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llama.cpp
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llama.cpp/examples/server/server.cpp

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#include "utils.hpp"
#include "common.h"
#include "llama.h"
#include "grammar-parser.h"
#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
#define CPPHTTPLIB_NO_EXCEPTIONS 1
#endif
// increase max payload length to allow use of larger context size
#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
#include "httplib.h"
#include "json.hpp"
// auto generated files (update with ./deps.sh)
#include "index.html.hpp"
#include "index.js.hpp"
#include "completion.js.hpp"
#include "json-schema-to-grammar.mjs.hpp"
#include <atomic>
#include <chrono>
#include <condition_variable>
#include <cstddef>
#include <set>
#include <mutex>
#include <thread>
#include <signal.h>
#include <memory>
using json = nlohmann::json;
bool server_verbose = false;
bool server_log_json = true;
enum stop_type {
STOP_TYPE_FULL,
STOP_TYPE_PARTIAL,
};
enum slot_state {
SLOT_STATE_IDLE,
SLOT_STATE_PROCESSING,
};
enum slot_command {
SLOT_COMMAND_NONE,
SLOT_COMMAND_LOAD_PROMPT,
SLOT_COMMAND_RELEASE,
};
enum server_state {
SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet
SERVER_STATE_READY, // Server is ready and model is loaded
SERVER_STATE_ERROR // An error occurred, load_model failed
};
enum server_task_type {
SERVER_TASK_TYPE_COMPLETION,
SERVER_TASK_TYPE_CANCEL,
SERVER_TASK_TYPE_NEXT_RESPONSE,
SERVER_TASK_TYPE_METRICS
};
struct server_task {
int id = -1; // to be filled by server_queue
int id_multi = -1;
int id_target = -1;
server_task_type type;
json data;
bool infill = false;
bool embedding = false;
};
struct server_task_result {
int id = -1;
int id_multi = -1;
json data;
bool stop;
bool error;
};
struct server_task_multi {
int id = -1;
std::set<int> subtasks_remaining;
std::vector<server_task_result> results;
};
struct slot_params {
bool stream = true;
bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
uint32_t seed = -1; // RNG seed
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_predict = -1; // new tokens to predict
std::vector<std::string> antiprompt;
json input_prefix;
json input_suffix;
};
struct server_params {
int32_t port = 8080;
int32_t read_timeout = 600;
int32_t write_timeout = 600;
int32_t n_threads_http = -1;
std::string hostname = "127.0.0.1";
std::string public_path = "";
std::string chat_template = "";
std::string system_prompt = "";
std::vector<std::string> api_keys;
#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
std::string ssl_key_file = "";
std::string ssl_cert_file = "";
#endif
bool slots_endpoint = true;
bool metrics_endpoint = false;
};
struct server_slot {
int id;
int id_task = -1;
int id_multi = -1;
struct slot_params params;
slot_state state = SLOT_STATE_IDLE;
slot_command command = SLOT_COMMAND_NONE;
// used to determine the slot that has been used the longest
int64_t t_last_used = -1;
// generation props
int32_t n_ctx = 0; // context size per slot
int32_t n_past = 0;
int32_t n_decoded = 0;
int32_t n_remaining = -1;
int32_t i_batch = -1;
int32_t n_predict = -1; // TODO: disambiguate from params.n_predict
int32_t n_prompt_tokens = 0;
int32_t n_prompt_tokens_processed = 0;
json prompt;
// when a task is submitted, we first tokenize the prompt and store it here
std::vector<llama_token> prompt_tokens;
std::string generated_text;
std::vector<llama_token> cache_tokens;
std::vector<completion_token_output> generated_token_probs;
bool infill = false;
bool embedding = false;
bool has_next_token = true;
bool truncated = false;
bool stopped_eos = false;
bool stopped_word = false;
bool stopped_limit = false;
bool oaicompat = false;
std::string oaicompat_model;
std::string stopping_word;
// sampling
llama_token sampled;
struct llama_sampling_params sparams;
llama_sampling_context * ctx_sampling = nullptr;
int32_t ga_i = 0; // group-attention state
int32_t ga_n = 1; // group-attention factor
int32_t ga_w = 512; // group-attention width
int32_t n_past_se = 0; // self-extend
// stats
size_t n_sent_text = 0; // number of sent text character
size_t n_sent_token_probs = 0;
