phi-coder-hf/llama.cpp/examples/training/finetune.cpp

#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama.h"

#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <vector>

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

int main(int argc, char ** argv) {
    common_params params;

    params.escape = false;

    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
        return 1;
    }

    if (params.use_mmap) {
        LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n", __func__);
        params.use_mmap = false;
    }
    if (params.cache_type_k != GGML_TYPE_F32) {
        LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
        params.cache_type_k = GGML_TYPE_F32;
    }
    if (params.cache_type_v != GGML_TYPE_F32) {
        LOG_INF("%s: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
        params.cache_type_v = GGML_TYPE_F32;
    }

    common_init();
    llama_backend_init();
    llama_numa_init(params.numa);

    // load the model and apply lora adapter, if any
    common_init_result llama_init = common_init_from_params(params);
    llama_model_ptr   & model = llama_init.model;
    llama_context_ptr & ctx   = llama_init.context;

    if (model == NULL) {
        LOG_ERR("%s: unable to load model\n", __func__);
        return 1;
    }

    // print system information
    {
        LOG_INF("\n");
        LOG_INF("%s\n", common_params_get_system_info(params).c_str());
    }

    constexpr float val_split = 0.05f;

    std::vector<llama_token> tokens = common_tokenize(ctx.get(), params.prompt, true);
    ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get())/2);

    struct ggml_opt_optimizer_params optimizer_params = ggml_opt_get_default_optimizer_params(nullptr);
    optimizer_params.adamw.alpha = 1e-7f; // learning rate

    struct llama_opt_params lopt_params {
        /*n_ctx_train     =*/ 0,
        /*param_filter    =*/ llama_opt_param_filter_all,
        /*param_filter_ud =*/ nullptr,
        /*get_opt_pars    =*/ ggml_opt_get_constant_optimizer_params,
        /*get_opt_pars_ud =*/ &optimizer_params,
    };
    llama_opt_init(ctx.get(), model.get(), lopt_params);

    const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - val_split);

    ggml_opt_result_t result_train = ggml_opt_result_init();
    ggml_opt_result_t result_eval  = ggml_opt_result_init();

    for (int epoch = 0; epoch < 2; ++epoch) {
        llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
            ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
        fprintf(stderr, "\n");

        ggml_opt_result_reset(result_train);
        ggml_opt_result_reset(result_eval);
    }
    ggml_opt_result_free(result_train);
    ggml_opt_result_free(result_eval);

    llama_model_save_to_file(model.get(), "finetuned-model.gguf");

    llama_backend_free();

    return 0;
}