c25557362a
more compact progress bar refactor: llama_prepare_sbatch/ubatch llama_save_model_to_file gqa_mode arg for repeat_back llama_opt_param_filter ggml_graph_dup force_grads refactor ggml_opt, fix test-opt
97 lines
3.1 KiB
C++
97 lines
3.1 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "log.h"
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#include "llama.h"
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <vector>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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int main(int argc, char ** argv) {
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common_params params;
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params.logits_all = true;
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params.escape = false;
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if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
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return 1;
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}
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if (params.use_mmap) {
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LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n", __func__);
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params.use_mmap = false;
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}
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if (params.cache_type_k == GGML_TYPE_F16) {
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LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
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params.cache_type_k = GGML_TYPE_F32;
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}
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if (params.cache_type_v == GGML_TYPE_F16) {
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LOG_INF("%s: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
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params.cache_type_v = GGML_TYPE_F32;
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}
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common_init();
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llama_backend_init();
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llama_numa_init(params.numa);
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// load the model and apply lora adapter, if any
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common_init_result llama_init = common_init_from_params(params);
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llama_model_ptr & model = llama_init.model;
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llama_context_ptr & ctx = llama_init.context;
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if (model == NULL) {
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LOG_ERR("%s: unable to load model\n", __func__);
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return 1;
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}
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// print system information
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{
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LOG_INF("\n");
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LOG_INF("%s\n", common_params_get_system_info(params).c_str());
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}
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constexpr float val_split = 0.05f;
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std::vector<llama_token> tokens = common_tokenize(ctx.get(), params.prompt, true);
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ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get())/2);
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struct ggml_opt_optimizer_params optimizer_params = ggml_opt_get_default_optimizer_params(nullptr);
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optimizer_params.adamw.alpha = 1e-7f; // learning rate
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struct llama_opt_params lopt_params {
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/*n_ctx_train =*/ 0,
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/*param_filter =*/ llama_opt_param_filter_all,
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/*param_filter_ud =*/ nullptr,
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/*get_opt_pars =*/ ggml_opt_get_constant_optimizer_params,
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/*get_opt_pars_ud =*/ &optimizer_params,
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};
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llama_opt_init(ctx.get(), model.get(), lopt_params);
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const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - val_split);
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ggml_opt_result_t result_train = ggml_opt_result_init();
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ggml_opt_result_t result_eval = ggml_opt_result_init();
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for (int epoch = 0; epoch < 2; ++epoch) {
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llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
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ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
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fprintf(stderr, "\n");
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ggml_opt_result_reset(result_train);
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ggml_opt_result_reset(result_eval);
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}
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ggml_opt_result_free(result_train);
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ggml_opt_result_free(result_eval);
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llama_model_save_to_file(model.get(), "finetuned-model.gguf");
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llama_backend_free();
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return 0;
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}
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