int64_t t_start_process_prompt;
int64_t t_start_generation;
double t_prompt_processing; // ms
double t_token_generation; // ms
void reset() {
n_prompt_tokens = 0;
generated_text = "";
truncated = false;
stopped_eos = false;
stopped_word = false;
stopped_limit = false;
stopping_word = "";
n_past = 0;
n_sent_text = 0;
n_sent_token_probs = 0;
infill = false;
ga_i = 0;
n_past_se = 0;
generated_token_probs.clear();
}
bool has_budget(gpt_params &global_params) {
if (params.n_predict == -1 && global_params.n_predict == -1) {
return true; // limitless
}
n_remaining = -1;
if (params.n_predict != -1) {
n_remaining = params.n_predict - n_decoded;
} else if (global_params.n_predict != -1) {
n_remaining = global_params.n_predict - n_decoded;
}
return n_remaining > 0; // no budget
}
bool available() const {
return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE;
}
bool is_processing() const {
return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING;
}
void add_token_string(const completion_token_output & token) {
if (command == SLOT_COMMAND_RELEASE) {
return;
}
generated_token_probs.push_back(token);
}
void release() {
if (state == SLOT_STATE_PROCESSING) {
t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
command = SLOT_COMMAND_RELEASE;
}
}
json get_formated_timings() const {
return json {
{"prompt_n", n_prompt_tokens_processed},
{"prompt_ms", t_prompt_processing},
{"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed},
{"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed},
{"predicted_n", n_decoded},
{"predicted_ms", t_token_generation},
{"predicted_per_token_ms", t_token_generation / n_decoded},
{"predicted_per_second", 1e3 / t_token_generation * n_decoded},
};
}
size_t find_stopping_strings(const std::string & text, const size_t last_token_size, const stop_type type) {
size_t stop_pos = std::string::npos;
for (const std::string & word : params.antiprompt) {
size_t pos;
if (type == STOP_TYPE_FULL) {
const size_t tmp = word.size() + last_token_size;
const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
pos = text.find(word, from_pos);
} else {
pos = find_partial_stop_string(word, text);
}
if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) {
if (type == STOP_TYPE_FULL) {
stopped_word = true;
stopping_word = word;
has_next_token = false;
}
stop_pos = pos;
}
}
return stop_pos;
}
void print_timings() const {
char buffer[512];
double t_token = t_prompt_processing / n_prompt_tokens_processed;
double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
snprintf(buffer, 512, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
t_prompt_processing, n_prompt_tokens_processed,
t_token, n_tokens_second);
LOG_INFO(buffer, {
{"id_slot", id},
{"id_task", id_task},
{"t_prompt_processing", t_prompt_processing},
{"n_prompt_tokens_processed", n_prompt_tokens_processed},
{"t_token", t_token},
{"n_tokens_second", n_tokens_second},
});
t_token = t_token_generation / n_decoded;
n_tokens_second = 1e3 / t_token_generation * n_decoded;
snprintf(buffer, 512, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
t_token_generation, n_decoded,
t_token, n_tokens_second);
LOG_INFO(buffer, {
{"id_slot", id},
{"id_task", id_task},
{"t_token_generation", t_token_generation},
{"n_decoded", n_decoded},
{"t_token", t_token},
{"n_tokens_second", n_tokens_second},
});
snprintf(buffer, 512, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
LOG_INFO(buffer, {
{"id_slot", id},
{"id_task", id_task},
{"t_prompt_processing", t_prompt_processing},
{"t_token_generation", t_token_generation},
{"t_total", t_prompt_processing + t_token_generation},
});
}
};
struct server_metrics {
int64_t t_start = 0;
uint64_t n_prompt_tokens_processed_total = 0;
uint64_t t_prompt_processing_total = 0;
uint64_t n_tokens_predicted_total = 0;
uint64_t t_tokens_generation_total = 0;
uint64_t n_prompt_tokens_processed = 0;
uint64_t t_prompt_processing = 0;
uint64_t n_tokens_predicted = 0;
uint64_t t_tokens_generation = 0;
void init() {
t_start = ggml_time_us();
}
void on_prompt_eval(const server_slot & slot) {
n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
n_prompt_tokens_processed += slot.n_prompt_tokens_processed;
