// Defines sigaction on msys: #ifndef _GNU_SOURCE #define _GNU_SOURCE #endif #include "common.h" #include "llama.h" #include "build-info.h" #include #include #include #include #include #include #include #include #include #include #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) #include #include #elif defined (_WIN32) #define WIN32_LEAN_AND_MEAN #define NOMINMAX #include #include #endif static console_state con_st; static llama_context ** g_ctx; static bool is_interacting = false; #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) void sigint_handler(int signo) { if (signo == SIGINT) { if (!is_interacting) { is_interacting=true; } else { console_cleanup(con_st); printf("\n"); llama_print_timings(*g_ctx); _exit(130); } } } #endif int main(int argc, char ** argv) { gpt_params params; params.model = "models/llama-7B/ggml-model.bin"; if (gpt_params_parse(argc, argv, params) == false) { return 1; } // save choice to use color for later // (note for later: this is a slightly awkward choice) con_st.use_color = params.use_color; con_st.multiline_input = params.multiline_input; console_init(con_st); atexit([]() { console_cleanup(con_st); }); if (params.perplexity) { printf("\n************\n"); printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__); printf("************\n\n"); return 0; } if (params.embedding) { printf("\n************\n"); printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__); printf("************\n\n"); return 0; } if (params.n_ctx > 2048) { fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);" "expect poor results\n", __func__, params.n_ctx); } fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); if (params.seed < 0) { params.seed = time(NULL); } fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); std::mt19937 rng(params.seed); if (params.random_prompt) { params.prompt = gpt_random_prompt(rng); } // params.prompt = R"(// this function checks if the number n is prime //bool is_prime(int n) {)"; llama_context * ctx; g_ctx = &ctx; // load the model and apply lora adapter, if any ctx = llama_init_from_gpt_params(params); if (ctx == NULL) { fprintf(stderr, "%s: error: unable to load model\n", __func__); return 1; } // print system information { fprintf(stderr, "\n"); fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } // determine the maximum memory usage needed to do inference for the given n_batch and n_predict parameters // uncomment the "used_mem" line in llama.cpp to see the results if (params.mem_test) { { const std::vector tmp(params.n_batch, llama_token_bos()); llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); } { const std::vector tmp = { 0, }; llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1, params.n_threads); } llama_print_timings(ctx); llama_free(ctx); return 0; } if (params.steering_add.size() || params.steering_sub.size()) { auto steering_add_tokens = ::llama_tokenize(ctx, params.steering_add, true); auto steering_sub_tokens = ::llama_tokenize(ctx, params.steering_sub, true); if (steering_add_tokens.size() != steering_sub_tokens.size()) { llama_token space; llama_tokenize(ctx, " ", &space, 1, 0); while (steering_add_tokens.size() < steering_sub_tokens.size()) steering_add_tokens.push_back(space); while (steering_sub_tokens.size() < steering_add_tokens.size()) steering_sub_tokens.push_back(space); } llama_set_steering_write(ctx, params.steering_lyr, params.steering_mul/2); llama_eval(ctx, steering_add_tokens.data(), std::min((int)steering_add_tokens.size(), params.n_ctx), 0, params.n_threads); llama_set_steering_write(ctx, params.steering_lyr, -params.steering_mul/2); llama_eval(ctx, steering_sub_tokens.data(), std::min((int)steering_sub_tokens.size(), params.n_ctx), 0, params.n_threads); llama_set_steering_read(ctx, params.steering_lyr, 1); } // Add a space in front of the first character to match OG llama tokenizer behavior params.prompt.insert(0, 1, ' '); std::string path_session = params.path_prompt_cache; std::vector session_tokens; if (!path_session.empty()) { fprintf(stderr, "%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str()); // fopen to check for existing session FILE * fp = std::fopen(path_session.c_str(), "rb"); if (fp != NULL) { std::fclose(fp); session_tokens.resize(params.n_ctx); size_t n_token_count_out = 0; if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) { fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str()); return 1; } session_tokens.resize(n_token_count_out); fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size()); } else { fprintf(stderr, "%s: session file does not exist, will create\n", __func__); } } // tokenize the prompt auto embd_inp = ::llama_tokenize(ctx, params.prompt, true); const int n_ctx = llama_n_ctx(ctx); if ((int) embd_inp.size() > n_ctx - 4) { fprintf(stderr, "%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); return 1; } // debug message about similarity of saved session, if applicable size_t n_matching_session_tokens = 0; if (session_tokens.size()) { for (llama_token id : session_tokens) { if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) { break; } n_matching_session_tokens++; } if (n_matching_session_tokens >= embd_inp.size()) { fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__); } else if (n_matching_session_tokens < (embd_inp.size() / 2)) { fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n", __func__, n_matching_session_tokens, embd_inp.size()); } else { fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n", __func__, n_matching_session_tokens, embd_inp.size()); } } // number of tokens to keep when resetting context if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct) { params.n_keep = (int)embd_inp.size(); } // prefix & suffix for instruct mode