/*-*-mode:c++;indent-tabs-mode:nil;c-basic-offset:4;tab-width:8;coding:utf-8-*-│ │vi: set net ft=c++ ts=4 sts=4 sw=4 fenc=utf-8 :vi│ ╚──────────────────────────────────────────────────────────────────────────────╝ │ │ │ llama.com │ │ Copyright (c) 2023 Justine Alexandra Roberts Tunney │ │ Copyright (c) 2023 Georgi Gerganov │ │ │ │ Permission is hereby granted, free of charge, to any person obtaining │ │ a copy of this software and associated documentation files (the │ │ "Software"), to deal in the Software without restriction, including │ │ without limitation the rights to use, copy, modify, merge, publish, │ │ distribute, sublicense, and/or sell copies of the Software, and to │ │ permit persons to whom the Software is furnished to do so, subject to │ │ the following conditions: │ │ │ │ The above copyright notice and this permission notice shall be │ │ included in all copies or substantial portions of the Software. │ │ │ │ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, │ │ EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF │ │ MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. │ │ IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY │ │ CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, │ │ TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE │ │ SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. │ │ │ ╚─────────────────────────────────────────────────────────────────────────────*/ #include "libc/assert.h" #include "libc/calls/calls.h" #include "libc/calls/struct/sigaction.h" #include "libc/calls/struct/stat.h" #include "libc/intrin/bits.h" #include "libc/log/log.h" #include "libc/nexgen32e/x86feature.h" #include "libc/stdio/stdio.h" #include "libc/sysv/consts/map.h" #include "libc/sysv/consts/msync.h" #include "libc/sysv/consts/o.h" #include "libc/sysv/consts/prot.h" #include "libc/sysv/consts/sig.h" #include "third_party/ggml/common.h" #include "third_party/ggml/llama.h" #include "third_party/ggml/llama_util.h" #include "third_party/libcxx/atomic" #include "third_party/libcxx/iostream" #include "third_party/libcxx/string" #include "third_party/libcxx/vector" asm(".ident\t\"\\n\\n\ llama.cpp (MIT License)\\n\ Copyright (c) 2023 Georgi Gerganov\""); asm(".include \"libc/disclaimer.inc\""); // clang-format off static std::atomic is_interacting; static std::atomic is_terminated; #define EPHEMERAL(fmt) "\r\e[K\033[1;35m" fmt " \033[0m" static void sigint_handler_batch(int signo) { is_terminated = true; } static void sigint_handler_interactive(int signo) { if (!is_interacting) { is_interacting = true; } else { is_terminated = true; } } static int CompareTime(struct timespec a, struct timespec b) { int cmp; if (!(cmp = (a.tv_sec > b.tv_sec) - (a.tv_sec < b.tv_sec))) { cmp = (a.tv_nsec > b.tv_nsec) - (a.tv_nsec < b.tv_nsec); } return cmp; } static int on_missing_feature(const char *name) { fprintf(stderr, "%s: error: cpuid %s not detected\n", __func__, name); fprintf(stderr, "%s: amd microprocessors made after 2017 usually work\n", __func__); fprintf(stderr, "%s: intel microprocessors made after 2013 usually work\n", __func__); return 1; } int main(int argc, char ** argv) { gpt_params params; ShowCrashReports(); setvbuf(stdin, NULL, _IONBF, 0); setvbuf(stdout, NULL, _IONBF, 0); setvbuf(stderr, NULL, _IONBF, 0); params.model = "models/llama-7B/ggml-model.bin"; #ifdef __x86_64__ if (!X86_HAVE(AVX2)) return on_missing_feature("avx2"); if (!X86_HAVE(AVX)) return on_missing_feature("avx"); if (!X86_HAVE(FMA)) return on_missing_feature("fma"); if (!X86_HAVE(SSE3)) return on_missing_feature("sse3"); if (!X86_HAVE(F16C)) { fprintf(stderr, "%s: warning: cpuid f16c not detected; inference might crash\n", __func__); } #endif /* __x86_64__ */ 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) static console_state con_st; 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); } if (params.seed < 0) { params.seed = time(NULL); } if (params.verbose) { 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; struct stat model_stat; // 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; } stat(params.model.c_str(), &model_stat); if (!params.lora_adapter.empty()) { int err = llama_apply_lora_from_file(ctx, params.lora_adapter.c_str(), params.lora_base.empty() ? NULL : params.lora_base.c_str(), params.n_threads); if (err != 0) { fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); return 1; } } // print system information if (params.verbose) { 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, 