t_prompt_processing += slot.t_prompt_processing;
t_prompt_processing_total += slot.t_prompt_processing;
}
void on_prediction(const server_slot & slot) {
n_tokens_predicted_total += slot.n_decoded;
n_tokens_predicted += slot.n_decoded;
t_tokens_generation += slot.t_token_generation;
t_tokens_generation_total += slot.t_token_generation;
}
void reset_bucket() {
n_prompt_tokens_processed = 0;
t_prompt_processing = 0;
n_tokens_predicted = 0;
t_tokens_generation = 0;
}
};
struct server_queue {
int id = 0;
bool running;
// queues
std::vector<server_task> queue_tasks;
std::vector<server_task> queue_tasks_deferred;
std::vector<server_task_multi> queue_multitasks;
std::mutex mutex_tasks;
std::condition_variable condition_tasks;
// callback functions
std::function<void(server_task &)> callback_new_task;
std::function<void(server_task_multi &)> callback_finish_multitask;
std::function<void(void)> callback_update_slots;
// Add a new task to the end of the queue
int post(server_task task) {
std::unique_lock<std::mutex> lock(mutex_tasks);
if (task.id == -1) {
task.id = id++;
LOG_VERBOSE("new task id", {{"new_id", task.id}});
}
queue_tasks.push_back(std::move(task));
condition_tasks.notify_one();
return task.id;
}
// Add a new task, but defer until one slot is available
void defer(server_task task) {
std::unique_lock<std::mutex> lock(mutex_tasks);
queue_tasks_deferred.push_back(std::move(task));
}
// Get the next id for creating anew task
int get_new_id() {
std::unique_lock<std::mutex> lock(mutex_tasks);
int new_id = id++;
LOG_VERBOSE("new task id", {{"new_id", new_id}});
return new_id;
}
// Register function to process a new task
void on_new_task(std::function<void(server_task &)> callback) {
callback_new_task = std::move(callback);
}
// Register function to process a multitask when it is finished
void on_finish_multitask(std::function<void(server_task_multi&)> callback) {
callback_finish_multitask = std::move(callback);
}
// Register the function to be called when all slots data is ready to be processed
void on_update_slots(std::function<void(void)> callback) {
callback_update_slots = std::move(callback);
}
// Call when the state of one slot is changed
void notify_slot_changed() {
// move deferred tasks back to main loop
std::unique_lock<std::mutex> lock(mutex_tasks);
for (auto & task : queue_tasks_deferred) {
queue_tasks.push_back(std::move(task));
}
queue_tasks_deferred.clear();
}
// end the start_loop routine
void terminate() {
std::unique_lock<std::mutex> lock(mutex_tasks);
running = false;
condition_tasks.notify_all();
}
/**
* Main loop consists of these steps:
* - Wait until a new task arrives
* - Process the task (i.e. maybe copy data into slot)
* - Check if multitask is finished
* - Update all slots
*/
void start_loop() {
running = true;
while (true) {
LOG_VERBOSE("new task may arrive", {});
while (true) {
std::unique_lock<std::mutex> lock(mutex_tasks);
if (queue_tasks.empty()) {
lock.unlock();
break;
}
server_task task = queue_tasks.front();
queue_tasks.erase(queue_tasks.begin());
lock.unlock();
LOG_VERBOSE("callback_new_task", {{"id_task", task.id}});
callback_new_task(task);
}
LOG_VERBOSE("update_multitasks", {});
// check if we have any finished multitasks
auto queue_iterator = queue_multitasks.begin();
while (queue_iterator != queue_multitasks.end()) {
if (queue_iterator->subtasks_remaining.empty()) {
// all subtasks done == multitask is done
server_task_multi current_multitask = *queue_iterator;
callback_finish_multitask(current_multitask);
// remove this multitask
queue_iterator = queue_multitasks.erase(queue_iterator);
} else {
++queue_iterator;
}
}
// all tasks in the current loop is processed, slots data is now ready
LOG_VERBOSE("callback_update_slots", {});
callback_update_slots();
LOG_VERBOSE("wait for new task", {});
{
std::unique_lock<std::mutex> lock(mutex_tasks);
if (queue_tasks.empty()) {
if (!running) {
LOG_VERBOSE("ending start_loop", {});
return;
}
condition_tasks.wait(lock, [&]{
return (!queue_tasks.empty() || !running);
});
}
}
}
}
//
// functions to manage multitasks
//
// add a multitask by specifying the id of all subtask (subtask is a server_task)
