data(), nval*sizeof(float)); + if (in.fail()) { + printf("%s: failed reading data for entry %d\n", __func__, i); + imatrix_data = {}; + exit(1); + } + if (ncall > 0) { + for (auto& v : e) v /= ncall; + } + + if (getenv("LLAMA_TRACE")) { + printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str()); + } + } + + // latest imatrix version contains the dataset filename at the end of the file + int m_last_call = 0; + if (in.peek() != EOF) { + in.read((char *)&m_last_call, sizeof(m_last_call)); + int dataset_len; + in.read((char *)&dataset_len, sizeof(dataset_len)); + std::vector dataset_as_vec(dataset_len); + in.read(dataset_as_vec.data(), dataset_len); + imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end()); + printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str()); + } + printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call); + return m_last_call; +} + +static int prepare_imatrix(const std::string & imatrix_file, + std::string & imatrix_dataset, + const std::vector & included_weights, + const std::vector & excluded_weights, + std::unordered_map> & imatrix_data) { + int m_last_call = -1; + if (!imatrix_file.empty()) { + m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data); + } + if (imatrix_data.empty()) { + return m_last_call; + } + if (!excluded_weights.empty()) { + for (auto& name : excluded_weights) { + for (auto it = imatrix_data.begin(); it != imatrix_data.end(); ) { + auto pos = it->first.find(name); + if (pos != std::string::npos) it = imatrix_data.erase(it); + else ++it; + } + } + } + if (!included_weights.empty()) { + std::unordered_map> tmp; + for (auto& name : included_weights) { + for (auto& e : imatrix_data) { + auto pos = e.first.find(name); + if (pos != std::string::npos) { + tmp.emplace(std::move(e)); + } + } + } + imatrix_data = std::move(tmp); + } + if (!imatrix_data.empty()) { + printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size())); + } + return m_last_call; +} + +static ggml_type parse_ggml_type(const char * arg) { + ggml_type result = GGML_TYPE_COUNT; + for (int j = 0; j < GGML_TYPE_COUNT; ++j) { + auto type = ggml_type(j); + const auto * name = ggml_type_name(type); + if (name && strcmp(arg, name) == 0) { + result = type; break; + } + } + return result; +} + +int main(int argc, char ** argv) { + if (argc < 3) { + usage(argv[0]); + } + + llama_model_quantize_params params = llama_model_quantize_default_params(); + + int arg_idx = 1; + std::string imatrix_file; + std::vector included_weights, excluded_weights; + std::vector kv_overrides; + + for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { + if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) { + params.quantize_output_tensor = false; + } else if (strcmp(argv[arg_idx], "--output-tensor-type") == 0) { + if (arg_idx < argc-1) { + params.output_tensor_type = parse_ggml_type(argv[++arg_idx]); + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--token-embedding-type") == 0) { + if (arg_idx < argc-1) { + params.token_embedding_type = parse_ggml_type(argv[++arg_idx]); + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--override-kv") == 0) { + if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) { + params.allow_requantize = true; + } else if (strcmp(argv[arg_idx], "--pure") == 0) { + params.pure = true; + } else if (strcmp(argv[arg_idx], "--imatrix") == 0) { + if (arg_idx < argc-1) { + imatrix_file = argv[++arg_idx]; + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--include-weights") == 0) { + if (arg_idx < argc-1) { + included_weights.emplace_back(argv[++arg_idx]); + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) { + if (arg_idx < argc-1) { + excluded_weights.emplace_back(argv[++arg_idx]); + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--keep-split") == 0) { + params.keep_split = true; + } else { + usage(argv[0]); + } + } + + if (argc - arg_idx < 2) { + printf("%s: bad arguments\n", argv[0]); + usage(argv[0]); + } + if (!included_weights.empty() && !excluded_weights.empty()) { + usage(argv[0]); + } + + std::string imatrix_dataset; + std::unordered_map> imatrix_data; + int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data); + if (!imatrix_data.empty()) { + params.imatrix = &imatrix_data; + { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + strncpy(kvo.val_str, imatrix_file.c_str(), 127); + kvo.val_str[127] = '\0'; + kv_overrides.emplace_back(std::move(kvo)); + } + if (!imatrix_dataset.empty()) { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + strncpy(kvo.val_str, imatrix_dataset.c_str(), 127); + kvo.val_str[127] = '\0'; + kv_overrides.emplace_back(std::move(kvo)); + } + + { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = imatrix_data.size(); + kv_overrides.emplace_back(std::move(kvo)); + } + + if (m_last_call > 0) { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = m_last_call; + kv_overrides.emplace_back(std::move(kvo)); + } + } + if (!kv_overrides.empty()) { + kv_overrides.emplace_back(); + kv_overrides.back().key[0] = 0; + params.kv_overrides = &kv_overrides; + } + + llama_backend_init(); + + // parse command