// Defines sigaction on msys: #ifndef _GNU_SOURCE #define _GNU_SOURCE #endif #include "run-inference.h" #include "console.h" #include "grammar-parser.h" #include <cassert> #include <cinttypes> #include <cmath> #include <cstdio> #include <cstring> #include <ctime> #include <fstream> #include <iostream> #include <string> #include <vector> #if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) #include <signal.h> #include <unistd.h> #elif defined(_WIN32) #define WIN32_LEAN_AND_MEAN #ifndef NOMINMAX #define NOMINMAX #endif #include <signal.h> #include <windows.h> #endif #if defined(_MSC_VER) #pragma warning(disable : 4244 4267) // possible loss of data #endif 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(); printf("\n"); llama_print_timings(*g_ctx); _exit(130); } } } #endif int run_inference(gpt_params params, token_callback on_token = nullptr) { // save choice to use color for later // (note for later: this is a slightly awkward choice) console::init(params.simple_io, params.use_color); atexit([]() { console::cleanup(); }); 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.rope_freq_base != 10000.0) { fprintf( stderr, "%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base); } if (params.rope_freq_scale != 1.0) { fprintf(stderr, "%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale); } if (params.n_ctx > 2048) { // TODO: determine the actual max context of the model (e.g. 4096 for LLaMA // v2) and use that instead of 2048 fprintf(stderr, "%s: warning: base model only supports context sizes no greater " "than 2048 tokens (%d specified)\n", __func__, params.n_ctx); } else if (params.n_ctx < 8) { fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; } fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); if (params.random_prompt) { params.prompt = gpt_random_prompt(rng); } llama_backend_init(params.numa); llama_model *model; llama_context *ctx; llama_context *ctx_guidance = NULL; g_ctx = &ctx; // load the model and apply lora adapter, if any std::tie(model, ctx) = llama_init_from_gpt_params(params); if (params.cfg_scale > 1.f) { struct llama_context_params lparams = llama_context_params_from_gpt_params(params); ctx_guidance = llama_new_context_with_model(model, lparams); } if (model == 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_ctx parameters uncomment the "used_mem" line in llama.cpp to // see the results if (params.mem_test) { { fprintf(stderr, "%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx); const std::vector<llama_token> tmp(params.n_batch, llama_token_bos()); llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads); } llama_print_timings(ctx); llama_free(ctx); llama_free_model(model); return 0; } // export the cgraph and exit if (params.export_cgraph) { llama_eval_export(ctx, "llama.ggml"); llama_free(ctx); llama_free_model(model); return 0; } std::string path_session = params.path_prompt_cache; std::vector<llama_token> 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); llama_set_rng_seed(ctx, params.seed); 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 std::vector<llama_token> embd_inp; // Add a space in front of the first character to match OG llama tokenizer // behavior params.prompt.insert(0, 1, ' '); if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) { embd_inp = ::llama_tokenize(ctx, params.prompt, true); } else { embd_inp = session_tokens; } // Tokenize negative prompt std::vector<llama_token> guidance_inp; int guidance_offset = 0; int original_prompt_len = 0; if (ctx_guidance) { params.cfg_negative_prompt.insert(0, 1, ' '); guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, true); std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true); original_prompt_len = original_inp.size(); guidance_offset = (int)guidance_inp.size() - original_prompt_len; } 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 (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) { fprintf(stderr, "%s: using full prompt from session file\n", __func__); } else 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()); } } // if we will use the cache for the full prompt without reaching the end of // the cache, force reevaluation of the last token token to recalculate the // cached logits if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) { session_tokens.resize(embd_inp.size() - 1); } // 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 interactive start is specified if (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 (ctx_guidance) { fprintf(stderr, "\n"); fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str()); fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size()); for (int i = 0; i < (int)guidance_inp.size(); i++) { fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_str(ctx, guidance_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<PHANDLER_ROUTINE>(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_bos) { fprintf(stderr, "Input prefix with BOS\n"); } 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"); grammar_parser::parse_state parsed_grammar; llama_grammar *grammar = NULL; if (!params.grammar.empty()) { parsed_grammar = grammar_parser::parse(params.grammar.c_str()); // will be empty (default) if there are parse errors if (parsed_grammar.rules.empty()) { return 1; } fprintf(stderr, "%s: grammar:\n", __func__); grammar_parser::print_grammar(stderr, parsed_grammar); fprintf(stderr, "\n"); { auto it = params.logit_bias.find(llama_token_eos()); if (it != params.logit_bias.end() && it->second == -INFINITY) { fprintf(stderr, "%s: warning: EOS token is disabled, which will cause most " "grammars to fail\n", __func__); } } std::vector<const llama_grammar_element *> grammar_rules( parsed_grammar.c_rules()); grammar = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); } // TODO: replace with ring-buffer std::vector<llama_token> last_n_tokens(n_ctx); std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); if (params.interactive) { const char *control_message; if (params.