mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-29 08:41:22 +00:00
2002bc96bf
* server : refactoring (wip) * server : remove llava/clip objects from build * server : fix empty prompt handling + all slots idle logic * server : normalize id vars * server : code style * server : simplify model chat template validation * server : code style * server : minor * llama : llama_chat_apply_template support null buf * server : do not process embedding requests when disabled * server : reorganize structs and enums + naming fixes * server : merge oai.hpp in utils.hpp * server : refactor system prompt update at start * server : disable cached prompts with self-extend * server : do not process more than n_batch tokens per iter * server: tests: embeddings use a real embeddings model (#5908) * server, tests : bump batch to fit 1 embedding prompt * server: tests: embeddings fix build type Debug is randomly failing (#5911) * server: tests: embeddings, use different KV Cache size * server: tests: embeddings, fixed prompt do not exceed n_batch, increase embedding timeout, reduce number of concurrent embeddings * server: tests: embeddings, no need to wait for server idle as it can timout * server: refactor: clean up http code (#5912) * server : avoid n_available var ggml-ci * server: refactor: better http codes * server : simplify json parsing + add comment about t_last * server : rename server structs * server : allow to override FQDN in tests ggml-ci * server : add comments --------- Co-authored-by: Pierrick Hymbert <pierrick.hymbert@gmail.com>
545 lines
19 KiB
C++
545 lines
19 KiB
C++
#pragma once
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#include "llama.h"
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#include "common.h"
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#include "json.hpp"
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#include <string>
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#include <vector>
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#include <sstream>
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#include <random>
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#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
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using json = nlohmann::json;
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extern bool server_verbose;
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extern bool server_log_json;
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#ifndef SERVER_VERBOSE
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#define SERVER_VERBOSE 1
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#endif
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#if SERVER_VERBOSE != 1
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#define LOG_VERBOSE(MSG, ...)
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#else
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#define LOG_VERBOSE(MSG, ...) \
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do \
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{ \
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if (server_verbose) \
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{ \
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server_log("VERB", __func__, __LINE__, MSG, __VA_ARGS__); \
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} \
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} while (0)
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#endif
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#define LOG_ERROR( MSG, ...) server_log("ERR", __func__, __LINE__, MSG, __VA_ARGS__)
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#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
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#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
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template <typename T>
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static T json_value(const json &body, const std::string &key, const T &default_value) {
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// Fallback null to default value
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return body.contains(key) && !body.at(key).is_null()
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? body.value(key, default_value)
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: default_value;
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}
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static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) {
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std::stringstream ss_tid;
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ss_tid << std::this_thread::get_id();
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json log = nlohmann::ordered_json{
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{"tid", ss_tid.str()},
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{"timestamp", time(nullptr)},
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};
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if (server_log_json) {
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log.merge_patch( {
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{"level", level},
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{"function", function},
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{"line", line},
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{"msg", message},
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});
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if (!extra.empty()) {
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log.merge_patch(extra);
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}
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printf("%s\n", log.dump(-1, ' ', false, json::error_handler_t::replace).c_str());
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} else {
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char buf[1024];
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snprintf(buf, 1024, "%4s [%24s] %s", level, function, message);
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if (!extra.empty()) {
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log.merge_patch(extra);
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}
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std::stringstream ss;
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ss << buf << " |";
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for (const auto& el : log.items())
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{
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const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
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ss << " " << el.key() << "=" << value;
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}
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const std::string str = ss.str();
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printf("%.*s\n", (int)str.size(), str.data());
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fflush(stdout);
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}
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}
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//
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// chat template utils
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//
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// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
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inline bool verify_custom_template(const std::string & tmpl) {
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llama_chat_message chat[] = {{"user", "test"}};
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int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
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return res >= 0;
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}
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// Format given chat. If tmpl is empty, we take the template from model metadata
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inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
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size_t alloc_size = 0;
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// vector holding all allocated string to be passed to llama_chat_apply_template
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std::vector<std::string> str(messages.size() * 2);
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std::vector<llama_chat_message> chat(messages.size());
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for (size_t i = 0; i < messages.size(); ++i) {
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const auto & curr_msg = messages[i];
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str[i*2 + 0] = json_value(curr_msg, "role", std::string(""));
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str[i*2 + 1] = json_value(curr_msg, "content", std::string(""));
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alloc_size += str[i*2 + 1].length();
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chat[i].role = str[i*2 + 0].c_str();
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chat[i].content = str[i*2 + 1].c_str();
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}
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const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
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std::vector<char> buf(alloc_size * 2);
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// run the first time to get the total output length
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int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
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// if it turns out that our buffer is too small, we resize it
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if ((size_t) res > buf.size()) {
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buf.resize(res);
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res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
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}
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const std::string formatted_chat(buf.data(), res);
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LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
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return formatted_chat;
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}
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//
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// base64 utils (TODO: move to common in the future)
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//
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static const std::string base64_chars =
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"ABCDEFGHIJKLMNOPQRSTUVWXYZ"
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"abcdefghijklmnopqrstuvwxyz"
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"0123456789+/";
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static inline bool is_base64(uint8_t c) {
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return (isalnum(c) || (c == '+') || (c == '/'));
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}
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static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string) {