0); 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); } if (params.verbose) { llama_print_timings(ctx); } llama_free(ctx); return 0; } // Add a space in front of the first character to match OG llama tokenizer behavior params.prompt.insert(0, 1, ' '); // 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; } // number of tokens to keep when resetting context int n_keep = params.n_keep; if (n_keep < 0 || n_keep > (int)embd_inp.size() || params.instruct) { n_keep = (int)embd_inp.size(); } if (!n_keep && !params.n_keep_str.empty()) { auto pivot = ::llama_tokenize(ctx, params.n_keep_str, false); auto pos = std::search(embd_inp.begin(), embd_inp.end(), pivot.begin(), pivot.end()); if (pos == embd_inp.end()) { fprintf(stderr, "%s: error: --n_keep %`'s substring not found within prompt\n", __func__, params.n_keep_str.c_str()); return 1; } n_keep = (pos - embd_inp.begin()) + (pivot.end() - pivot.begin()); } // 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 %6d -> %`'s\n", i, embd_inp[i], llama_token_to_str(ctx, embd_inp[i])); } fprintf(stderr, "%s: first part of prompt: \"", __func__); for (int i = 0; i < n_keep; i++) { fprintf(stderr, "%'s", llama_token_to_str(ctx, embd_inp[i])); } fprintf(stderr, "\"\n"); fprintf(stderr, "%s: second part of prompt: \"", __func__); for (int i = n_keep; i < embd_inp.size(); i++) { fprintf(stderr, "%'s", llama_token_to_str(ctx, embd_inp[i])); } fprintf(stderr, "\"\n"); fprintf(stderr, "\n"); } // setup ctrl-c handler struct sigaction sa; sa.sa_flags = 0; sigemptyset(&sa.sa_mask); if (params.interactive) { sa.sa_handler = sigint_handler_interactive; } else { sa.sa_handler = sigint_handler_batch; } sigaction(SIGINT, &sa, NULL); if (params.interactive) { if (params.verbose) { fprintf(stderr, "%s: interactive mode on.\n", __func__); } if (params.verbose && params.antiprompt.size()) { for (auto antiprompt : params.antiprompt) { fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str()); } } if (params.verbose && !params.input_prefix.empty()) { fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str()); } } if (params.verbose) { fprintf(stderr, "sampling: 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); fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, 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.verbose && params.interactive) { fprintf(stderr, "== Running in interactive mode. ==\n" " - Press Ctrl+C to interject at any time.\n" " - Press Return to return control to LLaMa.\n" " - If you want to submit another line, end your input in '\\'.\n\n"); is_interacting = params.interactive_first; } const uint32_t kJtlpMagic = READ32LE("jtlp"); const uint32_t kJtlpVersion = 0; struct jtlp_header { uint8_t magic[4]; uint8_t version[4]; uint8_t state_size[8]; uint8_t model_dev[8]; uint8_t model_ino[8]; uint8_t model_mtim_sec[8]; uint8_t model_mtim_nsec[8]; uint8_t prompt_size[8]; }; enum jtlp_status { kPromptPending, kPromptCompleted, kPromptFinished }; enum jtlp_status prompt_status = kPromptPending; bool is_antiprompt = false; bool input_noecho = !params.verbose; int n_past = 0; int n_remain = params.n_predict; int n_consumed = 0; // instantly reload prompt if it's cached int fd = open(params.prompt_path.c_str(), O_RDONLY); if (fd != -1) { size_t state_size; size_t prompt_size; struct timespec mtim; struct jtlp_header *header; off_t rc = lseek(fd, 0, SEEK_END); LLAMA_ASSERT(rc != -1); void *map = MAP_FAILED; size_t file_size = rc; if (file_size < sizeof(header)) { fprintf(stderr, "%s: prompt file too small\n", params.prompt_path.c_str()); goto CantReloadPrompt; } map = mmap(0, file_size, PROT_READ, MAP_SHARED, fd, 0); if (map == MAP_FAILED) { fprintf(stderr, "%s: mmap failed: %s\n", params.prompt_path.c_str(), strerror(errno)); goto CantReloadPrompt; } header = (struct jtlp_header *)map; // check file format magic if (READ32LE(header->magic) != kJtlpMagic) { fprintf(stderr, "%s: prompt file has wrong magic\n", params.prompt_path.c_str()); goto CantReloadPrompt; } // check file format version if (READ32LE(header->version) > kJtlpVersion) { fprintf(stderr, "%s: prompt has future file format version\n", params.prompt_path.c_str()); goto CantReloadPrompt; } // check expected state size state_size = llama_get_state_size(ctx); if (READ64LE(header->state_size) != state_size) { if (params.verbose) { fprintf(stderr, "%s: prompt has stale data state size\n", params.prompt_path.c_str()); } goto CantReloadPrompt; } // check model device id if (READ64LE(header->model_dev) != model_stat.st_dev) { fprintf(stderr, "%s: prompt is for different model (dev)\n", params.prompt_path.c_str()); goto CantReloadPrompt; } // check model inode id if (READ64LE(header->model_ino) != model_stat.st_ino) { fprintf(stderr, "%s: prompt is for different model (ino)\n", params.prompt_path.c_str()); goto CantReloadPrompt; } // check model modified timestamp mtim.tv_sec = READ64LE(header->model_mtim_sec); mtim.tv_nsec = READ64LE(header->model_mtim_nsec); if (CompareTime(model_stat.st_mtim, mtim) > 0) { if (params.verbose) { fprintf(stderr, "%s: model file timestamp changed; will reload and regenerate prompt\n", params.prompt_path.c_str()); } goto CantReloadPrompt; } // check prompt file size prompt_size = READ64LE(header->prompt_size); if (sizeof(struct jtlp_header) + prompt_size + state_size > file_size) { fprintf(stderr, "%s: prompt file size unexpected\n", params.prompt_path.c_str()); goto CantReloadPrompt; } // check prompt textus if (prompt_size != params.prompt.size() || memcmp(header + 1, params.prompt.c_str(), prompt_size) != 0) { if (params.verbose) { fprintf(stderr, "%s: prompt text changed; will reload and regenerate\n", params.prompt_path.c_str()); } goto CantReloadPrompt; } // read the transformer state llama_set_state_data(ctx, (uint8_t *)(header + 1) + prompt_size); // we're finished loading the prompt file if (params.verbose) { fprintf(stderr, "%s: %s: reloaded previously saved prompt\n", __func__, params.prompt_path.c_str()); } // now setup the business logic llama_set_rng_seed(ctx, params.seed); while ((int) embd_inp.size() > n_consumed) { last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.push_back(embd_inp[n_consumed++]); } n_past = n_consumed; prompt_status = kPromptFinished; if (params.interactive) { is_interacting = true; for (std::string & antiprompt : params.antiprompt) { auto toks = ::llama_tokenize(ctx, antiprompt, false); if (std::equal(last_n_tokens.end() - toks.size(), last_n_tokens.end(), toks.begin(), toks.end())) { console_set_color(con_st, CONSOLE_COLOR_PROMPT); printf("%s", antiprompt.c_str()); fflush(stdout); break; } } console_set_color(con_st, CONSOLE_COLOR_USER_INPUT); } CantReloadPrompt: if (map != MAP_FAILED) { munmap(map, file_size); } close(fd); } if (prompt_status == kPromptPending && params.verbose) { // 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; if (prompt_status == kPromptPending && !params.verbose && con_st.use_color) { fprintf(stderr, EPHEMERAL("loading weights...")); } while ((n_remain != 0 || params.interactive) && !is_terminated) { // perform evaluation if (embd.size() > 0) { if (n_past + (int) embd.size() > n_ctx) { n_past = n_keep; embd.insert(embd.begin(), last_n_tokens.end() - (n_past - n_keep) / 2 - embd.size(), last_n_tokens.end() - embd.size()); } 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__); console_set_color(con_st, CONSOLE_COLOR_DEFAULT); return 1; } n_past += n_eval; if (prompt_status == kPromptPending && !params.verbose && con_st.use_color && embd_inp.size()) { fprintf(stderr, EPHEMERAL("loading prompt %d%% ..."), (int)(n_consumed / (double)embd_inp.size() * 100)); } } embd.clear(); } // save prompt to disk atomically as soon as it's finished loading bool was_completed = prompt_status == kPromptCompleted; if (was_completed && !params.prompt_path.empty()) { int fd = -1; int close_rc; uint8_t buf[8]; size_t file_size; size_t state_size; std::string tmppath; void *map = MAP_FAILED; struct jtlp_header header; if (!params.verbose && con_st.use_color) { fprintf(stderr, EPHEMERAL("caching prompt...")); } state_size = llama_get_state_size(ctx); WRITE32LE(header.magic, kJtlpMagic); WRITE32LE(header.version, kJtlpVersion); WRITE64LE(header.state_size, state_size); WRITE64LE(header.model_dev, model_stat.st_dev); WRITE64LE(header.model_ino, model_stat.st_ino); WRITE64LE(header.model_mtim_sec, model_stat.st_mtim.tv_sec); WRITE64LE(header.model_mtim_nsec, model_stat.st_mtim.tv_nsec); WRITE64LE(header.prompt_size, params.prompt.size()); file_size = sizeof(header) + params.prompt.size() + state_size; tmppath.append(params.prompt_path); tmppath.append(".XXXXXX"); fd = mkstemp(&tmppath[0]); if (fd == -1) { fprintf(stderr, "%s: mkstemp failed: %s\n", tmppath.c_str(), strerror(errno)); goto CouldNotSavePrompt; } if (ftruncate(fd, file_size)) { fprintf(stderr, "%s: ftruncate failed: %s\n", tmppath.c_str(), strerror(errno)); goto CouldNotSavePrompt; } map = mmap(0, file_size, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0); if (map == MAP_FAILED) { fprintf(stderr, "%s: mmap failed: %s\n", tmppath.c_str(), strerror(errno)); goto CouldNotSavePrompt; } llama_copy_state_data(ctx, (uint8_t *)map + sizeof(header) + params.prompt.size()); memcpy((uint8_t *)map + sizeof(header), params.prompt.c_str(), params.prompt.size()); memcpy(map, &header, sizeof(header)); if (msync(map, file_size, MS_ASYNC) && params.verbose) { fprintf(stderr, "%s: msync failed: %s\n", tmppath.c_str(), strerror(errno)); } if (munmap(map, file_size) && params.verbose) { fprintf(stderr, "%s: munmap failed: %s\n", tmppath.c_str(), strerror(errno)); } map = MAP_FAILED; close_rc = close(fd); fd = -1; if (close_rc) { fprintf(stderr, "%s: close failed: %s\n", tmppath.c_str(), strerror(errno)); goto CouldNotSavePrompt; } if (rename(tmppath.c_str(), params.prompt_path.c_str())) { fprintf(stderr, "%s -> %s: rename failed: %s\n", tmppath.c_str(), params.prompt_path.c_str(), strerror(errno)); goto CouldNotSavePrompt; } tmppath.clear(); CouldNotSavePrompt: if (map != MAP_FAILED) munmap(map, file_size); if (fd != -1) close(fd); if (!tmppath.empty()) unlink(tmppath.c_str()); } if (was_completed) { if (!params.verbose && con_st.use_color) { fprintf(stderr, EPHEMERAL("")); } if (params.interactive) { is_interacting = true; } prompt_status = kPromptFinished; if (params.interactive) { is_interacting = true; fflush(stdout); std::string last_output; for (auto id : last_n_tokens) { last_output += llama_token_to_str(ctx, id); } for (std::string & antiprompt : params.antiprompt) { if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) { console_set_color(con_st, CONSOLE_COLOR_PROMPT); printf("%s", antiprompt.c_str()); fflush(stdout); break; } } console_set_color(con_st, CONSOLE_COLOR_USER_INPUT); } } 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; 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); } } 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_noecho = false; // 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++]); if ((int) embd.size() >= params.n_batch) { break; } } // we've nearly finished loading the prompt if (prompt_status == kPromptPending && (int) embd_inp.size() <= n_consumed) { prompt_status = kPromptCompleted; } } // checks for reverse prompt // // 1. in interactive mode, this lets us detect when the llm is // prompting the user, so we can pause for input, e.g. // // --interactive // --prompt $'CompanionAI: How can I help you?\nHuman:' // --reverse-prompt 'Human:' // // 2. in normal mode, the reverse prompt can be used to specify // a custom EOS token, e.g. // // --prompt 'Question: How old are you?\nAnswer: ' // --reverse-prompt $'\n' // 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_antiprompt = true; break; } } if (is_antiprompt && !params.interactive) { printf("\n"); break; } } // display text if (!input_noecho) { for (auto id : embd) { printf("%s", llama_token_to_str(ctx, id)); } fflush(stdout); } if (prompt_status == kPromptCompleted) { continue; // avoid reading line before last token loads } // reset color to default if we there is no pending user input if (params.verbose && !input_noecho && (int)embd_inp.size() == n_consumed) { console_set_color(con_st, CONSOLE_COLOR_DEFAULT); } if (is_antiprompt) { is_interacting = true; console_set_color(con_st, CONSOLE_COLOR_USER_INPUT); fflush(stdout); } // 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) { if (n_past > 0 && is_interacting) { // potentially set color to indicate we are taking user input console_set_color(con_st, CONSOLE_COLOR_USER_INPUT); if (params.instruct) { printf("\n> "); } std::string buffer; if (!params.input_prefix.empty()) { buffer += params.input_prefix; printf("%s", buffer.c_str()); } // display a "waiting for input" indicator, just in case // the model doesn't halt on the antiprompt. if (con_st.use_color) { fprintf(stdout, "?\b"); fflush(stdout); } 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_noecho = true; // do not echo this again } if (n_past > 0) { is_interacting = false; } assert(!is_interacting); } // end of text token if (!embd.empty() && embd.back() == llama_token_eos()) { if (params.instruct) { is_interacting = true; } else if (params.verbose) { 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 (is_terminated) { console_cleanup(con_st); printf("\n"); if (params.verbose) { llama_print_timings(ctx); } _exit(128 + SIGINT); } if (params.verbose) { llama_print_timings(ctx); } llama_free(ctx); console_set_color(con_st, CONSOLE_COLOR_DEFAULT); return 0; }