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Diffstat (limited to 'llama.cpp/tools/quantize/quantize.cpp')
| -rw-r--r-- | llama.cpp/tools/quantize/quantize.cpp | 733 |
1 files changed, 733 insertions, 0 deletions
diff --git a/llama.cpp/tools/quantize/quantize.cpp b/llama.cpp/tools/quantize/quantize.cpp new file mode 100644 index 0000000..c0f4927 --- /dev/null +++ b/llama.cpp/tools/quantize/quantize.cpp @@ -0,0 +1,733 @@ +#include "common.h" +#include "llama.h" +#include "gguf.h" + +#include <cstdio> +#include <cstring> +#include <vector> +#include <string> +#include <unordered_map> +#include <map> +#include <fstream> +#include <cmath> +#include <cctype> +#include <algorithm> +#include <filesystem> + +struct quant_option { + std::string name; + llama_ftype ftype; + std::string desc; +}; + +static const std::vector<quant_option> 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", }, + { "MXFP4_MOE",LLAMA_FTYPE_MOSTLY_MXFP4_MOE," MXFP4 MoE", }, + { "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", }, + { "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", }, + { "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", }, + { "TQ1_0", LLAMA_FTYPE_MOSTLY_TQ1_0, " 1.69 bpw ternarization", }, + { "TQ2_0", LLAMA_FTYPE_MOSTLY_TQ2_0, " 2.06 bpw ternarization", }, + { "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", }, + { "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", }, + { "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", }, + { "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", }, +}; + +// Quantization types. Changes to this struct must be replicated in llama-quantize.cpp +struct tensor_quantization { + std::string name; + ggml_type quant = GGML_TYPE_COUNT; +}; + +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"; + +// TODO: share with imatrix.cpp +static const char * const LLM_KV_IMATRIX_DATASETS = "imatrix.datasets"; +static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count"; +static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size"; + +static bool striequals(const char * a, const char * b) { + while (*a && *b) { + if (std::tolower(*a) != std::tolower(*b)) { + return false; + } + a++; b++; + } + return *a == *b; +} + +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 (const auto & it : QUANT_OPTIONS) { + if (striequals(it.name.c_str(), ftype_str.c_str())) { + ftype = it.ftype; + ftype_str_out = it.name; + return true; + } + } + try { + int ftype_int = std::stoi(ftype_str); + for (const auto & it : QUANT_OPTIONS) { + if (it.ftype == ftype_int) { + ftype = it.ftype; + ftype_str_out = it.name; + return true; + } + } + } + catch (...) { + // stoi failed + } + return false; +} + +[[noreturn]] +static void usage(const char * executable) { + printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights]\n", executable); + printf(" [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--tensor-type-file]\n"); + printf(" [--prune-layers] [--keep-split] [--override-kv]\n"); + printf(" model-f32.gguf [model-quant.gguf] type [nthreads]\n\n"); + printf(" --allow-requantize\n"); + printf(" allow requantizing tensors that have already been quantized\n"); + printf(" WARNING: this can severely reduce quality compared to quantizing\n"); + printf(" from 16bit or 32bit!\n"); + printf(" --leave-output-tensor\n"); + printf(" leave output.weight un(re)quantized\n"); + printf(" increases model size but may also increase quality, especially when requantizing\n"); + printf(" --pure\n"); + printf(" disable k-quant mixtures and quantize all tensors to the same type\n"); + printf(" --imatrix file_name\n"); + printf(" use data in file_name as importance matrix for quant optimizations\n"); + printf(" --include-weights tensor_name\n"); + printf(" use importance matrix for this/these tensor(s)\n"); + printf(" --exclude-weights tensor_name\n"); + printf(" do not use importance matrix for this/these tensor(s)\n"); + printf(" --output-tensor-type ggml_type\n"); + printf(" use this ggml_type for the output.weight tensor\n"); + printf(" --token-embedding-type ggml_type\n"); + printf(" use this ggml_type for the token embeddings tensor\n"); + printf(" --tensor-type tensor_name=ggml_type\n"); + printf(" quantize this tensor to this ggml_type\n"); + printf(" this is an advanced option to selectively quantize tensors. may be specified multiple times.\n"); + printf(" example: --tensor-type attn_q=q8_0\n"); + printf(" --tensor-type-file tensor_types.txt\n"); + printf(" list of tensors to quantize to a specific ggml_type\n"); + printf(" this is an advanced option to selectively quantize a long list of tensors.\n"); + printf(" the file should use the same format as above, separated by spaces or newlines.\n"); + printf(" --prune-layers L0,L1,L2...\n"); + printf(" comma-separated list of layer numbers to prune from the model\n"); + printf(" WARNING: this is an advanced option, use with care.