#include "utils.hpp" #include "common.h" #include "json-schema-to-grammar.h" #include "llama.h" #include "grammar-parser.h" #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error #define CPPHTTPLIB_NO_EXCEPTIONS 1 #endif // increase max payload length to allow use of larger context size #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576 #include "httplib.h" #include "json.hpp" // auto generated files (update with ./deps.sh) #include "index.html.hpp" #include "index.js.hpp" #include "completion.js.hpp" #include "json-schema-to-grammar.mjs.hpp" #include #include #include #include #include #include #include #include #include using json = nlohmann::ordered_json; bool server_verbose = false; bool server_log_json = true; enum stop_type { STOP_TYPE_FULL, STOP_TYPE_PARTIAL, }; enum slot_state { SLOT_STATE_IDLE, SLOT_STATE_PROCESSING, }; enum slot_command { SLOT_COMMAND_NONE, SLOT_COMMAND_LOAD_PROMPT, SLOT_COMMAND_RELEASE, }; enum server_state { SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet SERVER_STATE_READY, // Server is ready and model is loaded SERVER_STATE_ERROR // An error occurred, load_model failed }; enum server_task_type { SERVER_TASK_TYPE_COMPLETION, SERVER_TASK_TYPE_CANCEL, SERVER_TASK_TYPE_NEXT_RESPONSE, SERVER_TASK_TYPE_METRICS, SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE, }; struct server_task { int id = -1; // to be filled by server_queue int id_multi = -1; int id_target = -1; server_task_type type; json data; bool infill = false; bool embedding = false; }; struct server_task_result { int id = -1; int id_multi = -1; json data; bool stop; bool error; }; struct server_task_multi { int id = -1; std::set subtasks_remaining; std::vector results; }; struct slot_params { bool stream = true; bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt uint32_t seed = -1; // RNG seed int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half int32_t n_predict = -1; // new tokens to predict std::vector antiprompt; json input_prefix; json input_suffix; }; struct server_params { int32_t port = 8080; int32_t read_timeout = 600; int32_t write_timeout = 600; int32_t n_threads_http = -1; std::string hostname = "127.0.0.1"; std::string public_path = ""; std::string chat_template = ""; std::string system_prompt = ""; std::vector api_keys; #ifdef CPPHTTPLIB_OPENSSL_SUPPORT std::string ssl_key_file = ""; std::string ssl_cert_file = ""; #endif bool slots_endpoint = true; bool metrics_endpoint = false; std::string slot_save_path; }; struct server_slot { int id; int id_task = -1; int id_multi = -1; struct slot_params params; slot_state state = SLOT_STATE_IDLE; slot_command command = SLOT_COMMAND_NONE; // used to determine the slot that has been used the longest int64_t t_last_used = -1; // generation props int32_t n_ctx = 0; // context size per slot int32_t n_past = 0; int32_t n_decoded = 0; int32_t n_remaining = -1; int32_t i_batch = -1; int32_t n_predict = -1; // TODO: disambiguate from params.n_predict int32_t n_prompt_tokens = 0; int32_t n_prompt_tokens_processed = 0; json prompt; // when a task is submitted, we first tokenize the prompt and store it here std::vector prompt_tokens; std::string generated_text; std::vector cache_tokens; std::vector generated_token_probs; bool infill = false; bool embedding = false; bool has_next_token = true; bool truncated = false; bool stopped_eos = false; bool stopped_word = false; bool stopped_limit = false; bool oaicompat = false; std::string oaicompat_model; std::string stopping_word; // sampling llama_token sampled; struct llama_sampling_params sparams; llama_sampling_context * ctx_sampling = nullptr; json json_schema; int32_t ga_i = 0; // group-attention state int32_t ga_n = 1; // group-attention factor int32_t ga_w = 512; // group-attention width int32_t n_past_se = 0; // self-extend // stats size_t n_sent_text = 0; // number of sent text character size_t n_sent_token_probs = 0; int64_t t_start_process_prompt; int64_t t_start_generation; double t_prompt_processing; // ms double t_token_generation; // ms void reset() { n_prompt_tokens = 0; generated_text = ""; truncated = false; stopped_eos = false; stopped_word = false; stopped_limit = false; stopping_word = ""; n_past = 0; n_sent_text = 0; n_sent_token_probs = 0; infill = false; ga_i = 0; n_past_se = 0; generated_token_probs.clear(); } bool has_budget(gpt_params &global_params) { if (params.n_predict == -1 && global_params.n_predict == -1) { return true; // limitless } n_remaining = -1; if (params.n_predict != -1) { n_remaining = params.n_predict - n_decoded; } else if (global_params.n_predict != -1) { n_remaining = global_params.n_predict - n_decoded; } return n_remaining > 0; // no budget } bool available() const { return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE; } bool is_processing() const { return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING; } void add_token_string(const completion_token_output & token) { if (command == SLOT_COMMAND_RELEASE) { return; } generated_token_probs.push_back(token); } void release() { if (state == SLOT_STATE_PROCESSING) { t_token_generation = (ggml_time_us() - t_start_generation) / 1e3; command = SLOT_COMMAND_RELEASE; } } json get_formated_timings() const { return json { {"prompt_n", n_prompt_tokens_processed}, {"prompt_ms", t_prompt_processing}, {"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed}, {"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed}, {"predicted_n", n_decoded}, {"predicted_ms", t_token_generation}, {"predicted_per_token_ms", t_token_generation / n_decoded}, {"predicted_per_second", 1e3 / t_token_generation * n_decoded}, }; } size_t find_stopping_strings(const std::string & text, const size_t last_token_size, const stop_type type) { size_t stop_pos = std::string::npos; for (const std::string & word : params.antiprompt) { size_t pos; if (type == STOP_TYPE_FULL) { const size_t tmp = word.size() + last_token_size; const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; pos = text.find(word, from_pos); } else { pos = find_partial_stop_string(word, text); } if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) { if (type == STOP_TYPE_FULL) { stopped_word = true; stopping_word = word; has_next_token = false; } stop_pos = pos; } } return stop_pos; } void print_timings() const { char buffer[512]; double t_token = t_prompt_processing / n_prompt_tokens_processed; double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; snprintf(buffer, 512, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", t_prompt_processing, n_prompt_tokens_processed, t_token, n_tokens_second); LOG_INFO(buffer, { {"id_slot", id}, {"id_task", id_task}, {"t_prompt_processing", t_prompt_processing}, {"n_prompt_tokens_processed", n_prompt_tokens_processed}, {"t_token", t_token}, {"n_tokens_second", n_tokens_second}, }); t_token = t_token_generation / n_decoded; n_tokens_second = 1e3 / t_token_generation * n_decoded; snprintf(buffer, 512, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)", t_token_generation, n_decoded, t_token, n_tokens_second); LOG_INFO(buffer, { {"id_slot", id}, {"id_task", id_task}, {"t_token_generation", t_token_generation}, {"n_decoded", n_decoded}, {"t_token", t_token}, {"n_tokens_second", n_tokens_second}, }); snprintf(buffer, 512, " total time = %10.2f ms", t_prompt_processing + t_token_generation); LOG_INFO(buffer, { {"id_slot", id}, {"id_task", id_task}, {"t_prompt_processing", t_prompt_processing}, {"t_token_generation", t_token_generation}, {"t_total", t_prompt_processing + t_token_generation}, }); } }; struct server_metrics { int64_t t_start = 0; uint64_t n_prompt_tokens_processed_total = 0; uint64_t t_prompt_processing_total = 0; uint64_t n_tokens_predicted_total = 0; uint64_t t_tokens_generation_total = 0; uint64_t n_prompt_tokens_processed = 0; uint64_t t_prompt_processing = 0; uint64_t n_tokens_predicted = 0; uint64_t t_tokens_generation = 0; void init() { t_start = ggml_time_us(); } void on_prompt_eval(const server_slot & slot) { n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; n_prompt_tokens_processed += slot.n_prompt_tokens_processed; t_prompt_processing += slot.t_prompt_processing; t_prompt_processing_total += slot.t_prompt_processing; } void on_prediction(const server_slot & slot) { n_tokens_predicted_total += slot.n_decoded; n_tokens_predicted += slot.n_decoded; t_tokens_generation += slot.t_token_generation; t_tokens_generation_total += slot.t_token_generation; } void reset_bucket() { n_prompt_tokens_processed = 0; t_prompt_processing = 0; n_tokens_predicted = 0; t_tokens_generation = 0; } }; struct server_queue { int id = 0; bool running; // queues std::vector queue_tasks; std::vector queue_tasks_deferred; std::vector queue_multitasks; std::mutex mutex_tasks; std::condition_variable condition_tasks; // callback functions std::function callback_new_task; std::function callback_finish_multitask; std::function callback_update_slots; // Add a new task to the end of the queue int post(server_task task) { std::unique_lock lock(mutex_tasks); if (task.id == -1) { task.id = id++; LOG_VERBOSE("new task id", {{"new_id", task.id}}); } queue_tasks.push_back(std::move(task)); condition_tasks.notify_one(); return task.id; } // Add a new task, but defer until one slot is available void defer(server_task task) { std::unique_lock lock(mutex_tasks); queue_tasks_deferred.push_back(std::move(task)); } // Get the next id for creating anew task int get_new_id() { std::unique_lock lock(mutex_tasks); int new_id = id++; LOG_VERBOSE("new task id", {{"new_id", new_id}}); return new_id; } // Register function to process a new task void on_new_task(std::function callback) { callback_new_task = std::move(callback); } // Register function to process a multitask when it is finished void on_finish_multitask(std::function callback) { callback_finish_multitask = std::move(callback); } // Register the function to be called when all slots data is ready to be processed void on_update_slots(std::function callback) { callback_update_slots = std::move(callback); } // Call when the state of one slot is changed void notify_slot_changed() { // move deferred tasks back to main loop std::unique_lock lock(mutex_tasks); for (auto & task : queue_tasks_deferred) { queue_tasks.push_back(std::move(task)); } queue_tasks_deferred.clear(); } // end the start_loop routine void terminate() { std::unique_lock lock(mutex_tasks); running = false; condition_tasks.notify_all(); } /** * Main loop consists of these steps: * - Wait until a new task arrives * - Process the task (i.e. maybe copy data into slot) * - Check if multitask is finished * - Update all slots */ void start_loop() { running = true; while (true) { LOG_VERBOSE("new task may arrive", {}); while (true) { std::unique_lock lock(mutex_tasks); if (queue_tasks.empty()) { lock.unlock(); break; } server_task task = queue_tasks.front(); queue_tasks.erase(queue_tasks.begin()); lock.unlock(); LOG_VERBOSE("callback_new_task", {{"id_task", task.id}}); callback_new_task(task); } LOG_VERBOSE("update_multitasks", {}); // check if we have any finished multitasks auto queue_iterator = queue_multitasks.begin(); while (queue_iterator != queue_multitasks.end()) { if (queue_iterator->subtasks_remaining.empty()) { // all subtasks done == multitask is done server_task_multi current_multitask = *queue_iterator; callback_finish_multitask(current_multitask); // remove this multitask queue_iterator = queue_multitasks.erase(queue_iterator); } else { ++queue_iterator; } } // all tasks in the current loop is processed, slots data is now ready LOG_VERBOSE("callback_update_slots", {}); callback_update_slots(); LOG_VERBOSE("wait for new task", {}); { std::unique_lock lock(mutex_tasks); if (queue_tasks.empty()) { if (!running) { LOG_VERBOSE("ending start_loop", {}); return; } condition_tasks.wait(lock, [&]{ return (!queue_tasks.empty() || !running); }); } } } } // // functions to manage