Felipe97/llama-cpp-compiled

1#include "arg.h"2#include "common.h"3#include "log.h"4#include "llama.h"5 6#include <clocale>7#include <cmath>8#include <cstdio>9#include <cstring>10#include <ctime>11#include <vector>12 13#if defined(_MSC_VER)14#pragma warning(disable: 4244 4267)  // possible loss of data15#endif16 17int main(int argc, char ** argv) {18    std::setlocale(LC_NUMERIC, "C");19 20    common_params params;21    params.escape = false;22 23    common_init();24 25    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_FINETUNE)) {26        return 1;27    }28 29    if (params.load_mode != LLAMA_LOAD_MODE_NONE) {30        LOG_INF("%s: forcing load_mode = none to enable writable pointers to the weights\n", __func__);31        params.load_mode = LLAMA_LOAD_MODE_NONE;32    }33    if (params.cache_type_k != GGML_TYPE_F32) {34        LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);35        params.cache_type_k = GGML_TYPE_F32;36    }37    if (params.cache_type_v != GGML_TYPE_F32) {38        LOG_INF("%s: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);39        params.cache_type_v = GGML_TYPE_F32;40    }41 42    llama_backend_init();43    llama_numa_init(params.numa);44    // load the model and apply lora adapter, if any45    auto llama_init = common_init_from_params(params);46 47    auto * model = llama_init->model();48    auto * ctx   = llama_init->context();49 50    if (model == NULL) {51        LOG_ERR("%s: unable to load model\n", __func__);52        return 1;53    }54 55    // print system information56    {57        LOG_INF("\n");58        LOG_INF("%s\n", common_params_get_system_info(params).c_str());59    }60 61    std::vector<llama_token> tokens  = common_tokenize(ctx, params.prompt, true);62    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx, tokens, llama_n_ctx(ctx) / 2);63 64    struct lr_opt & lr = params.lr;65    LOG_INF("-optimizer %s -lr0 %.2g -wd %.2g -lr-min %.2g -min-epochs %.2g -epochs %d -period %.2g -val %.2g\n",66            ggml_opt_optimizer_name(params.optimizer), (double) lr.lr0, (double) lr.wd, (double) lr.lr_min, (double) lr.decay_epochs,67            (unsigned) lr.epochs, (double) params.n_batch / params.n_ubatch, (double) params.val_split);68 69    struct llama_opt_params lopt_params{70        /*n_ctx_train     =*/0,71        /*param_filter    =*/llama_opt_param_filter_all,72        /*param_filter_ud =*/nullptr,73        /*get_opt_pars    =*/common_opt_lr_pars,74        /*get_opt_pars_ud =*/&params.lr,75        /*optimizer_type  =*/params.optimizer,76    };77    llama_opt_init(ctx, model, lopt_params);78 79    const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - params.val_split);80 81    ggml_opt_result_t result_train = ggml_opt_result_init();82    ggml_opt_result_t result_eval  = ggml_opt_result_init();83 84    for (lr.epoch = 0; lr.epoch < lr.epochs; ++lr.epoch) {85        llama_opt_epoch(ctx, dataset, result_train, result_eval, idata_split,86                        ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);87        fprintf(stderr, "\n");88 89        ggml_opt_result_reset(result_train);90        ggml_opt_result_reset(result_eval);91    }92    ggml_opt_result_free(result_train);93    ggml_opt_result_free(result_eval);94 95    llama_model_save_to_file(model, params.out_file.c_str());96 97    llama_backend_free();98 99    return 0;100}101