* server: tests: add truncated prompt tests, better size * server, tests : update regex --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
3417 lines
136 KiB
C++
3417 lines
136 KiB
C++
#include "utils.hpp"
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#include "common.h"
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#include "llama.h"
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#include "grammar-parser.h"
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#ifndef NDEBUG
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// crash the server in debug mode, otherwise send an http 500 error
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#define CPPHTTPLIB_NO_EXCEPTIONS 1
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#endif
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// increase max payload length to allow use of larger context size
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#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
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#include "httplib.h"
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#include "json.hpp"
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// auto generated files (update with ./deps.sh)
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#include "index.html.hpp"
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#include "index.js.hpp"
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#include "completion.js.hpp"
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#include "json-schema-to-grammar.mjs.hpp"
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#include <atomic>
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#include <chrono>
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#include <condition_variable>
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#include <cstddef>
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#include <set>
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#include <mutex>
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#include <thread>
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#include <signal.h>
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using json = nlohmann::json;
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bool server_verbose = false;
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bool server_log_json = true;
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enum stop_type {
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STOP_TYPE_FULL,
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STOP_TYPE_PARTIAL,
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};
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enum slot_state {
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SLOT_STATE_IDLE,
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SLOT_STATE_PROCESSING,
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};
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enum slot_command {
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SLOT_COMMAND_NONE,
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SLOT_COMMAND_LOAD_PROMPT,
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SLOT_COMMAND_RELEASE,
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};
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enum server_state {
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SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet
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SERVER_STATE_READY, // Server is ready and model is loaded
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SERVER_STATE_ERROR // An error occurred, load_model failed
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};
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enum server_task_type {
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SERVER_TASK_TYPE_COMPLETION,
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SERVER_TASK_TYPE_CANCEL,
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SERVER_TASK_TYPE_NEXT_RESPONSE,
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SERVER_TASK_TYPE_METRICS
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};
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struct server_task {
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int id = -1; // to be filled by server_queue
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int id_multi = -1;
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int id_target = -1;
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server_task_type type;
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json data;
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bool infill = false;
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bool embedding = false;
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};
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struct server_task_result {
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int id = -1;
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int id_multi = -1;
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json data;
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bool stop;
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bool error;
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};
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struct server_task_multi {
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int id = -1;
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std::set<int> subtasks_remaining;
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std::vector<server_task_result> results;
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};
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struct slot_params {
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bool stream = true;
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bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
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uint32_t seed = -1; // RNG seed
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_predict = -1; // new tokens to predict
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std::vector<std::string> antiprompt;
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json input_prefix;
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json input_suffix;
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};
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struct server_params {
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int32_t port = 8080;
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int32_t read_timeout = 600;
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int32_t write_timeout = 600;
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int32_t n_threads_http = -1;
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std::string hostname = "127.0.0.1";
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std::string public_path = "examples/server/public";
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std::string chat_template = "";
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std::string system_prompt = "";
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std::vector<std::string> api_keys;
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bool slots_endpoint = true;
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bool metrics_endpoint = false;
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};
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struct server_slot {
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int id;
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int id_task = -1;
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int id_multi = -1;
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struct slot_params params;
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slot_state state = SLOT_STATE_IDLE;
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slot_command command = SLOT_COMMAND_NONE;
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// used to determine the slot that has been used the longest
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int64_t t_last_used = -1;
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// generation props
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int32_t n_ctx = 0; // context size per slot
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int32_t n_past = 0;
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int32_t n_decoded = 0;
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int32_t n_remaining = -1;
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int32_t i_batch = -1;
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int32_t n_predict = -1;
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int32_t n_prompt_tokens = 0;
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int32_t n_prompt_tokens_processed = 0;
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json prompt;
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// when a task is submitted, we first tokenize the prompt and store it here
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std::vector<llama_token> prompt_tokens;
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std::string generated_text;
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std::vector<llama_token> cache_tokens;
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std::vector<completion_token_output> generated_token_probs;
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bool infill = false;
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bool embedding = false;
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bool has_next_token = true;
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bool truncated = false;
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bool stopped_eos = false;
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bool stopped_word = false;
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bool stopped_limit = false;
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bool oaicompat = false;
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std::string oaicompat_model;
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std::string stopping_word;
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// sampling
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llama_token sampled;
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struct llama_sampling_params sparams;
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llama_sampling_context * ctx_sampling = nullptr;
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int32_t ga_i = 0; // group-attention state
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int32_t ga_n = 1; // group-attention factor
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int32_t ga_w = 512; // group-attention width
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int32_t n_past_se = 0; // self-extend
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// stats
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size_t n_sent_text = 0; // number of sent text character
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size_t n_sent_token_probs = 0;
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int64_t t_start_process_prompt;
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int64_t t_start_generation;
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double t_prompt_processing; // ms
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double t_token_generation; // ms
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void reset() {
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n_prompt_tokens = 0;
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generated_text = "";
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truncated = false;
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stopped_eos = false;
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stopped_word = false;
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stopped_limit = false;
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stopping_word = "";
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n_past = 0;
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n_sent_text = 0;
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n_sent_token_probs = 0;
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infill = false;
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ga_i = 0;
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n_past_se = 0;
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generated_token_probs.clear();
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}
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bool has_budget(gpt_params &global_params) {
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if (params.n_predict == -1 && global_params.n_predict == -1) {
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return true; // limitless
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}
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n_remaining = -1;
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if (params.n_predict != -1) {
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n_remaining = params.n_predict - n_decoded;
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} else if (global_params.n_predict != -1) {
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n_remaining = global_params.n_predict - n_decoded;
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}
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return n_remaining > 0; // no budget
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}
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bool available() const {
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return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE;
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}
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bool is_processing() const {
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return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING;
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}
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void add_token_string(const completion_token_output & token) {
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if (command == SLOT_COMMAND_RELEASE) {
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return;
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}
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generated_token_probs.push_back(token);
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}
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void release() {
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if (state == SLOT_STATE_PROCESSING) {
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t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
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command = SLOT_COMMAND_RELEASE;
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}
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}
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json get_formated_timings() const {
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return json {
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{"prompt_n", n_prompt_tokens_processed},
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{"prompt_ms", t_prompt_processing},
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{"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed},
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{"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed},
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{"predicted_n", n_decoded},
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{"predicted_ms", t_token_generation},
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{"predicted_per_token_ms", t_token_generation / n_decoded},
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{"predicted_per_second", 1e3 / t_token_generation * n_decoded},
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};
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}
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size_t find_stopping_strings(const std::string & text, const size_t last_token_size, const stop_type type) {
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size_t stop_pos = std::string::npos;
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for (const std::string & word : params.antiprompt) {
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size_t pos;
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if (type == STOP_TYPE_FULL) {
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const size_t tmp = word.size() + last_token_size;
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const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
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pos = text.find(word, from_pos);
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} else {
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pos = find_partial_stop_string(word, text);
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}
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if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) {
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if (type == STOP_TYPE_FULL) {
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stopped_word = true;
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stopping_word = word;
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has_next_token = false;
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}
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stop_pos = pos;
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}
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}
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return stop_pos;
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}
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void print_timings() const {
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char buffer[512];
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double t_token = t_prompt_processing / n_prompt_tokens_processed;
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double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
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snprintf(buffer, 512, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
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t_prompt_processing, n_prompt_tokens_processed,
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t_token, n_tokens_second);
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LOG_INFO(buffer, {
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{"id_slot", id},
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{"id_task", id_task},
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{"t_prompt_processing", t_prompt_processing},
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{"n_prompt_tokens_processed", n_prompt_tokens_processed},
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{"t_token", t_token},
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{"n_tokens_second", n_tokens_second},
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});
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t_token = t_token_generation / n_decoded;
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n_tokens_second = 1e3 / t_token_generation * n_decoded;
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snprintf(buffer, 512, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
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t_token_generation, n_decoded,
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t_token, n_tokens_second);
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LOG_INFO(buffer, {
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{"id_slot", id},
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{"id_task", id_task},
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{"t_token_generation", t_token_generation},
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{"n_decoded", n_decoded},
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{"t_token", t_token},
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{"n_tokens_second", n_tokens_second},
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});
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snprintf(buffer, 512, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
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LOG_INFO(buffer, {
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{"id_slot", id},
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{"id_task", id_task},
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{"t_prompt_processing", t_prompt_processing},
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{"t_token_generation", t_token_generation},
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{"t_total", t_prompt_processing + t_token_generation},
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});
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}
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};