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int i = 0;
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int j = 0;
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int in_ = 0;
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int in_len = encoded_string.size();
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uint8_t char_array_4[4];
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uint8_t char_array_3[3];
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std::vector<uint8_t> ret;
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while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) {
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char_array_4[i++] = encoded_string[in_]; in_++;
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if (i == 4) {
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for (i = 0; i < 4; i++) {
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char_array_4[i] = base64_chars.find(char_array_4[i]);
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}
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char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
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char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
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char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
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for (i = 0; (i < 3); i++) {
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ret.push_back(char_array_3[i]);
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}
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i = 0;
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}
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}
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if (i) {
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for (j = i; j < 4; j++) {
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char_array_4[j] = 0;
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}
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for (j = 0; j < 4; j++) {
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char_array_4[j] = base64_chars.find(char_array_4[j]);
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}
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char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
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char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
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char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
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for (j = 0; j < i - 1; j++) {
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ret.push_back(char_array_3[j]);
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}
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}
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return ret;
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}
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//
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// random string / id
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//
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static std::string random_string() {
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static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
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std::random_device rd;
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std::mt19937 generator(rd());
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std::string result(32, ' ');
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for (int i = 0; i < 32; ++i) {
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result[i] = str[generator() % str.size()];
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}
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return result;
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}
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static std::string gen_chatcmplid() {
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std::stringstream chatcmplid;
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chatcmplid << "chatcmpl-" << random_string();
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return chatcmplid.str();
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}
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//
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// other common utils
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//
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static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
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size_t i;
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for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
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return i;
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}
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static bool ends_with(const std::string & str, const std::string & suffix) {
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return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
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}
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static size_t find_partial_stop_string(const std::string &stop, const std::string &text) {
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if (!text.empty() && !stop.empty()) {
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const char text_last_char = text.back();
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for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
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if (stop[char_index] == text_last_char) {
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const std::string current_partial = stop.substr(0, char_index + 1);
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if (ends_with(text, current_partial)) {
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return text.size() - char_index - 1;
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}
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}
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}
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}
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return std::string::npos;
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}
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// TODO: reuse llama_detokenize
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template <class Iter>
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static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
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std::string ret;
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for (; begin != end; ++begin) {
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ret += llama_token_to_piece(ctx, *begin);
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}
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return ret;
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}
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// format incomplete utf-8 multibyte character for output
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static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
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std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
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// if the size is 1 and first bit is 1, meaning it's a partial character
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// (size > 1 meaning it's already a known token)
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if (out.size() == 1 && (out[0] & 0x80) == 0x80) {
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std::stringstream ss;
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ss << std::hex << (out[0] & 0xff);
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std::string res(ss.str());
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out = "byte: \\x" + res;
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}
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return out;
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}
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struct completion_token_output {
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llama_token tok;
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std::string text_to_send;
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struct token_prob {
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llama_token tok;
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float prob;
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};
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std::vector<token_prob> probs;
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};
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// convert a vector of completion_token_output to json
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static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> & probs) {
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json out = json::array();
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for (const auto & prob : probs) {
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json probs_for_token = json::array();
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for (const auto & p : prob.probs) {
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const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
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probs_for_token.push_back(json {
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{"tok_str", tok_str},
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{"prob", p.prob},
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});
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}
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const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
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out.push_back(json {
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{"content", tok_str},
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{"probs", probs_for_token},
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});
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}
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return out;
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}
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//
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// OAI utils
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//
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static json oaicompat_completion_params_parse(
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const struct llama_model * model,
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const json & body, /* openai api json semantics */
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const std::string & chat_template) {
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json llama_params;
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llama_params["__oaicompat"] = true;
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// Map OpenAI parameters to llama.cpp parameters
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//
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// For parameters that are defined by the OpenAI documentation (e.g.