\n"); + printf(" --keep-split\n"); + printf(" generate quantized model in the same shards as input\n"); + printf(" --override-kv KEY=TYPE:VALUE\n"); + printf(" override model metadata by key in the quantized model. may be specified multiple times.\n"); + printf(" WARNING: this is an advanced option, use with care.\n\n"); + printf("note: --include-weights and --exclude-weights cannot be used together\n\n"); + printf("-----------------------------------------------------------------------------\n"); + printf(" allowed quantization types\n"); + printf("-----------------------------------------------------------------------------\n\n"); + for (const 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_legacy_imatrix(const std::string & imatrix_file, std::vector<std::string> & imatrix_datasets, std::unordered_map<std::string, std::vector<float>> & 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<char> 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 legacy 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<char> dataset_as_vec(dataset_len); + in.read(dataset_as_vec.data(), dataset_len); + imatrix_datasets.resize(1); + imatrix_datasets[0].assign(dataset_as_vec.begin(), dataset_as_vec.end()); + printf("%s: imatrix dataset='%s'\n", __func__, imatrix_datasets[0].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 load_imatrix(const std::string & imatrix_file, std::vector<std::string> & imatrix_datasets, std::unordered_map<std::string, std::vector<float>> & imatrix_data) { + + struct ggml_context * ctx = nullptr; + struct gguf_init_params meta_gguf_params = { + /* .no_alloc = */ false, // the data is needed + /* .ctx = */ &ctx, + }; + struct gguf_context * ctx_gguf = gguf_init_from_file(imatrix_file.c_str(), meta_gguf_params); + if (!ctx_gguf) { + fprintf(stderr, "%s: imatrix file '%s' is using old format\n", __func__, imatrix_file.c_str()); + return load_legacy_imatrix(imatrix_file, imatrix_datasets, imatrix_data); + } + const int32_t n_entries = gguf_get_n_tensors(ctx_gguf); + if (n_entries < 1) { + fprintf(stderr, "%s: no data in file %s\n", __func__, imatrix_file.c_str()); + gguf_free(ctx_gguf); + ggml_free(ctx); + exit(1); + } + + const int dataset_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASETS); + const int chunk_count_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT); + const int chunk_size_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE); + if (dataset_idx < 0 || chunk_count_idx < 0 || chunk_size_idx < 0) { + fprintf(stderr, "%s: missing imatrix metadata in file %s\n", __func__, imatrix_file.c_str()); + gguf_free(ctx_gguf); + ggml_free(ctx); + exit(1); + } + + const uint32_t chunk_size = gguf_get_val_u32(ctx_gguf, chunk_size_idx); + + const std::string sums_suffix{ ".in_sum2" }; + const std::string counts_suffix{ ".counts" }; + + // Using an ordered map to get a deterministic iteration order. + std::map<std::string, std::pair<struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for; + + for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + std::string name = cur->name; + + if (name.empty()) { continue; } + + if (string_remove_suffix(name, sums_suffix)) { + // in_sum2 + sums_counts_for[std::move(name)].first = cur; + } else if (string_remove_suffix(name, counts_suffix)) { + // counts + sums_counts_for[std::move(name)].second = cur; + } else { + // ignore other tensors + } + } + + for (const auto & sc : sums_counts_for) { + const std::string & name = sc.first; + const struct ggml_tensor * sums = sc.second.first; + const struct ggml_tensor * counts = sc.second.second; + + if (!sums || !counts) { + fprintf(stderr, "%s: mismatched sums and counts for %s\n", __func__, name.c_str()); + gguf_free(ctx_gguf); + ggml_free(ctx); + exit(1); + } + + const int64_t ne0 = sums->ne[0]; + const int64_t ne1 = sums->ne[1]; + + auto & e = imatrix_data[name]; + e.resize(ggml_nelements(sums)); + float max_count = 0.0f; + for (int64_t j = 0; j < ne1; ++j) { + const float count = ((const float *) counts->data)[j]; + if (count > 0.0f) { + for (int64_t i = 0; i < ne0; ++i) { + e[j*ne0 + i] = ((const float *) sums->data)[j*ne0 + i] / count; + } + } else { + // Partial imatrix data, this tensor never got any input during calibration + for (int64_t i = 0; i < ne0; ++i) { + e[j*ne0 + i] = 1; + } + } + if (count > max_count) { + max_count = count; + } + } + if (getenv("LLAMA_TRACE")) { + printf("%s: loaded data (size = %6d, n_tokens = %6d, n_chunks = %6d) for '%s'\n", __func__, int(e.size()), int(max_count), int(max_count / chunk_size), name.c_str()); + } + } + + int m_last_chunk = gguf_get_val_u32(ctx_gguf, chunk_count_idx); + + int64_t n_datasets = gguf_get_arr_n(ctx_gguf, dataset_idx); + imatrix_datasets.reserve(n_datasets); + for (int64_t i = 0; i < n_datasets; ++i) { + imatrix_datasets.push_back(gguf_get_arr_str(ctx_gguf, dataset_idx, i)); + } + printf("%s: imatrix datasets=['%s'", __func__, imatrix_datasets[0].c_str()); + for (size_t i = 1; i < imatrix_datasets.size(); ++i) { + printf(", '%s'", imatrix_datasets[i].c_str()); + } + printf("]\n"); + + 