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struct server_metrics {
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const int64_t t_start = ggml_time_us();
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uint64_t n_prompt_tokens_processed_total = 0;
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uint64_t t_prompt_processing_total = 0;
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uint64_t n_tokens_predicted_total = 0;
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uint64_t t_tokens_generation_total = 0;
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uint64_t n_prompt_tokens_processed = 0;
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uint64_t t_prompt_processing = 0;
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uint64_t n_tokens_predicted = 0;
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uint64_t t_tokens_generation = 0;
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void on_prompt_eval(const server_slot &slot) {
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n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
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n_prompt_tokens_processed += slot.n_prompt_tokens_processed;
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t_prompt_processing += slot.t_prompt_processing;
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t_prompt_processing_total += slot.t_prompt_processing;
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}
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void on_prediction(const server_slot &slot) {
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n_tokens_predicted_total += slot.n_decoded;
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n_tokens_predicted += slot.n_decoded;
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t_tokens_generation += slot.t_token_generation;
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t_tokens_generation_total += slot.t_token_generation;
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}
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void reset_bucket() {
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n_prompt_tokens_processed = 0;
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t_prompt_processing = 0;
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n_tokens_predicted = 0;
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t_tokens_generation = 0;
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}
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};
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struct server_queue {
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int id = 0;
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bool running;
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// queues
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std::vector<server_task> queue_tasks;
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std::vector<server_task> queue_tasks_deferred;
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std::vector<server_task_multi> queue_multitasks;
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std::mutex mutex_tasks;
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std::condition_variable condition_tasks;
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// callback functions
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std::function<void(server_task &)> callback_new_task;
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std::function<void(server_task_multi &)> callback_finish_multitask;
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std::function<void(void)> callback_run_slots;
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// Add a new task to the end of the queue
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int post(server_task task) {
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std::unique_lock<std::mutex> lock(mutex_tasks);
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if (task.id == -1) {
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task.id = id++;
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LOG_VERBOSE("new task id", {{"new_id", task.id}});
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}
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queue_tasks.push_back(std::move(task));
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condition_tasks.notify_one();
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return task.id;
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}
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// Add a new task, but defer until one slot is available
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void defer(server_task task) {
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std::unique_lock<std::mutex> lock(mutex_tasks);
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queue_tasks_deferred.push_back(std::move(task));
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}
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// Get the next id for creating anew task
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int get_new_id() {
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std::unique_lock<std::mutex> lock(mutex_tasks);
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int new_id = id++;
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LOG_VERBOSE("new task id", {{"new_id", new_id}});
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return new_id;
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}
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// Register function to process a new task
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void on_new_task(std::function<void(server_task &)> callback) {
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callback_new_task = std::move(callback);
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}
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// Register function to process a multitask when it is finished
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void on_finish_multitask(std::function<void(server_task_multi&)> callback) {
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callback_finish_multitask = std::move(callback);
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}
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// Register the function to be called when all slots data is ready to be processed
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void on_run_slots(std::function<void(void)> callback) {
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callback_run_slots = std::move(callback);
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}
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// Call when the state of one slot is changed
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void notify_slot_changed() {
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// move deferred tasks back to main loop
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std::unique_lock<std::mutex> lock(mutex_tasks);
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for (auto & task : queue_tasks_deferred) {
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queue_tasks.push_back(std::move(task));
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}
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queue_tasks_deferred.clear();
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}
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// end the start_loop routine
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void terminate() {
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std::unique_lock<std::mutex> lock(mutex_tasks);
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running = false;
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condition_tasks.notify_all();
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}
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/**
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* Main loop consists of these steps:
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* - Wait until a new task arrives
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* - Process the task (i.e. maybe copy data into slot)
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* - Check if multitask is finished
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* - Run all slots
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*/
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void start_loop() {
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running = true;
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while (true) {
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LOG_VERBOSE("new task may arrive", {});
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while (true) {
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std::unique_lock<std::mutex> lock(mutex_tasks);
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if (queue_tasks.empty()) {
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lock.unlock();
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break;
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}
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server_task task = queue_tasks.front();
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queue_tasks.erase(queue_tasks.begin());
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lock.unlock();
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LOG_VERBOSE("callback_new_task", {{"id_task", task.id}});
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callback_new_task(task);
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}
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LOG_VERBOSE("update_multitasks", {});
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// check if we have any finished multitasks
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auto queue_iterator = queue_multitasks.begin();
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while (queue_iterator != queue_multitasks.end()) {
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if (queue_iterator->subtasks_remaining.empty()) {
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// all subtasks done == multitask is done
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server_task_multi current_multitask = *queue_iterator;
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callback_finish_multitask(current_multitask);
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// remove this multitask
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queue_iterator = queue_multitasks.erase(queue_iterator);
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} else {
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++queue_iterator;
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}
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}
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// all tasks in the current loop is processed, slots data is now ready
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LOG_VERBOSE("callback_run_slots", {});
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callback_run_slots();
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LOG_VERBOSE("wait for new task", {});
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{
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std::unique_lock<std::mutex> lock(mutex_tasks);
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if (queue_tasks.empty()) {
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if (!running) {
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LOG_VERBOSE("ending start_loop", {});
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return;
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}
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condition_tasks.wait(lock, [&]{
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return (!queue_tasks.empty() || !running);
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});
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}
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}
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}
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}
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//
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// functions to manage multitasks
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//
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// add a multitask by specifying the id of all subtask (subtask is a server_task)
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void add_multitask(int id_multi, std::vector<int> & sub_ids) {
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std::lock_guard<std::mutex> lock(mutex_tasks);
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server_task_multi multi;
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multi.id = id_multi;
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std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
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queue_multitasks.push_back(multi);
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}
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// updatethe remaining subtasks, while appending results to multitask
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void update_multitask(int id_multi, int id_sub, server_task_result & result) {
|
|
std::lock_guard<std::mutex> lock(mutex_tasks);
|
|
for (auto & multitask : queue_multitasks) {
|
|
if (multitask.id == id_multi) {
|
|
multitask.subtasks_remaining.erase(id_sub);
|
|
multitask.results.push_back(result);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
struct server_response {
|
|
typedef std::function<void(int, int, server_task_result &)> callback_multitask_t;
|
|
callback_multitask_t callback_update_multitask;
|
|
|
|
// for keeping track of all tasks waiting for the result
|
|
std::set<int> waiting_task_ids;
|
|
|
|
// the main result queue
|
|
std::vector<server_task_result> queue_results;
|
|
|
|
std::mutex mutex_results;
|
|
std::condition_variable condition_results;
|
|
|
|
// add the id_task to the list of tasks waiting for response
|
|
void add_waiting_task_id(int id_task) {
|
|
LOG_VERBOSE("waiting for task id", {{"id_task", id_task}});
|
|
|
|
std::unique_lock<std::mutex> lock(mutex_results);
|
|
waiting_task_ids.insert(id_task);
|
|
}
|
|
|
|
// when the request is finished, we can remove task associated with it
|
|
void remove_waiting_task_id(int id_task) {
|
|
LOG_VERBOSE("remove waiting for task id", {{"id_task", id_task}});
|
|
|
|
std::unique_lock<std::mutex> lock(mutex_results);
|
|
waiting_task_ids.erase(id_task);
|
|
}
|
|
|
|
// This function blocks the thread until there is a response for this id_task
|
|
server_task_result recv(int id_task) {
|
|
while (true) {
|
|
std::unique_lock<std::mutex> lock(mutex_results);
|
|
condition_results.wait(lock, [&]{
|
|
return !queue_results.empty();
|
|
});
|
|
|
|
for (int i = 0; i < (int) queue_results.size(); i++) {
|
|
if (queue_results[i].id == id_task) {
|
|
assert(queue_results[i].id_multi == -1);
|
|
server_task_result res = queue_results[i];
|
|
queue_results.erase(queue_results.begin() + i);
|
|
return res;
|
|
}
|
|
}
|
|
}
|
|
|
|
// should never reach here
|
|
}
|
|
|
|
// Register the function to update multitask
|
|
void on_multitask_update(callback_multitask_t callback) {
|
|
callback_update_multitask = std::move(callback);
|
|
}
|
|
|
|
// Send a new result to a waiting id_task
|
|
void send(server_task_result result) {
|
|
LOG_VERBOSE("send new result", {{"id_task", result.id}});
|
|
|
|
std::unique_lock<std::mutex> lock(mutex_results);
|
|
for (const auto & id_task : waiting_task_ids) {
|
|
// LOG_TEE("waiting task id %i \n", id_task);
|
|
// for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
|
|
if (result.id_multi == id_task) {
|
|
LOG_VERBOSE("callback_update_multitask", {{"id_task", id_task}});
|
|
callback_update_multitask(id_task, result.id, result);
|
|
continue;
|
|
}
|
|
|
|
if (result.id == id_task) {
|
|
LOG_VERBOSE("queue_results.push_back", {{"id_task", id_task}});
|
|
queue_results.push_back(result);
|
|
condition_results.notify_all();
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
struct server_context {
|
|
llama_model * model = nullptr;
|
|
llama_context * ctx = nullptr;
|
|
|
|
gpt_params params;
|
|
|
|
llama_batch batch;
|
|
|
|
bool clean_kv_cache = true;
|
|
bool add_bos_token = true;
|
|
|
|
int32_t n_ctx; // total context for all clients / slots
|
|
|
|
// system prompt
|
|
bool system_need_update = false;
|
|
|
|
std::string system_prompt;
|
|
std::vector<llama_token> system_tokens;
|
|
|
|
std::string name_user; // this should be the antiprompt
|
|
std::string name_assistant;
|
|
|
|
// slots / clients
|
|
std::vector<server_slot> slots;
|
|
json default_generation_settings_for_props;
|
|
|
|
server_queue queue_tasks;
|
|
server_response queue_results;
|
|
|
|
server_metrics metrics;
|
|
|
|
~server_context() {
|
|
if (ctx) {
|
|
llama_free(ctx);
|
|
ctx = nullptr;
|
|
}
|
|
|
|
if (model) {
|
|
llama_free_model(model);
|
|
model = nullptr;
|
|
}
|
|
}
|
|
|
|
bool load_model(const gpt_params & params_) {
|
|
params = params_;
|
|
|
|
// dedicate one sequence to the system prompt
|
|
params.n_parallel += 1;
|
|
|
|
std::tie(model, ctx) = llama_init_from_gpt_params(params);
|
|
params.n_parallel -= 1; // but be sneaky about it
|
|
if (model == nullptr) {
|
|
LOG_ERROR("unable to load model", {{"model", params.model}});
|
|
return false;
|
|
}
|
|
|
|
n_ctx = llama_n_ctx(ctx);
|
|
|
|
add_bos_token = llama_should_add_bos_token(model);
|
|
|
|
return true;
|
|
}
|
|
|
|
bool validate_model_chat_template() const {
|
|
llama_chat_message chat[] = {{"user", "test"}};
|
|
|
|
const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0);
|
|
|
|
return res > 0;
|
|
}
|
|
|
|
void initialize() {
|
|
const int32_t n_ctx_slot = n_ctx / params.n_parallel;
|
|
|
|
LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}});
|
|
for (int i = 0; i < params.n_parallel; i++) {
|
|
server_slot slot;
|
|
|
|
slot.id = i;
|
|
slot.n_ctx = n_ctx_slot;
|
|
slot.n_predict = params.n_predict;
|
|
|
|
LOG_INFO("new slot", {
|
|
{"id_slot", slot.id},
|
|
{"n_ctx_slot", slot.n_ctx}
|
|
});
|
|
|
|
const int ga_n = params.grp_attn_n;
|
|
const int ga_w = params.grp_attn_w;
|
|
|
|
if (ga_n != 1) {
|
|
GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT
|
|
GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
|
|
//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT
|
|
//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
|
|
|
|
LOG_INFO("slot self-extend", {
|
|
{"id_slot", slot.id},
|
|
{"ga_n", ga_n},
|
|
{"ga_w", ga_w}
|
|
});
|
|
}
|
|
|
|
slot.ga_i = 0;
|
|
slot.ga_n = ga_n;
|
|
slot.ga_w = ga_w;
|
|
|
|
slot.reset();
|
|
|
|
slots.push_back(slot);
|
|
}
|
|
|
|
default_generation_settings_for_props = get_formated_generation(slots.front());
|
|
default_generation_settings_for_props["seed"] = -1;
|
|
|
|
batch = llama_batch_init(n_ctx, 0, params.n_parallel);
|
|
}
|
|
|
|
std::vector<llama_token> 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<llama_token> 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::string>();
|
|
|
|
std::vector<llama_token> 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<llama_token>());
|
|
}
|
|
}
|
|
} else {
|
|
auto s = json_prompt.template get<std::string>();
|
|
prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
|
|
}
|
|
|
|
return prompt_tokens;
|
|
}
|
|
|
|
server_slot * get_slot(int id) {
|
|
int64_t t_last = ggml_time_us();
|
|
|
|
server_slot * last_used = nullptr;
|
|
|
|
for (server_slot & slot : slots) {
|
|
if (slot.id == id && slot.available()) {
|
|
return &slot;
|
|
}
|
|
|
|
// among all available slots, find the one that has been least recently used
|
|
if (slot.available() && slot.t_last_used < t_last) {
|
|
last_used = &slot;
|
|
t_last = slot.t_last_used;
|
|
}
|
|
}
|
|
|
|
return last_used;
|
|
}
|
|
|
|
bool launch_slot_with_data(server_slot & slot, json data) const {
|
|
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.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
|
|
slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
|
|
slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
|
|
slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
|
|
slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
|
|
slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
|
|
slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
|
|
slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
|
|
slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
|
|
slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
|
|
slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
|
|
slot.params.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);
|
|
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
|
|
|
|
if (slot.params.cache_prompt && slot.ga_n != 1) {
|
|
LOG_WARNING("cache_prompt is not supported with group-attention", {});
|
|
slot.params.cache_prompt = false;
|
|
}
|
|
|
|
if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
|
|
// Might be better to reject the request with a 400 ?