line arguments + const std::string fname_inp = argv[arg_idx]; + arg_idx++; + std::string fname_out; + + std::string ftype_str; + std::string suffix = ".gguf"; + if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { + std::string fpath; + const size_t pos = fname_inp.find_last_of("/\\"); + if (pos != std::string::npos) { + fpath = fname_inp.substr(0, pos + 1); + } + + // export as [inp path]/ggml-model-[ftype]. Only add extension if there is no splitting + fname_out = fpath + "ggml-model-" + ftype_str; + if (!params.keep_split) { + fname_out += suffix; + } + arg_idx++; + if (ftype_str == "COPY") { + params.only_copy = true; + } + } else { + fname_out = argv[arg_idx]; + if (params.keep_split && fname_out.find(suffix) != std::string::npos) { + fname_out = fname_out.substr(0, fname_out.length() - suffix.length()); + } + arg_idx++; + + if (argc <= arg_idx) { + fprintf(stderr, "%s: missing ftype\n", __func__); + return 1; + } + if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { + fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]); + return 1; + } + if (ftype_str == "COPY") { + params.only_copy = true; + } + arg_idx++; + } + + // parse nthreads + if (argc > arg_idx) { + try { + params.nthread = std::stoi(argv[arg_idx]); + } + catch (const std::exception & e) { + fprintf(stderr, "%s: invalid nthread '%s' (%s)\n", __func__, argv[arg_idx], e.what()); + return 1; + } + } + + if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || + params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || + params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || + params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || + params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) { + fprintf(stderr, "\n==========================================================================================================\n"); + fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); + fprintf(stderr, "==========================================================================================================\n\n\n"); + return 1; + } + + print_build_info(); + + fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str()); + if (params.nthread > 0) { + fprintf(stderr, " using %d threads", params.nthread); + } + fprintf(stderr, "\n"); + + const int64_t t_main_start_us = llama_time_us(); + + int64_t t_quantize_us = 0; + + // load the model + { + const int64_t t_start_us = llama_time_us(); + + if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ¶ms)) { + fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str()); + return 1; + } + + t_quantize_us = llama_time_us() - t_start_us; + } + + // report timing + { + const int64_t t_main_end_us = llama_time_us(); + + printf("\n"); + printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0); + printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0); + } + + llama_backend_free(); + + return 0; +} diff --git a/src/llama.cpp b/src/llama.cpp index f5347a97d..27ebbd05c 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -4480,6 +4480,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_IQ2_XL: return "IQ2_XL - 2.9 bpw"; case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw"; case LLAMA_FTYPE_MOSTLY_IQ3_XXS: return "IQ3_XXS - 3.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ1_XS: return "IQ1_S mix - 1.6-1.7 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_S: return "IQ1_S - 1.5625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_M: return "IQ1_M - 1.75 bpw"; case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw"; @@ -15474,7 +15475,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { new_type = GGML_TYPE_Q8_0; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) { + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_XS ||ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) { new_type = GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || @@ -15490,7 +15491,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n if (qs.params->token_embedding_type < GGML_TYPE_COUNT) { new_type = qs.params->token_embedding_type; } else { - if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_S; + if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_XS ||ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) { + new_type = GGML_TYPE_IQ2_S; + } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) new_type = GGML_TYPE_Q2_K; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_XXS; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S; @@ -15504,7 +15507,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n // TODO: explore better strategies if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || - ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || + ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XL || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_L) new_type = GGML_TYPE_Q6_K; else new_type = GGML_TYPE_Q8_0; } @@ -15516,7 +15519,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_IQ3_S || new_type == GGML_TYPE_IQ4_XS) new_type = GGML_TYPE_Q5_K; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) { + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_XS ||ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) { new_type = (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2) ? GGML_TYPE_IQ4_XS : GGML_TYPE_IQ3_XXS; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { @@ -15560,7 +15563,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n ftype == LLAMA_FTYPE_MOSTLY_IQ2_XL || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_L) new_type = GGML_TYPE_Q6_K; else new_type = GGML_TYPE_Q8_0; } - else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2)) { + else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ1_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && + (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2)) { new_type = GGML_TYPE_IQ1_M; } else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2)) { @@ -15608,6 +15612,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n int i_layer = info.first, n_layer = info.second; if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_L) new_type = GGML_TYPE_Q3_K; else if ((ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) && (use_more_bits(i_layer, n_layer))) new_type = GGML_TYPE_Q3_K; + else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ1_XS) && (i_layer < n_layer/8)) new_type = GGML_TYPE_IQ2_XXS; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) { if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_IQ2_XXS; } @@ -15668,10 +15673,12 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) new_type = GGML_TYPE_Q5_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || - ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) { + ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || + ftype == LLAMA_FTYPE_MOSTLY_IQ1_XS) { if (qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K; else { - if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS; + if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) + new_type = GGML_TYPE_IQ2_XXS; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S) new_type = GGML_TYPE_IQ2_S; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_XXS; } @@ -15691,10 +15698,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n new_type = GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_L) new_type = GGML_TYPE_Q3_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ1_M; - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS; - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS) new_type = GGML_TYPE_IQ2_XS; - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) new_type = GGML_TYPE_IQ2_S; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { + new_type = GGML_TYPE_IQ2_XS; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) new_type = GGML_TYPE_IQ2_S; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S) new_type = GGML_TYPE_IQ3_XXS; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_XXS; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XL) new_type = GGML_TYPE_IQ3_S; @@ -15717,7 +15724,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ2_S) && (i_layer < n_layer/8)) new_type = GGML_TYPE_IQ2_S; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) && (i_layer < n_layer/8)) new_type = GGML_TYPE_IQ3_XXS; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ2_XL) && (use_more_bits(i_layer, n_layer))) new_type = GGML_TYPE_IQ3_XXS; - else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) && (i_layer < n_layer/8)) new_type = GGML_TYPE_IQ3_S; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) && (use_more_bits(i_layer, n_layer))) new_type = GGML_TYPE_IQ3_S; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) && (i_layer < n_layer/8)) new_type = GGML_TYPE_IQ4_XS; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XL) && (use_more_bits(i_layer, n_layer))) new_type = GGML_TYPE_IQ4_XS; @@ -15735,7 +15741,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ2_S) && (i_layer < n_layer/8)) new_type = GGML_TYPE_IQ2_S; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) && (i_layer < n_layer/8)) new_type = GGML_TYPE_IQ3_XXS; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ2_XL) && (use_more_bits(i_layer, n_layer))) new_type = GGML_TYPE_IQ3_XXS; - else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) && (i_layer < n_layer/8)) new_type = GGML_TYPE_IQ3_S; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) && (use_more_bits(i_layer, n_layer))) new_type = GGML_TYPE_IQ3_S; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) && (i_layer < n_layer/8)) new_type = GGML_TYPE_IQ4_XS; else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XL) && (use_more_bits(i_layer, n_layer))) new_type = GGML_TYPE_IQ4_XS; @@ -15879,6 +15884,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break; case LLAMA_FTYPE_MOSTLY_IQ2_XL: default_type = GGML_TYPE_IQ2_S; break; case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break; + case LLAMA_FTYPE_MOSTLY_IQ1_XS: default_type = GGML_TYPE_IQ1_S; break; case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break; case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break; case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;