const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true); const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false); // in instruct mode, we inject a prefix and a suffix to each input by the user if (params.instruct) { params.interactive_first = true; params.antiprompt.push_back("### Instruction:\n\n"); } // enable interactive mode if reverse prompt or interactive start is specified if (params.antiprompt.size() != 0 || params.interactive_first) { params.interactive = true; } // determine newline token auto llama_token_newline = ::llama_tokenize(ctx, "\n", false); if (params.verbose_prompt) { fprintf(stderr, "\n"); fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str()); fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); for (int i = 0; i < (int) embd_inp.size(); i++) { fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i])); } if (params.n_keep > 0) { fprintf(stderr, "%s: static prompt based on n_keep: '", __func__); for (int i = 0; i < params.n_keep; i++) { fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i])); } fprintf(stderr, "'\n"); } fprintf(stderr, "\n"); } if (params.interactive) { #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) struct sigaction sigint_action; sigint_action.sa_handler = sigint_handler; sigemptyset (&sigint_action.sa_mask); sigint_action.sa_flags = 0; sigaction(SIGINT, &sigint_action, NULL); #elif defined (_WIN32) auto console_ctrl_handler = [](DWORD ctrl_type) -> BOOL { return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false; }; SetConsoleCtrlHandler(static_cast(console_ctrl_handler), true); #endif fprintf(stderr, "%s: interactive mode on.\n", __func__); if (params.antiprompt.size()) { for (auto antiprompt : params.antiprompt) { fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str()); } } if (!params.input_prefix.empty()) { fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str()); } if (!params.input_suffix.empty()) { fprintf(stderr, "Input suffix: '%s'\n", params.input_suffix.c_str()); } } fprintf(stderr, "sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n", params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau); fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); fprintf(stderr, "\n\n"); // TODO: replace with ring-buffer std::vector last_n_tokens(n_ctx); std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); if (params.interactive) { const char *control_message; if (con_st.multiline_input) { control_message = " - To return control to LLaMa, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { control_message = " - Press Return to return control to LLaMa.\n" " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } fprintf(stderr, "== Running in interactive mode. ==\n" #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) " - Press Ctrl+C to interject at any time.\n" #endif "%s\n", control_message); is_interacting = params.interactive_first; } bool is_antiprompt = false; bool input_echo = true; bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size(); int n_past = 0; int n_remain = params.n_predict; int n_consumed = 0; int n_session_consumed = 0; // the first thing we will do is to output the prompt, so set color accordingly console_set_color(con_st, CONSOLE_COLOR_PROMPT); std::vector embd; while (n_remain != 0 || params.interactive) { // predict if (embd.size() > 0) { // infinite text generation via context swapping // if we run out of context: // - take the n_keep first tokens from the original prompt (via n_past) // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches if (n_past + (int) embd.size() > n_ctx) { const int n_left = n_past - params.n_keep; // always keep the first token - BOS n_past = std::max(1, params.n_keep); // insert n_left/2 tokens at the start of embd from last_n_tokens embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size()); // stop saving session if we run out of context path_session.clear(); //printf("\n---\n"); //printf("resetting: '"); //for (int i = 0; i < (int) embd.size(); i++) { // printf("%s", llama_token_to_str(ctx, embd[i])); //} //printf("'\n"); //printf("\n---\n"); } // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past) if (n_session_consumed < (int) session_tokens.size()) { size_t i = 0; for ( ; i < embd.size(); i++) { if (embd[i] != session_tokens[n_session_consumed]) { session_tokens.resize(n_session_consumed); break; } n_past++; n_session_consumed++; if (n_session_consumed >= (int) session_tokens.size()) { ++i; break; } } if (i > 0) { embd.erase(embd.begin(), embd.begin() + i); } } // evaluate tokens in batches // embd is typically prepared beforehand to fit within a batch, but not always for (int i = 0; i < (int) embd.size(); i += params.n_batch) { int n_eval = (int) embd.size() - i; if (n_eval > params.n_batch) { n_eval = params.n_batch; } if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) { fprintf(stderr, "%s : failed to eval\n", __func__); return 1; } n_past += n_eval; } if (embd.size() > 0 && !path_session.empty()) { session_tokens.insert(session_tokens.end(), embd.begin(), embd.end()); n_session_consumed = session_tokens.size(); } } embd.