|
|
LOG_WARNING("Max tokens to predict exceeds server configuration", {
|
|
{"params.n_predict", slot.params.n_predict},
|
|
{"slot.n_predict", slot.n_predict},
|
|
});
|
|
slot.params.n_predict = slot.n_predict;
|
|
}
|
|
|
|
// infill
|
|
slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
|
|
slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
|
|
slot.prompt = json_value(data, "prompt", std::string(""));
|
|
|
|
// penalize user-provided tokens
|
|
{
|
|
slot.sparams.penalty_prompt_tokens.clear();
|
|
slot.sparams.use_penalty_prompt_tokens = false;
|
|
|
|
const auto & penalty_prompt = data.find("penalty_prompt");
|
|
|
|
if (penalty_prompt != data.end()) {
|
|
if (penalty_prompt->is_string()) {
|
|
const auto penalty_prompt_string = penalty_prompt->get<std::string>();
|
|
slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false);
|
|
|
|
if (slot.params.n_predict > 0) {
|
|
slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() + slot.params.n_predict);
|
|
}
|
|
slot.sparams.use_penalty_prompt_tokens = true;
|
|
|
|
LOG_VERBOSE("penalty_prompt_tokens", {
|
|
{"id_slot", slot.id},
|
|
{"tokens", slot.sparams.penalty_prompt_tokens},
|
|
});
|
|
}
|
|
else if (penalty_prompt->is_array()) {
|
|
const auto n_tokens = penalty_prompt->size();
|
|
slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict));
|
|
|
|
const int n_vocab = llama_n_vocab(model);
|
|
for (const auto & penalty_token : *penalty_prompt) {
|
|
if (penalty_token.is_number_integer()) {
|
|
const auto tok = penalty_token.get<llama_token>();
|
|
if (tok >= 0 && tok < n_vocab) {
|
|
slot.sparams.penalty_prompt_tokens.push_back(tok);
|
|
}
|
|
}
|
|
}
|
|
slot.sparams.use_penalty_prompt_tokens = true;
|
|
|
|
LOG_VERBOSE("penalty_prompt_tokens", {
|
|
{"id_slot", slot.id},
|
|
{"tokens", slot.sparams.penalty_prompt_tokens},
|
|
});
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
slot.sparams.logit_bias.clear();
|
|
|
|
if (json_value(data, "ignore_eos", false)) {
|
|
slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
|
|
}
|
|
|
|
const auto & logit_bias = data.find("logit_bias");
|
|
if (logit_bias != data.end() && logit_bias->is_array()) {
|
|
const int n_vocab = llama_n_vocab(model);
|
|
for (const auto & el : *logit_bias) {
|
|
if (el.is_array() && el.size() == 2) {
|
|
float bias;
|
|
if (el[1].is_number()) {
|
|
bias = el[1].get<float>();
|
|
} else if (el[1].is_boolean() && !el[1].get<bool>()) {
|
|
bias = -INFINITY;
|
|
} else {
|
|
continue;
|
|
}
|
|
|
|
if (el[0].is_number_integer()) {
|
|
llama_token tok = el[0].get<llama_token>();
|
|
if (tok >= 0 && tok < n_vocab) {
|
|
slot.sparams.logit_bias[tok] = bias;
|
|
}
|
|
} else if (el[0].is_string()) {
|
|
auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
|
|
for (auto tok : toks) {
|
|
slot.sparams.logit_bias[tok] = bias;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
slot.params.antiprompt.clear();
|
|
|
|
const auto & stop = data.find("stop");
|
|
if (stop != data.end() && stop->is_array()) {
|
|
for (const auto & word : *stop) {
|
|
if (!word.empty()) {
|
|
slot.params.antiprompt.push_back(word);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
const auto & samplers_sequence = data.find("samplers");
|
|
if (samplers_sequence != data.end() && samplers_sequence->is_array()) {
|
|
std::vector<std::string> sampler_names;
|
|
for (const auto & sampler_name : *samplers_sequence) {
|
|
if (sampler_name.is_string()) {
|
|
sampler_names.emplace_back(sampler_name);
|
|
}
|
|
}
|
|
slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
|
|
} else {
|
|
slot.sparams.samplers_sequence = default_sparams.samplers_sequence;
|
|
}
|
|
}
|
|
|
|
{
|
|
if (slot.ctx_sampling != nullptr) {
|
|
llama_sampling_free(slot.ctx_sampling);
|
|
}
|
|
slot.ctx_sampling = llama_sampling_init(slot.sparams);
|
|
llama_set_rng_seed(ctx, slot.params.seed);
|
|
}
|
|
|
|
slot.command = SLOT_COMMAND_LOAD_PROMPT;
|
|
slot.prompt_tokens.clear();
|
|
|
|
LOG_INFO("slot is processing task", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
});
|
|
|
|
return true;
|
|
}
|
|
|
|
void kv_cache_clear() {
|
|
LOG_VERBOSE("clearing KV cache", {});
|
|
|
|
// clear the entire KV cache
|
|
llama_kv_cache_clear(ctx);
|
|
clean_kv_cache = false;
|
|
}
|
|
|
|
void system_prompt_update() {
|
|
LOG_VERBOSE("system prompt update", {
|
|
{"system_prompt", system_prompt},
|
|
});
|
|
|
|
kv_cache_clear();
|
|
system_tokens.clear();
|
|
|
|
if (!system_prompt.empty()) {
|
|
system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
|
|
|
|
llama_batch_clear(batch);
|
|
|
|
for (int i = 0; i < (int)system_tokens.size(); ++i) {
|
|
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
|
|
}
|
|
|
|
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) {
|
|
const int32_t n_tokens = std::min(params.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) != 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, -1, -1);
|
|
}
|
|
}
|
|
|
|
system_need_update = false;
|
|
}
|
|
|
|
void system_prompt_set(const json & sys_props) {
|
|
system_prompt = sys_props.value("prompt", "");
|
|
name_user = sys_props.value("anti_prompt", "");
|
|
name_assistant = sys_props.value("assistant_name", "");
|
|
|
|
LOG_VERBOSE("system prompt process", {
|
|
{"system_prompt", system_prompt},
|
|
{"name_user", name_user},
|
|
{"name_assistant", name_assistant},
|
|
});
|
|
|
|
// release all slots
|
|
for (server_slot & slot : slots) {
|
|
slot.release();
|
|
}
|
|
|
|
system_need_update = true;
|
|
}
|
|
|
|
bool process_token(completion_token_output & result, server_slot & slot) {
|
|
// remember which tokens were sampled - used for repetition penalties during sampling
|
|
const std::string token_str = llama_token_to_piece(ctx, result.tok);
|
|
slot.sampled = result.tok;
|
|
|
|
// search stop word and delete it
|
|
slot.generated_text += token_str;
|
|
slot.has_next_token = true;
|
|
|
|
if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) {
|
|
// we can change penalty_prompt_tokens because it is always created from scratch each request
|
|
slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
|
|
}
|
|
|
|
// check if there is incomplete UTF-8 character at the end
|
|
bool incomplete = false;
|
|
for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) {
|
|
unsigned char c = slot.generated_text[slot.generated_text.size() - i];
|
|
if ((c & 0xC0) == 0x80) {
|
|
// continuation byte: 10xxxxxx
|
|
continue;
|
|
}
|
|
if ((c & 0xE0) == 0xC0) {
|
|
// 2-byte character: 110xxxxx ...
|
|
incomplete = i < 2;
|
|
} else if ((c & 0xF0) == 0xE0) {
|
|
// 3-byte character: 1110xxxx ...
|
|
incomplete = i < 3;
|
|
} else if ((c & 0xF8) == 0xF0) {
|
|
// 4-byte character: 11110xxx ...
|
|
incomplete = i < 4;
|
|
}
|
|
// else 1-byte character or invalid byte
|
|
break;
|
|
}
|
|
|
|
if (!incomplete) {
|
|
size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());
|
|
|
|
const std::string str_test = slot.generated_text.substr(pos);
|
|
bool is_stop_full = false;
|
|
|
|
size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL);
|
|
if (stop_pos != std::string::npos) {
|
|
is_stop_full = true;
|
|
slot.generated_text.erase(
|
|
slot.generated_text.begin() + pos + stop_pos,
|
|
slot.generated_text.end());
|
|
pos = std::min(slot.n_sent_text, slot.generated_text.size());
|
|
} else {
|
|
is_stop_full = false;
|
|
stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL);
|
|
}
|
|
|
|
// check if there is any token to predict
|
|
if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) {
|
|
// no send the stop word in the response
|
|
result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
|
|
slot.n_sent_text += result.text_to_send.size();
|
|
// add the token to slot queue and cache
|
|
}
|
|
|
|
slot.add_token_string(result);
|
|
if (slot.params.stream) {
|
|
send_partial_response(slot, result);
|
|
}
|
|
}
|
|
|
|
if (incomplete) {
|
|
slot.has_next_token = true;
|
|
}
|
|
|
|
// check the limits
|
|
if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) {
|
|
slot.stopped_limit = true;
|
|
slot.has_next_token = false;
|
|
|
|
LOG_VERBOSE("stopped by limit", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_decoded", slot.n_decoded},
|
|
{"n_predict", slot.params.n_predict},
|
|
});
|
|
}
|
|
|
|
if (result.tok == llama_token_eos(model)) {
|
|
slot.stopped_eos = true;
|
|
slot.has_next_token = false;
|
|
|
|
LOG_VERBOSE("eos token found", {});
|
|
}
|
|
|
|
LOG_VERBOSE("next token", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"token", result.tok},
|
|
{"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
|
|
{"has_next_token", slot.has_next_token},
|
|
{"n_remain", slot.n_remaining},
|
|
{"n_decoded", slot.n_decoded},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
});
|
|
|
|
return slot.has_next_token; // continue
|
|
}
|
|
|
|
json get_formated_generation(const server_slot & slot) const {
|
|
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
|
|
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second);
|
|
|
|
std::vector<std::string> samplers_sequence;
|
|
samplers_sequence.reserve(slot.sparams.samplers_sequence.size());
|
|
for (const auto & sampler_type : slot.sparams.samplers_sequence) {
|
|
samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type));
|
|
}
|
|
|
|
return json {
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_predict", slot.n_predict},
|
|
{"model", params.model_alias},
|
|
{"seed", slot.params.seed},
|
|
{"temperature", slot.sparams.temp},
|
|
{"dynatemp_range", slot.sparams.dynatemp_range},
|
|
{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
|
|
{"top_k", slot.sparams.top_k},
|
|
{"top_p", slot.sparams.top_p},
|
|
{"min_p", slot.sparams.min_p},
|
|
{"tfs_z", slot.sparams.tfs_z},
|
|
{"typical_p", slot.sparams.typical_p},
|
|
{"repeat_last_n", slot.sparams.penalty_last_n},
