#include "common.h" #include "llama.h" #include "grammar-parser.h" #include "../llava/clip.h" #include "stb_image.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 #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 #endif #define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" using json = nlohmann::json; struct server_params { std::string hostname = "127.0.0.1"; std::string api_key; std::string public_path = "examples/server/public"; int32_t port = 8080; int32_t read_timeout = 600; int32_t write_timeout = 600; }; static bool server_verbose = false; #if SERVER_VERBOSE != 1 #define LOG_VERBOSE(MSG, ...) #else #define LOG_VERBOSE(MSG, ...) \ do \ { \ if (server_verbose) \ { \ server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ } \ } while (0) #endif #define LOG_ERROR( MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) json oaicompat_completion_params_parse(const json &body); std::string format_chatml(std::vector messages); // // base64 utils (TODO: move to common in the future) // static const std::string base64_chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" "abcdefghijklmnopqrstuvwxyz" "0123456789+/"; static inline bool is_base64(uint8_t c) { return (isalnum(c) || (c == '+') || (c == '/')); } static std::vector base64_decode(std::string const &encoded_string) { int i = 0; int j = 0; int in_ = 0; int in_len = encoded_string.size(); uint8_t char_array_4[4]; uint8_t char_array_3[3]; std::vector ret; while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { char_array_4[i++] = encoded_string[in_]; in_++; if (i == 4) { for (i = 0; i <4; i++) { char_array_4[i] = base64_chars.find(char_array_4[i]); } char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; for (i = 0; (i < 3); i++) { ret.push_back(char_array_3[i]); } i = 0; } } if (i) { for (j = i; j <4; j++) { char_array_4[j] = 0; } for (j = 0; j <4; j++) { char_array_4[j] = base64_chars.find(char_array_4[j]); } char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; for (j = 0; (j < i - 1); j++) { ret.push_back(char_array_3[j]); } } return ret; } // // parallel // enum task_type { COMPLETION_TASK, CANCEL_TASK }; struct task_server { int id; int target_id; task_type type; json data; bool infill_mode = false; bool embedding_mode = false; int multitask_id = -1; }; struct task_result { int id; int multitask_id = -1; bool stop; bool error; json result_json; }; struct task_multi { int id; std::set subtasks_remaining{}; std::vector results{}; }; // TODO: can become bool if we can't find use of more states enum slot_state { IDLE, PROCESSING, }; enum slot_command { NONE, LOAD_PROMPT, RELEASE, }; 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_predict = -1; // new tokens to predict std::vector antiprompt; json input_prefix; json input_suffix; }; struct slot_image { int32_t id; bool request_encode_image = false; float* image_embedding = nullptr; int32_t image_tokens = 0; clip_image_u8 img_data; std::string prefix_prompt; // before of this image }; // completion token output with probabilities struct completion_token_output { struct token_prob { llama_token tok; float prob; }; std::vector probs; llama_token tok; std::string text_to_send; }; static size_t common_part(const std::vector &a, const std::vector &b) { size_t i; for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) { } return i; } enum stop_type { STOP_FULL, STOP_PARTIAL, }; static bool ends_with(const std::string &str, const std::string &suffix) { return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); } static size_t find_partial_stop_string(const std::string &stop, const std::string &text) { if (!text.empty() && !stop.empty()) { const char text_last_char = text.back(); for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) { if (stop[char_index] == text_last_char) { const std::string current_partial = stop.substr(0, char_index + 1); if (ends_with(text, current_partial)) { return text.size() - char_index - 1; } } } } return std::string::npos; } // TODO: reuse llama_detokenize template static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) { std::string ret; for (; begin != end; ++begin) { ret += llama_token_to_piece(ctx, *begin); } return ret; } static void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { nlohmann::ordered_json log { {"timestamp", time(nullptr)}, {"level", level}, {"function", function}, {"line", line}, {"message", message}, }; if (!extra.empty()) { log.merge_patch(extra); } const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace); printf("%.*s\n", (int)str.size(), str.data()); fflush(stdout); } // format incomplete utf-8 multibyte character for output static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) { std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); // if the size is 1 and first bit is 1, meaning it's a partial character // (size > 1 meaning it's already a known token) if (out.size() == 1 && (out[0] & 0x80) == 0x80) { std::stringstream ss; ss << std::hex << (out[0] & 0xff); std::string res(ss.str()); out = "byte: \\x" + res; } return out; } // convert a vector of completion_token_output to json static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) { json out = json::array(); for (const auto &prob : probs) { json probs_for_token = json::array(); for (const auto &p : prob.probs) { std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); probs_for_token.push_back(json { {"tok_str", tok_str}, {"prob", p.prob}, }); } std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); out.push_back(json{ {"content", tok_str}, {"probs", probs_for_token}, }); } return out; } template static T json_value(const json &body, const std::string &key, const T &default_value) { // Fallback null to default value return body.contains(key) && !body.at(key).is_null() ? body.value(key, default_value) : default_value; } struct llama_client_slot { int id; int task_id = -1; struct slot_params params; slot_state state = IDLE; slot_command 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 num_prompt_tokens = 0; int32_t num_prompt_tokens_processed = 0; json prompt; std::string generated_text; llama_token sampled; 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 struct llama_sampling_params sparams; llama_sampling_context *ctx_sampling = nullptr; // multimodal std::vector images; // stats size_t sent_count = 0; size_t sent_token_probs_index = 0; int64_t t_start_process_prompt; int64_t t_start_genereration; double t_prompt_processing; // ms double t_token_generation; // ms // multitasks int multitask_id = -1; void reset() { num_prompt_tokens = 0; generated_text = ""; truncated = false; stopped_eos = false; stopped_word = false; stopped_limit = false; stopping_word = ""; n_past = 0; sent_count = 0; sent_token_probs_index = 0; infill = false; generated_token_probs.clear(); for (slot_image &img : images) { free(img.image_embedding); delete[] img.img_data.data; img.prefix_prompt = ""; } images.clear(); // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } bool has_budget(gpt_params &global_params) { 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 || n_remaining == -1; // no budget || limitless } bool available() const { return state == IDLE && command == NONE; } bool is_processing() const { return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING; } void add_token_string(const completion_token_output &token) { if (command == RELEASE) { return; } cache_tokens.push_back(token.tok); generated_token_probs.push_back(token); } void release() { if (state == IDLE || state == PROCESSING) { t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; command = RELEASE; } } json get_formated_timings() { return json { {"prompt_n", num_prompt_tokens_processed}, {"prompt_ms", t_prompt_processing}, {"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed}, {"prompt_per_second", 1e3 / t_prompt_processing * num_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}, }; } void print_timings() const { LOG_TEE("\n"); LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n", __func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed); LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", __func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded); LOG_TEE("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation); } }; struct llama_server_context { llama_model *model = nullptr; llama_context *ctx = nullptr; clip_ctx *clp_ctx = nullptr; gpt_params params; llama_batch batch; bool multimodal = false; bool clean_kv_cache = true; bool all_slots_are_idle = false; bool add_bos_token = true; int32_t id_gen; 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; std::vector queue_tasks; std::vector queue_results; std::vector queue_multitasks; std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks std::mutex mutex_results; ~llama_server_context() { if (ctx) { llama_free(ctx); ctx = nullptr; } if (model) { llama_free_model(model); model = nullptr; } } bool load_model(const gpt_params ¶ms_) { params = params_; if (!params.mmproj.empty()) { multimodal = true; LOG_TEE("Multi Modal Mode Enabled"); clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); if(clp_ctx == nullptr) { LOG_ERROR("unable to load clip model", {{"model", params.mmproj}}); return false; } if (params.n_ctx < 2048) { // request larger context for the image embedding params.n_ctx = 2048; } } std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == nullptr) { LOG_ERROR("unable to load model", {{"model", params.model}}); return false; } if (multimodal) { const int n_embd_clip = clip_n_mmproj_embd(clp_ctx); const int n_embd_llm = llama_n_embd(model); if (n_embd_clip != n_embd_llm) { LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm); llama_free(ctx); llama_free_model(model); return false; } } n_ctx = llama_n_ctx(ctx); add_bos_token = llama_should_add_bos_token(model); return true; } void initialize() { id_gen = 0; // create slots all_slots_are_idle = true; const int32_t n_ctx_slot = n_ctx / params.n_parallel; LOG_TEE("Available slots:\n"); for (int i = 0; i < params.n_parallel; i++) { llama_client_slot slot; slot.id = i; slot.n_ctx = n_ctx_slot; slot.reset(); LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot); slots.push_back(slot); } batch = llama_batch_init(n_ctx, 0, params.n_parallel); // empty system prompt system_prompt = ""; system_tokens.clear(); } std::vector tokenize(const json & json_prompt, bool add_bos) 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_bos, 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_bos, TMP_FORCE_SPECIAL); } return prompt_tokens; } llama_client_slot* get_slot(int id) { int64_t t_last = ggml_time_us(); llama_client_slot *last_used = nullptr; for (llama_client_slot & slot : slots) { if (slot.id == id && slot.available()) { return &slot; } 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_data(llama_client_slot* &slot, json data) { slot_params default_params; llama_sampling_params default_sparams; 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.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.seed = json_value(data, "seed", default_params.seed); slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); // infill if (data.count("input_prefix") != 0) { slot->params.input_prefix = data["input_prefix"]; } else { slot->params.input_prefix = ""; } if (data.count("input_suffix") != 0) { slot->params.input_suffix = data["input_suffix"]; } else { slot->params.input_suffix = ""; } if (data.count("prompt") != 0) { slot->prompt = data["prompt"]; } else { slot->prompt = ""; } 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) { if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) { llama_token tok = el[0].get(); if (tok >= 0 && tok < n_vocab) { if (el[1].is_number()) { slot->sparams.logit_bias[tok] = el[1].get(); } else if (el[1].is_boolean() && !el[1].get()) { slot->sparams.logit_bias[tok] = -INFINITY; } } } } } 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); } } } if (multimodal) { const auto &images_data = data.find("image_data"); if (images_data != data.end() && images_data->is_array()) { for (const auto &img : *images_data) { std::string data_b64 = img["data"].get(); slot_image img_sl; img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); int width, height, channels; std::vector image_buffer = base64_decode(data_b64); data_b64.clear(); auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); if (!data) { LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id); return false; } LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height); img_sl.img_data.nx = width; img_sl.img_data.ny = height; img_sl.img_data.size = width * height * 3; img_sl.img_data.data = new uint8_t[width * height * 3](); memcpy(img_sl.img_data.data, data, width * height * 3); stbi_image_free(data); img_sl.request_encode_image = true; slot->images.push_back(img_sl); } // process prompt // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} if (slot->images.size() > 0 && !slot->prompt.is_array()) { std::string prompt = slot->prompt.get(); size_t pos = 0, begin_prefix = 0; std::string pattern = "[img-"; while ((pos = prompt.find(pattern, pos)) != std::string::npos) { size_t end_prefix = pos; pos += pattern.length(); size_t end_pos = prompt.find("]", pos); if (end_pos != std::string::npos) { std::string image_id = prompt.substr(pos, end_pos - pos); try { int img_id = std::stoi(image_id); bool found = false; for (slot_image &img : slot->images) { if (img.id == img_id) { found = true; img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); begin_prefix = end_pos + 1; break; } } if (!found) { LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id); slot->images.clear(); return false; } } catch (const std::invalid_argument& e) { LOG_TEE("Invalid image number id in prompt\n"); slot->images.clear(); return false; } } } slot->prompt = ""; slot->params.input_suffix = prompt.substr(begin_prefix); slot->params.cache_prompt = false; // multimodal doesn't support cache prompt } } } if (slot->ctx_sampling != nullptr) { llama_sampling_free(slot->ctx_sampling); } slot->ctx_sampling = llama_sampling_init(slot->sparams); slot->command = LOAD_PROMPT; all_slots_are_idle = false; LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id); return true; } void kv_cache_clear() { // clear the entire KV cache llama_kv_cache_clear(ctx); clean_kv_cache = false; } void update_system_prompt() { system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token); llama_batch_clear(batch); kv_cache_clear(); for (int i = 0; i < (int) system_tokens.size(); ++i) { llama_batch_add(batch, system_tokens[i], i, { 0 }, false); } if (llama_decode(ctx, batch) != 0) { LOG_TEE("%s: llama_decode() failed\n", __func__); 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, 0, system_tokens.size()); } LOG_TEE("system prompt updated\n"); system_need_update = false; } void notify_system_prompt_changed() { // release all slots for (llama_client_slot &slot : slots) { slot.release(); } system_need_update = true; } void process_system_prompt_data(const json &sys_props) { system_prompt = sys_props.value("prompt", ""); name_user = sys_props.value("anti_prompt", ""); name_assistant = sys_props.value("assistant_name", ""); if (slots.size() > 0) { notify_system_prompt_changed(); } } static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, const stop_type type, llama_client_slot &slot) { size_t stop_pos = std::string::npos; for (const std::string &word : slot.params.antiprompt) { size_t pos; if (type == STOP_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_FULL) { slot.stopped_word = true; slot.stopping_word = word; slot.has_next_token = false; } stop_pos = pos; } } return stop_pos; } bool process_token(completion_token_output &result, llama_client_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); slot.sampled = result.tok; // search stop word and delete it slot.generated_text += token_str; slot.has_next_token = true; // 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.sent_count, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); bool is_stop_full = false; size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); 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.sent_count, slot.generated_text.size()); } else { is_stop_full = false; stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); } // 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.sent_count += 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 > 2 && slot.has_next_token && !slot.has_budget(params)) { slot.stopped_limit = true; slot.has_next_token = false; } if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) { slot.stopped_eos = true; slot.has_next_token = false; LOG_VERBOSE("eos token found", {}); } LOG_VERBOSE("next token", { {"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}, {"num_tokens_predicted", 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 } bool process_images(llama_client_slot &slot) const { for (slot_image &img : slot.images) { if (!img.request_encode_image) { continue; } clip_image_f32 img_res; if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true)) { LOG_TEE("Error processing the given image"); clip_free(clp_ctx); return false; } img.image_tokens = clip_n_patches(clp_ctx); img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); if (!img.image_embedding) { LOG_TEE("Unable to allocate memory for image embeddings\n"); clip_free(clp_ctx); return false; } LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id); if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding)) { LOG_TEE("Unable to encode image\n"); return false; } img.request_encode_image = false; } return slot.images.size() > 0; } void send_error(task_server& task, std::string error) { std::lock_guard lock(mutex_results); task_result res; res.id = task.id; res.multitask_id = task.multitask_id; res.stop = false; res.error = true; res.result_json = { { "content", error } }; queue_results.push_back(res); } void add_multi_task(int id, std::vector& sub_ids) { std::lock_guard lock(mutex_tasks); task_multi multi; multi.id = id; std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); queue_multitasks.push_back(multi); } void update_multi_task(int multitask_id, int subtask_id, task_result& result) { std::lock_guard lock(mutex_tasks); for (auto& multitask : queue_multitasks) { if (multitask.id == multitask_id) { multitask.subtasks_remaining.erase(subtask_id); multitask.results.push_back(result); } } } json get_model_props() { return get_formated_generation(slots[0]); } json get_formated_generation(llama_client_slot &slot) { 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); return json { {"n_ctx", slot.n_ctx}, {"model", params.model_alias}, {"seed", slot.params.seed}, {"temp", slot.sparams.temp}, {"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}, {"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}, {"n_keep", params.n_keep}, {"ignore_eos", ignore_eos}, {"stream", slot.params.stream}, {"logit_bias", slot.sparams.logit_bias}, {"n_probs", slot.sparams.n_probs}, {"grammar", slot.sparams.grammar}, }; } void send_partial_response(llama_client_slot &slot, completion_token_output tkn) { std::lock_guard lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; res.error = false; res.stop = false; res.result_json = json { {"content", tkn.text_to_send}, {"stop", false}, {"slot_id", slot.id}, {"multimodal", multimodal} }; if (slot.sparams.n_probs > 0) { std::vector probs_output = {}; const std::vector to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size()); size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size()); 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.sent_token_probs_index = probs_stop_pos; res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); } if (slot.oaicompat) { res.result_json["oaicompat_token_ctr"] = slot.n_decoded; res.result_json["model"] = slot.oaicompat_model; } queue_results.push_back(res); } void send_final_response(llama_client_slot &slot) { std::lock_guard lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; res.error = false; res.stop = true; res.result_json = json { {"content", !slot.params.stream ? slot.generated_text : ""}, {"slot_id", slot.id}, {"stop", true}, {"model", params.model_alias}, {"tokens_predicted", slot.n_decoded}, {"tokens_evaluated", slot.num_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); probs = std::vector(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size()); } else { probs = std::vector( slot.generated_token_probs.begin(), slot.generated_token_probs.begin() + slot.sent_token_probs_index); } res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs); } if (slot.oaicompat) { res.result_json["oaicompat_token_ctr"] = slot.n_decoded; res.result_json["model"] = slot.oaicompat_model; } // parent multitask, if any, needs to be updated if (slot.multitask_id != -1) { update_multi_task(slot.multitask_id, slot.task_id, res); } queue_results.push_back(res); } void send_embedding(llama_client_slot &slot) { std::lock_guard lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; res.error = false; res.stop = true; const int n_embd = llama_n_embd(model); if (!params.embedding) { LOG_WARNING("embedding disabled", { {"params.embedding", params.embedding}, }); res.result_json = json { {"embedding", std::vector(n_embd, 0.0f)}, }; } else { const float *data = llama_get_embeddings(ctx); std::vector embedding(data, data + n_embd); res.result_json = json { {"embedding", embedding }, }; } queue_results.push_back(res); } int request_completion(json data, bool infill, bool embedding, int multitask_id) { std::unique_lock lock(mutex_tasks); task_server task; task.id = id_gen++; task.target_id = 0; task.data = std::move(data); task.infill_mode = infill; task.embedding_mode = embedding; task.type = COMPLETION_TASK; task.multitask_id = multitask_id; // when a completion task's prompt array is not a singleton, we split it into multiple requests if (task.data.at("prompt").size() > 1) { lock.unlock(); // entering new func scope return split_multiprompt_task(task); } // otherwise, it's a single-prompt task, we actually queue it queue_tasks.push_back(task); return task.id; } task_result next_result(int task_id) { while (true) { std::this_thread::sleep_for(std::chrono::microseconds(5)); std::lock_guard lock(mutex_results); if (queue_results.empty()) { continue; } for (int i = 0; i < (int) queue_results.size(); i++) { // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result if (queue_results[i].multitask_id == task_id) { update_multi_task(task_id, queue_results[i].id, queue_results[i]); queue_results.erase(queue_results.begin() + i); continue; } if (queue_results[i].id == task_id) { assert(queue_results[i].multitask_id == -1); task_result res = queue_results[i]; queue_results.erase(queue_results.begin() + i); return res; } } } // never reached //return task_result{-1, false, false, {}}; } // for multiple images processing bool ingest_images(llama_client_slot &slot, int n_batch) { int image_idx = 0; while (image_idx < (int) slot.images.size()) { slot_image &img = slot.images[image_idx]; // process prefix prompt for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(n_batch, (int32_t) (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)) { LOG_TEE("%s : failed to eval\n", __func__); return false; } } // process image with llm for (int i = 0; i < img.image_tokens; i += n_batch) { int n_eval = img.image_tokens - i; if (n_eval > n_batch) { n_eval = n_batch; } const int n_embd = llama_n_embd(model); llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; if (llama_decode(ctx, batch_img)) { LOG_TEE("%s : failed to eval image\n", __func__); return false; } slot.n_past += n_eval; } image_idx++; llama_batch_clear(batch); // append prefix of next image const auto json_prompt = (image_idx >= (int) slot.images.size()) ? slot.params.input_suffix : // no more images, then process suffix prompt (json)(slot.images[image_idx].prefix_prompt); std::vector append_tokens = tokenize(json_prompt, false); // has next image for (int i = 0; i < (int) append_tokens.size(); ++i) { llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); slot.n_past += 1; } } return true; } void request_cancel(int task_id) { std::lock_guard lock(mutex_tasks); task_server task; task.id = id_gen++; task.type = CANCEL_TASK; task.target_id = task_id; queue_tasks.push_back(task); } int split_multiprompt_task(task_server& multiprompt_task) { int prompt_count = multiprompt_task.data.at("prompt").size(); assert(prompt_count > 1); int multitask_id = id_gen++; std::vector subtask_ids(prompt_count); 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.) subtask_ids[i] = request_completion(subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id); } // queue up the multitask so we can track its subtask progression add_multi_task(multitask_id, subtask_ids); return multitask_id; } void process_tasks() { std::lock_guard lock(mutex_tasks); while (!queue_tasks.empty()) { task_server task = queue_tasks.front(); queue_tasks.erase(queue_tasks.begin()); switch (task.type) { case COMPLETION_TASK: { llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); if (slot == nullptr) { LOG_TEE("slot unavailable\n"); // send error result send_error(task, "slot unavailable"); return; } if (task.data.contains("system_prompt")) { process_system_prompt_data(task.data["system_prompt"]); } slot->reset(); slot->infill = task.infill_mode; slot->embedding = task.embedding_mode; slot->task_id = task.id; slot->multitask_id = task.multitask_id; if (!launch_slot_with_data(slot, task.data)) { // send error result send_error(task, "internal_error"); break; } } break; case CANCEL_TASK: { // release slot linked with the task id for (auto & slot : slots) { if (slot.task_id == task.target_id) { slot.release(); break; } } } break; } } // remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue auto queue_iterator = queue_multitasks.begin(); while (queue_iterator != queue_multitasks.end()) { if (queue_iterator->subtasks_remaining.empty()) { // all subtasks done == multitask is done task_result aggregate_result; aggregate_result.id = queue_iterator->id; aggregate_result.stop = true; aggregate_result.error = false; // collect json results into one json result std::vector result_jsons; for (auto& subres : queue_iterator->results) { result_jsons.push_back(subres.result_json); aggregate_result.error = aggregate_result.error && subres.error; } aggregate_result.result_json = json{ "results", result_jsons }; std::lock_guard lock(mutex_results); queue_results.push_back(aggregate_result); queue_iterator = queue_multitasks.erase(queue_iterator); } else { ++queue_iterator; } } } bool update_slots() { // attend tasks process_tasks(); // update the system prompt wait until all slots are idle state if (system_need_update && all_slots_are_idle) { LOG_TEE("updating system prompt\n"); update_system_prompt(); } llama_batch_clear(batch); if (all_slots_are_idle) { if (system_prompt.empty() && clean_kv_cache) { LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n"); kv_cache_clear(); } // avoid 100% usage of cpu all time std::this_thread::sleep_for(std::chrono::milliseconds(5)); } for (llama_client_slot &slot : slots) { if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx) { // Shift context const int n_left = slot.n_past - slot.params.n_keep - 1; const int n_discard = n_left / 2; LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); for (size_t i = slot.params.n_keep + 1 + 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; LOG_VERBOSE("context shift", { {"n_ctx", n_ctx}, {"n_keep", params.n_keep}, {"n_left", n_left}, }); } } // decode any currently ongoing sequences for (auto & slot : slots) { // release the slot if (slot.command == RELEASE) { slot.state = IDLE; slot.command = NONE; slot.t_last_used = ggml_time_us(); LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size()); continue; } if (slot.state == IDLE) { continue; } slot.i_batch = batch.n_tokens; llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true); slot.n_decoded += 1; slot.n_past += 1; } // process in chunks of params.n_batch int32_t n_batch = params.n_batch; // assign workload to the slots if (params.cont_batching || batch.n_tokens == 0) { for (auto & slot : slots) { const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()) || !slot.images.empty(); // empty prompt passed -> release the slot and send empty response if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt) { slot.release(); slot.print_timings(); send_final_response(slot); continue; } // need process the prompt if (slot.state == IDLE && slot.command == LOAD_PROMPT) { slot.state = PROCESSING; slot.command = NONE; std::vector prompt_tokens; slot.t_start_process_prompt = ggml_time_us(); slot.t_start_genereration = 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_token); // add BOS if there isn't system prompt } slot.num_prompt_tokens = prompt_tokens.size(); if (slot.params.n_keep < 0) { slot.params.n_keep = slot.num_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 (slot.num_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.num_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()); LOG_VERBOSE("input truncated", { {"n_ctx", slot.n_ctx}, {"n_keep", slot.params.n_keep}, {"n_left", n_left}, {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, }); slot.truncated = true; prompt_tokens = new_tokens; slot.num_prompt_tokens = prompt_tokens.size(); GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); } if (!slot.params.cache_prompt) { llama_sampling_reset(slot.ctx_sampling); slot.n_past = 0; slot.num_prompt_tokens_processed = slot.num_prompt_tokens; } else { // push the prompt into the sampling context (do not apply grammar) for (auto &token : prompt_tokens) { llama_sampling_accept(slot.ctx_sampling, ctx, token, false); } slot.n_past = common_part(slot.cache_tokens, prompt_tokens); slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); } LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past); llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1); slot.cache_tokens = prompt_tokens; if (slot.n_past == slot.num_prompt_tokens) { // we have to evaluate at least 1 token to generate logits. LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); slot.n_past--; } LOG_VERBOSE("prompt ingested", { {"n_past", slot.n_past}, {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); const bool has_images = process_images(slot); // process the prefix of first image std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens; for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) { llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false); } if (has_images && !ingest_images(slot, n_batch)) { LOG_TEE("failed processing images\n"); return false; } // extract the logits only for the last token if (batch.n_tokens > 0) { batch.logits[batch.n_tokens - 1] = true; } slot.n_decoded = 0; slot.i_batch = batch.n_tokens - 1; } } } if (batch.n_tokens == 0) { all_slots_are_idle = true; return true; } for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(n_batch, (int32_t) (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 }; 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_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); return false; } LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2); // retry with half the batch size to try to find a free slot in the KV cache n_batch /= 2; i -= n_batch; continue; } for (auto & slot : slots) { if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { continue; } // prompt evaluated for embedding if (slot.embedding) { send_embedding(slot); slot.release(); slot.i_batch = -1; return true; } 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); if (slot.n_decoded == 1) { slot.t_start_genereration = ggml_time_us(); slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3; } llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false }; result.tok = id; const int32_t n_probs = slot.sparams.n_probs; if (slot.sparams.temp <= 0 && n_probs > 0) { // for llama_sample_token_greedy we need to sort candidates llama_sample_softmax(ctx, &cur_p); } for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) { result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); } if (!process_token(result, slot)) { slot.release(); slot.print_timings(); send_final_response(slot); } slot.i_batch = -1; } } return true; } }; static void server_print_usage(const char *argv0, const gpt_params ¶ms, 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(" -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(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); if (llama_mlock_supported()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa attempt optimizations that help on some NUMA systems\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD printf(" -ngl N, --n-gpu-layers N\n"); printf(" number of layers to store in VRAM\n"); printf(" -ts SPLIT --tensor-split SPLIT\n"); printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); printf(" -nommq, --no-mul-mat-q\n"); printf(" use cuBLAS instead of custom mul_mat_q CUDA kernels.