Remove direct access to std streams from llama_main
The goal is to allow running llama_main while connected to other streams, such as TCP sockets. Signed-off-by: Thiago Padilha <thiago@padilha.cc>
This commit is contained in:
Thiago Padilha
parent
1088d2dd04
commit
edc17cfa9f
3 changed files with 41 additions and 35 deletions
69
llama.cpp
69
llama.cpp
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@ -718,13 +718,16 @@ int llama_main(
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gpt_vocab vocab,
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llama_model model,
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int64_t t_load_us,
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int64_t t_main_start_us) {
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int64_t t_main_start_us,
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FILE *instream,
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FILE *outstream,
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FILE *errstream) {
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if (params.seed < 0) {
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params.seed = time(NULL);
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}
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fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
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fprintf(errstream, "%s: seed = %d\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.prompt.empty()) {
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@ -751,13 +754,13 @@ int llama_main(
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// tokenize the reverse prompt
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std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
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fprintf(stderr, "\n");
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fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
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fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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fprintf(errstream, "\n");
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fprintf(errstream, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
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fprintf(errstream, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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for (int i = 0; i < (int) embd_inp.size(); i++) {
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fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
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fprintf(errstream, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
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}
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fprintf(stderr, "\n");
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fprintf(errstream, "\n");
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if (params.interactive) {
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
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struct sigaction sigint_action;
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@ -769,19 +772,19 @@ int llama_main(
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signal(SIGINT, sigint_handler);
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#endif
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fprintf(stderr, "%s: interactive mode on.\n", __func__);
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fprintf(errstream, "%s: interactive mode on.\n", __func__);
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if(antiprompt_inp.size()) {
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fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
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fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
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fprintf(errstream, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
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fprintf(errstream, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
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for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
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fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
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fprintf(errstream, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
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}
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fprintf(stderr, "\n");
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fprintf(errstream, "\n");
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}
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}
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fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
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fprintf(stderr, "\n\n");
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fprintf(errstream, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
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fprintf(errstream, "\n\n");
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std::vector<gpt_vocab::id> embd;
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@ -795,7 +798,7 @@ int llama_main(
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if (params.interactive) {
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fprintf(stderr, "== Running in interactive mode. ==\n"
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fprintf(errstream, "== Running in interactive mode. ==\n"
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
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" - Press Ctrl+C to interject at any time.\n"
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#endif
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@ -814,7 +817,7 @@ int llama_main(
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// set the color for the prompt which will be output initially
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if (params.use_color) {
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printf(ANSI_COLOR_YELLOW);
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fprintf(outstream, ANSI_COLOR_YELLOW);
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}
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while (remaining_tokens > 0) {
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@ -823,7 +826,7 @@ int llama_main(
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const int64_t t_start_us = ggml_time_us();
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if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
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fprintf(stderr, "Failed to predict\n");
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fprintf(errstream, "Failed to predict\n");
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return 1;
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}
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@ -877,16 +880,16 @@ int llama_main(
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// reset color to default if we there is no pending user input
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if (!input_noecho && params.use_color && embd_inp.size() == input_consumed) {
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printf(ANSI_COLOR_RESET);
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fprintf(outstream, ANSI_COLOR_RESET);
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}
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}
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// display text
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if (!input_noecho) {
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for (auto id : embd) {
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printf("%s", vocab.id_to_token[id].c_str());
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fprintf(outstream, "%s", vocab.id_to_token[id].c_str());
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}
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fflush(stdout);
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fflush(outstream);
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}
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// in interactive mode, and not currently processing queued inputs;
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@ -901,16 +904,16 @@ int llama_main(
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// currently being interactive
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bool another_line=true;
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while (another_line) {
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fflush(stdout);
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fflush(outstream);
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char buf[256] = {0};
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int n_read;
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if(params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
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if (scanf("%255[^\n]%n%*c", buf, &n_read) <= 0) {
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if(params.use_color) fprintf(outstream, ANSI_BOLD ANSI_COLOR_GREEN);
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if (fscanf(instream, "%255[^\n]%n%*c", buf, &n_read) <= 0) {
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// presumable empty line, consume the newline
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std::ignore = scanf("%*c");
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std::ignore = fscanf(instream, "%*c");
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n_read=0;
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}
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if(params.use_color) printf(ANSI_COLOR_RESET);
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if(params.use_color) fprintf(outstream, ANSI_COLOR_RESET);
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if (n_read > 0 && buf[n_read-1]=='\\') {
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another_line = true;
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@ -936,7 +939,7 @@ int llama_main(
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// end of text token
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if (embd.back() == 2) {
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fprintf(stderr, " [end of text]\n");
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fprintf(errstream, " [end of text]\n");
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break;
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}
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}
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@ -949,18 +952,18 @@ int llama_main(
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{
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const int64_t t_main_end_us = ggml_time_us();
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fprintf(stderr, "\n\n");
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fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
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fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
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fprintf(stderr, "%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
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fprintf(stderr, "%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
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fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
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fprintf(errstream, "\n\n");
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fprintf(errstream, "%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
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fprintf(errstream, "%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
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fprintf(errstream, "%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
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fprintf(errstream, "%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
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fprintf(errstream, "%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
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}
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ggml_free(model.ctx);
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if (params.use_color) {
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printf(ANSI_COLOR_RESET);
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fprintf(outstream, ANSI_COLOR_RESET);
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}
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return 0;
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5
llama.h
5
llama.h
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@ -64,5 +64,8 @@ int llama_main(
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gpt_vocab vocab,
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llama_model model,
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int64_t t_load_us,
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int64_t t_main_start_us);
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int64_t t_main_start_us,
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FILE *instream,
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FILE *outstream,
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FILE *errstream);
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bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx);
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2
main.cpp
2
main.cpp
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@ -56,5 +56,5 @@ int main(int argc, char ** argv) {
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params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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}
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return llama_main(params, vocab, model, t_main_start_us, t_load_us);
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return llama_main(params, vocab, model, t_main_start_us, t_load_us, stdin, stdout, stderr);
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}
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