|
|
{"repeat_penalty", slot.sparams.penalty_repeat},
|
|
{"presence_penalty", slot.sparams.penalty_present},
|
|
{"frequency_penalty", slot.sparams.penalty_freq},
|
|
{"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
|
|
{"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
|
|
{"mirostat", slot.sparams.mirostat},
|
|
{"mirostat_tau", slot.sparams.mirostat_tau},
|
|
{"mirostat_eta", slot.sparams.mirostat_eta},
|
|
{"penalize_nl", slot.sparams.penalize_nl},
|
|
{"stop", slot.params.antiprompt},
|
|
{"n_predict", slot.params.n_predict},
|
|
{"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},
|
|
{"min_keep", slot.sparams.min_keep},
|
|
{"grammar", slot.sparams.grammar},
|
|
{"samplers", samplers_sequence}
|
|
};
|
|
}
|
|
|
|
void send_error(const server_task & task, const std::string & error) {
|
|
LOG_TEE("task %i - error: %s\n", task.id, error.c_str());
|
|
|
|
server_task_result res;
|
|
res.id = task.id;
|
|
res.id_multi = task.id_multi;
|
|
res.stop = false;
|
|
res.error = true;
|
|
res.data = { { "content", error } };
|
|
|
|
queue_results.send(res);
|
|
}
|
|
|
|
void send_partial_response(server_slot & slot, completion_token_output tkn) {
|
|
server_task_result res;
|
|
res.id = slot.id_task;
|
|
res.id_multi = slot.id_multi;
|
|
res.error = false;
|
|
res.stop = false;
|
|
res.data = json {
|
|
{"content", tkn.text_to_send},
|
|
{"stop", false},
|
|
{"id_slot", slot.id},
|
|
{"multimodal", false}
|
|
};
|
|
|
|
if (slot.sparams.n_probs > 0) {
|
|
const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
|
|
const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size());
|
|
const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());
|
|
|
|
std::vector<completion_token_output> probs_output;
|
|
if (probs_pos < probs_stop_pos) {
|
|
probs_output = std::vector<completion_token_output>(
|
|
slot.generated_token_probs.begin() + probs_pos,
|
|
slot.generated_token_probs.begin() + probs_stop_pos);
|
|
}
|
|
slot.n_sent_token_probs = probs_stop_pos;
|
|
|
|
res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
|
|
}
|
|
|
|
if (slot.oaicompat) {
|
|
res.data["oaicompat_token_ctr"] = slot.n_decoded;
|
|
res.data["model"] = slot.oaicompat_model;
|
|
}
|
|
|
|
queue_results.send(res);
|
|
}
|
|
|
|
void send_final_response(const server_slot & slot) {
|
|
server_task_result res;
|
|
res.id = slot.id_task;
|
|
res.id_multi = slot.id_multi;
|
|
res.error = false;
|
|
res.stop = true;
|
|
res.data = json {
|
|
{"content", !slot.params.stream ? slot.generated_text : ""},
|
|
{"id_slot", slot.id},
|
|
{"stop", true},
|
|
{"model", params.model_alias},
|
|
{"tokens_predicted", slot.n_decoded},
|
|
{"tokens_evaluated", slot.n_prompt_tokens},
|
|
{"generation_settings", get_formated_generation(slot)},
|
|
{"prompt", slot.prompt},
|
|
{"truncated", slot.truncated},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
{"tokens_cached", slot.n_past},
|
|
{"timings", slot.get_formated_timings()}
|
|
};
|
|
|
|
if (slot.sparams.n_probs > 0) {
|
|
std::vector<completion_token_output> probs;
|
|
if (!slot.params.stream && slot.stopped_word) {
|
|
const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
|
|
|
|
probs = std::vector<completion_token_output>(
|
|
slot.generated_token_probs.begin(),
|
|
slot.generated_token_probs.end() - stop_word_toks.size());
|
|
} else {
|
|
probs = std::vector<completion_token_output>(
|
|
slot.generated_token_probs.begin(),
|
|
slot.generated_token_probs.end());
|
|
}
|
|
|
|
res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs);
|
|
}
|
|
|
|
if (slot.oaicompat) {
|
|
res.data["oaicompat_token_ctr"] = slot.n_decoded;
|
|
res.data["model"] = slot.oaicompat_model;
|
|
}
|
|
|
|
queue_results.send(res);
|
|
}
|
|
|
|
void send_embedding(const server_slot & slot, const llama_batch & batch) {
|
|
server_task_result res;
|
|
res.id = slot.id_task;
|
|
res.id_multi = slot.id_multi;
|
|
res.error = false;
|
|
res.stop = true;
|
|
|
|
const int n_embd = llama_n_embd(model);
|
|
|
|
for (int i = 0; i < batch.n_tokens; ++i) {
|
|
if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
|
|
continue;
|
|
}
|
|
|
|
const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
|
|
if (embd == NULL) {
|
|
embd = llama_get_embeddings_ith(ctx, i);
|
|
}
|
|
|
|
if (embd == NULL) {
|
|
LOG_ERROR("failed to get embeddings", {
|
|
{"token", batch.token [i]},
|
|
{"seq_id", batch.seq_id[i][0]}
|
|
});
|
|
|
|
res.data = json {
|
|
{"embedding", std::vector<float>(n_embd, 0.0f)},
|
|
};
|
|
|
|
continue;
|
|
}
|
|
|
|
res.data = json {
|
|
{"embedding", std::vector<float>(embd, embd + n_embd)},
|
|
};
|
|
}
|
|
|
|
queue_results.send(res);
|
|
}
|
|
|
|
void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding) {
|
|
server_task task;
|
|
task.id = id_task;
|
|
task.id_multi = id_multi;
|
|
task.id_target = 0;
|
|
task.data = std::move(data);
|
|
task.infill = infill;
|
|
task.embedding = embedding;
|
|
task.type = SERVER_TASK_TYPE_COMPLETION;
|
|
|
|
// when a completion task's prompt array is not a singleton, we split it into multiple requests
|
|
// otherwise, it's a single-prompt task, we actually queue it
|
|
// if there's numbers in the prompt array it will be treated as an array of tokens
|
|
if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
|
|
bool numbers = false;
|
|
for (const auto & e : task.data.at("prompt")) {
|
|
if (e.is_number()) {
|
|
numbers = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
|
|
// it will completely stall the server. I don't know where the bug for this is.
|
|
//
|
|
// if there are numbers, it needs to be treated like a single prompt,
|
|
// queue_tasks handles a mix of strings and numbers just fine.
|
|
if (numbers) {
|
|
queue_tasks.post(task);
|
|
} else {
|
|
split_multiprompt_task(id_task, task);
|
|
}
|
|
} else {
|
|
queue_tasks.post(task);
|
|
}
|
|
}
|
|
|
|
void request_cancel(int id_task) {
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_CANCEL;
|
|
task.id_target = id_task;
|
|
|
|
queue_tasks.post(task);
|
|
}
|
|
|
|
void split_multiprompt_task(int id_multi, const server_task & multiprompt_task) {
|
|
const int prompt_count = multiprompt_task.data.at("prompt").size();
|
|
if (prompt_count <= 1) {
|
|
send_error(multiprompt_task, "error while handling multiple prompts");
|
|
return;
|
|
}
|
|
|
|
// generate all the ID for subtask
|
|
std::vector<int> subtask_ids(prompt_count);
|
|
for (int i = 0; i < prompt_count; i++) {
|
|
subtask_ids[i] = queue_tasks.get_new_id();
|
|
}
|
|
|
|
// queue up the multitask so we can track its subtask progression
|
|
queue_tasks.add_multitask(id_multi, subtask_ids);
|
|
|
|
// add subtasks
|
|
for (int i = 0; i < prompt_count; i++) {
|
|
json subtask_data = multiprompt_task.data;
|
|
subtask_data["prompt"] = subtask_data["prompt"][i];
|
|
|
|
// subtasks inherit everything else (infill mode, embedding mode, etc.)
|
|
request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding);
|
|
}
|
|
}
|
|
|
|
void process_single_task(const server_task & task) {
|
|
switch (task.type) {
|
|
case SERVER_TASK_TYPE_COMPLETION:
|
|
{
|
|
server_slot * slot = get_slot(json_value(task.data, "id_slot", -1));
|
|
if (slot == nullptr) {
|
|
// if no slot is available, we defer this task for processing later
|
|
LOG_VERBOSE("no slot is available", {{"id_task", task.id}});
|
|
queue_tasks.defer(task);
|
|
break;
|
|
}
|
|
|
|
if (task.data.contains("system_prompt")) {
|
|
system_prompt_set(task.data["system_prompt"]);
|
|
|
|
for (server_slot & slot : slots) {
|
|
slot.n_past = 0;
|
|
slot.n_past_se = 0;
|
|
}
|
|
}
|
|
|
|
slot->reset();
|
|
|
|
slot->id_task = task.id;
|
|
slot->id_multi = task.id_multi;
|
|
slot->infill = task.infill;
|
|
slot->embedding = task.embedding;
|
|
|
|
if (!launch_slot_with_data(*slot, task.data)) {
|
|
// send error result
|
|
send_error(task, "internal_error");
|
|
break;
|
|
}
|
|
} break;
|
|
case SERVER_TASK_TYPE_CANCEL:
|
|
{
|
|
// release slot linked with the task id
|
|
for (auto & slot : slots) {
|
|
if (slot.id_task == task.id_target) {
|
|
slot.release();
|
|
break;
|
|
}
|
|
}
|
|
} break;
|
|
case SERVER_TASK_TYPE_NEXT_RESPONSE:
|
|
{
|
|
// do nothing
|
|
} break;
|
|
case SERVER_TASK_TYPE_METRICS:
|
|
{
|
|
json slots_data = json::array();
|
|
|
|
int n_idle_slots = 0;
|
|
int n_processing_slots = 0;
|
|
|
|
for (server_slot & slot : slots) {
|
|
json slot_data = get_formated_generation(slot);
|
|
slot_data["id"] = slot.id;
|
|
slot_data["id_task"] = slot.id_task;
|
|
slot_data["state"] = slot.state;
|
|
slot_data["prompt"] = slot.prompt;
|
|
slot_data["next_token"] = {
|
|
{"has_next_token", slot.has_next_token},
|
|
{"n_remain", slot.n_remaining},
|
|
{"n_decoded", slot.n_decoded},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
};
|
|
|
|
if (slot_data["state"] == SLOT_STATE_IDLE) {
|
|
n_idle_slots++;
|
|
} else {
|
|
n_processing_slots++;
|
|
}
|
|
|
|
slots_data.push_back(slot_data);
|
|
}
|
|
LOG_INFO("slot data", {
|
|
{"id_task", task.id},
|
|
{"n_idle_slots", n_idle_slots},
|
|
{"n_processing_slots", n_processing_slots}
|
|
});
|
|
|
|
LOG_VERBOSE("slot data", {
|
|
{"id_task", task.id},
|
|
{"n_idle_slots", n_idle_slots},
|
|
{"n_processing_slots", n_processing_slots},
|
|
{"slots", slots_data}
|
|
});
|
|
|
|
server_task_result res;
|
|
res.id = task.id;
|
|
res.id_multi = task.id_multi;
|
|
res.stop = true;
|
|
res.error = false;
|
|
res.data = {
|
|
{ "idle", n_idle_slots },
|
|
{ "processing", n_processing_slots },
|
|
{ "deferred", queue_tasks.queue_tasks_deferred.size() },
|
|
{ "t_start", metrics.t_start},
|
|
|
|
{ "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
|
|
{ "t_tokens_generation_total", metrics.t_tokens_generation_total},
|
|
{ "n_tokens_predicted_total", metrics.n_tokens_predicted_total},
|
|
{ "t_prompt_processing_total", metrics.t_prompt_processing_total},
