mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 19:34:35 +00:00
16bc66d947
* llama.cpp : split llama_context_params into model and context params ggml-ci * fix metal build * fix freq_base/scale default to model value * llama-bench : keep the same model between tests when possible * move n_threads to llama_context_params, add n_threads_batch * fix mpi build * remove kv_size(), cuda scratch fixes * remove low-vram option * add n_threads_batch to system info, refactor to get_system_info() * add documentation about --threads-batch to the READMEs * llama-bench fix * main : fix rope freq/scale warning * llama.cpp : add llama_get_model common : add llama_tokenize from model * remove duplicated ctx/model functions ggml-ci * cuda : print total VRAM used
164 lines
5.1 KiB
C++
164 lines
5.1 KiB
C++
#include "build-info.h"
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#include "common.h"
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#include "llama.h"
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#include <vector>
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#include <cstdio>
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#include <chrono>
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int main(int argc, char ** argv) {
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gpt_params params;
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params.seed = 42;
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params.n_threads = 4;
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params.repeat_last_n = 64;
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params.prompt = "The quick brown fox";
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if (!gpt_params_parse(argc, argv, params)) {
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return 1;
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}
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print_build_info();
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if (params.n_predict < 0) {
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params.n_predict = 16;
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}
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auto n_past = 0;
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auto last_n_tokens_data = std::vector<llama_token>(params.repeat_last_n, 0);
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// init
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llama_model * model;
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llama_context * ctx;
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std::tie(model, ctx) = llama_init_from_gpt_params( params );
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if (model == nullptr) {
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return 1;
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}
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if (ctx == nullptr) {
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llama_free_model(model);
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return 1;
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}
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auto tokens = llama_tokenize(ctx, params.prompt, true);
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auto n_prompt_tokens = tokens.size();
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if (n_prompt_tokens < 1) {
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fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
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llama_free(ctx);
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llama_free_model(model);
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return 1;
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}
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// evaluate prompt
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llama_decode(ctx, llama_batch_get_one(tokens.data(), n_prompt_tokens, n_past, 0));
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last_n_tokens_data.insert(last_n_tokens_data.end(), tokens.data(), tokens.data() + n_prompt_tokens);
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n_past += n_prompt_tokens;
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const size_t state_size = llama_get_state_size(ctx);
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uint8_t * state_mem = new uint8_t[state_size];
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// Save state (rng, logits, embedding and kv_cache) to file
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{
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FILE *fp_write = fopen("dump_state.bin", "wb");
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llama_copy_state_data(ctx, state_mem); // could also copy directly to memory mapped file
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fwrite(state_mem, 1, state_size, fp_write);
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fclose(fp_write);
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}
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// save state (last tokens)
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const auto last_n_tokens_data_saved = std::vector<llama_token>(last_n_tokens_data);
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const auto n_past_saved = n_past;
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// first run
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printf("\n%s", params.prompt.c_str());
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for (auto i = 0; i < params.n_predict; i++) {
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auto * logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(model);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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auto next_token = llama_sample_token(ctx, &candidates_p);
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auto next_token_str = llama_token_to_piece(ctx, next_token);
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last_n_tokens_data.push_back(next_token);
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printf("%s", next_token_str.c_str());
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if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_free(ctx);
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llama_free_model(model);
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return 1;
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}
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n_past += 1;
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}
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printf("\n\n");
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// free old context
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llama_free(ctx);
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// make new context
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auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
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// Load state (rng, logits, embedding and kv_cache) from file
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{
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FILE *fp_read = fopen("dump_state.bin", "rb");
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if (state_size != llama_get_state_size(ctx2)) {
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fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
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llama_free(ctx2);
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llama_free_model(model);
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return 1;
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}
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const size_t ret = fread(state_mem, 1, state_size, fp_read);
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if (ret != state_size) {
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fprintf(stderr, "\n%s : failed to read state\n", __func__);
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llama_free(ctx2);
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llama_free_model(model);
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return 1;
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}
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llama_set_state_data(ctx2, state_mem); // could also read directly from memory mapped file
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fclose(fp_read);
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}
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delete[] state_mem;
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// restore state (last tokens)
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last_n_tokens_data = last_n_tokens_data_saved;
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n_past = n_past_saved;
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// second run
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for (auto i = 0; i < params.n_predict; i++) {
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auto * logits = llama_get_logits(ctx2);
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auto n_vocab = llama_n_vocab(model);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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auto next_token = llama_sample_token(ctx2, &candidates_p);
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auto next_token_str = llama_token_to_piece(ctx2, next_token);
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last_n_tokens_data.push_back(next_token);
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printf("%s", next_token_str.c_str());
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if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_free(ctx2);
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llama_free_model(model);
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return 1;
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}
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n_past += 1;
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}
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printf("\n\n");
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llama_free(ctx2);
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llama_free_model(model);
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return 0;
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}
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