\n"); printf(" Not recommended since this is both slower and uses more VRAM.\n"); #endif printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); 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 %s)\n", sparams.public_path.c_str()); printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" --embedding 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: disabled)\n"); 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(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf(" --log-disable disables logging to a file.\n"); printf("\n"); } static void server_params_parse(int argc, char **argv, server_params &sparams, gpt_params ¶ms, llama_server_context& llama) { 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_key = argv[i]; } 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 == "-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_NONE; } else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; } else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_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 == "--threads" || arg == "-t") { if (++i >= argc) { invalid_param = true; break; } params.n_threads = 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 == "-b" || arg == "--batch-size") { if (++i >= argc) { invalid_param = true; break; } params.n_batch = std::stoi(argv[i]); params.n_batch = std::min(512, params.n_batch); } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { if (++i >= argc) { invalid_param = true; break; } #ifdef 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}}); #endif } else if (arg == "--tensor-split" || arg == "-ts") { if (++i >= argc) { invalid_param = true; break; } #ifdef GGML_USE_CUBLAS 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 cuBLAS. It is not possible to set a tensor split.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--no-mul-mat-q" || arg == "-nommq") { #ifdef GGML_USE_CUBLAS params.mul_mat_q = false; #else LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--main-gpu" || arg == "-mg") { if (++i >= argc) { invalid_param = true; break; } #ifdef GGML_USE_CUBLAS params.main_gpu = std::stoi(argv[i]); #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {}); #endif } else if (arg == "--lora") { if (++i >= argc) { invalid_param = true; break; } params.lora_adapter.push_back(std::make_tuple(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.push_back(std::make_tuple(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") { params.numa = true; } else if (arg == "--embedding") { params.embedding = true; } else if (arg == "-cb" || arg == "--cont-batching") { params.cont_batching = 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 systm_content; std::copy( std::istreambuf_iterator(file), std::istreambuf_iterator(), std::back_inserter(systm_content) ); llama.process_system_prompt_data(json::parse(systm_content)); } else if(arg == "--mmproj") { if (++i >= argc) { invalid_param = true; break; } params.mmproj = argv[i]; } else if (arg == "--log-disable") { log_set_target(stdout); LOG_INFO("logging to file is disabled.", {}); } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); exit(1); } } 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 std::string random_string() { static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); std::random_device rd; std::mt19937 generator(rd()); std::string result(32, ' '); for (int i = 0; i < 32; ++i) { result[i] = str[generator() % str.size()]; } return result; } static std::string gen_chatcmplid() { std::stringstream chatcmplid; chatcmplid << "chatcmpl-" << random_string(); return chatcmplid.str(); } std::string format_chatml(std::vector messages) { std::ostringstream chatml_msgs; for (auto it = messages.begin(); it != messages.end(); ++it) { chatml_msgs << "<|im_start|>" << json_value(*it, "role", std::string("user")) << '\n'; chatml_msgs << json_value(*it, "content", std::string("")) << "<|im_end|>\n"; } chatml_msgs << "<|im_start|>assistant" << '\n'; return chatml_msgs.str(); } /* llama.cpp completion api semantics */ json oaicompat_completion_params_parse( const json &body /* openai api json semantics */) { json llama_params; llama_params["__oaicompat"] = true; // Map OpenAI parameters to llama.cpp parameters llama_params["model"] = json_value(body, "model", std::string("uknown")); llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); llama_params["temperature"] = json_value(body, "temperature", 0.8); llama_params["top_k"] = json_value(body, "top_k", 40); llama_params["top_p"] = json_value(body, "top_p", 0.95); llama_params["n_predict"] = json_value(body, "max_tokens", -1); llama_params["logit_bias"] = json_value(body, "logit_bias",json::object()); llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); llama_params["seed"] = json_value(body, "seed", 0); llama_params["stream"] = json_value(body, "stream", false); llama_params["mirostat"] = json_value(body, "mirostat", false); llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0); llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0); llama_params["penalize_nl"] = json_value(body, "penalize_nl", false); llama_params["typical_p"] = json_value(body, "typical_p", 0.0); llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0); llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0); if (body.count("grammar") != 0) { llama_params["grammar"] = json_value(body, "grammar", json::object()); } // Handle 'stop' field if (body.contains("stop") && body["stop"].is_string()) { llama_params["stop"] = json::array({body["stop"].get()}); } else { llama_params["stop"] = json_value(body, "stop", json::array()); } // Ensure there is ChatML-specific end sequence among stop words llama_params["stop"].push_back("<|im_end|>"); return llama_params; } static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false) { json result = response.result_json; bool stopped_word = result.count("stopped_word") != 0; bool stopped_eos = json_value(result, "stopped_eos", false); int num_tokens_predicted = json_value(result, "tokens_predicted", 0); int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); std::string content = json_value(result, "content", std::string("")); std::string finish_reason = "length"; if (stopped_word || stopped_eos) { finish_reason = "stop"; } json choices = streaming ? json::array({json{{"finish_reason", finish_reason}, {"index", 