|
|
|
|
{ "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed},
|
|
{ "t_prompt_processing", metrics.t_prompt_processing},
|
|
{ "n_tokens_predicted", metrics.n_tokens_predicted},
|
|
{ "t_tokens_generation", metrics.t_tokens_generation},
|
|
|
|
{ "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)},
|
|
{ "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)},
|
|
|
|
{ "slots", slots_data },
|
|
};
|
|
|
|
if (json_value(task.data, "reset_bucket", false)) {
|
|
metrics.reset_bucket();
|
|
}
|
|
queue_results.send(res);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
void on_finish_multitask(const server_task_multi & multitask) {
|
|
// all subtasks done == multitask is done
|
|
server_task_result result;
|
|
result.id = multitask.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
|
|
// collect json results into one json result
|
|
std::vector<json> result_jsons;
|
|
for (const auto & subres : multitask.results) {
|
|
result_jsons.push_back(subres.data);
|
|
result.error = result.error && subres.error;
|
|
}
|
|
result.data = json {
|
|
{ "results", result_jsons }
|
|
};
|
|
|
|
queue_results.send(result);
|
|
}
|
|
|
|
bool update_slots() {
|
|
if (system_need_update) {
|
|
system_prompt_update();
|
|
}
|
|
|
|
// release slots
|
|
for (auto & slot : slots) {
|
|
if (slot.command == SLOT_COMMAND_RELEASE) {
|
|
slot.state = SLOT_STATE_IDLE;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.t_last_used = ggml_time_us();
|
|
|
|
LOG_INFO("slot released", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", n_ctx},
|
|
{"n_past", slot.n_past},
|
|
{"n_system_tokens", system_tokens.size()},
|
|
{"n_cache_tokens", slot.cache_tokens.size()},
|
|
{"truncated", slot.truncated}
|
|
});
|
|
|
|
queue_tasks.notify_slot_changed();
|
|
}
|
|
}
|
|
|
|
// check if all slots are idle
|
|
{
|
|
bool all_idle = true;
|
|
|
|
for (auto & slot : slots) {
|
|
if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) {
|
|
all_idle = false;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (all_idle) {
|
|
LOG_INFO("all slots are idle", {});
|
|
if (system_prompt.empty() && clean_kv_cache) {
|
|
kv_cache_clear();
|
|
}
|
|
|
|
return true;
|
|
}
|
|
}
|
|
|
|
{
|
|
LOG_VERBOSE("posting NEXT_RESPONSE", {});
|
|
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_NEXT_RESPONSE;
|
|
task.id_target = -1;
|
|
|
|
queue_tasks.post(task);
|
|
}
|
|
|
|
// apply context-shift if needed
|
|
// TODO: simplify and improve
|
|
for (server_slot & slot : slots) {
|
|
if (slot.ga_n == 1) {
|
|
if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
|
|
// Shift context
|
|
const int n_keep = slot.params.n_keep + add_bos_token;
|
|
const int n_left = (int) system_tokens.size() + slot.n_past - n_keep;
|
|
const int n_discard = n_left / 2;
|
|
|
|
LOG_INFO("slot context shift", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_keep", n_keep},
|
|
{"n_left", n_left},
|
|
{"n_discard", n_discard},
|
|
{"n_ctx", n_ctx},
|
|
{"n_past", slot.n_past},
|
|
{"n_system_tokens", system_tokens.size()},
|
|
{"n_cache_tokens", slot.cache_tokens.size()}
|
|
});
|
|
|
|
llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep , n_keep + n_discard);
|
|
llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);
|
|
|
|
if (slot.params.cache_prompt) {
|
|
for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
|
|
slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
|
|
}
|
|
|
|
slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
|
|
}
|
|
|
|
slot.n_past -= n_discard;
|
|
|
|
slot.truncated = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
// start populating the batch for this iteration
|
|
llama_batch_clear(batch);
|
|
|
|
// frist, add sampled tokens from any ongoing sequences
|
|
for (auto & slot : slots) {
|
|
if (slot.state == SLOT_STATE_IDLE) {
|
|
continue;
|
|
}
|
|
|
|
slot.i_batch = batch.n_tokens;
|
|
|
|
const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
|
|
|
|
// TODO: we always have to take into account the "system_tokens"
|
|
// this is not great and needs to be improved somehow
|
|
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true);
|
|
|
|
slot.n_past += 1;
|
|
|
|
if (slot.params.cache_prompt) {
|
|
slot.cache_tokens.push_back(slot.sampled);
|
|
}
|
|
|
|
LOG_VERBOSE("slot decode token", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", n_ctx},
|
|
{"n_past", slot.n_past},
|
|
{"n_system_tokens", system_tokens.size()},
|
|
{"n_cache_tokens", slot.cache_tokens.size()},
|
|
{"truncated", slot.truncated}
|
|
});
|
|
}
|
|
|
|
// process in chunks of params.n_batch
|
|
int32_t n_batch = params.n_batch;
|
|
|
|
// next, batch any pending prompts without exceeding n_batch
|
|
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<std::string>().empty());
|
|
|
|
// empty prompt passed -> release the slot and send empty response
|
|
// note: infill mode allows empty prompt
|
|
if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT && !has_prompt && !slot.infill) {
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
continue;
|
|
}
|
|
|
|
// this slot still has a prompt to be processed
|
|
if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) {
|
|
auto & prompt_tokens = slot.prompt_tokens;
|
|
|
|
// we haven't tokenized the prompt yet - do it now:
|
|
if (prompt_tokens.empty()) {
|
|
LOG_VERBOSE("tokenizing prompt", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task}
|
|
});
|
|
|
|
slot.t_start_process_prompt = ggml_time_us();
|
|
slot.t_start_generation = 0;
|
|
|
|
if (slot.infill) {
|
|
bool suff_rm_leading_spc = true;
|
|
if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
|
|
params.input_suffix.erase(0, 1);
|
|
suff_rm_leading_spc = false;
|
|
}
|
|
|
|
auto prefix_tokens = tokenize(slot.params.input_prefix, false);
|
|
auto suffix_tokens = tokenize(slot.params.input_suffix, false);
|
|
|
|
const int space_token = 29871; // TODO: this should not be hardcoded
|
|
if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
|
|
suffix_tokens.erase(suffix_tokens.begin());
|
|
}
|
|
|
|
prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
|
|
prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
|
|
prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model));
|
|
prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end());
|
|
prefix_tokens.push_back(llama_token_middle(model));
|
|
prompt_tokens = prefix_tokens;
|
|
} else {
|
|
prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt
|
|
}
|
|
|
|
slot.n_past = 0;
|
|
slot.n_prompt_tokens = prompt_tokens.size();
|
|
|
|
LOG_VERBOSE("prompt tokenized", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_prompt_tokens", slot.n_prompt_tokens},
|
|
{"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())},
|
|
});
|
|
|
|
if (slot.embedding) {
|
|
// this prompt is too large to process - discard it
|
|
if (slot.n_prompt_tokens > n_batch) {
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
continue;
|
|
}
|
|
} else {
|
|
if (slot.params.n_keep < 0) {
|
|
slot.params.n_keep = slot.n_prompt_tokens;
|
|
}
|
|
slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
|
|
|
|
// if input prompt is too big, truncate it (if group attention self-extend is disabled)
|
|
if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) {
|
|
const int n_left = slot.n_ctx - slot.params.n_keep;
|
|
|
|
const int n_block_size = n_left / 2;
|
|
const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
|
|
|
|
std::vector<llama_token> new_tokens(
|
|
prompt_tokens.begin(),
|
|
prompt_tokens.begin() + slot.params.n_keep);
|
|
|
|
new_tokens.insert(
|
|
new_tokens.end(),
|
|
prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size,
|
|
prompt_tokens.end());
|
|
|
|
prompt_tokens = std::move(new_tokens);
|
|
|
|
slot.truncated = true;
|
|
slot.n_prompt_tokens = prompt_tokens.size();
|
|
|
|
LOG_VERBOSE("input truncated", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_left", n_left},
|
|
{"n_prompt_tokens", slot.n_prompt_tokens},
|
|
{"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())},
|
|
});
|
|
|
|
GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
|
|
}
|
|
|
|
llama_sampling_reset(slot.ctx_sampling);
|
|
|
|
if (!slot.params.cache_prompt) {
|
|
slot.n_past_se = 0;
|
|
slot.ga_i = 0;
|
|
} else {
|
|
GGML_ASSERT(slot.ga_n == 1);
|
|
|
|
// reuse any previously computed tokens that are common with the new prompt
|
|
slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
|
|
|
|
// push the prompt into the sampling context (do not apply grammar)
|
|
for (int i = 0; i < slot.n_past; ++i) {
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) {
|
|
// we have to evaluate at least 1 token to generate logits.
|
|
LOG_INFO("we have to evaluate at least 1 token to generate logits", {
|
|
{ "id_slot", slot.id },
|
|
{ "id_task", slot.id_task }
|
|
});
|
|
|
|
slot.n_past--;
|
|
if (slot.ga_i > 0) {
|
|
slot.n_past_se--;
|
|
}
|
|
}
|
|
|
|
slot.n_prompt_tokens_processed = 0;
|
|
}
|
|
|
|
if (slot.embedding) {
|
|
// cannot fit the prompt in the current batch - will try next iter
|
|
if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
|
|
continue;
|
|
}
|
|
}
|
|
|
|
// keep only the common part
|
|
int p0 = (int) system_tokens.size() + slot.n_past;
|
|
if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) {
|
|
// could not partially delete (likely using a non-Transformer model)
|
|
llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1);
|
|
|
|
p0 = (int) system_tokens.size();
|
|
if (p0 != 0) {
|
|
// copy over the system prompt when there is one
|
|
llama_kv_cache_seq_cp(ctx, 0, slot.id + 1, -1, -1);
|
|
}
|
|
|
|
// there is no common part left (except for the system prompt)
|
|
slot.n_past = 0;
|
|
slot.n_past_se = 0;
|
|
slot.ga_i = 0;
|
|
// TODO: is the system prompt ever in the sampling context?