void add_multitask(int id_multi, std::vector<int> & sub_ids) {
std::lock_guard<std::mutex> lock(mutex_tasks);
server_task_multi multi;
multi.id = id_multi;
std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
queue_multitasks.push_back(multi);
}
// updatethe remaining subtasks, while appending results to multitask
void update_multitask(int id_multi, int id_sub, server_task_result & result) {
std::lock_guard<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 init() {
const int32_t n_ctx_slot = n_ctx / params.n_parallel;
LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}});
for (int i = 0; i < params.n_parallel; i++) {
server_slot slot;
slot.id = i;
slot.n_ctx = n_ctx_slot;
slot.n_predict = params.n_predict;
LOG_INFO("new slot", {
{"id_slot", slot.id},
{"n_ctx_slot", slot.n_ctx}
});
const int ga_n = params.grp_attn_n;
const int ga_w = params.grp_attn_w;
if (ga_n != 1) {
GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT
GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT
//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
LOG_INFO("slot self-extend", {
{"id_slot", slot.id},
{"ga_n", ga_n},
{"ga_w", ga_w}
});
}
slot.ga_i = 0;
slot.ga_n = ga_n;
slot.ga_w = ga_w;
slot.reset();
slots.push_back(slot);
}
default_generation_settings_for_props = get_formated_generation(slots.front());
default_generation_settings_for_props["seed"] = -1;
// the update_slots() logic will always submit a maximum of n_batch tokens
// note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
{
const int32_t n_batch = llama_n_batch(ctx);
batch = llama_batch_init(n_batch, 0, params.n_parallel);
}
metrics.init();
}
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_task(server_slot & slot, const server_task & task) {
slot_params default_params;
llama_sampling_params default_sparams;
auto & data = task.data;
if (data.count("__oaicompat") != 0) {
slot.oaicompat = true;
slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
} else {
slot.oaicompat = false;
slot.oaicompat_model = "";
}
slot.params.stream = json_value(data, "stream", false);
slot.params.cache_prompt = json_value(data, "cache_prompt", false);
slot.params.n_predict = json_value(data, "n_predict", default_params.n_predict);
slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
slot.sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
slot.sparams.temp = json_value(data, "temperature", default_sparams.temp);
slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
slot.params.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);
// get prompt
{
const auto & prompt = data.find("prompt");
if (prompt == data.end()) {
send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST);
return false;
} else {
slot.prompt = *prompt;
}
if (slot.prompt.is_array() && slot.prompt.size() == 0) {
send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST);
return false;
}
}
// penalize user-provided tokens
{
slot.sparams.penalty_prompt_tokens.clear();
slot.sparams.use_penalty_prompt_tokens = false;
const auto & penalty_prompt = data.find("penalty_prompt");
if (penalty_prompt != data.end()) {
if (penalty_prompt->is_string()) {
const auto penalty_prompt_string = penalty_prompt->get<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) {
// TODO: we may want to throw errors here, in case "el" is incorrect
if (el.is_array() && el.size() == 2) {
float bias;
if (el[1].is_number()) {
bias = el[1].get<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);
if (slot.ctx_sampling == nullptr) {
// for now, the only error that may happen here is invalid grammar
send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
return false;
}
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);
}
const int32_t n_batch = llama_n_batch(ctx);
for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
batch.pos + i,
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};
if (llama_decode(ctx, batch_view) != 0) {
LOG_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}, // TODO: fix duplicate key 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, const enum error_type type = ERROR_TYPE_SERVER) {
send_error(task.id, task.id_multi, error, type);