multitasks // // add a multitask by specifying the id of all subtask (subtask is a server_task) void add_multitask(int id_multi, std::vector & sub_ids) { std::lock_guard lock(mutex_tasks); server_task_multi multi; multi.id = id_multi; std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); queue_multitasks.push_back(multi); } // updatethe remaining subtasks, while appending results to multitask void update_multitask(int id_multi, int id_sub, server_task_result & result) { std::lock_guard lock(mutex_tasks); for (auto & multitask : queue_multitasks) { if (multitask.id == id_multi) { multitask.subtasks_remaining.erase(id_sub); multitask.results.push_back(result); } } } }; struct server_response { typedef std::function callback_multitask_t; callback_multitask_t callback_update_multitask; // for keeping track of all tasks waiting for the result std::set waiting_task_ids; // the main result queue std::vector queue_results; std::mutex mutex_results; std::condition_variable condition_results; // add the id_task to the list of tasks waiting for response void add_waiting_task_id(int id_task) { LOG_VERBOSE("waiting for task id", {{"id_task", id_task}}); std::unique_lock lock(mutex_results); waiting_task_ids.insert(id_task); } // when the request is finished, we can remove task associated with it void remove_waiting_task_id(int id_task) { LOG_VERBOSE("remove waiting for task id", {{"id_task", id_task}}); std::unique_lock lock(mutex_results); waiting_task_ids.erase(id_task); } // This function blocks the thread until there is a response for this id_task server_task_result recv(int id_task) { while (true) { std::unique_lock lock(mutex_results); condition_results.wait(lock, [&]{ return !queue_results.empty(); }); for (int i = 0; i < (int) queue_results.size(); i++) { if (queue_results[i].id == id_task) { assert(queue_results[i].id_multi == -1); server_task_result res = queue_results[i]; queue_results.erase(queue_results.begin() + i); return res; } } } // should never reach here } // Register the function to update multitask void on_multitask_update(callback_multitask_t callback) { callback_update_multitask = std::move(callback); } // Send a new result to a waiting id_task void send(server_task_result result) { LOG_VERBOSE("send new result", {{"id_task", result.id}}); std::unique_lock lock(mutex_results); for (const auto & id_task : waiting_task_ids) { // LOG_TEE("waiting task id %i \n", id_task); // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result if (result.id_multi == id_task) { LOG_VERBOSE("callback_update_multitask", {{"id_task", id_task}}); callback_update_multitask(id_task, result.id, result); continue; } if (result.id == id_task) { LOG_VERBOSE("queue_results.push_back", {{"id_task", id_task}}); queue_results.push_back(result); condition_results.notify_all(); return; } } } }; struct server_context { llama_model * model = nullptr; llama_context * ctx = nullptr; gpt_params params; llama_batch batch; bool clean_kv_cache = true; bool add_bos_token = true; int32_t n_ctx; // total context for all clients / slots // system prompt bool system_need_update = false; std::string system_prompt; std::vector system_tokens; std::string name_user; // this should be the antiprompt std::string name_assistant; // slots / clients std::vector slots; json default_generation_settings_for_props; server_queue queue_tasks; server_response queue_results; server_metrics metrics; ~server_context() { if (ctx) { llama_free(ctx); ctx = nullptr; } if (model) { llama_free_model(model); model = nullptr; } } bool load_model(const gpt_params & params_) { params = params_; // dedicate one sequence to the system prompt params.n_parallel += 1; std::tie(model, ctx) = llama_init_from_gpt_params(params); params.n_parallel -= 1; // but be sneaky about it if (model == nullptr) { LOG_ERROR("unable to load model", {{"model", params.model}}); return false; } n_ctx = llama_n_ctx(ctx); add_bos_token = llama_should_add_bos_token(model); GGML_ASSERT(llama_add_eos_token(model) != 1); return true; } bool validate_model_chat_template() const { llama_chat_message chat[] = {{"user", "test"}}; const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0); return res > 0; } void init() { const int32_t n_ctx_slot = n_ctx / params.n_parallel; LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}}); for (int i = 0; i < params.n_parallel; i++) { server_slot slot; slot.id = i; slot.n_ctx = n_ctx_slot; slot.n_predict = params.n_predict; LOG_INFO("new slot", { {"id_slot", slot.id}, {"n_ctx_slot", slot.n_ctx} }); const int ga_n = params.grp_attn_n; const int ga_w = params.grp_attn_w; if (ga_n != 1) { GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT //GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT LOG_INFO("slot self-extend", { {"id_slot", slot.id}, {"ga_n", ga_n}, {"ga_w", ga_w} }); } slot.ga_i = 0; slot.ga_n = ga_n; slot.ga_w = ga_w; slot.reset(); slots.push_back(slot); } default_generation_settings_for_props = get_formated_generation(slots.front()); default_generation_settings_for_props["seed"] = -1; // the update_slots() logic will always submit a maximum of n_batch tokens // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used) { const int32_t n_batch = llama_n_batch(ctx); // only a single seq_id per token is needed batch = llama_batch_init(n_batch, 0, 1); } metrics.init(); } std::vector tokenize(const json & json_prompt, bool add_special) const { // TODO: currently, we tokenize using special tokens by default // this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216) // but it's better compared to completely ignoring ChatML and other chat templates const bool TMP_FORCE_SPECIAL = true; // If `add_bos` is true, we only add BOS, when json_prompt is a string, // or the first element of the json_prompt array is a string. std::vector prompt_tokens; if (json_prompt.is_array()) { bool first = true; for (const auto & p : json_prompt) { if (p.is_string()) { auto s = p.template get(); std::vector p; if (first) { p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL); first = false; } else { p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL); } prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); } else { if (first) { first = false; } prompt_tokens.push_back(p.template get()); } } } else { auto s = json_prompt.template get(); prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL); } return prompt_tokens; } server_slot * get_slot(int id) { int64_t t_last = ggml_time_us(); server_slot * last_used = nullptr; for (server_slot & slot : slots) { if (slot.id == id && slot.available()) { return &slot; } // among all available slots, find the one that has been least recently used if (slot.available() && slot.t_last_used < t_last) { last_used = &slot; t_last = slot.t_last_used; } } return last_used; } bool launch_slot_with_task(server_slot & slot, const server_task & task) { slot_params default_params; llama_sampling_params default_sparams; auto & data = task.data; if (data.count("__oaicompat") != 0) { slot.oaicompat = true; slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); } else { slot.oaicompat = false; slot.oaicompat_model = ""; } slot.params.stream = json_value(data, "stream", false); slot.params.cache_prompt = json_value(data, "cache_prompt", false); slot.params.n_predict = json_value(data, "n_predict", default_params.n_predict); slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k); slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p); slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); slot.sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); slot.sparams.temp = json_value(data, "temperature", default_sparams.temp); slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep); slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard); slot.sparams.seed = json_value(data, "seed", default_sparams.seed); slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); // process "json_schema" and "grammar" if (data.contains("json_schema") && !data["json_schema"].is_null() && data.contains("grammar") && !data["grammar"].is_null()) { send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST); return false; } else if (data.contains("json_schema") && !data.contains("grammar")) { try { auto schema = json_value(data, "json_schema", json::object()); slot.sparams.grammar = json_schema_to_grammar(schema); } catch (const std::exception & e) { send_error(task, std::string("\"json_schema\": ") + e.what(), ERROR_TYPE_INVALID_REQUEST); return false; } } else { slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar); } if (slot.params.cache_prompt && slot.ga_n != 1) { LOG_WARNING("cache_prompt is not supported with group-attention", {}); slot.params.cache_prompt = false; } if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) { // Might be better to reject the request with a 400 ? LOG_WARNING("Max tokens to predict exceeds server configuration", { {"params.n_predict", slot.params.n_predict}, {"slot.n_predict", slot.n_predict}, }); slot.params.n_predict = slot.n_predict; } // infill slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); // get prompt { const auto & prompt = data.find("prompt"); if (prompt == data.end()) { send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST); return false; } else { slot.prompt = *prompt; } if (slot.prompt.is_array() && slot.prompt.size() == 0) { send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST); return false; } } // penalize user-provided tokens { slot.sparams.penalty_prompt_tokens.clear(); slot.sparams.use_penalty_prompt_tokens = false; const auto & penalty_prompt = data.find("penalty_prompt"); if (penalty_prompt != data.end()) { if (penalty_prompt->is_string()) { const auto penalty_prompt_string = penalty_prompt->get(); slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false); if (slot.params.n_predict > 0) { slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() + slot.params.n_predict); } slot.sparams.use_penalty_prompt_tokens = true; LOG_VERBOSE("penalty_prompt_tokens", { {"id_slot", slot.id}, {"tokens", slot.sparams.penalty_prompt_tokens}, }); } else if (penalty_prompt->is_array()) { const auto n_tokens = penalty_prompt->size(); slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict)); const int n_vocab = llama_n_vocab(model); for (const auto & penalty_token : *penalty_prompt) { if (penalty_token.is_number_integer()) { const auto tok = penalty_token.get(); if (tok >= 0 && tok < n_vocab) { slot.sparams.penalty_prompt_tokens.push_back(tok); } } } slot.sparams.use_penalty_prompt_tokens = true; LOG_VERBOSE("penalty_prompt_tokens", { {"id_slot", slot.id}, {"tokens", slot.sparams.penalty_prompt_tokens}, }); } } } { slot.sparams.logit_bias.clear(); if (json_value(data, "ignore_eos", false)) { slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; } const auto & logit_bias = data.find("logit_bias"); if (logit_bias != data.end() && logit_bias->is_array()) { const int n_vocab = llama_n_vocab(model); for (const auto & el : *logit_bias) { // TODO: we may want to throw errors here, in case "el" is incorrect if (el.is_array() && el.size() == 2) { float bias; if (el[1].is_number()) { bias = el[1].get(); } else if (el[1].is_boolean() && !el[1].get()) { bias = -INFINITY; } else { continue; } if (el[0].is_number_integer()) { llama_token tok = el[0].get(); if (tok >= 0 && tok < n_vocab) { slot.sparams.logit_bias[tok] = bias; } } else if (el[0].is_string()) { auto toks = llama_tokenize(model, el[0].get(), false); for (auto tok : toks) { slot.sparams.logit_bias[tok] = bias; } } } } } } { slot.params.antiprompt.clear(); const auto & stop = data.find("stop"); if (stop != data.end() && stop->is_array()) { for (const auto & word : *stop) { if (!word.empty()) { slot.params.antiprompt.push_back(word); } } } } { const auto & samplers_sequence = data.find("samplers"); if (samplers_sequence != data.end() && samplers_sequence->is_array()) { std::vector