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_chunk); + + gguf_free(ctx_gguf); + ggml_free(ctx); + + return m_last_chunk; +} + +static int prepare_imatrix(const std::string & imatrix_file, + std::vector<std::string> & imatrix_dataset, + const std::vector<std::string> & included_weights, + const std::vector<std::string> & excluded_weights, + std::unordered_map<std::string, std::vector<float>> & 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 (const 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<std::string, std::vector<float>> tmp; + for (const 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) { + for (int i = 0; i < GGML_TYPE_COUNT; ++i) { + auto type = (ggml_type)i; + const auto * name = ggml_type_name(type); + if (name && striequals(name, arg)) { + return type; + } + } + fprintf(stderr, "\n%s: invalid ggml_type '%s'\n\n", __func__, arg); + return GGML_TYPE_COUNT; +} + +static bool parse_tensor_type(const char * data, std::vector<tensor_quantization> & tensor_type) { + const char * sep = strchr(data, '='); + if (sep == nullptr) { + printf("\n%s: malformed tensor type '%s'\n\n", __func__, data); + return false; + } + + const size_t tn_len = sep - data; + if (tn_len == 0) { + printf("\n%s: missing tensor name\n\n", __func__); + return false; + } + if (const size_t qt_len = strlen(sep); qt_len == 1) { + printf("\n%s: missing quantization type\n\n", __func__); + return false; + } + + std::string tn(data, tn_len); + std::transform(tn.begin(), tn.end(), tn.begin(), tolower); + sep++; + tensor_quantization tqz; + tqz.name = tn; + tqz.quant = parse_ggml_type(sep); + tensor_type.emplace_back(std::move(tqz)); + if (tqz.quant == GGML_TYPE_COUNT) { + printf("\n%s: invalid quantization type '%s'\n\n", __func__, sep); + return false; + } + + return true; +} + +static bool parse_tensor_type_file(const char * filename, std::vector<tensor_quantization> & tensor_type) { + std::ifstream file(filename); + if (!file) { + printf("\n%s: failed to open file '%s': %s\n\n", __func__, filename, std::strerror(errno)); + return false; + } + + std::string arg; + while (file >> arg) { + if (!parse_tensor_type(arg.c_str(), tensor_type)) { + return false; + } + } + + return true; +} + +static bool parse_layer_prune(const char * data, std::vector<int> & prune_layers) { + if (!data) { + printf("\n%s: no layer pruning ids provided\n\n", __func__); + return false; + } + + const auto block_ids = string_split<std::string>(data, ','); + for (const auto & block_id : block_ids) { + int id; + try { + id = std::stoi(block_id); + } catch (...) { + id = -1; + } + if (id < 0) { + printf("\n%s: invalid layer id '%s'\n\n", __func__, block_id.c_str()); + return false; + } + prune_layers.emplace_back(id); + } + + sort(prune_layers.begin(), prune_layers.end()); + prune_layers.erase(std::unique(prune_layers.begin(), prune_layers.end()), prune_layers.end()); + return true; +} + +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<std::string> included_weights, excluded_weights; + std::vector<llama_model_kv_override> kv_overrides; + std::vector<tensor_quantization> tensor_types; + std::vector<int> prune_layers; + + 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]); + if (params.output_tensor_type == GGML_TYPE_COUNT) { + usage(argv[0]); + } + } 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]); + if (params.token_embedding_type == GGML_TYPE_COUNT) { + usage(argv[0]); + } + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--tensor-type") == 0) { + if (arg_idx == argc-1 || !parse_tensor_type(argv[++arg_idx], tensor_types)) { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--tensor-type-file") == 0) { + if (arg_idx == argc-1 || !parse_tensor_type_file(argv[++arg_idx], tensor_types)) { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--prune-layers") == 0) { + if (arg_idx == argc-1 || !parse_layer_prune(argv[++arg_idx], prune_layers)) { + 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::vector<std::string> imatrix_datasets; + std::unordered_map<std::string, std::vector<float>> imatrix_data; + int m_last_call = prepare_imatrix(imatrix_file, imatrix_datasets, 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_datasets.empty()) { + llama_model_kv_override kvo; + // TODO: list multiple datasets when there are more than one + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + strncpy(kvo.val_str, imatrix_datasets[0].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; + } + if (!tensor_types.empty()) { + params.tensor_types = &tensor_types; + } + if (!prune_layers.empty()) { + params.prune_layers = &prune_layers; + } + + 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[arg_idx]); + 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; + } + + if (std::error_code ec; std::filesystem::equivalent(fname_inp, fname_out, ec)) { + fprintf(stderr, "%s: error: input and output files are the same: '%s'\n", __func__, fname_inp.c_str()); + 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; +} + |