common : reimplement logging (#9418)
https://github.com/ggerganov/llama.cpp/pull/9418
This commit is contained in:
Georgi Gerganov
GitHub
parent
e6deac31f7
commit
6262d13e0b
54 changed files with 2092 additions and 2419 deletions
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@ -5,13 +5,12 @@
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#include "llama.h"
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#include "ggml.h"
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#include <cmath>
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#include <cstdint>
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#include <cstdio>
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#include <cinttypes>
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#include <fstream>
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#include <string>
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#include <vector>
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#include <unordered_map>
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int main(int argc, char ** argv){
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gpt_params params;
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@ -20,6 +19,8 @@ int main(int argc, char ** argv){
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return 1;
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}
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gpt_init();
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const int n_draft = params.n_draft;
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// init llama.cpp
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@ -49,7 +50,7 @@ int main(int argc, char ** argv){
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try {
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ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
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} catch (std::ifstream::failure const &) {
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fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
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LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
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exit(1);
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}
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}
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@ -128,7 +129,7 @@ int main(int argc, char ** argv){
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const int64_t eta_min = eta_ms / (60*1000);
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const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000;
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LOG_TEE("lookup-stats: %d/%d done, ETA: %02" PRId64 ":%02" PRId64 "\n", i_start, n_input, eta_min, eta_s);
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LOG_INF("lookup-stats: %d/%d done, ETA: %02" PRId64 ":%02" PRId64 "\n", i_start, n_input, eta_min, eta_s);
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}
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// After each chunk, update the dynamic ngram cache with the context ngram cache:
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@ -136,24 +137,24 @@ int main(int argc, char ** argv){
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ngram_cache_context.clear();
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}
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LOG_TEE("\n");
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LOG("\n");
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LOG_TEE("\n");
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LOG_TEE("n_draft = %d\n", n_draft);
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LOG_TEE("n_predict = %d\n", n_input - n_input % n_ctx);
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LOG_TEE("n_drafted = %d\n", n_drafted);
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LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
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LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n",
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LOG_INF("\n");
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LOG_INF("n_draft = %d\n", n_draft);
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LOG_INF("n_predict = %d\n", n_input - n_input % n_ctx);
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LOG_INF("n_drafted = %d\n", n_drafted);
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LOG_INF("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
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LOG_INF("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n",
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t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
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LOG_TEE("n_accept = %d\n", n_accept);
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LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
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LOG_INF("n_accept = %d\n", n_accept);
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LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
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llama_free(ctx);
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llama_free_model(model);
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llama_backend_free();
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fprintf(stderr, "\n\n");
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LOG("\n\n");
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return 0;
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}
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@ -3,6 +3,7 @@
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#include "common.h"
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#include "ngram-cache.h"
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#include "sampling.h"
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#include "log.h"
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#include "llama.h"
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#include <cstdint>
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@ -18,17 +19,13 @@ int main(int argc, char ** argv){
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return 1;
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}
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gpt_init();
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// max. number of additional tokens to draft if match is found
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const int n_draft = params.n_draft;
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const bool dump_kv_cache = params.dump_kv_cache;
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#ifndef LOG_DISABLE_LOGS
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log_set_target(log_filename_generator("lookup", "log"));
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LOG_TEE("Log start\n");
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log_dump_cmdline(argc, argv);
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#endif // LOG_DISABLE_LOGS
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// init llama.cpp
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llama_backend_init();
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llama_numa_init(params.numa);
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@ -58,7 +55,7 @@ int main(int argc, char ** argv){
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try {
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ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
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} catch (std::ifstream::failure const &) {
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fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
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LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
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exit(1);
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}
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}
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@ -76,14 +73,14 @@ int main(int argc, char ** argv){
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const int max_tokens_list_size = max_context_size - 4;
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if ((int) inp.size() > max_tokens_list_size) {
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fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
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LOG_ERR("%s: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
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return 1;
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}
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fprintf(stderr, "\n\n");
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LOG("\n\n");
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for (auto id : inp) {
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fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
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LOG("%s", llama_token_to_piece(ctx, id).c_str());
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}
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fflush(stderr);
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@ -124,7 +121,7 @@ int main(int argc, char ** argv){
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}
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// print current draft sequence
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LOG("drafted %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, draft).c_str());
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LOG_DBG("drafted %s\n", string_from(ctx, draft).c_str());
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int i_dft = 0;
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while (true) {
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@ -136,7 +133,7 @@ int main(int argc, char ** argv){
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const std::string token_str = llama_token_to_piece(ctx, id);
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if (!params.use_color) {
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printf("%s", token_str.c_str());
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LOG("%s", token_str.c_str());
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}
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if (llama_token_is_eog(model, id)) {
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@ -147,7 +144,7 @@ int main(int argc, char ** argv){
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// check if the target token matches the draft
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if (i_dft < (int) draft.size() && id == draft[i_dft]) {
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LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
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LOG_DBG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
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++n_accept;
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++n_past;
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++i_dft;
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@ -161,19 +158,19 @@ int main(int argc, char ** argv){
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if (params.use_color) {
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// color accepted draft token
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printf("\033[34m%s\033[0m", token_str.c_str());
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LOG("\033[34m%s\033[0m", token_str.c_str());
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fflush(stdout);
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}
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continue;
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}
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if (params.use_color) {
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printf("%s", token_str.c_str());
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LOG("%s", token_str.c_str());
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}
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fflush(stdout);
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LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
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LOG_DBG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
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draft.clear();
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draft.push_back(id);
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@ -224,22 +221,22 @@ int main(int argc, char ** argv){
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llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
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llama_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic);
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LOG_TEE("\n\n");
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LOG("\n\n");
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LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
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LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
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LOG_INF("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
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LOG_INF("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
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LOG_TEE("\n");
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LOG_TEE("n_draft = %d\n", n_draft);
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LOG_TEE("n_predict = %d\n", n_predict);
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LOG_TEE("n_drafted = %d\n", n_drafted);
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LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
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LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n",
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LOG_INF("\n");
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LOG_INF("n_draft = %d\n", n_draft);
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LOG_INF("n_predict = %d\n", n_predict);
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LOG_INF("n_drafted = %d\n", n_drafted);
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LOG_INF("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
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LOG_INF("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n",
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t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
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LOG_TEE("n_accept = %d\n", n_accept);
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LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
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LOG_INF("n_accept = %d\n", n_accept);
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LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
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LOG_TEE("\ntarget:\n\n");
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LOG_INF("\ntarget:\n\n");
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gpt_perf_print(ctx, smpl);
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gpt_sampler_free(smpl);
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@ -251,7 +248,7 @@ int main(int argc, char ** argv){
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llama_backend_free();
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fprintf(stderr, "\n\n");
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LOG("\n\n");
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return 0;
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}
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