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// temperature), we explicitly specify OpenAI's intended default; we
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// need to do that because sometimes OpenAI disagrees with llama.cpp
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//
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// https://platform.openai.com/docs/api-reference/chat/create
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llama_sampling_params default_sparams;
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llama_params["model"] = json_value(body, "model", std::string("unknown"));
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llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
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llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
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llama_params["temperature"] = json_value(body, "temperature", 0.0);
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llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
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llama_params["top_p"] = json_value(body, "top_p", 1.0);
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llama_params["n_predict"] = json_value(body, "max_tokens", -1);
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llama_params["logit_bias"] = json_value(body, "logit_bias", json::object());
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llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
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llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
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llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED);
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llama_params["stream"] = json_value(body, "stream", false);
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llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat);
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llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
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llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
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llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl);
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llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p);
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llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
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llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
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llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z);
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if (body.count("grammar") != 0) {
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llama_params["grammar"] = json_value(body, "grammar", json::object());
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}
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// Handle 'stop' field
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if (body.contains("stop") && body["stop"].is_string()) {
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llama_params["stop"] = json::array({body["stop"].get<std::string>()});
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} else {
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llama_params["stop"] = json_value(body, "stop", json::array());
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}
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// Ensure there is ChatML-specific end sequence among stop words
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llama_params["stop"].push_back("<|im_end|>");
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return llama_params;
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}
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static json format_final_response_oaicompat(const json & request, json result, bool streaming = false) {
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bool stopped_word = result.count("stopped_word") != 0;
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bool stopped_eos = json_value(result, "stopped_eos", false);
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int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
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int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
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std::string content = json_value(result, "content", std::string(""));
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std::string finish_reason = "length";
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if (stopped_word || stopped_eos) {
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finish_reason = "stop";
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}
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json choices =
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streaming ? json::array({json{{"finish_reason", finish_reason},
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{"index", 0},
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{"delta", json::object()}}})
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: json::array({json{{"finish_reason", finish_reason},
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{"index", 0},
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{"message", json{{"content", content},
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{"role", "assistant"}}}}});
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std::time_t t = std::time(0);
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json res = json {
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{"choices", choices},
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{"created", t},
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{"model",
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json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
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{"object", streaming ? "chat.completion.chunk" : "chat.completion"},
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{"usage", json {
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{"completion_tokens", num_tokens_predicted},
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{"prompt_tokens", num_prompt_tokens},
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{"total_tokens", num_tokens_predicted + num_prompt_tokens}
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}},
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{"id", gen_chatcmplid()}
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};
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if (server_verbose) {
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res["__verbose"] = result;
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}
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if (result.contains("completion_probabilities")) {
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res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
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}
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return res;
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}
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// return value is vector as there is one case where we might need to generate two responses
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static std::vector<json> format_partial_response_oaicompat(json result) {
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if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
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return std::vector<json>({result});
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}
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bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
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std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
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bool stopped_word = json_value(result, "stopped_word", false);
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bool stopped_eos = json_value(result, "stopped_eos", false);
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bool stopped_limit = json_value(result, "stopped_limit", false);
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std::string content = json_value(result, "content", std::string(""));
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std::string finish_reason;
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if (stopped_word || stopped_eos) {
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finish_reason = "stop";
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|
}
|
|
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<json>({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>({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<json>({ret});
|
|
}
|
|
|
|
static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
|
|
json res = json {
|
|
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
|
|
{"object", "list"},
|
|
{"usage", json {
|
|
{"prompt_tokens", 0},
|
|
{"total_tokens", 0}
|
|
}},
|
|
{"data", embeddings}
|
|
};
|
|
|
|
return res;
|
|
}
|
|
|
|
static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
|
|
return json {
|
|
{"tokens", tokens}
|
|
};
|
|
}
|
|
|
|
static json format_detokenized_response(const std::string & content) {
|
|
return json {
|
|
{"content", content}
|
|
};
|
|
}
|