|
|
llama_sampling_reset(slot.ctx_sampling);
|
|
}
|
|
|
|
// remove the non-common part from the cache
|
|
slot.cache_tokens.resize(slot.n_past);
|
|
|
|
LOG_INFO("kv cache rm [p0, end)", {
|
|
{ "id_slot", slot.id },
|
|
{ "id_task", slot.id_task },
|
|
{ "p0", p0 }
|
|
});
|
|
|
|
int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
|
|
|
|
int32_t ga_i = slot.ga_i;
|
|
int32_t ga_n = slot.ga_n;
|
|
int32_t ga_w = slot.ga_w;
|
|
|
|
// add prompt tokens for processing in the current batch
|
|
// TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow
|
|
for (; slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch; ++slot.n_past) {
|
|
if (slot.ga_n != 1) {
|
|
while (slot_npast >= ga_i + ga_w) {
|
|
const int bd = (ga_w/ga_n)*(ga_n - 1);
|
|
slot_npast -= bd;
|
|
ga_i += ga_w/ga_n;
|
|
}
|
|
}
|
|
|
|
llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false);
|
|
|
|
if (slot.params.cache_prompt) {
|
|
slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
|
|
}
|
|
|
|
slot.n_prompt_tokens_processed++;
|
|
slot_npast++;
|
|
}
|
|
|
|
LOG_VERBOSE("prompt processing progress", {
|
|
{"id_slot", slot.id},
|
|
{"n_past", slot.n_past},
|
|
{"n_ctx", n_ctx},
|
|
{"n_tokens", batch.n_tokens},
|
|
{"progress", (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens},
|
|
});
|
|
|
|
// entire prompt has been processed - start decoding new tokens
|
|
if (slot.n_past == slot.n_prompt_tokens) {
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
|
|
GGML_ASSERT(batch.n_tokens > 0);
|
|
|
|
// extract the logits only for the last token
|
|
batch.logits[batch.n_tokens - 1] = true;
|
|
|
|
slot.n_decoded = 0;
|
|
slot.i_batch = batch.n_tokens - 1;
|
|
|
|
LOG_VERBOSE("prompt done", {
|
|
{"id_slot", slot.id},
|
|
{"n_past", slot.n_past},
|
|
{"n_ctx", n_ctx},
|
|
{"n_tokens", batch.n_tokens},
|
|
});
|
|
}
|
|
}
|
|
|
|
if (batch.n_tokens >= n_batch) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (batch.n_tokens == 0) {
|
|
LOG_VERBOSE("no tokens to decode", {});
|
|
|
|
return true;
|
|
}
|
|
|
|
LOG_VERBOSE("decoding batch", {
|
|
{"n_tokens", batch.n_tokens},
|
|
});
|
|
|
|
// process the created batch of tokens
|
|
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
|
|
const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
|
|
|
|
for (auto & slot : slots) {
|
|
if (slot.ga_n != 1) {
|
|
// context extension via Self-Extend
|
|
// TODO: simplify and/or abstract this
|
|
while (slot.n_past_se >= slot.ga_i + slot.ga_w) {
|
|
const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
|
|
const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
|
|
const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
|
|
|
|
LOG_TEE("\n");
|
|
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
|
|
LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
|
|
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
|
|
|
|
llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i, slot.n_past_se, ib * bd);
|
|
llama_kv_cache_seq_div(ctx, slot.id + 1, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n);
|
|
llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd);
|
|
|
|
slot.n_past_se -= bd;
|
|
|
|
slot.ga_i += slot.ga_w / slot.ga_n;
|
|
|
|
LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
|
|
}
|
|
|
|
slot.n_past_se += n_tokens;
|
|
}
|
|
}
|
|
|
|
llama_batch batch_view = {
|
|
n_tokens,
|
|
batch.token + i,
|
|
nullptr,
|
|
batch.pos + i,
|
|
batch.n_seq_id + i,
|
|
batch.seq_id + i,
|
|
batch.logits + i,
|
|
0, 0, 0, // unused
|
|
};
|
|
|
|
const int ret = llama_decode(ctx, batch_view);
|
|
|
|
if (ret != 0) {
|
|
if (n_batch == 1 || ret < 0) {
|
|
// if you get here, it means the KV cache is full - try increasing it via the context size
|
|
LOG_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.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
|
|
continue;
|
|
}
|
|
|
|
// prompt evaluated for embedding
|
|
if (slot.embedding) {
|
|
send_embedding(slot, batch_view);
|
|
slot.release();
|
|
slot.i_batch = -1;
|
|
continue;
|
|
}
|
|
|
|
completion_token_output result;
|
|
const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i);
|
|
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
|
|
|
|
slot.n_decoded += 1;
|
|
if (slot.n_decoded == 1) {
|
|
slot.t_start_generation = ggml_time_us();
|
|
slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
|
|
metrics.on_prompt_eval(slot);
|
|
}
|
|
|
|
llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
|
|
result.tok = id;
|
|
|
|
const 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);
|
|
metrics.on_prediction(slot);
|
|
}
|
|
|
|
slot.i_batch = -1;
|
|
}
|
|
}
|
|
|
|
LOG_VERBOSE("slots updated", {});
|
|
|
|
return true;
|
|
}
|
|
|
|
json model_meta() const {
|
|
return json {
|
|
{"vocab_type", llama_vocab_type (model)},
|
|
{"n_vocab", llama_n_vocab (model)},
|
|
{"n_ctx_train", llama_n_ctx_train (model)},
|
|
{"n_embd", llama_n_embd (model)},
|
|
{"n_params", llama_model_n_params(model)},
|
|
{"size", llama_model_size (model)},
|
|
};
|
|
}
|
|
};
|
|
|
|
static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) {
|
|
printf("usage: %s [options]\n", argv0);
|
|
printf("\n");
|
|
printf("options:\n");
|
|
printf(" -h, --help show this help message and exit\n");
|
|
printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
|
|
printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
|
printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n");
|
|
printf(" --threads-http N number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n");
|
|
printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
|
printf(" --rope-scaling {none,linear,yarn}\n");
|
|
printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n");
|
|
printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n");
|
|
printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n");
|
|
printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
|
|
printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
|
|
printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
|
|
printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
|
|
printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
|
|
printf(" -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_supports_mlock()) {
|
|
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
|
}
|
|
if (llama_supports_mmap()) {
|
|
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
|
}
|
|
printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n");
|
|
printf(" - distribute: spread execution evenly over all nodes\n");
|
|
printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n");
|
|
printf(" - numactl: use the CPU map provided my numactl\n");
|
|
if (llama_supports_gpu_offload()) {
|
|
printf(" -ngl N, --n-gpu-layers N\n");
|
|
printf(" number of layers to store in VRAM\n");
|
|
printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
|
|
printf(" how to split the model across multiple GPUs, one of:\n");
|
|
printf(" - none: use one GPU only\n");
|
|
printf(" - layer (default): split layers and KV across GPUs\n");
|
|
printf(" - row: split rows across GPUs\n");
|
|
printf(" -ts SPLIT --tensor-split SPLIT\n");
|
|
printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
|
|
printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n");
|
|
printf(" or for intermediate results and KV (with split-mode = row)\n");
|
|
}
|
|
printf(" -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(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
|
|
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
|
|
printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
|
|
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
|
|
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: 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(" -ctk TYPE, --cache-type-k TYPE\n");
|
|
printf(" KV cache data type for K (default: f16)\n");
|
|
printf(" -ctv TYPE, --cache-type-v TYPE\n");
|
|
printf(" KV cache data type for V (default: f16)\n");
|
|
printf(" --log-format log output format: json or text (default: json)\n");
|
|
printf(" --log-disable disables logging to a file.\n");
|
|
printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n");
|
|
printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled");
|
|
printf("\n");
|
|
printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict);
|
|
printf(" --override-kv KEY=TYPE:VALUE\n");
|
|
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
|
|
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
|
|
printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
|
|
printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
|
|
printf(" --chat-template JINJA_TEMPLATE\n");
|
|
printf(" set custom jinja chat template (default: template taken from model's metadata)\n");
|
|
printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n");
|
|
printf("\n");
|
|
}
|
|
|
|
static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) {
|
|
gpt_params default_params;
|
|
server_params default_sparams;
|
|
|
|
std::string arg;
|
|
bool invalid_param = false;
|
|
|
|
for (int i = 1; i < argc; i++) {
|
|
arg = argv[i];
|
|
if (arg == "--port") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.port = std::stoi(argv[i]);
|
|
} else if (arg == "--host") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.hostname = argv[i];
|
|
} else if (arg == "--path") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.public_path = argv[i];
|
|
} else if (arg == "--api-key") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.api_keys.emplace_back(argv[i]);
|
|
} else if (arg == "--api-key-file") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::ifstream key_file(argv[i]);
|
|
if (!key_file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string key;
|
|
while (std::getline(key_file, key)) {
|
|
if (key.size() > 0) {
|
|
sparams.api_keys.push_back(key);
|
|
}
|
|
}
|
|
key_file.close();
|
|
} 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_TYPE_NONE; }
|
|
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
|
|
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
|
|
else { invalid_param = true; break; }
|
|
} else if (arg == "--rope-freq-base") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rope_freq_base = std::stof(argv[i]);
|
|
} else if (arg == "--rope-freq-scale") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rope_freq_scale = std::stof(argv[i]);
|
|
} else if (arg == "--yarn-ext-factor") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_ext_factor = std::stof(argv[i]);
|
|
}
|
|
else if (arg == "--yarn-attn-factor") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_attn_factor = std::stof(argv[i]);
|
|
} else if (arg == "--yarn-beta-fast") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_beta_fast = std::stof(argv[i]);
|
|
} else if (arg == "--yarn-beta-slow") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_beta_slow = std::stof(argv[i]);
|
|
} else if (arg == "--pooling") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
|
|
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
|
|
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
|
|
else { invalid_param = true; break; }
|
|
} else if (arg == "--threads" || arg == "-t") {
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_threads = std::stoi(argv[i]);
|
|
} else if (arg == "--grp-attn-n" || arg == "-gan") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
|
|
params.grp_attn_n = std::stoi(argv[i]);
|
|
} else if (arg == "--grp-attn-w" || arg == "-gaw") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
|
|
params.grp_attn_w = std::stoi(argv[i]);
|
|
} else if (arg == "--threads-batch" || arg == "-tb") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_threads_batch = std::stoi(argv[i]);
|
|
} else if (arg == "--threads-http") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.n_threads_http = std::stoi(argv[i]);
|
|
} else if (arg == "-b" || arg == "--batch-size") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_batch = std::stoi(argv[i]);
|
|
} else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
if (llama_supports_gpu_offload()) {
|
|
params.n_gpu_layers = std::stoi(argv[i]);
|
|
} else {
|
|
LOG_WARNING(
|
|
"Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
|
|
"See main README.md for information on enabling GPU BLAS support",
|
|
{{"n_gpu_layers", params.n_gpu_layers}});
|
|
}
|
|
} else if (arg == "--split-mode" || arg == "-sm") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string arg_next = argv[i];
|
|
if (arg_next == "none") {
|
|
params.split_mode = LLAMA_SPLIT_MODE_NONE;
|
|
} else if (arg_next == "layer") {
|
|
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
|
|
} else if (arg_next == "row") {
|
|
params.split_mode = LLAMA_SPLIT_MODE_ROW;
|
|
} else {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#ifndef GGML_USE_CUBLAS
|
|
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n");
|
|
#endif // GGML_USE_CUBLAS
|
|
} else if (arg == "--tensor-split" || arg == "-ts") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
|
|
std::string arg_next = argv[i];
|
|
|
|
// split string by , and /
|
|
const std::regex regex{R"([,/]+)"};
|
|
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
|
|
std::vector<std::string> 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 == "--main-gpu" || arg == "-mg") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