0}, {"delta", json::object()}}}) : json::array({json{{"finish_reason", finish_reason}, {"index", 0}, {"message", json{{"content", content}, {"role", "assistant"}}}}}); std::time_t t = std::time(0); json res = json{{"choices", choices}, {"created", t}, {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, {"usage", json{{"completion_tokens", num_tokens_predicted}, {"prompt_tokens", num_prompt_tokens}, {"total_tokens", num_tokens_predicted + num_prompt_tokens}}}, {"id", gen_chatcmplid()}}; if (server_verbose) { res["__verbose"] = result; } if (result.contains("completion_probabilities")) { res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); } return res; } // return value is vector as there is one case where we might need to generate two responses static std::vector format_partial_response_oaicompat(const task_result &response) { json result = response.result_json; if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { return std::vector({response.result_json}); } bool first = json_value(result, "oaicompat_token_ctr", 0) == 0; std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); bool stopped_word = json_value(result, "stopped_word", false); bool stopped_eos = json_value(result, "stopped_eos", false); bool stopped_limit = json_value(result, "stopped_limit", false); std::string content = json_value(result, "content", std::string("")); std::string finish_reason; if (stopped_word || stopped_eos) { finish_reason = "stop"; } if (stopped_limit) { finish_reason = "length"; } std::time_t t = std::time(0); json choices; if (!finish_reason.empty()) { choices = json::array({json{{"finish_reason", finish_reason}, {"index", 0}, {"delta", json::object()}}}); } else { if (first) { if (content.empty()) { choices = json::array({json{{"finish_reason", nullptr}, {"index", 0}, {"delta", json{{"role", "assistant"}}}}}); } else { // We have to send this as two updates to conform to openai behavior json initial_ret = json{{"choices", json::array({json{ {"finish_reason", nullptr}, {"index", 0}, {"delta", json{ {"role", "assistant"} }}}})}, {"created", t}, {"id", gen_chatcmplid()}, {"model", modelname}, {"object", "chat.completion.chunk"}}; json second_ret = json{ {"choices", json::array({json{{"finish_reason", nullptr}, {"index", 0}, {"delta", json{ {"content", content}}} }})}, {"created", t}, {"id", gen_chatcmplid()}, {"model", modelname}, {"object", "chat.completion.chunk"}}; return std::vector({initial_ret, second_ret}); } } else { // Some idiosyncrasy in task processing logic makes several trailing calls // with empty content, we ignore these at the calee site. if (content.empty()) { return std::vector({json::object()}); } choices = json::array({json{ {"finish_reason", nullptr}, {"index", 0}, {"delta", json{ {"content", content}, }}, }}); } } json ret = json{{"choices", choices}, {"created", t}, {"id", gen_chatcmplid()}, {"model", modelname}, {"object", "chat.completion.chunk"}}; return std::vector({ret}); } static json format_partial_response( llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector &probs ) { json res = json { {"content", content }, {"stop", false}, {"slot_id", slot->id }, {"multimodal", llama.multimodal } }; if (slot->sparams.n_probs > 0) { res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); } return res; } static json format_tokenizer_response(const std::vector &tokens) { return json{ {"tokens", tokens}}; } static json format_detokenized_response(std::string content) { return json{ {"content", content}}; } static void log_server_request(const httplib::Request &req, const httplib::Response &res) { 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}, }); } struct token_translator { llama_context * ctx; std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); } }; static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot) { auto & gtps = slot->generated_token_probs; auto translator = token_translator{llama.ctx}; auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); }; const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen); if (slot->generated_text.capacity() < slot->generated_text.size() + len) { slot->generated_text.reserve(slot->generated_text.size() + len); } for (const completion_token_output & cto : gtps) { slot->generated_text += translator(cto); } } 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 llama_server_context llama; server_params_parse(argc, argv, sparams, params, llama); if (params.model_alias == "unknown") { params.model_alias = params.model; } llama_backend_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()}, }); // load the model if (!llama.load_model(params)) { return 1; } llama.initialize(); httplib::Server svr; // Middleware for API key validation auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool { // If API key is not set, skip validation if (sparams.api_key.empty()) { 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 (received_api_key == sparams.api_key) { return true; // API key is valid } } // API key is invalid or not provided res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8"); res.status = 401; // Unauthorized LOG_WARNING("Unauthorized: Invalid API Key", {}); return false; }; svr.set_default_headers({{"Server", "llama.cpp"}, {"Access-Control-Allow-Origin", "*"}, {"Access-Control-Allow-Headers", "content-type"}}); // this is only called if no index.html is found in the public --path svr.Get("/", [](const httplib::Request &, httplib::Response &res) { res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html; charset=utf-8"); return false; }); // this is only called if no index.js is found in the public --path svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res) { res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript; charset=utf-8"); return false; }); // this is only called if no index.html is found in the public --path svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res) { res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript; charset=utf-8"); return false; }); // this is only called if no index.html is found in the public --path svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res) { res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8"); return false; }); svr.Get("/props", [&llama](const httplib::Request & /*req*/, httplib::Response &res) { res.set_header("Access-Control-Allow-Origin", "*"); json data = { { "user_name", llama.name_user.c_str() }, { "assistant_name", llama.name_assistant.c_str() } }; res.set_content(data.dump(), "application/json; charset=utf-8"); }); svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { if (!validate_api_key(req, res)) { return; } json data = json::parse(req.body); const int task_id = llama.request_completion(data, false, false, -1); if (!json_value(data, "stream", false)) { std::string completion_text; task_result result = llama.next_result(task_id); if (!result.error && result.stop) { res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res.status = 404; res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); return; } } else { const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { while (true) { task_result result = llama.next_result(task_id); if (!result.error) { const std::string str = "data: " + result.result_json.