sampler_names; for (const auto & sampler_name : *samplers_sequence) { if (sampler_name.is_string()) { sampler_names.emplace_back(sampler_name); } } slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); } else { slot.sparams.samplers_sequence = default_sparams.samplers_sequence; } } { if (slot.ctx_sampling != nullptr) { llama_sampling_free(slot.ctx_sampling); } slot.ctx_sampling = llama_sampling_init(slot.sparams); if (slot.ctx_sampling == nullptr) { // for now, the only error that may happen here is invalid grammar send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST); return false; } } slot.command = SLOT_COMMAND_LOAD_PROMPT; slot.prompt_tokens.clear(); LOG_INFO("slot is processing task", { {"id_slot", slot.id}, {"id_task", slot.id_task}, }); return true; } void kv_cache_clear() { LOG_VERBOSE("clearing KV cache", {}); // clear the entire KV cache llama_kv_cache_clear(ctx); clean_kv_cache = false; } void system_prompt_update() { LOG_VERBOSE("system prompt update", { {"system_prompt", system_prompt}, }); kv_cache_clear(); system_tokens.clear(); if (!system_prompt.empty()) { system_tokens = ::llama_tokenize(ctx, system_prompt, true); llama_batch_clear(batch); for (int i = 0; i < (int)system_tokens.size(); ++i) { llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } const int32_t n_batch = llama_n_batch(ctx); for (int32_t i = 0; i < batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i); llama_batch batch_view = { n_tokens, batch.token + i, nullptr, batch.pos + i, batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, 0, 0, 0, // unused }; if (llama_decode(ctx, batch_view) != 0) { LOG_ERROR("llama_decode() failed", {}); return; } } // assign the system KV cache to all parallel sequences for (int32_t i = 1; i <= params.n_parallel; ++i) { llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } } system_need_update = false; } void system_prompt_set(const json & sys_props) { system_prompt = sys_props.value("prompt", ""); name_user = sys_props.value("anti_prompt", ""); name_assistant = sys_props.value("assistant_name", ""); LOG_VERBOSE("system prompt process", { {"system_prompt", system_prompt}, {"name_user", name_user}, {"name_assistant", name_assistant}, }); // release all slots for (server_slot & slot : slots) { slot.release(); } system_need_update = true; } bool process_token(completion_token_output & result, server_slot & slot) { // remember which tokens were sampled - used for repetition penalties during sampling const std::string token_str = llama_token_to_piece(ctx, result.tok, false); slot.sampled = result.tok; // search stop word and delete it slot.generated_text += token_str; slot.has_next_token = true; if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) { // we can change penalty_prompt_tokens because it is always created from scratch each request slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok); } // check if there is incomplete UTF-8 character at the end bool incomplete = false; for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) { unsigned char c = slot.generated_text[slot.generated_text.size() - i]; if ((c & 0xC0) == 0x80) { // continuation byte: 10xxxxxx continue; } if ((c & 0xE0) == 0xC0) { // 2-byte character: 110xxxxx ... incomplete = i < 2; } else if ((c & 0xF0) == 0xE0) { // 3-byte character: 1110xxxx ... incomplete = i < 3; } else if ((c & 0xF8) == 0xF0) { // 4-byte character: 11110xxx ... incomplete = i < 4; } // else 1-byte character or invalid byte break; } if (!incomplete) { size_t pos = std::min(slot.n_sent_text, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); bool is_stop_full = false; size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL); if (stop_pos != std::string::npos) { is_stop_full = true; slot.generated_text.erase( slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); pos = std::min(slot.n_sent_text, slot.generated_text.size()); } else { is_stop_full = false; stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL); } // check if there is any token to predict if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.n_sent_text += result.text_to_send.size(); // add the token to slot queue and cache } slot.add_token_string(result); if (slot.params.stream) { send_partial_response(slot, result); } } if (incomplete) { slot.has_next_token = true; } // check the limits if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) { slot.stopped_limit = true; slot.has_next_token = false; LOG_VERBOSE("stopped by limit", { {"id_slot", slot.id}, {"id_task", slot.id_task}, {"n_decoded", slot.n_decoded}, {"n_predict", slot.params.n_predict}, }); } if (llama_token_is_eog(model, result.tok)) { slot.stopped_eos = true; slot.has_next_token = false; LOG_VERBOSE("eos token found", {}); } auto n_ctx_train = llama_n_ctx_train(model); if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1 && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) { LOG_WARNING("n_predict is not set and self-context extend is disabled." " Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop", { { "id_slot", slot.id }, { "params.n_predict", slot.params.n_predict }, { "slot.n_prompt_tokens", slot.n_prompt_tokens }, { "slot.n_decoded", slot.n_decoded }, { "slot.n_predict", slot.n_predict }, { "n_slots", params.n_parallel }, { "slot.n_ctx", slot.n_ctx }, { "n_ctx", n_ctx }, { "n_ctx_train", n_ctx_train }, { "ga_n", slot.ga_n }, }); slot.truncated = true; slot.stopped_limit = true; slot.has_next_token = false; // stop prediction } LOG_VERBOSE("next token", { {"id_slot", slot.id}, {"id_task", slot.id_task}, {"token", result.tok}, {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, {"has_next_token", slot.has_next_token}, {"n_remain", slot.n_remaining}, {"n_decoded", slot.n_decoded}, {"stopped_eos", slot.stopped_eos}, {"stopped_word", slot.stopped_word}, {"stopped_limit", slot.stopped_limit}, {"stopping_word", slot.stopping_word}, }); return slot.has_next_token; // continue } json get_formated_generation(const server_slot & slot) const { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); std::vector samplers_sequence; samplers_sequence.reserve(slot.sparams.samplers_sequence.size()); for (const auto & sampler_type : slot.sparams.samplers_sequence) { samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type)); } return json { {"n_ctx", slot.n_ctx}, {"n_predict", slot.n_predict}, {"model", params.model_alias}, {"seed", slot.params.seed}, {"temperature", slot.sparams.temp}, {"dynatemp_range", slot.sparams.dynatemp_range}, {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, {"min_p", slot.sparams.min_p}, {"tfs_z", slot.sparams.tfs_z}, {"typical_p", slot.sparams.typical_p}, {"repeat_last_n", slot.sparams.penalty_last_n}, {"repeat_penalty", slot.sparams.penalty_repeat}, {"presence_penalty", slot.sparams.penalty_present}, {"frequency_penalty", slot.sparams.penalty_freq}, {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens}, {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens}, {"mirostat", slot.sparams.mirostat}, {"mirostat_tau", slot.sparams.mirostat_tau}, {"mirostat_eta", slot.sparams.mirostat_eta}, {"penalize_nl", slot.sparams.penalize_nl}, {"stop", slot.params.antiprompt}, {"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict {"n_keep", slot.params.n_keep}, {"n_discard", slot.params.n_discard}, {"ignore_eos", ignore_eos}, {"stream", slot.params.stream}, {"logit_bias", slot.sparams.logit_bias}, {"n_probs", slot.sparams.n_probs}, {"min_keep", slot.sparams.min_keep}, {"grammar", slot.sparams.grammar}, {"samplers", samplers_sequence} }; } void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { send_error(task.id, task.id_multi, error, type); } void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { send_error(slot.id_task, slot.id_multi, error, type); } void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { LOG_ERROR("task error", { {"id_multi", id_multi}, {"id_task", id_task}, {"error", error}, }); server_task_result res; res.id = id_task; res.id_multi = id_multi; res.stop = false; res.error = true; res.data = format_error_response(error, type); queue_results.send(res); } void send_partial_response(server_slot & slot, completion_token_output tkn) { server_task_result res; res.id = slot.id_task; res.id_multi = slot.id_multi; res.error = false; res.stop = false; res.data = json { {"content", tkn.text_to_send}, {"stop", false}, {"id_slot", slot.id}, {"multimodal", false} }; if (slot.sparams.n_probs > 0) { const std::vector to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); std::vector probs_output; if (probs_pos < probs_stop_pos) { probs_output = std::vector( slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos); } slot.n_sent_token_probs = probs_stop_pos; res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); } if (slot.oaicompat) { res.data["oaicompat_token_ctr"] = slot.n_decoded; res.data["model"] = slot.oaicompat_model; } queue_results.send(res); } void send_final_response(const server_slot & slot) { server_task_result res; res.id = slot.id_task; res.id_multi = slot.id_multi; res.error = false; res.stop = true; res.data = json { {"content", !slot.params.stream ? slot.generated_text : ""}, {"id_slot", slot.id}, {"stop", true}, {"model", params.model_alias}, {"tokens_predicted", slot.n_decoded}, {"tokens_evaluated", slot.n_prompt_tokens}, {"generation_settings", get_formated_generation(slot)}, {"prompt", slot.prompt}, {"truncated", slot.truncated}, {"stopped_eos", slot.stopped_eos}, {"stopped_word", slot.stopped_word}, {"stopped_limit", slot.stopped_limit}, {"stopping_word", slot.stopping_word}, {"tokens_cached", slot.n_past}, {"timings", slot.get_formated_timings()} }; if (slot.sparams.n_probs > 0) { std::vector probs; if (!slot.params.stream && slot.stopped_word) { const std::vector stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false); size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size()); probs = std::vector( slot.generated_token_probs.begin(), slot.generated_token_probs.end() - safe_offset); } else { probs = std::vector( slot.generated_token_probs.begin(), slot.generated_token_probs.end()); } res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs); } if (slot.oaicompat) { res.data["oaicompat_token_ctr"] = slot.n_decoded; res.data["model"] = slot.oaicompat_model; } queue_results.send(res); } void send_embedding(const server_slot & slot, const llama_batch & batch) { server_task_result res; res.id = slot.id_task; res.id_multi = slot.id_multi; res.error = false; res.stop = true; const int n_embd = llama_n_embd(model); std::vector embd_res(n_embd, 0.0f); for (int i = 0; i < batch.n_tokens; ++i) { if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { continue; } const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); if (embd == NULL) { embd = llama_get_embeddings_ith(ctx, i); } if (embd == NULL) { LOG_ERROR("failed to get embeddings", { {"token", batch.token [i]}, {"seq_id", batch.seq_id[i][0]} }); res.data = json { {"embedding", std::vector(n_embd, 0.0f)}, }; continue; } llama_embd_normalize(embd, embd_res.data(), n_embd); res.data = json { {"embedding", embd_res}, }; } queue_results.send(res); } void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding) { server_task task; task.id = id_task; task.id_multi = id_multi; task.id_target = 0; task.data = std::move(data); task.infill = infill; task.embedding = embedding; task.type = SERVER_TASK_TYPE_COMPLETION; // when a completion task's prompt array is not a singleton, we split it into multiple requests // otherwise, it's a single-prompt task, we actually queue it // if there's numbers in the prompt array it will be treated as an array of tokens if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) { bool numbers = false; for (const auto & e : task.data.at("prompt")) { if (e.is_number()) { numbers = true; break; } } // NOTE: split_multiprompt_task() does not handle a mix of strings and numbers, // it will completely