|
|
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.emplace_back(argv[i], 1.0f);
|
|
params.use_mmap = false;
|
|
} else if (arg == "--lora-scaled") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
const char * lora_adapter = argv[i];
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
|
|
params.use_mmap = false;
|
|
} else if (arg == "--lora-base") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_base = argv[i];
|
|
} else if (arg == "-v" || arg == "--verbose") {
|
|
#if SERVER_VERBOSE != 1
|
|
LOG_WARNING("server.cpp is not built with verbose logging.", {});
|
|
#else
|
|
server_verbose = true;
|
|
#endif
|
|
} else if (arg == "--mlock") {
|
|
params.use_mlock = true;
|
|
} else if (arg == "--no-mmap") {
|
|
params.use_mmap = false;
|
|
} else if (arg == "--numa") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
} else {
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
|
|
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
|
|
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
|
|
else { invalid_param = true; break; }
|
|
}
|
|
} else if (arg == "--embedding" || arg == "--embeddings") {
|
|
params.embedding = true;
|
|
} else if (arg == "-cb" || arg == "--cont-batching") {
|
|
params.cont_batching = true;
|
|
} else if (arg == "-np" || arg == "--parallel") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_parallel = std::stoi(argv[i]);
|
|
} else if (arg == "-n" || arg == "--n-predict") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_predict = std::stoi(argv[i]);
|
|
} else if (arg == "-spf" || arg == "--system-prompt-file") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string system_prompt;
|
|
std::copy(
|
|
std::istreambuf_iterator<char>(file),
|
|
std::istreambuf_iterator<char>(),
|
|
std::back_inserter(system_prompt)
|
|
);
|
|
sparams.system_prompt = system_prompt;
|
|
} else if (arg == "-ctk" || arg == "--cache-type-k") {
|
|
params.cache_type_k = argv[++i];
|
|
} else if (arg == "-ctv" || arg == "--cache-type-v") {
|
|
params.cache_type_v = argv[++i];
|
|
} else if (arg == "--log-format") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
if (std::strcmp(argv[i], "json") == 0) {
|
|
server_log_json = true;
|
|
} else if (std::strcmp(argv[i], "text") == 0) {
|
|
server_log_json = false;
|
|
} else {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
} else if (arg == "--log-disable") {
|
|
log_set_target(stdout);
|
|
LOG_INFO("logging to file is disabled.", {});
|
|
} else if (arg == "--slots-endpoint-disable") {
|
|
sparams.slots_endpoint = false;
|
|
} else if (arg == "--metrics") {
|
|
sparams.metrics_endpoint = true;
|
|
} else if (arg == "--chat-template") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
if (!verify_custom_template(argv[i])) {
|
|
fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
|
|
fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.chat_template = argv[i];
|
|
} else if (arg == "--override-kv") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
char * sep = strchr(argv[i], '=');
|
|
if (sep == nullptr || sep - argv[i] >= 128) {
|
|
fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
|
|
struct llama_model_kv_override kvo;
|
|
std::strncpy(kvo.key, argv[i], sep - argv[i]);
|
|
kvo.key[sep - argv[i]] = 0;
|
|
sep++;
|
|
if (strncmp(sep, "int:", 4) == 0) {
|
|
sep += 4;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
|
|
kvo.int_value = std::atol(sep);
|
|
} else if (strncmp(sep, "float:", 6) == 0) {
|
|
sep += 6;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
|
|
kvo.float_value = std::atof(sep);
|
|
} else if (strncmp(sep, "bool:", 5) == 0) {
|
|
sep += 5;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
|
|
if (std::strcmp(sep, "true") == 0) {
|
|
kvo.bool_value = true;
|
|
} else if (std::strcmp(sep, "false") == 0) {
|
|
kvo.bool_value = false;
|
|
} else {
|
|
fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
} else {
|
|
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.kv_overrides.push_back(kvo);
|
|
} else {
|
|
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
|
server_print_usage(argv[0], default_params, default_sparams);
|
|
exit(1);
|
|
}
|
|
}
|
|
|
|
if (!params.kv_overrides.empty()) {
|
|
params.kv_overrides.emplace_back();
|
|
params.kv_overrides.back().key[0] = 0;
|
|
}
|
|
|
|
if (invalid_param) {
|
|
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
|
|
server_print_usage(argv[0], default_params, default_sparams);
|
|
exit(1);
|
|
}
|
|
}
|
|
|
|
static void log_server_request(const httplib::Request & req, const httplib::Response & res) {
|
|
// skip GH copilot requests when using default port
|
|
if (req.path == "/v1/health" || req.path == "/v1/completions") {
|
|
return;
|
|
}
|
|
|
|
LOG_INFO("request", {
|
|
{"remote_addr", req.remote_addr},
|
|
{"remote_port", req.remote_port},
|
|
{"status", res.status},
|
|
{"method", req.method},
|
|
{"path", req.path},
|
|
{"params", req.params},
|
|
});
|
|
|
|
LOG_VERBOSE("request", {
|
|
{"request", req.body},
|
|
{"response", res.body},
|
|
});
|
|
}
|
|
|
|
std::function<void(int)> shutdown_handler;
|
|
std::atomic_flag is_terminating = ATOMIC_FLAG_INIT;
|
|
|
|
inline void signal_handler(int signal) {
|
|
if (is_terminating.test_and_set()) {
|
|
// in case it hangs, we can force terminate the server by hitting Ctrl+C twice
|
|
// this is for better developer experience, we can remove when the server is stable enough
|
|
fprintf(stderr, "Received second interrupt, terminating immediately.\n");
|
|
exit(1);
|
|
}
|
|
|
|
shutdown_handler(signal);
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
#if SERVER_VERBOSE != 1
|
|
log_disable();
|
|
#endif
|
|
// own arguments required by this example
|
|
gpt_params params;
|
|
server_params sparams;
|
|
|
|
// struct that contains llama context and inference
|
|
server_context ctx_server;
|
|
|
|
server_params_parse(argc, argv, sparams, params);
|
|
|
|
if (!sparams.system_prompt.empty()) {
|
|
ctx_server.system_prompt_set(json::parse(sparams.system_prompt));
|
|
}
|
|
|
|
if (params.model_alias == "unknown") {
|
|
params.model_alias = params.model;
|
|
}
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
LOG_INFO("build info", {
|
|
{"build", LLAMA_BUILD_NUMBER},
|
|
{"commit", LLAMA_COMMIT}
|
|
});
|
|
|
|
LOG_INFO("system info", {
|
|
{"n_threads", params.n_threads},
|
|
{"n_threads_batch", params.n_threads_batch},
|
|
{"total_threads", std::thread::hardware_concurrency()},
|
|
{"system_info", llama_print_system_info()},
|
|
});
|
|
|
|
httplib::Server svr;
|
|
|
|
std::atomic<server_state> state{SERVER_STATE_LOADING_MODEL};
|
|
|
|
svr.set_default_headers({{"Server", "llama.cpp"}});
|
|
|
|
// CORS preflight
|
|
svr.Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
res.set_header("Access-Control-Allow-Credentials", "true");
|
|
res.set_header("Access-Control-Allow-Methods", "POST");
|
|
res.set_header("Access-Control-Allow-Headers", "*");
|
|
});
|
|
|
|
svr.Get("/health", [&](const httplib::Request & req, httplib::Response & res) {
|
|
server_state current_state = state.load();
|
|
switch (current_state) {
|
|
case SERVER_STATE_READY:
|
|
{
|
|
// request slots data using task queue
|
|
server_task task;
|
|
task.id = ctx_server.queue_tasks.get_new_id();
|
|
task.type = SERVER_TASK_TYPE_METRICS;
|
|
task.id_target = -1;
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(task.id);
|
|
ctx_server.queue_tasks.post(task);
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(task.id);
|
|
ctx_server.queue_results.remove_waiting_task_id(task.id);
|
|
|
|
const int n_idle_slots = result.data["idle"];
|
|
const int n_processing_slots = result.data["processing"];
|
|
|
|
json health = {
|
|
{"status", "ok"},
|
|
{"slots_idle", n_idle_slots},
|
|
{"slots_processing", n_processing_slots}
|
|
};
|
|
|
|
res.status = 200; // HTTP OK
|
|
if (sparams.slots_endpoint && req.has_param("include_slots")) {
|
|
health["slots"] = result.data["slots"];
|
|
}
|
|
|
|
if (n_idle_slots == 0) {
|
|
health["status"] = "no slot available";
|
|
if (req.has_param("fail_on_no_slot")) {
|
|
res.status = 503; // HTTP Service Unavailable
|
|
}
|
|
}
|
|
|
|
res.set_content(health.dump(), "application/json");
|
|
break;
|
|
}
|
|
case SERVER_STATE_LOADING_MODEL:
|
|
{
|
|
res.set_content(R"({"status": "loading model"})", "application/json");
|
|
res.status = 503; // HTTP Service Unavailable
|
|
} break;
|
|
case SERVER_STATE_ERROR:
|
|
{
|
|
res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json");
|
|
res.status = 500; // HTTP Internal Server Error
|
|
} break;
|
|
}
|
|
});
|
|
|
|
if (sparams.slots_endpoint) {
|
|
svr.Get("/slots", [&](const httplib::Request &, httplib::Response & res) {
|
|
// request slots data using task queue
|
|
server_task task;
|
|
task.id = ctx_server.queue_tasks.get_new_id();
|
|
task.id_multi = -1;
|
|
task.id_target = -1;
|
|
task.type = SERVER_TASK_TYPE_METRICS;
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(task.id);
|
|
ctx_server.queue_tasks.post(task);
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(task.id);
|
|
ctx_server.queue_results.remove_waiting_task_id(task.id);
|
|
|
|
res.set_content(result.data["slots"].dump(), "application/json");
|
|
res.status = 200; // HTTP OK
|
|
});
|
|
}
|
|
|
|
if (sparams.metrics_endpoint) {
|
|
svr.Get("/metrics", [&](const httplib::Request &, httplib::Response & res) {
|
|
// request slots data using task queue
|
|
server_task task;
|
|
task.id = ctx_server.queue_tasks.get_new_id();
|
|
task.id_multi = -1;
|
|
task.id_target = -1;
|
|
task.type = SERVER_TASK_TYPE_METRICS;
|
|
task.data.push_back({{"reset_bucket", true}});
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(task.id);
|
|
ctx_server.queue_tasks.post(task);
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(task.id);
|
|
ctx_server.queue_results.remove_waiting_task_id(task.id);
|
|
|
|
json data = result.data;
|
|
|
|
const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"];
|
|
const uint64_t t_prompt_processing = data["t_prompt_processing"];
|
|
|
|
const uint64_t n_tokens_predicted = data["n_tokens_predicted"];
|
|
const uint64_t t_tokens_generation = data["t_tokens_generation"];
|
|
|
|
const int32_t kv_cache_used_cells = data["kv_cache_used_cells"];
|
|
|
|
// metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
|
|
json all_metrics_def = json {
|
|
{"counter", {{
|
|
{"name", "prompt_tokens_total"},
|
|
{"help", "Number of prompt tokens processed."},
|
|
{"value", (uint64_t) data["n_prompt_tokens_processed_total"]}
|
|
}, {
|
|
{"name", "prompt_seconds_total"},
|
|
{"help", "Prompt process time"},
|
|
{"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3}
|
|
}, {
|
|
{"name", "tokens_predicted_total"},
|
|
{"help", "Number of generation tokens processed."},
|
|
{"value", (uint64_t) data["n_tokens_predicted_total"]}
|
|
}, {
|
|
{"name", "tokens_predicted_seconds_total"},
|
|
{"help", "Predict process time"},
|
|
{"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3}
|
|
}}},
|
|
{"gauge", {{
|
|
{"name", "prompt_tokens_seconds"},
|
|
{"help", "Average prompt throughput in tokens/s."},
|
|
{"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.}
|
|
},{
|
|
{"name", "predicted_tokens_seconds"},
|
|
{"help", "Average generation throughput in tokens/s."},
|
|
{"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.}
|
|
},{
|
|
{"name", "kv_cache_usage_ratio"},
|
|
{"help", "KV-cache usage. 1 means 100 percent usage."},
|
|
{"value", 1. * kv_cache_used_cells / params.n_ctx}
|
|
},{
|
|
{"name", "kv_cache_tokens"},
|
|
{"help", "KV-cache tokens."},
|
|
{"value", (uint64_t) data["kv_cache_tokens_count"]}
|
|
},{
|
|
{"name", "requests_processing"},
|
|
{"help", "Number of request processing."},
|
|
{"value", (uint64_t) data["processing"]}
|
|
},{
|
|
{"name", "requests_deferred"},
|
|
{"help", "Number of request deferred."},
|
|
{"value", (uint64_t) data["deferred"]}
|
|
}}}
|
|
};
|
|
|
|
std::stringstream prometheus;
|
|
|
|
for (const auto & el : all_metrics_def.items()) {
|
|
const auto & type = el.key();
|
|
const auto & metrics_def = el.value();
|
|
|
|
for (const auto & metric_def : metrics_def) {
|
|
const std::string name = metric_def["name"];
|
|
const std::string help = metric_def["help"];
|
|
|
|
auto value = json_value(metric_def, "value", 0.);
|
|
prometheus << "# HELP llamacpp:" << name << " " << help << "\n"
|
|
<< "# TYPE llamacpp:" << name << " " << type << "\n"
|
|
<< "llamacpp:" << name << " " << value << "\n";
|
|
}
|
|
}
|
|
|
|
const int64_t t_start = data["t_start"];
|
|
res.set_header("Process-Start-Time-Unix", std::to_string(t_start));
|
|
|
|
res.set_content(prometheus.str(), "text/plain; version=0.0.4");
|
|
res.status = 200; // HTTP OK
|
|
});
|
|
}
|
|
|
|
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");
|
|
}
|
|
if (res.status == 404) {
|
|
res.set_content("File Not Found", "text/plain; charset=utf-8");
|
|
}
|
|
});
|
|
|
|
// 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);
|
|
|
|
std::unordered_map<std::string, std::string> log_data;
|
|
|
|
log_data["hostname"] = sparams.hostname;
|
|
log_data["port"] = std::to_string(sparams.port);
|
|
|
|
if (sparams.api_keys.size() == 1) {
|
|
log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4);