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())) { return false; } if (result.stop) { break; } } else { const std::string str = "error: " + result.result_json.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())) { return false; } break; } } sink.done(); return true; }; auto on_complete = [task_id, &llama] (bool) { // cancel llama.request_cancel(task_id); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); svr.Get("/v1/models", [¶ms](const httplib::Request&, httplib::Response& res) { std::time_t t = std::time(0); json models = { {"object", "list"}, {"data", { { {"id", params.model_alias}, {"object", "model"}, {"created", t}, {"owned_by", "llamacpp"} }, }} }; res.set_content(models.dump(), "application/json; charset=utf-8"); }); // TODO: add mount point without "/v1" prefix -- how? svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { if (!validate_api_key(req, res)) { return; } json data = oaicompat_completion_params_parse(json::parse(req.body)); const int task_id = llama.request_completion(data, false, false, -1); if (!json_value(data, "stream", false)) { std::string completion_text; task_result result = llama.next_result(task_id); if (!result.error && result.stop) { json oaicompat_result = format_final_response_oaicompat(data, result); res.set_content(oaicompat_result.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res.status = 500; res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); return; } } else { const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) { while (true) { task_result llama_result = llama.next_result(task_id); if (!llama_result.error) { std::vector result_array = format_partial_response_oaicompat( llama_result); 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())) { return false; } } } if (llama_result.stop) { break; } } else { const std::string str = "error: " + llama_result.result_json.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())) { return false; } break; } } sink.done(); return true; }; auto on_complete = [task_id, &llama](bool) { // cancel request llama.request_cancel(task_id); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { if (!validate_api_key(req, res)) { return; } json data = json::parse(req.body); const int task_id = llama.request_completion(data, true, false, -1); if (!json_value(data, "stream", false)) { std::string completion_text; task_result result = llama.next_result(task_id); if (!result.error && result.stop) { res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } else { res.status = 404; res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); return; } } else { const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { while (true) { task_result result = llama.next_result(task_id); if (!result.error) { const std::string str = "data: " + result.result_json.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())) { return false; } if (result.stop) { break; } } else { break; } } sink.done(); return true; }; auto on_complete = [task_id, &llama] (bool) { // cancel llama.request_cancel(task_id); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res) { const json data = llama.get_model_props(); return res.set_content(data.dump(), "application/json; charset=utf-8"); }); svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) { return res.set_content("", "application/json; charset=utf-8"); }); svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) { const json body = json::parse(req.body); std::vector tokens; if (body.count("content") != 0) { tokens = llama.tokenize(body["content"], false); } const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json; charset=utf-8"); }); svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) { 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(llama.ctx, tokens.cbegin(), tokens.cend()); } const json data = format_detokenized_response(content); return res.set_content(data.dump(), "application/json; charset=utf-8"); }); svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) { const json body = json::parse(req.body); json prompt; if (body.count("content") != 0) { prompt = body["content"]; } else { prompt = ""; } const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1); task_result result = llama.next_result(task_id); return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); }); svr.set_logger(log_server_request); svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) { const char fmt[] = "500 Internal Server Error\n%s"; char buf[BUFSIZ]; try { std::rethrow_exception(std::move(ep)); } catch (std::exception &e) { snprintf(buf, sizeof(buf), fmt, e.what()); } catch (...) { snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); } res.set_content(buf, "text/plain; charset=utf-8"); res.status = 500; }); svr.set_error_handler([](const httplib::Request &, httplib::Response &res) { if (res.status == 401) { res.set_content("Unauthorized", "text/plain; charset=utf-8"); } if (res.status == 400) { res.set_content("Invalid request", "text/plain; charset=utf-8"); } else if (res.status == 404) { res.set_content("File Not Found", "text/plain; charset=utf-8"); res.status = 404; } }); // 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; } // Set the base directory for serving static files svr.set_base_dir(sparams.public_path); // to make it ctrl+clickable: LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); std::unordered_map log_data; log_data["hostname"] = sparams.hostname; log_data["port"] = std::to_string(sparams.port); if (!sparams.api_key.empty()) { log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4); } 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()) { return 1; } return 0; }); // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!? // "Bus error: 10" - this is on macOS, it does not crash on Linux //std::thread t2([&]() { bool running = true; while (running) { running = llama.update_slots(); } } //); t.join(); llama_backend_free(); return 0; }