stall the server. I don't know where the bug for this is. // // if there are numbers, it needs to be treated like a single prompt, // queue_tasks handles a mix of strings and numbers just fine. if (numbers) { queue_tasks.post(task); } else { split_multiprompt_task(id_task, task); } } else { queue_tasks.post(task); } } void request_cancel(int id_task) { server_task task; task.type = SERVER_TASK_TYPE_CANCEL; task.id_target = id_task; queue_tasks.post(task); } void split_multiprompt_task(int id_multi, const server_task & multiprompt_task) { const int prompt_count = multiprompt_task.data.at("prompt").size(); if (prompt_count <= 1) { send_error(multiprompt_task, "error while handling multiple prompts"); return; } // generate all the ID for subtask std::vector subtask_ids(prompt_count); for (int i = 0; i < prompt_count; i++) { subtask_ids[i] = queue_tasks.get_new_id(); } // queue up the multitask so we can track its subtask progression queue_tasks.add_multitask(id_multi, subtask_ids); // add subtasks for (int i = 0; i < prompt_count; i++) { json subtask_data = multiprompt_task.data; subtask_data["prompt"] = subtask_data["prompt"][i]; // subtasks inherit everything else (infill mode, embedding mode, etc.) request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding); } } void process_single_task(const server_task & task) { switch (task.type) { case SERVER_TASK_TYPE_COMPLETION: { server_slot * slot = get_slot(json_value(task.data, "id_slot", -1)); if (slot == nullptr) { // if no slot is available, we defer this task for processing later LOG_VERBOSE("no slot is available", {{"id_task", task.id}}); queue_tasks.defer(task); break; } if (task.data.contains("system_prompt")) { system_prompt_set(task.data["system_prompt"]); for (server_slot & slot : slots) { slot.n_past = 0; slot.n_past_se = 0; } } slot->reset(); slot->id_task = task.id; slot->id_multi = task.id_multi; slot->infill = task.infill; slot->embedding = task.embedding; if (!launch_slot_with_task(*slot, task)) { LOG_ERROR("error while launching slot", task.data); break; } } break; case SERVER_TASK_TYPE_CANCEL: { // release slot linked with the task id for (auto & slot : slots) { if (slot.id_task == task.id_target) { slot.release(); break; } } } break; case SERVER_TASK_TYPE_NEXT_RESPONSE: { // do nothing } break; case SERVER_TASK_TYPE_METRICS: { json slots_data = json::array(); int n_idle_slots = 0; int n_processing_slots = 0; for (server_slot & slot : slots) { json slot_data = get_formated_generation(slot); slot_data["id"] = slot.id; slot_data["id_task"] = slot.id_task; slot_data["state"] = slot.state; slot_data["prompt"] = slot.prompt; slot_data["next_token"] = { {"has_next_token", slot.has_next_token}, {"n_remain", slot.n_remaining}, {"n_decoded", slot.n_decoded}, {"stopped_eos", slot.stopped_eos}, {"stopped_word", slot.stopped_word}, {"stopped_limit", slot.stopped_limit}, {"stopping_word", slot.stopping_word}, }; if (slot_data["state"] == SLOT_STATE_IDLE) { n_idle_slots++; } else { n_processing_slots++; } slots_data.push_back(slot_data); } LOG_INFO("slot data", { {"id_task", task.id}, {"n_idle_slots", n_idle_slots}, {"n_processing_slots", n_processing_slots} }); LOG_VERBOSE("slot data", { {"id_task", task.id}, {"n_idle_slots", n_idle_slots}, {"n_processing_slots", n_processing_slots}, {"slots", slots_data} }); server_task_result res; res.id = task.id; res.id_multi = task.id_multi; res.stop = true; res.error = false; res.data = { { "idle", n_idle_slots }, { "processing", n_processing_slots }, { "deferred", queue_tasks.queue_tasks_deferred.size() }, { "t_start", metrics.t_start}, { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total}, { "t_tokens_generation_total", metrics.t_tokens_generation_total}, { "n_tokens_predicted_total", metrics.n_tokens_predicted_total}, { "t_prompt_processing_total", metrics.t_prompt_processing_total}, { "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed}, { "t_prompt_processing", metrics.t_prompt_processing}, { "n_tokens_predicted", metrics.n_tokens_predicted}, { "t_tokens_generation", metrics.t_tokens_generation}, { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, { "slots", slots_data }, }; if (json_value(task.data, "reset_bucket", false)) { metrics.reset_bucket(); } queue_results.send(res); } break; case SERVER_TASK_TYPE_SLOT_SAVE: { int id_slot = task.data["id_slot"]; server_slot * slot = get_slot(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } const size_t token_count = slot->cache_tokens.size(); const int64_t t_start = ggml_time_us(); std::string filename = task.data["filename"]; std::string filepath = task.data["filepath"]; const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count); const int64_t t_end = ggml_time_us(); const double t_save_ms = (t_end - t_start) / 1000.0; server_task_result result; result.id = task.id; result.stop = true; result.error = false; result.data = json { { "id_slot", id_slot }, { "filename", filename }, { "n_saved", token_count }, // tokens saved { "n_written", nwrite }, // bytes written { "timings", { { "save_ms", t_save_ms } } } }; queue_results.send(result); } break; case SERVER_TASK_TYPE_SLOT_RESTORE: { int id_slot = task.data["id_slot"]; server_slot * slot = get_slot(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } const int64_t t_start = ggml_time_us(); std::string filename = task.data["filename"]; std::string filepath = task.data["filepath"]; slot->cache_tokens.resize(slot->n_ctx); size_t token_count = 0; size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count); if (nread == 0) { slot->cache_tokens.resize(0); send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST); break; } slot->cache_tokens.resize(token_count); const int64_t t_end = ggml_time_us(); const double t_restore_ms = (t_end - t_start) / 1000.0; server_task_result result; result.id = task.id; result.stop = true; result.error = false; result.data = json { { "id_slot", id_slot }, { "filename", filename }, { "n_restored", token_count }, // tokens restored { "n_read", nread }, // bytes read { "timings", { { "restore_ms", t_restore_ms } } } }; queue_results.send(result); } break; case SERVER_TASK_TYPE_SLOT_ERASE: { int id_slot = task.data["id_slot"]; server_slot * slot = get_slot(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } // Erase token cache const size_t n_erased = slot->cache_tokens.size(); llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1); slot->cache_tokens.clear(); server_task_result result; result.id = task.id; result.stop = true; result.error = false; result.data = json { { "id_slot", id_slot }, { "n_erased", n_erased } }; queue_results.send(result); } break; } } void on_finish_multitask(const server_task_multi & multitask) { // all subtasks done == multitask is done server_task_result result; result.id = multitask.id; result.stop = true; result.error = false; // collect json results into one json result std::vector result_jsons; for (const auto & subres : multitask.results) { result_jsons.push_back(subres.data); result.error = result.error && subres.error; } result.data = json { { "results", result_jsons } }; queue_results.send(result); } void update_slots() { if (system_need_update) { system_prompt_update(); } // release slots for (auto & slot : slots) { if (slot.command == SLOT_COMMAND_RELEASE) { slot.state = SLOT_STATE_IDLE; slot.command = SLOT_COMMAND_NONE; slot.t_last_used = ggml_time_us(); LOG_INFO("slot released", { {"id_slot", slot.id}, {"id_task", slot.id_task}, {"n_ctx", n_ctx}, {"n_past", slot.n_past}, {"n_system_tokens", system_tokens.size()}, {"n_cache_tokens", slot.cache_tokens.size()}, {"truncated", slot.truncated} }); queue_tasks.notify_slot_changed(); } } // check if all slots are idle { bool all_idle = true; for (auto & slot : slots) { if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) { all_idle = false; break; } } if (all_idle) { LOG_INFO("all slots are idle", {}); if (system_prompt.empty() && clean_kv_cache) { kv_cache_clear(); } return; } } { LOG_VERBOSE("posting NEXT_RESPONSE", {}); server_task task; task.type = SERVER_TASK_TYPE_NEXT_RESPONSE; task.id_target = -1; queue_tasks.post(task); } // apply context-shift if needed // TODO: simplify and improve for (server_slot & slot : slots) { if (slot.ga_n == 1) { if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) { // Shift context const int n_keep = slot.params.n_keep + add_bos_token; const int n_left = (int) system_tokens.size() + slot.n_past - n_keep; const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2); LOG_INFO("slot context shift", { {"id_slot", slot.id}, {"id_task", slot.id_task}, {"n_keep", n_keep}, {"n_left", n_left}, {"n_discard", n_discard}, {"n_ctx", n_ctx}, {"n_past", slot.n_past}, {"n_system_tokens", system_tokens.size()}, {"n_cache_tokens", slot.cache_tokens.size()} }); llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep , n_keep + n_discard); llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); if (slot.params.cache_prompt) { for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; } slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); } slot.n_past -= n_discard; slot.truncated = true; } } } // start populating the batch for this iteration llama_batch_clear(batch); // frist, add sampled tokens from any ongoing sequences for (auto & slot : slots) { if (slot.state == SLOT_STATE_IDLE) { continue; } slot.i_batch = batch.n_tokens; const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; // TODO: we always have to take into account the "system_tokens" // this is not great and needs to be improved somehow llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true); slot.n_past += 1; if (slot.params.cache_prompt) { slot.cache_tokens.push_back(slot.sampled); } LOG_VERBOSE("slot decode token", { {"id_slot", slot.id}, {"id_task", slot.id_task}, {"n_ctx", n_ctx}, {"n_past", slot.n_past}, {"n_system_tokens", system_tokens.size()}, {"n_cache_tokens", slot.cache_tokens.size()}, {"truncated", slot.truncated} }); } // process in chunks of params.n_batch int32_t n_batch = llama_n_batch(ctx); int32_t n_ubatch = llama_n_ubatch(ctx); // next, batch any pending prompts without exceeding n_batch if (params.cont_batching || batch.n_tokens == 0) { for (auto & slot : slots) { // this slot still has a prompt to be processed if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) { auto & prompt_tokens = slot.prompt_tokens; // we haven't tokenized the prompt yet - do it now: if (prompt_tokens.empty()) { LOG_VERBOSE("tokenizing prompt", { {"id_slot", slot.id}, {"id_task", slot.id_task} }); slot.t_start_process_prompt = ggml_time_us(); slot.t_start_generation = 0; if (slot.infill) { bool suff_rm_leading_spc = true; if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { params.input_suffix.erase(0, 1); suff_rm_leading_spc = false; } auto prefix_tokens = tokenize(slot.params.input_prefix, false); auto suffix_tokens = tokenize(slot.params.input_suffix, false); const int space_token = 29871; // TODO: this should not be hardcoded if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) { suffix_tokens.erase(suffix_tokens.begin()); } prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model)); prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); prefix_tokens.push_back(llama_token_middle(model)); prompt_tokens = prefix_tokens; } else { prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt } slot.n_past = 0; slot.n_prompt_tokens = prompt_tokens.size(); LOG_VERBOSE("prompt tokenized", { {"id_slot", slot.id}, {"id_task", slot.id_task}, {"n_ctx", slot.n_ctx}, {"n_keep", slot.params.n_keep}, {"n_prompt_tokens", slot.n_prompt_tokens}, {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, }); // empty prompt passed -> release the slot and send empty response if (prompt_tokens.empty()) { LOG_INFO("empty prompt - releasing slot", { {"id_slot", slot.id}, {"id_task", slot.id_task} }); slot.state = SLOT_STATE_PROCESSING; slot.command = SLOT_COMMAND_NONE; slot.release(); slot.print_timings(); send_final_response(slot); continue; } if (slot.embedding) { // this prompt is too large to process - discard it if (slot.n_prompt_tokens > n_ubatch) { slot.state = SLOT_STATE_PROCESSING; slot.command = SLOT_COMMAND_NONE; slot.release(); slot.print_timings(); send_final_response(slot); continue; } } else { if (slot.params.n_keep < 0) { slot.params.n_keep = slot.n_prompt_tokens; } slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); // if input prompt is too big, truncate it (if group attention self-extend is disabled) if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) { const int n_left = slot.n_ctx - slot.params.n_keep; const int n_block_size = n_left / 2; const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; std::vector new_tokens( prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); new_tokens.insert( new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); prompt_tokens = std::move(new_tokens); slot.truncated = true; slot.n_prompt_tokens = prompt_tokens.size(); LOG_VERBOSE("input truncated", { {"id_slot", slot.id}, {"id_task", slot.id_task}, {"n_ctx", slot.n_ctx}, {"n_keep", slot.params.n_keep}, {"n_left", n_left}, {"n_prompt_tokens", slot.n_prompt_tokens}, {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())}, }); GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); } llama_sampling_reset(slot.ctx_sampling); if (!slot.params.cache_prompt) { slot.n_past_se = 0; slot.ga_i = 0; } else { GGML_ASSERT(slot.ga_n == 1); // reuse any previously computed tokens that are common with the new prompt slot.n_past = common_part(slot.cache_tokens, prompt_tokens); // push the prompt into the sampling context (do not apply grammar) for (int i = 0; i < slot.n_past; ++i) { llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false); } } } if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) { // we have to evaluate at least 1 token to generate logits. LOG_INFO("we have to evaluate at least 1 token to generate logits", { { "id_slot", slot.id }, { "id_task", slot.id_task } }); slot.n_past--; if (slot.ga_i > 0) { slot.n_past_se--; } } slot.n_prompt_tokens_processed = 0; } if (slot.embedding) { // cannot fit the prompt in the current batch - will try next iter if (batch.n_tokens + slot.n_prompt_tokens > n_batch) { continue; } } // keep only the common part int p0 = (int) system_tokens.size() + slot.n_past; if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) { // could not partially delete (likely using a non-Transformer model) llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1); p0 = (int) system_tokens.size(); if (p0 != 0) { // copy over the system prompt when there is one llama_kv_cache_seq_cp(ctx, 0, slot.id + 1, -1, -1); } // there is no common part left (except for the system prompt) slot.n_past = 0; slot.n_past_se = 0; slot.ga_i = 0; // TODO: is the system prompt ever in the sampling context? llama_sampling_reset(slot.ctx_sampling); } // remove the non-common part from the cache slot.cache_tokens.resize(slot.n_past); LOG_INFO("kv cache rm [p0, end)", { { "id_slot", slot.id }, { "id_task", slot.id_task }, { "p0", p0 } }); int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; int32_t ga_i = slot.ga_i; int32_t ga_n = slot.ga_n; int32_t ga_w = slot.ga_w; // add prompt tokens for processing in the current batch // TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow for (; slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch; ++slot.n_past) { if (slot.ga_n != 1) { while (slot_npast >= ga_i + ga_w) { const int bd = (ga_w/ga_n)*(ga_n - 1); slot_npast -= bd; ga_i += ga_w/ga_n; } } llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false); if (slot.params.cache_prompt) { slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); } slot.n_prompt_tokens_processed++; slot_npast++; } LOG_VERBOSE("prompt processing progress", { {"id_slot", slot.id}, {"n_past", slot.n_past}, {"n_ctx", n_ctx}, {"n_tokens", batch.n_tokens}, {"progress", (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens}, }); // entire prompt has been processed - start decoding new tokens if (slot.n_past == slot.n_prompt_tokens) { slot.state = SLOT_STATE_PROCESSING; slot.command = SLOT_COMMAND_NONE; GGML_ASSERT(batch.n_tokens > 0); // extract the logits only for the last token batch.logits[batch.n_tokens - 1] = true; slot.n_decoded = 0; slot.i_batch = batch.n_tokens - 1; LOG_VERBOSE("prompt done", { {"id_slot", slot.id}, {"n_past", slot.n_past}, {"n_ctx", n_ctx}, {"n_tokens", batch.n_tokens}, }); } } if (batch.n_tokens >= n_batch) { break; } } } if (batch.n_tokens == 0) { LOG_VERBOSE("no tokens to decode", {}); return; } LOG_VERBOSE("decoding batch", { {"n_tokens", batch.n_tokens}, }); // process the created batch of tokens for (int32_t i = 0; i < batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i); for (auto & slot : slots) { if (slot.ga_n != 1) { // context extension via Self-Extend // TODO: simplify and/or abstract this while (slot.n_past_se >= slot.ga_i + slot.ga_w) { const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w; const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1); const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w; LOG_TEE("\n"); LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd); LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n); LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd); llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i, slot.n_past_se, ib * bd); llama_kv_cache_seq_div(ctx, slot.id + 1, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n); llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd); slot.n_past_se -= bd; slot.ga_i += slot.ga_w / slot.ga_n; LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i); } slot.n_past_se += n_tokens; } } llama_batch batch_view = { n_tokens, batch.token + i, nullptr, batch.pos + i, batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); if (ret != 0) { if (n_batch == 1 || ret < 0) { // if you get here, it means the KV cache is full - try increasing it via the context size LOG_ERROR("failed to decode the batch: KV cache is full - try increasing it via the context size", { {"i", i}, {"n_batch", ret}, {"ret", ret}, }); for (auto & slot : slots) { slot.state = SLOT_STATE_PROCESSING; slot.command = SLOT_COMMAND_NONE; slot.release(); send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size."); } break; // break loop of n_batch } // retry with half the batch size to try to find a free slot in the KV cache n_batch /= 2; i -= n_batch; LOG_WARNING("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation", { {"i", i}, {"n_batch", n_batch}, {"ret", ret}, }); continue; // continue loop of n_batch } for (auto & slot : slots) { if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { continue; // continue loop of slots } // prompt evaluated for embedding if (slot.embedding) { send_embedding(slot, batch_view); slot.release(); slot.i_batch = -1; continue; // continue loop of slots } completion_token_output result; const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i); llama_sampling_accept(slot.ctx_sampling, ctx, id, true); slot.n_decoded += 1; if (slot.n_decoded == 1) { slot.t_start_generation = ggml_time_us(); slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3; metrics.on_prompt_eval(slot); } llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false }; result.tok = id; const size_t n_probs = std::min(cur_p.size, (size_t) slot.sparams.n_probs); if (n_probs > 0) { const size_t n_considered = slot.ctx_sampling->n_considered; // Make sure at least n_probs top tokens are at the front of the vector: if (slot.sparams.temp == 0.0f && n_probs > n_considered) { llama_sample_top_k(ctx, &cur_p, n_probs, 0); } if (slot.sparams.temp == 0.0f) { // With greedy sampling the probabilities have possibly not been calculated. for (size_t i = 0; i < n_probs; ++i) { result.probs.push_back({ cur_p.data[i].id, i == 0 ? 1.0f : 0.0f }); } } else { for (size_t i = 0; i < n_probs; ++i) { result.probs.push_back({ cur_p.data[i].id, i >= n_considered ? 0.0f : cur_p.data[i].p // Tokens filtered out due to e.g. top_k have 0 probability. }); } } } if (!process_token(result, slot)) { slot.release(); slot.print_timings(); send_final_response(slot); metrics.on_prediction(slot); } slot.i_batch = -1; } } LOG_VERBOSE("run slots completed", {}); } json model_meta() const { return json { {"vocab_type", llama_vocab_type (model)}, {"n_vocab", llama_n_vocab (model)}, {"n_ctx_train", llama_n_ctx_train (model)}, {"n_embd", llama_n_embd (model)}, {"n_params", llama_model_n_params(model)}, {"size", llama_model_size (model)}, }; } }; static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) { printf("usage: %s [options]\n", argv0); printf("\n"); printf("options:\n"); printf(" -h, --help show this help message and exit\n"); printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); printf(" --threads-http N number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n"); printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); printf(" --rope-scaling {none,linear,yarn}\n"); printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n"); printf(" -dt N, --defrag-thold N\n"); printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch); printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch); if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); printf(" - distribute: spread execution evenly over all nodes\n"); printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n"); printf(" - numactl: use the CPU map provided my numactl\n"); if (llama_supports_gpu_offload()) { printf(" -ngl N, --n-gpu-layers N\n"); printf(" number of layers to store in VRAM\n"); printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); printf(" how to split the model across multiple GPUs, one of:\n"); printf(" - none: use one GPU only\n"); printf(" - layer (default): split layers and KV across GPUs\n"); printf(" - row: split rows across GPUs\n"); printf(" -ts SPLIT --tensor-split SPLIT\n"); printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); printf(" or for intermediate results and KV (with split-mode = row)\n"); printf(" -nkvo, --no-kv-offload\n"); printf(" disable KV offload\n"); } printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH); printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); printf(" model download url (default: unused)\n"); printf(" -hfr REPO, --hf-repo REPO\n"); printf(" Hugging Face model repository (default: unused)\n"); printf(" -hff FILE, --hf-file FILE\n"); printf(" Hugging Face model file (default: unused)\n"); printf(" -a ALIAS, --alias ALIAS\n"); printf(" set an alias for the model, will be added as `model` field in completion response\n"); printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); printf(" --path PUBLIC_PATH path from which to serve static files (default: disabled)\n"); printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); #ifdef CPPHTTPLIB_OPENSSL_SUPPORT printf(" --ssl-key-file FNAME path to file a PEM-encoded SSL private key\n"); printf(" --ssl-cert-file FNAME path to file a PEM-encoded SSL certificate\n"); #endif printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: enabled)\n"); printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled"); printf(" -spf FNAME, --system-prompt-file FNAME\n"); printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); printf(" -ctk TYPE, --cache-type-k TYPE\n"); printf(" KV cache data type for K (default: f16)\n"); printf(" -ctv TYPE, --cache-type-v TYPE\n"); printf(" KV cache data type for V (default: f16)\n"); printf(" --log-format log output format: json or text (default: json)\n"); printf(" --log-disable disables logging to a file.