|
|
} else if (sparams.api_keys.size() > 1) {
|
|
log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded";
|
|
}
|
|
|
|
// load the model
|
|
if (!ctx_server.load_model(params)) {
|
|
state.store(SERVER_STATE_ERROR);
|
|
return 1;
|
|
} else {
|
|
ctx_server.initialize();
|
|
state.store(SERVER_STATE_READY);
|
|
}
|
|
|
|
LOG_INFO("model loaded", {});
|
|
|
|
const auto model_meta = ctx_server.model_meta();
|
|
|
|
if (sparams.chat_template.empty()) { // custom chat template is not supplied
|
|
if (!ctx_server.validate_model_chat_template()) {
|
|
LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
|
|
sparams.chat_template = "chatml";
|
|
}
|
|
}
|
|
|
|
// 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_keys.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 (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) {
|
|
return true; // API key is valid
|
|
}
|
|
}
|
|
|
|
// API key is invalid or not provided
|
|
res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8");
|
|
res.status = 401; // Unauthorized
|
|
|
|
LOG_WARNING("Unauthorized: Invalid API Key", {});
|
|
|
|
return false;
|
|
};
|
|
|
|
// 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<const char*>(&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<const char *>(&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<const char*>(&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<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8");
|
|
return false;
|
|
});
|
|
|
|
svr.Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
json data = {
|
|
{ "user_name", ctx_server.name_user.c_str() },
|
|
{ "assistant_name", ctx_server.name_assistant.c_str() },
|
|
{ "default_generation_settings", ctx_server.default_generation_settings_for_props },
|
|
{ "total_slots", ctx_server.params.n_parallel }
|
|
};
|
|
|
|
res.set_content(data.dump(), "application/json; charset=utf-8");
|
|
});
|
|
|
|
const auto completions = [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
if (!validate_api_key(req, res)) {
|
|
return;
|
|
}
|
|
|
|
json data = json::parse(req.body);
|
|
|
|
const int id_task = ctx_server.queue_tasks.get_new_id();
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
ctx_server.request_completion(id_task, -1, data, false, false);
|
|
|
|
if (!json_value(data, "stream", false)) {
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
if (!result.error && result.stop) {
|
|
res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
|
|
} else {
|
|
res.status = 500;
|
|
res.set_content(result.data["content"], "text/plain; charset=utf-8");
|
|
}
|
|
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
} else {
|
|
const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
|
|
while (true) {
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
if (!result.error) {
|
|
const std::string str =
|
|
"data: " +
|
|
result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
|
|
"\n\n";
|
|
|
|
LOG_VERBOSE("data stream", {
|
|
{ "to_send", str }
|
|
});
|
|
|
|
if (!sink.write(str.c_str(), str.size())) {
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return false;
|
|
}
|
|
|
|
if (result.stop) {
|
|
break;
|
|
}
|
|
} else {
|
|
const std::string str =
|
|
"error: " +
|
|
result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
|
|
"\n\n";
|
|
|
|
LOG_VERBOSE("data stream", {
|
|
{ "to_send", str }
|
|
});
|
|
|
|
if (!sink.write(str.c_str(), str.size())) {
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return false;
|
|
}
|
|
|
|
break;
|
|
}
|
|
}
|
|
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
sink.done();
|
|
|
|
return true;
|
|
};
|
|
|
|
auto on_complete = [id_task, &ctx_server] (bool) {
|
|
// cancel
|
|
ctx_server.request_cancel(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
};
|
|
|
|
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
|
}
|
|
};
|
|
|
|
svr.Post("/completion", completions); // legacy
|
|
svr.Post("/completions", completions);
|
|
svr.Post("/v1/completions", completions);
|
|
|
|
svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
|
|
json models = {
|
|
{"object", "list"},
|
|
{"data", {
|
|
{
|
|
{"id", params.model_alias},
|
|
{"object", "model"},
|
|
{"created", std::time(0)},
|
|
{"owned_by", "llamacpp"},
|
|
{"meta", model_meta}
|
|
},
|
|
}}
|
|
};
|
|
|
|
res.set_content(models.dump(), "application/json; charset=utf-8");
|
|
});
|
|
|
|
const auto chat_completions = [&ctx_server, &validate_api_key, &sparams](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
if (!validate_api_key(req, res)) {
|
|
return;
|
|
}
|
|
|
|
json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template);
|
|
|
|
const int id_task = ctx_server.queue_tasks.get_new_id();
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
ctx_server.request_completion(id_task, -1, data, false, false);
|
|
|
|
if (!json_value(data, "stream", false)) {
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
|
|
if (!result.error && result.stop) {
|
|
json result_oai = format_final_response_oaicompat(data, result.data);
|
|
|
|
res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
|
|
} else {
|
|
res.status = 500;
|
|
res.set_content(result.data["content"], "text/plain; charset=utf-8");
|
|
}
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
} else {
|
|
const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
|
|
while (true) {
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
if (!result.error) {
|
|
std::vector<json> result_array = format_partial_response_oaicompat(result.data);
|
|
|
|
for (auto it = result_array.begin(); it != result_array.end(); ++it) {
|
|
if (!it->empty()) {
|
|
const std::string str =
|
|
"data: " +
|
|
it->dump(-1, ' ', false, json::error_handler_t::replace) +
|
|
"\n\n";
|
|
LOG_VERBOSE("data stream", {{"to_send", str}});
|
|
if (!sink.write(str.c_str(), str.size())) {
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
if (result.stop) {
|
|
break;
|
|
}
|
|
} else {
|
|
const std::string str =
|
|
"error: " +
|
|
result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
|
|
"\n\n";
|
|
LOG_VERBOSE("data stream", {{"to_send", str}});
|
|
if (!sink.write(str.c_str(), str.size())) {
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return false;
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
sink.done();
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return true;
|
|
};
|
|
|
|
auto on_complete = [id_task, &ctx_server](bool) {
|
|
// cancel request
|
|
ctx_server.request_cancel(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
};
|
|
|
|
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
|
}
|
|
};
|
|
|
|
svr.Post("/chat/completions", chat_completions);
|
|
svr.Post("/v1/chat/completions", chat_completions);
|
|
|
|
svr.Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
if (!validate_api_key(req, res)) {
|
|
return;
|
|
}
|
|
|
|
json data = json::parse(req.body);
|
|
|
|
const int id_task = ctx_server.queue_tasks.get_new_id();
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
ctx_server.request_completion(id_task, -1, data, true, false);
|
|
|
|
if (!json_value(data, "stream", false)) {
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
if (!result.error && result.stop) {
|
|
res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
|
|
} else {
|
|
res.status = 404;
|
|
res.set_content(result.data["content"], "text/plain; charset=utf-8");
|
|
}
|
|
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
} else {
|
|
const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
|
|
while (true) {
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
if (!result.error) {
|
|
const std::string str =
|
|
"data: " +
|
|
result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
|
|
"\n\n";
|
|
|
|
LOG_VERBOSE("data stream", {
|
|
{ "to_send", str }
|
|
});
|
|
|
|
if (!sink.write(str.c_str(), str.size())) {
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return false;
|
|
}
|
|
|
|
if (result.stop) {
|
|
break;
|
|
}
|
|
} else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
sink.done();
|
|
|
|
return true;
|
|
};
|
|
|
|
auto on_complete = [id_task, &ctx_server] (bool) {
|
|
ctx_server.request_cancel(id_task);
|
|
};
|
|
|
|
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
|
}
|
|
});
|
|
|
|
svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) {
|
|
return res.set_content("", "application/json; charset=utf-8");
|
|
});
|
|
|
|
svr.Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
const json body = json::parse(req.body);
|
|
|
|
std::vector<llama_token> tokens;
|
|
if (body.count("content") != 0) {
|
|
tokens = ctx_server.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", [&ctx_server](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
const json body = json::parse(req.body);
|
|
|
|
std::string content;
|
|
if (body.count("tokens") != 0) {
|
|
const std::vector<llama_token> tokens = body["tokens"];
|
|
content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend());
|
|
}
|
|
|
|
const json data = format_detokenized_response(content);
|
|
return res.set_content(data.dump(), "application/json; charset=utf-8");
|
|
});
|
|
|
|
svr.Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
if (!params.embedding) {
|
|
res.status = 501;
|
|
res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
|
|
return;
|
|
}
|
|
|
|
const json body = json::parse(req.body);
|
|
|
|
json prompt;
|
|
if (body.count("content") != 0) {
|
|
prompt = body["content"];
|
|
} else {
|
|
prompt = "";
|
|
}
|
|
|
|
// create and queue the task
|
|
const int id_task = ctx_server.queue_tasks.get_new_id();
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0} }, false, true);
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
|
|
// send the result
|
|
return res.set_content(result.data.dump(), "application/json; charset=utf-8");
|
|
});
|
|
|
|
svr.Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
if (!params.embedding) {
|
|
res.status = 501;
|
|
res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
|
|
return;
|
|
}
|
|
|
|
const json body = json::parse(req.body);
|
|
|
|
json prompt;
|
|
if (body.count("input") != 0) {
|
|
prompt = body["input"];
|
|
if (prompt.is_array()) {
|
|
json data = json::array();
|
|
|
|
int i = 0;
|
|
for (const json & elem : prompt) {
|
|
const int id_task = ctx_server.queue_tasks.get_new_id();
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
ctx_server.request_completion(id_task, -1, { {"prompt", elem}, { "n_predict", 0} }, false, true);
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
|
|
json embedding = json{
|
|
{"embedding", json_value(result.data, "embedding", json::array())},
|
|
{"index", i++},
|
|
{"object", "embedding"}
|
|
};
|
|
|
|
data.push_back(embedding);
|
|
}
|
|
|
|
json result = format_embeddings_response_oaicompat(body, data);
|
|
|
|
return res.set_content(result.dump(), "application/json; charset=utf-8");
|
|
}
|
|
} else {
|
|
prompt = "";
|
|
}
|
|
|
|
// create and queue the task
|
|
const int id_task = ctx_server.queue_tasks.get_new_id();
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true);
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
|
|
json data = json::array({json{
|
|
{"embedding", json_value(result.data, "embedding", json::array())},
|
|
{"index", 0},
|
|
{"object", "embedding"}
|
|
}}
|
|
);
|
|
|
|
json root = format_embeddings_response_oaicompat(body, data);
|
|
|
|
return res.set_content(root.dump(), "application/json; charset=utf-8");
|
|
});
|
|
|
|
if (sparams.n_threads_http < 1) {
|
|
// +2 threads for monitoring endpoints
|
|
sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1);
|
|
}
|
|
log_data["n_threads_http"] = std::to_string(sparams.n_threads_http);
|
|
svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); };
|
|
|
|
LOG_INFO("HTTP server listening", log_data);
|
|
|
|
// run the HTTP server in a thread - see comment below
|
|
std::thread t([&]() {
|
|
if (!svr.listen_after_bind()) {
|
|
state.store(SERVER_STATE_ERROR);
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
});
|
|
|
|
ctx_server.queue_tasks.on_new_task(std::bind(
|
|
&server_context::process_single_task, &ctx_server, std::placeholders::_1));
|
|
ctx_server.queue_tasks.on_finish_multitask(std::bind(
|
|
&server_context::on_finish_multitask, &ctx_server, std::placeholders::_1));
|
|
ctx_server.queue_tasks.on_run_slots(std::bind(
|
|
&server_context::update_slots, &ctx_server));
|
|
ctx_server.queue_results.on_multitask_update(std::bind(
|
|
&server_queue::update_multitask,
|
|
&ctx_server.queue_tasks,
|
|
std::placeholders::_1,
|
|
std::placeholders::_2,
|
|
std::placeholders::_3
|
|
));
|
|
|
|
shutdown_handler = [&](int) {
|
|
ctx_server.queue_tasks.terminate();
|
|
};
|
|
|
|
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
|
|
struct sigaction sigint_action;
|
|
sigint_action.sa_handler = signal_handler;
|
|
sigemptyset (&sigint_action.sa_mask);
|
|
sigint_action.sa_flags = 0;
|
|
sigaction(SIGINT, &sigint_action, NULL);
|
|
#elif defined (_WIN32)
|
|
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
|
|
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
|
|
};
|
|
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
|
|
#endif
|
|
|
|
ctx_server.queue_tasks.start_loop();
|
|
|
|
svr.stop();
|
|
t.join();
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|