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 = false; 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; int n_past_guidance = 0; // the first thing we will do is to output the prompt, so set color // accordingly console::set_display(console::prompt); std::vector<llama_token> embd; std::vector<llama_token> embd_guidance; // do one empty run to warm up the model { const std::vector<llama_token> tmp = { llama_token_bos(), }; llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); llama_reset_timings(ctx); } while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict if (embd.size() > 0) { // Note: n_ctx - 4 here is to match the logic for commandline prompt // handling via // --prompt or --file which uses the same value. auto max_embd_size = n_ctx - 4; // Ensure the input doesn't exceed the context size by truncating embd if // necessary. if ((int)embd.size() > max_embd_size) { auto skipped_tokens = embd.size() - max_embd_size; console::set_display(console::error); printf("<<input too long: skipped %zu token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); console::set_display(console::reset); fflush(stdout); embd.resize(max_embd_size); } // 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() + std::max<int>(0, guidance_offset) > 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); n_past_guidance = std::max(1, params.n_keep + guidance_offset); // 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 if (ctx_guidance) { int input_size = 0; llama_token *input_buf = NULL; if (n_past_guidance < (int)guidance_inp.size()) { // Guidance context should have the same data with these // modifications: // // * Replace the initial prompt // * Shift everything by guidance_offset embd_guidance = guidance_inp; if (embd.begin() + original_prompt_len < embd.end()) { embd_guidance.insert(embd_guidance.end(), embd.begin() + original_prompt_len, embd.end()); } input_buf = embd_guidance.data(); input_size = embd_guidance.size(); // fprintf(stderr, "\n---------------------\n"); // for (int i = 0; i < (int) embd_guidance.size(); i++) { // fprintf(stderr, "%s", llama_token_to_str(ctx, embd_guidance[i])); //} // fprintf(stderr, "\n---------------------\n"); } else { input_buf = embd.data(); input_size = embd.size(); } for (int i = 0; i < input_size; i += params.n_batch) { int n_eval = std::min(input_size - i, params.n_batch); if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, params.n_threads)) { fprintf(stderr, "%s : failed to eval\n", __func__); return 1; } n_past_guidance += n_eval; } } 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(); embd_guidance.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 && !params.prompt_cache_ro) { 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<llama_token_data> 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}; if (ctx_guidance) { llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale); } // 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 (grammar != NULL) { llama_sample_grammar(ctx, &candidates_p, grammar); } 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); if (grammar != NULL) { llama_grammar_accept_token(ctx, grammar, id); } last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.push_back(id); } // 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) { if (on_token != nullptr) { for (auto id : embd) { if (!on_token(llama_token_to_str(ctx, id))) { n_remain = 0; } } } } // reset color to default if we there is no pending user input if (input_echo && (int)embd_inp.size() == n_consumed) { console::set_display(console::reset); } // if not currently processing queued inputs; if ((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. If we're not running interactively, the reverse prompt might // be tokenized with some following characters so we'll compensate for // that by widening the search window a bit. for (std::string &antiprompt : params.antiprompt) { size_t extra_padding = params.interactive ? 0 : 2; size_t search_start_pos = last_output.length() > static_cast<size_t>(antiprompt.length() + extra_padding) ? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding) : 0; if (last_output.find(antiprompt.c_str(), search_start_pos) != std::string::npos) { if (params.interactive) { is_interacting = true; console::set_display(console::user_input); } is_antiprompt = true; fflush(stdout); break; } } } // deal with end of text token in interactive mode if (last_n_tokens.back() == llama_token_eos()) { if (params.interactive) { 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()); is_antiprompt = true; } is_interacting = true; printf("\n"); console::set_display(console::user_input); fflush(stdout); } else if (params.instruct) { is_interacting = true; } } if (n_past > 0 && is_interacting) { if (params.instruct) { printf("\n> "); } if (params.input_prefix_bos) { embd_inp.push_back(llama_token_bos()); } 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(line, params.multiline_input); buffer += line; } while (another_line); // done taking input, reset color console::set_display(console::reset); // 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) { if (is_interacting) { // reset grammar state if we're restarting generation if (grammar != NULL) { llama_grammar_free(grammar); std::vector<const llama_grammar_element *> grammar_rules( parsed_grammar.c_rules()); grammar = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); } } is_interacting = false; } } // end of text token if (!embd.empty() && embd.back() == llama_token_eos() && !(params.instruct || params.interactive)) { 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 && !params.prompt_cache_ro) { 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); if (ctx_guidance) { llama_free(ctx_guidance); } llama_free(ctx); llama_free_model(model); if (grammar != NULL) { llama_grammar_free(grammar); } llama_backend_free(); return 0; }