\n"); printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled"); printf(" --slot-save-path PATH path to save slot kv cache (default: disabled)\n"); printf("\n"); printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict); printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n"); printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); printf(" --chat-template JINJA_TEMPLATE\n"); printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); printf(" only commonly used templates are accepted:\n"); printf(" https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n"); printf("\n"); } static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) { gpt_params default_params; server_params default_sparams; std::string arg; bool invalid_param = false; for (int i = 1; i < argc; i++) { arg = argv[i]; if (arg == "--port") { if (++i >= argc) { invalid_param = true; break; } sparams.port = std::stoi(argv[i]); } else if (arg == "--host") { if (++i >= argc) { invalid_param = true; break; } sparams.hostname = argv[i]; } else if (arg == "--path") { if (++i >= argc) { invalid_param = true; break; } sparams.public_path = argv[i]; } else if (arg == "--api-key") { if (++i >= argc) { invalid_param = true; break; } sparams.api_keys.push_back(argv[i]); } else if (arg == "--api-key-file") { if (++i >= argc) { invalid_param = true; break; } std::ifstream key_file(argv[i]); if (!key_file) { fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); invalid_param = true; break; } std::string key; while (std::getline(key_file, key)) { if (key.size() > 0) { sparams.api_keys.push_back(key); } } key_file.close(); } #ifdef CPPHTTPLIB_OPENSSL_SUPPORT else if (arg == "--ssl-key-file") { if (++i >= argc) { invalid_param = true; break; } sparams.ssl_key_file = argv[i]; } else if (arg == "--ssl-cert-file") { if (++i >= argc) { invalid_param = true; break; } sparams.ssl_cert_file = argv[i]; } #endif else if (arg == "--timeout" || arg == "-to") { if (++i >= argc) { invalid_param = true; break; } sparams.read_timeout = std::stoi(argv[i]); sparams.write_timeout = std::stoi(argv[i]); } else if (arg == "-m" || arg == "--model") { if (++i >= argc) { invalid_param = true; break; } params.model = argv[i]; } else if (arg == "-mu" || arg == "--model-url") { if (++i >= argc) { invalid_param = true; break; } params.model_url = argv[i]; } else if (arg == "-hfr" || arg == "--hf-repo") { if (++i >= argc) { invalid_param = true; break; } params.hf_repo = argv[i]; } else if (arg == "-hff" || arg == "--hf-file") { if (++i >= argc) { invalid_param = true; break; } params.hf_file = argv[i]; } else if (arg == "-a" || arg == "--alias") { if (++i >= argc) { invalid_param = true; break; } params.model_alias = argv[i]; } else if (arg == "-h" || arg == "--help") { server_print_usage(argv[0], default_params, default_sparams); exit(0); } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") { if (++i >= argc) { invalid_param = true; break; } params.n_ctx = std::stoi(argv[i]); } else if (arg == "--rope-scaling") { if (++i >= argc) { invalid_param = true; break; } std::string value(argv[i]); /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } else { invalid_param = true; break; } } else if (arg == "--rope-freq-base") { if (++i >= argc) { invalid_param = true; break; } params.rope_freq_base = std::stof(argv[i]); } else if (arg == "--rope-freq-scale") { if (++i >= argc) { invalid_param = true; break; } params.rope_freq_scale = std::stof(argv[i]); } else if (arg == "--yarn-ext-factor") { if (++i >= argc) { invalid_param = true; break; } params.yarn_ext_factor = std::stof(argv[i]); } else if (arg == "--yarn-attn-factor") { if (++i >= argc) { invalid_param = true; break; } params.yarn_attn_factor = std::stof(argv[i]); } else if (arg == "--yarn-beta-fast") { if (++i >= argc) { invalid_param = true; break; } params.yarn_beta_fast = std::stof(argv[i]); } else if (arg == "--yarn-beta-slow") { if (++i >= argc) { invalid_param = true; break; } params.yarn_beta_slow = std::stof(argv[i]); } else if (arg == "--pooling") { if (++i >= argc) { invalid_param = true; break; } std::string value(argv[i]); /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } else { invalid_param = true; break; } } else if (arg == "--defrag-thold" || arg == "-dt") { if (++i >= argc) { invalid_param = true; break; } params.defrag_thold = std::stof(argv[i]); } else if (arg == "--threads" || arg == "-t") { if (++i >= argc) { invalid_param = true; break; } params.n_threads = std::stoi(argv[i]); } else if (arg == "--grp-attn-n" || arg == "-gan") { if (++i >= argc) { invalid_param = true; break; } params.grp_attn_n = std::stoi(argv[i]); } else if (arg == "--grp-attn-w" || arg == "-gaw") { if (++i >= argc) { invalid_param = true; break; } params.grp_attn_w = std::stoi(argv[i]); } else if (arg == "--threads-batch" || arg == "-tb") { if (++i >= argc) { invalid_param = true; break; } params.n_threads_batch = std::stoi(argv[i]); } else if (arg == "--threads-http") { if (++i >= argc) { invalid_param = true; break; } sparams.n_threads_http = std::stoi(argv[i]); } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) { invalid_param = true; break; } params.n_batch = std::stoi(argv[i]); } else if (arg == "-ub" || arg == "--ubatch-size") { if (++i >= argc) { invalid_param = true; break; } params.n_ubatch = std::stoi(argv[i]); } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { if (++i >= argc) { invalid_param = true; break; } if (llama_supports_gpu_offload()) { params.n_gpu_layers = std::stoi(argv[i]); } else { LOG_WARNING( "Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " "See main README.md for information on enabling GPU BLAS support", {{"n_gpu_layers", params.n_gpu_layers}}); } } else if (arg == "-nkvo" || arg == "--no-kv-offload") { params.no_kv_offload = true; } else if (arg == "--split-mode" || arg == "-sm") { if (++i >= argc) { invalid_param = true; break; } std::string arg_next = argv[i]; if (arg_next == "none") { params.split_mode = LLAMA_SPLIT_MODE_NONE; } else if (arg_next == "layer") { params.split_mode = LLAMA_SPLIT_MODE_LAYER; } else if (arg_next == "row") { params.split_mode = LLAMA_SPLIT_MODE_ROW; } else { invalid_param = true; break; } #ifndef GGML_USE_CUDA fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n"); #endif // GGML_USE_CUDA } else if (arg == "--tensor-split" || arg == "-ts") { if (++i >= argc) { invalid_param = true; break; } #if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL) std::string arg_next = argv[i]; // split string by , and / const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; GGML_ASSERT(split_arg.size() <= llama_max_devices()); for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) { if (i_device < split_arg.size()) { params.tensor_split[i_device] = std::stof(split_arg[i_device]); } else { params.tensor_split[i_device] = 0.0f; } } #else LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {}); #endif // GGML_USE_CUDA } else if (arg == "--main-gpu" || arg == "-mg") { if (++i >= argc) { invalid_param = true; break; } #if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL) params.main_gpu = std::stoi(argv[i]); #else LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a main GPU.", {}); #endif } else if (arg == "--lora") { if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; } else if (arg == "--lora-scaled") { if (++i >= argc) { invalid_param = true; break; } const char * lora_adapter = argv[i]; if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; } else if (arg == "--lora-base") { if (++i >= argc) { invalid_param = true; break; } params.lora_base = argv[i]; } else if (arg == "-v" || arg == "--verbose") { #if SERVER_VERBOSE != 1 LOG_WARNING("server.cpp is not built with verbose logging.", {}); #else server_verbose = true; #endif } else if (arg == "--mlock") { params.use_mlock = true; } else if (arg == "--no-mmap") { params.use_mmap = false; } else if (arg == "--numa") { if (++i >= argc) { invalid_param = true; break; } else { std::string value(argv[i]); /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } else { invalid_param = true; break; } } } else if (arg == "--embedding" || arg == "--embeddings") { params.embedding = true; } else if (arg == "-cb" || arg == "--cont-batching") { params.cont_batching = true; } else if (arg == "-fa" || arg == "--flash-attn") { params.flash_attn = true; } else if (arg == "-np" || arg == "--parallel") { if (++i >= argc) { invalid_param = true; break; } params.n_parallel = std::stoi(argv[i]); } else if (arg == "-n" || arg == "--n-predict") { if (++i >= argc) { invalid_param = true; break; } params.n_predict = std::stoi(argv[i]); } else if (arg == "-spf" || arg == "--system-prompt-file") { if (++i >= argc) { invalid_param = true; break; } std::ifstream file(argv[i]); if (!file) { fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); invalid_param = true; break; } std::string system_prompt; std::copy( std::istreambuf_iterator(file), std::istreambuf_iterator(), std::back_inserter(system_prompt) ); sparams.system_prompt = system_prompt; } else if (arg == "-ctk" || arg == "--cache-type-k") { params.cache_type_k = argv[++i]; } else if (arg == "-ctv" || arg == "--cache-type-v") { params.cache_type_v = argv[++i]; } else if (arg == "--log-format") { if (++i >= argc) { invalid_param = true; break; } if (std::strcmp(argv[i], "json") == 0) { server_log_json = true; } else if (std::strcmp(argv[i], "text") == 0) { server_log_json = false; } else { invalid_param = true; break; } } else if (arg == "--log-disable") { log_set_target(stdout); LOG_INFO("logging to file is disabled.", {}); } else if (arg == "--slots-endpoint-disable") { sparams.slots_endpoint = false; } else if (arg == "--metrics") { sparams.metrics_endpoint = true; } else if (arg == "--slot-save-path") { if (++i >= argc) { invalid_param = true; break; } sparams.slot_save_path = argv[i]; // if doesn't end with DIRECTORY_SEPARATOR, add it if (!sparams.slot_save_path.empty() && sparams.slot_save_path[sparams.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) { sparams.slot_save_path += DIRECTORY_SEPARATOR; } } else if (arg == "--chat-template") { if (++i >= argc) { invalid_param = true; break; } if (!verify_custom_template(argv[i])) { fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]); fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n"); invalid_param = true; break; } sparams.chat_template = argv[i]; } else if (arg == "--override-kv") { if (++i >= argc) { invalid_param = true; break; } if (!parse_kv_override(argv[i], params.kv_overrides)) { fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); invalid_param = true; break; } } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } gpt_params_handle_model_default(params); if (!params.kv_overrides.empty()) { params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; } if (invalid_param) { fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } static void log_server_request(const httplib::Request & req, const httplib::Response & res) { // skip GH copilot requests when using default port if (req.path == "/v1/health" || req.path == "/v1/completions") { return; } LOG_INFO("request", { {"remote_addr", req.remote_addr}, {"remote_port", req.remote_port}, {"status", res.status}, {"method", req.method}, {"path", req.path}, {"params", req.params}, }); LOG_VERBOSE("request", { {"request", req.body}, {"response", res.body}, }); } std::function shutdown_handler; std::atomic_flag is_terminating = ATOMIC_FLAG_INIT; inline void signal_handler(int signal) { if (is_terminating.test_and_set()) { // in case it hangs, we can force terminate the server by hitting Ctrl+C twice // this is for better developer experience, we can remove when the server is stable enough fprintf(stderr, "Received second interrupt, terminating immediately.