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { // out of user input, sample next token const float temp = params.temp; const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; const float top_p = params.top_p; const float tfs_z = params.tfs_z; const float typical_p = params.typical_p; const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; const float repeat_penalty = params.repeat_penalty; const float alpha_presence = params.presence_penalty; const float alpha_frequency = params.frequency_penalty; const int mirostat = params.mirostat; const float mirostat_tau = params.mirostat_tau; const float mirostat_eta = params.mirostat_eta; const bool penalize_nl = params.penalize_nl; // optionally save the session on first sample (for faster prompt loading next time) if (!path_session.empty() && need_to_save_session) { need_to_save_session = false; llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); } llama_token id = 0; { auto logits = llama_get_logits(ctx); auto n_vocab = llama_n_vocab(ctx); // Apply params.logit_bias map for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { logits[it->first] += it->second; } std::vector candidates; candidates.reserve(n_vocab); for (llama_token token_id = 0; token_id < n_vocab; token_id++) { candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); } llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; // Apply penalties float nl_logit = logits[llama_token_nl()]; auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx); llama_sample_repetition_penalty(ctx, &candidates_p, last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, last_n_repeat, repeat_penalty); llama_sample_frequency_and_presence_penalties(ctx, &candidates_p, last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, last_n_repeat, alpha_frequency, alpha_presence); if (!penalize_nl) { logits[llama_token_nl()] = nl_logit; } if (temp <= 0) { // Greedy sampling id = llama_sample_token_greedy(ctx, &candidates_p); } else { if (mirostat == 1) { static float mirostat_mu = 2.0f * mirostat_tau; const int mirostat_m = 100; llama_sample_temperature(ctx, &candidates_p, temp); id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); } else if (mirostat == 2) { static float mirostat_mu = 2.0f * mirostat_tau; llama_sample_temperature(ctx, &candidates_p, temp); id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); } else { // Temperature sampling llama_sample_top_k(ctx, &candidates_p, top_k, 1); llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1); llama_sample_typical(ctx, &candidates_p, typical_p, 1); llama_sample_top_p(ctx, &candidates_p, top_p, 1); llama_sample_temperature(ctx, &candidates_p, temp); id = llama_sample_token(ctx, &candidates_p); } } // printf("`%d`", candidates_p.size); last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.push_back(id); } // replace end of text token with newline token when in interactive mode if (id == llama_token_eos() && params.interactive && !params.instruct) { id = llama_token_newline.front(); if (params.antiprompt.size() != 0) { // tokenize and inject first reverse prompt const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false); embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); } } // add it to the context embd.push_back(id); // echo this to console input_echo = true; // decrement remaining sampling budget --n_remain; } else { // some user input remains from prompt or interaction, forward it to processing while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.push_back(embd_inp[n_consumed]); ++n_consumed; if ((int) embd.size() >= params.n_batch) { break; } } } // display text if (input_echo) { for (auto id : embd) { printf("%s", llama_token_to_str(ctx, id)); } fflush(stdout); } // reset color to default if we there is no pending user input if (input_echo && (int)embd_inp.size() == n_consumed) { console_set_color(con_st, CONSOLE_COLOR_DEFAULT); } // in interactive mode, and not currently processing queued inputs; // check if we should prompt the user for more if (params.interactive && (int) embd_inp.size() <= n_consumed) { // check for reverse prompt if (params.antiprompt.size()) { std::string last_output; for (auto id : last_n_tokens) { last_output += llama_token_to_str(ctx, id); } is_antiprompt = false; // Check if each of the reverse prompts appears at the end of the output. for (std::string & antiprompt : params.antiprompt) { if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) { is_interacting = true; is_antiprompt = true; break; } } } if (n_past > 0 && is_interacting) { if (params.instruct) { printf("\n> "); } std::string buffer; if (!params.input_prefix.empty()) { buffer += params.input_prefix; printf("%s", buffer.c_str()); } std::string line; bool another_line = true; do { another_line = console_readline(con_st, line); buffer += line; } while (another_line); // done taking input, reset color console_set_color(con_st, CONSOLE_COLOR_DEFAULT); // Add tokens to embd only if the input buffer is non-empty // Entering a empty line lets the user pass control back if (buffer.length() > 1) { // append input suffix if any if (!params.input_suffix.empty()) { buffer += params.input_suffix; printf("%s", params.input_suffix.c_str()); } // instruct mode: insert instruction prefix if (params.instruct && !is_antiprompt) { n_consumed = embd_inp.size(); embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end()); } auto line_inp = ::llama_tokenize(ctx, buffer, false); embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); // instruct mode: insert response suffix if (params.instruct) { embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); } n_remain -= line_inp.size(); } input_echo = false; // do not echo this again } if (n_past > 0) { is_interacting = false; } } // end of text token if (!embd.empty() && embd.back() == llama_token_eos()) { if (params.instruct) { is_interacting = true; } else { fprintf(stderr, " [end of text]\n"); break; } } // In interactive mode, respect the maximum number of tokens and drop back to user input when reached. if (params.interactive && n_remain <= 0 && params.n_predict != -1) { n_remain = params.n_predict; is_interacting = true; } } if (!path_session.empty() && params.prompt_cache_all) { fprintf(stderr, "\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str()); llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); } llama_print_timings(ctx); llama_free(ctx); return 0; }