\n"); exit(1); } shutdown_handler(signal); } int main(int argc, char ** argv) { #if SERVER_VERBOSE != 1 log_disable(); #endif // own arguments required by this example gpt_params params; server_params sparams; // struct that contains llama context and inference server_context ctx_server; server_params_parse(argc, argv, sparams, params); if (!sparams.system_prompt.empty()) { ctx_server.system_prompt_set(json::parse(sparams.system_prompt)); } if (params.model_alias == "unknown") { params.model_alias = params.model; } llama_backend_init(); llama_numa_init(params.numa); LOG_INFO("build info", { {"build", LLAMA_BUILD_NUMBER}, {"commit", LLAMA_COMMIT} }); LOG_INFO("system info", { {"n_threads", params.n_threads}, {"n_threads_batch", params.n_threads_batch}, {"total_threads", std::thread::hardware_concurrency()}, {"system_info", llama_print_system_info()}, }); std::unique_ptr svr; #ifdef CPPHTTPLIB_OPENSSL_SUPPORT if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") { LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}}); svr.reset( new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str()) ); } else { LOG_INFO("Running without SSL", {}); svr.reset(new httplib::Server()); } #else svr.reset(new httplib::Server()); #endif std::atomic state{SERVER_STATE_LOADING_MODEL}; svr->set_default_headers({{"Server", "llama.cpp"}}); // CORS preflight svr->Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); res.set_header("Access-Control-Allow-Credentials", "true"); res.set_header("Access-Control-Allow-Methods", "POST"); res.set_header("Access-Control-Allow-Headers", "*"); return res.set_content("", "application/json; charset=utf-8"); }); svr->set_logger(log_server_request); auto res_error = [](httplib::Response & res, json error_data) { json final_response {{"error", error_data}}; res.set_content(final_response.dump(), "application/json; charset=utf-8"); res.status = json_value(error_data, "code", 500); }; svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) { std::string message; try { std::rethrow_exception(std::move(ep)); } catch (std::exception & e) { message = e.what(); } catch (...) { message = "Unknown Exception"; } json formatted_error = format_error_response(message, ERROR_TYPE_SERVER); LOG_VERBOSE("Got exception", formatted_error); res_error(res, formatted_error); }); svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) { if (res.status == 404) { res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND)); } // for other error codes, we skip processing here because it's already done by res_error() }); // set timeouts and change hostname and port svr->set_read_timeout (sparams.read_timeout); svr->set_write_timeout(sparams.write_timeout); if (!svr->bind_to_port(sparams.hostname, sparams.port)) { fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } std::unordered_map log_data; log_data["hostname"] = sparams.hostname; log_data["port"] = std::to_string(sparams.port); if (sparams.api_keys.size() == 1) { auto key = sparams.api_keys[0]; log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0)); } else if (sparams.api_keys.size() > 1) { log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded"; } // load the model if (!ctx_server.load_model(params)) { state.store(SERVER_STATE_ERROR); return 1; } else { ctx_server.init(); state.store(SERVER_STATE_READY); } LOG_INFO("model loaded", {}); const auto model_meta = ctx_server.model_meta(); // if a custom chat template is not supplied, we will use the one that comes with the model (if any) if (sparams.chat_template.empty()) { if (!ctx_server.validate_model_chat_template()) { LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); sparams.chat_template = "chatml"; } } // print sample chat example to make it clear which template is used { json chat; chat.push_back({{"role", "system"}, {"content", "You are a helpful assistant"}}); chat.push_back({{"role", "user"}, {"content", "Hello"}}); chat.push_back({{"role", "assistant"}, {"content", "Hi there"}}); chat.push_back({{"role", "user"}, {"content", "How are you?"}}); const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat); LOG_INFO("chat template", { {"chat_example", chat_example}, {"built_in", sparams.chat_template.empty()}, }); } // // Middlewares // auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) { // TODO: should we apply API key to all endpoints, including "/health" and "/models"? static const std::set protected_endpoints = { "/props", "/completion", "/completions", "/v1/completions", "/chat/completions", "/v1/chat/completions", "/infill", "/tokenize", "/detokenize", "/embedding", "/embeddings", "/v1/embeddings", }; // If API key is not set, skip validation if (sparams.api_keys.empty()) { return true; } // If path is not in protected_endpoints list, skip validation if (protected_endpoints.find(req.path) == protected_endpoints.end()) { return true; } // Check for API key in the header auto auth_header = req.get_header_value("Authorization"); std::string prefix = "Bearer "; if (auth_header.substr(0, prefix.size()) == prefix) { std::string received_api_key = auth_header.substr(prefix.size()); if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) { return true; // API key is valid } } // API key is invalid or not provided // TODO: make another middleware for CORS related logic res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION)); LOG_WARNING("Unauthorized: Invalid API Key", {}); return false; }; // register server middlewares svr->set_pre_routing_handler([&middleware_validate_api_key](const httplib::Request & req, httplib::Response & res) { if (!middleware_validate_api_key(req, res)) { return httplib::Server::HandlerResponse::Handled; } return httplib::Server::HandlerResponse::Unhandled; }); // // Route handlers (or controllers) // const auto handle_health = [&](const httplib::Request & req, httplib::Response & res) { server_state current_state = state.load(); switch (current_state) { case SERVER_STATE_READY: { // request slots data using task queue server_task task; task.id = ctx_server.queue_tasks.get_new_id(); task.type = SERVER_TASK_TYPE_METRICS; task.id_target = -1; ctx_server.queue_results.add_waiting_task_id(task.id); ctx_server.queue_tasks.post(task); // get the result server_task_result result = ctx_server.queue_results.recv(task.id); ctx_server.queue_results.remove_waiting_task_id(task.id); const int n_idle_slots = result.data["idle"]; const int n_processing_slots = result.data["processing"]; json health = { {"status", "ok"}, {"slots_idle", n_idle_slots}, {"slots_processing", n_processing_slots} }; res.status = 200; // HTTP OK if (sparams.slots_endpoint && req.has_param("include_slots")) { health["slots"] = result.data["slots"]; } if (n_idle_slots == 0) { health["status"] = "no slot available"; if (req.has_param("fail_on_no_slot")) { res.status = 503; // HTTP Service Unavailable } } res.set_content(health.dump(), "application/json"); break; } case SERVER_STATE_LOADING_MODEL: { res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE)); } break; case SERVER_STATE_ERROR: { res_error(res, format_error_response("Model failed to load", ERROR_TYPE_SERVER)); } break; } }; const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) { if (!sparams.slots_endpoint) { res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED)); return; } // request slots data using task queue server_task task; task.id = ctx_server.queue_tasks.get_new_id(); task.id_multi = -1; task.id_target = -1; task.type = SERVER_TASK_TYPE_METRICS; ctx_server.queue_results.add_waiting_task_id(task.id); ctx_server.queue_tasks.post(task); // get the result server_task_result result = ctx_server.queue_results.recv(task.id); ctx_server.queue_results.remove_waiting_task_id(task.id); res.set_content(result.data["slots"].dump(), "application/json"); res.status = 200; // HTTP OK }; const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) { if (!sparams.metrics_endpoint) { res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED)); return; } // request slots data using task queue server_task task; task.id = ctx_server.queue_tasks.get_new_id(); task.id_multi = -1; task.id_target = -1; task.type = SERVER_TASK_TYPE_METRICS; task.data.push_back({{"reset_bucket", true}}); ctx_server.queue_results.add_waiting_task_id(task.id); ctx_server.queue_tasks.post(task); // get the result server_task_result result = ctx_server.queue_results.recv(task.id); ctx_server.queue_results.remove_waiting_task_id(task.id); json data = result.data; const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; const uint64_t t_prompt_processing = data["t_prompt_processing"]; const uint64_t n_tokens_predicted = data["n_tokens_predicted"]; const uint64_t t_tokens_generation = data["t_tokens_generation"]; const int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names json all_metrics_def = json { {"counter", {{ {"name", "prompt_tokens_total"}, {"help", "Number of prompt tokens processed."}, {"value", (uint64_t) data["n_prompt_tokens_processed_total"]} }, { {"name", "prompt_seconds_total"}, {"help", "Prompt process time"}, {"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3} }, { {"name", "tokens_predicted_total"}, {"help", "Number of generation tokens processed."}, {"value", (uint64_t) data["n_tokens_predicted_total"]} }, { {"name", "tokens_predicted_seconds_total"}, {"help", "Predict process time"}, {"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3} }}}, {"gauge", {{ {"name", "prompt_tokens_seconds"}, {"help", "Average prompt throughput in tokens/s."}, {"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.} },{ {"name", "predicted_tokens_seconds"}, {"help", "Average generation throughput in tokens/s."}, {"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.} },{ {"name", "kv_cache_usage_ratio"}, {"help", "KV-cache usage. 1 means 100 percent usage."}, {"value", 1. * kv_cache_used_cells / params.n_ctx} },{ {"name", "kv_cache_tokens"}, {"help", "KV-cache tokens."}, {"value", (uint64_t) data["kv_cache_tokens_count"]} },{ {"name", "requests_processing"}, {"help", "Number of request processing."}, {"value", (uint64_t) data["processing"]} },{ {"name", "requests_deferred"}, {"help", "Number of request deferred."}, {"value", (uint64_t) data["deferred"]} }}} }; std::stringstream prometheus; for (const auto & el : all_metrics_def.items()) { const auto & type = el.key(); const auto & metrics_def = el.value(); for (const auto & metric_def : metrics_def) { const std::string name = metric_def["name"]; const std::string help = metric_def["help"]; auto value = json_value(metric_def, "value", 0.); prometheus << "# HELP llamacpp:" << name << " " << help << "\n" << "# TYPE llamacpp:" << name << " " << type << "\n" << "llamacpp:" << name << " " << value << "\n"; } } const int64_t t_start = data["t_start"]; res.set_header("Process-Start-Time-Unix", std::to_string(t_start)); res.set_content(prometheus.str(), "text/plain; version=0.0.4"); res.status = 200; // HTTP OK }; const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data["filename"]; if (!validate_file_name(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } std::string filepath = sparams.slot_save_path + filename; server_task task; task.type = SERVER_TASK_TYPE_SLOT_SAVE; task.data = { { "id_slot", id_slot }, { "filename", filename }, { "filepath", filepath } }; const int id_task = ctx_server.queue_tasks.post(task); ctx_server.queue_results.add_waiting_task_id(id_task); server_task_result result = ctx_server.queue_results.recv(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); if (result.error) { res_error(res, result.data); } else { res.set_content(result.data.dump(), "application/json"); } }; const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data["filename"]; if (!validate_file_name(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } std::string filepath = sparams.slot_save_path + filename; server_task task; task.type = SERVER_TASK_TYPE_SLOT_RESTORE; task.data = { { "id_slot", id_slot }, { "filename", filename }, { "filepath", filepath } }; const int id_task = ctx_server.queue_tasks.post(task); ctx_server.queue_results.add_waiting_task_id(id_task); server_task_result result = ctx_server.queue_results.recv(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); if (result.error) { res_error(res, result.data); } else { res.set_content(result.data.dump(), "application/json"); } }; const auto handle_slots_erase = [&ctx_server, &res_error](const httplib::Request & /* req */, httplib::Response & res, int id_slot) { server_task task; task.type = SERVER_TASK_TYPE_SLOT_ERASE; task.data = { { "id_slot", id_slot }, }; const int id_task = ctx_server.queue_tasks.post(task); ctx_server.queue_results.add_waiting_task_id(id_task); server_task_result result = ctx_server.queue_results.recv(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); if (result.error) { res_error(res, result.data); } else { res.set_content(result.data.dump(), "application/json"); } }; const auto handle_slots_action = [&res_error, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); std::string id_slot_str = req.path_params.at("id_slot"); int id_slot; try { id_slot = std::stoi(id_slot_str); } catch (const std::exception &) { res_error(res, format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST)); return; } std::string action = req.get_param_value("action"); if (action == "save") { handle_slots_save(req, res, id_slot); } else if (action == "restore") { handle_slots_restore(req, res, id_slot); } else if (action == "erase") { handle_slots_erase(req, res, id_slot); } else { res_error(res, format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST)); } }; const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "user_name", ctx_server.name_user.c_str() }, { "assistant_name", ctx_server.name_assistant.c_str() }, { "default_generation_settings", ctx_server.default_generation_settings_for_props }, { "total_slots", ctx_server.params.n_parallel } }; res.set_content(data.dump(), "application/json; charset=utf-8"); }; const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = json::parse(req.body); const int id_task = ctx_server.queue_tasks.get_new_id(); ctx_server.queue_results.add_waiting_task_id(id_task); ctx_server.request_completion(id_task, -1, data, false, false); if (!json_value(data, "stream", false)) { server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error && result.stop) { res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res_error(res, result.data); } ctx_server.queue_results.remove_waiting_task_id(id_task); } else { const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) { while (true) { server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error) { const std::string str = "data: " + result.data.dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; LOG_VERBOSE("data stream", { { "to_send", str } }); if (!sink.write(str.c_str(), str.size())) { ctx_server.queue_results.remove_waiting_task_id(id_task); return false; } if (result.stop) { break; } } else { const std::string str = "error: " + result.data.dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; LOG_VERBOSE("data stream", { { "to_send", str } }); if (!sink.write(str.c_str(), str.size())) { ctx_server.queue_results.remove_waiting_task_id(id_task); return false; } break; } } ctx_server.queue_results.remove_waiting_task_id(id_task); sink.done(); return true; }; auto on_complete = [id_task, &ctx_server] (bool) { // cancel ctx_server.request_cancel(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }; const auto handle_models = [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json models = { {"object", "list"}, {"data", { { {"id", params.model_alias}, {"object", "model"}, {"created", std::time(0)}, {"owned_by", "llamacpp"}, {"meta", model_meta} }, }} }; res.set_content(models.dump(), "application/json; charset=utf-8"); }; const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template); const int id_task = ctx_server.queue_tasks.get_new_id(); ctx_server.queue_results.add_waiting_task_id(id_task); ctx_server.request_completion(id_task, -1, data, false, false); const auto completion_id = gen_chatcmplid(); if (!json_value(data, "stream", false)) { server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error && result.stop) { json result_oai = format_final_response_oaicompat(data, result.data, completion_id); res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res_error(res, result.data); } ctx_server.queue_results.remove_waiting_task_id(id_task); } else { const auto chunked_content_provider = [id_task, &ctx_server, completion_id](size_t, httplib::DataSink & sink) { while (true) { server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error) { std::vector result_array = format_partial_response_oaicompat(result.data, completion_id); for (auto it = result_array.begin(); it != result_array.end(); ++it) { if (!it->empty()) { const std::string str = "data: " + it->dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; LOG_VERBOSE("data stream", {{"to_send", str}}); if (!sink.write(str.c_str(), str.size())) { ctx_server.queue_results.remove_waiting_task_id(id_task); return false; } } } if (result.stop) { break; } } else { const std::string str = "error: " + result.data.dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; LOG_VERBOSE("data stream", {{"to_send", str}}); if (!sink.write(str.c_str(), str.size())) { ctx_server.queue_results.remove_waiting_task_id(id_task); return false; } break; } } sink.done(); ctx_server.queue_results.remove_waiting_task_id(id_task); return true; }; auto on_complete = [id_task, &ctx_server](bool) { // cancel request ctx_server.request_cancel(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }; const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = json::parse(req.body); const int id_task = ctx_server.queue_tasks.get_new_id(); ctx_server.queue_results.add_waiting_task_id(id_task); ctx_server.request_completion(id_task, -1, data, true, false); if (!json_value(data, "stream", false)) { server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error && result.stop) { res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res_error(res, result.data); } ctx_server.queue_results.remove_waiting_task_id(id_task); } else { const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) { while (true) { server_task_result result = ctx_server.queue_results.recv(id_task); if (!result.error) { const std::string str = "data: " + result.data.dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; LOG_VERBOSE("data stream", { { "to_send", str } }); if (!sink.write(str.c_str(), str.size())) { ctx_server.queue_results.remove_waiting_task_id(id_task); return false; } if (result.stop) { break; } } else { break; } } ctx_server.queue_results.remove_waiting_task_id(id_task); sink.done(); return true; }; auto on_complete = [id_task, &ctx_server] (bool) { ctx_server.request_cancel(id_task); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }; const auto handle_tokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); std::vector tokens; if (body.count("content") != 0) { const bool add_special = json_value(body, "add_special", false); tokens = ctx_server.tokenize(body["content"], add_special); } const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json; charset=utf-8"); }; const auto handle_detokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); const json body = json::parse(req.body); std::string content; if (body.count("tokens") != 0) { const std::vector tokens = body["tokens"]; content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend()); } const json data = format_detokenized_response(content); return res.set_content(data.dump(), "application/json; charset=utf-8"); }; const auto handle_embeddings = [¶ms, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!params.embedding) { res.status = 501; res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8"); return; } const json body = json::parse(req.body); bool is_openai = false; // an input prompt can be a string or a list of tokens (integer) json prompt; if (body.count("input") != 0) { is_openai = true; prompt = body["input"]; } else if (body.count("content") != 0) { // with "content", we only support single prompt prompt = std::vector{body["content"]}; } else { res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST)); return; } // create and queue the task json responses; { const int id_task = ctx_server.queue_tasks.get_new_id(); ctx_server.queue_results.add_waiting_task_id(id_task); ctx_server.request_completion(id_task, -1, {{"prompt", prompt}}, false, true); // get the result server_task_result result = ctx_server.queue_results.recv(id_task); ctx_server.queue_results.remove_waiting_task_id(id_task); if (!result.error) { if (result.data.count("results")) { // result for multi-task responses = result.data["results"]; } else { // result for single task responses = std::vector{result.data}; } } else { // error received, ignore everything else res_error(res, result.data); return; } } // write JSON response json root = is_openai ? format_embeddings_response_oaicompat(body, responses) : responses[0]; return res.set_content(root.dump(), "application/json; charset=utf-8"); }; auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) { return [content, len, mime_type](const httplib::Request &, httplib::Response & res) { res.set_content(reinterpret_cast(content), len, mime_type); return false; }; }; // // Router // // register static assets routes if (!sparams.public_path.empty()) { // Set the base directory for serving static files svr->set_base_dir(sparams.public_path); } // using embedded static files svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); svr->Get("/json-schema-to-grammar.mjs", handle_static_file( json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); // register API routes svr->Get ("/health", handle_health); svr->Get ("/slots", handle_slots); svr->Get ("/metrics", handle_metrics); svr->Get ("/props", handle_props); svr->Get ("/v1/models", handle_models); svr->Post("/completion", handle_completions); // legacy svr->Post("/completions", handle_completions); svr->Post("/v1/completions", handle_completions); svr->Post("/chat/completions", handle_chat_completions); svr->Post("/v1/chat/completions", handle_chat_completions); svr->Post("/infill", handle_infill); svr->Post("/embedding", handle_embeddings); // legacy svr->Post("/embeddings", handle_embeddings); svr->Post("/v1/embeddings", handle_embeddings); svr->Post("/tokenize", handle_tokenize); svr->Post("/detokenize", handle_detokenize); if (!sparams.slot_save_path.empty()) { // only enable slot endpoints if slot_save_path is set svr->Post("/slots/:id_slot", handle_slots_action); } // // Start the server // if (sparams.n_threads_http < 1) { // +2 threads for monitoring endpoints sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); } log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below std::thread t([&]() { if (!svr->listen_after_bind()) { state.store(SERVER_STATE_ERROR); return 1; } return 0; }); ctx_server.queue_tasks.on_new_task(std::bind( &server_context::process_single_task, &ctx_server, std::placeholders::_1)); ctx_server.queue_tasks.on_finish_multitask(std::bind( &server_context::on_finish_multitask, &ctx_server, std::placeholders::_1)); ctx_server.queue_tasks.on_update_slots(std::bind( &server_context::update_slots, &ctx_server)); ctx_server.queue_results.on_multitask_update(std::bind( &server_queue::update_multitask, &ctx_server.queue_tasks, std::placeholders::_1, std::placeholders::_2, std::placeholders::_3 )); shutdown_handler = [&](int) { ctx_server.queue_tasks.terminate(); }; #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) struct sigaction sigint_action; sigint_action.sa_handler = signal_handler; sigemptyset (&sigint_action.sa_mask); sigint_action.sa_flags = 0; sigaction(SIGINT, &sigint_action, NULL); sigaction(SIGTERM, &sigint_action, NULL); #elif defined (_WIN32) auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false; }; SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); #endif ctx_server.queue_tasks.start_loop(); svr->stop(); t.join(); llama_backend_free(); return 0; }