}
void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
send_error(slot.id_task, slot.id_multi, error, type);
}
void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
LOG_TEE("task %i - error: %s\n", id_task, error.c_str());
server_task_result res;
res.id = id_task;
res.id_multi = id_multi;
res.stop = false;
res.error = true;
res.data = format_error_response(error, type);
queue_results.send(res);
}
void send_partial_response(server_slot & slot, completion_token_output tkn) {
server_task_result res;
res.id = slot.id_task;
res.id_multi = slot.id_multi;
res.error = false;
res.stop = false;
res.data = json {
{"content", tkn.text_to_send},
{"stop", false},
{"id_slot", slot.id},
{"multimodal", false}
};
if (slot.sparams.n_probs > 0) {
const std::vector<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);
std::vector<float> embd_res(n_embd, 0.0f);
for (int i = 0; i < batch.n_tokens; ++i) {
if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
continue;
}
const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
if (embd == NULL) {
embd = llama_get_embeddings_ith(ctx, i);
}
if (embd == NULL) {
LOG_ERROR("failed to get embeddings", {
{"token", batch.token [i]},
{"seq_id", batch.seq_id[i][0]}
});
res.data = json {
{"embedding", std::vector<float>(n_embd, 0.0f)},
};
continue;
}
llama_embd_normalize(embd, embd_res.data(), n_embd);
res.data = json {
{"embedding", embd_res},
};
}
queue_results.send(res);
}
void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding) {
server_task task;
task.id = id_task;
task.id_multi = id_multi;
task.id_target = 0;
task.data = std::move(data);
task.infill = infill;
task.embedding = embedding;
task.type = SERVER_TASK_TYPE_COMPLETION;
// when a completion task's prompt array is not a singleton, we split it into multiple requests
// otherwise, it's a single-prompt task, we actually queue it
// if there's numbers in the prompt array it will be treated as an array of tokens
if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
bool numbers = false;
for (const auto & e : task.data.at("prompt")) {
if (e.is_number()) {
numbers = true;
break;
}
}
// NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
// it will completely stall the server. I don't know where the bug for this is.
//
// if there are numbers, it needs to be treated like a single prompt,
// queue_tasks handles a mix of strings and numbers just fine.
if (numbers) {
queue_tasks.post(task);
} else {
split_multiprompt_task(id_task, task);
}
} else {
queue_tasks.post(task);
}
}
void request_cancel(int id_task) {
server_task task;
task.type = SERVER_TASK_TYPE_CANCEL;
task.id_target = id_task;
queue_tasks.post(task);
}
void split_multiprompt_task(int id_multi, const server_task & multiprompt_task) {
const int prompt_count = multiprompt_task.data.at("prompt").size();
if (prompt_count <= 1) {
send_error(multiprompt_task, "error while handling multiple prompts");
return;
}
// generate all the ID for subtask
std::vector<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_task(*slot, task)) {
LOG_ERROR("error while launching slot", task.data);
break;
}
} break;
case SERVER_TASK_TYPE_CANCEL:
{
// release slot linked with the task id
for (auto & slot : slots) {
if (slot.id_task == task.id_target) {
slot.release();
break;
}
}
} break;
case SERVER_TASK_TYPE_NEXT_RESPONSE:
{
// do nothing
} break;
case SERVER_TASK_TYPE_METRICS:
{
json slots_data = json::array();
int n_idle_slots = 0;
int n_processing_slots = 0;
for (server_slot & slot : slots) {
json slot_data = get_formated_generation(slot);
slot_data["id"] = slot.id;
slot_data["id_task"] = slot.id_task;
slot_data["state"] = slot.state;
slot_data["prompt"] = slot.prompt;
slot_data["next_token"] = {
{"has_next_token", slot.has_next_token},
{"n_remain", slot.n_remaining},
{"n_decoded", slot.n_decoded},
{"stopped_eos", slot.stopped_eos},
{"stopped_word", slot.stopped_word},
{"stopped_limit", slot.stopped_limit},
{"stopping_word", slot.stopping_word},
};
if (slot_data["state"] == SLOT_STATE_IDLE) {
n_idle_slots++;
} else {
n_processing_slots++;
}
slots_data.push_back(slot_data);
}
LOG_INFO("slot data", {
{"id_task", task.id},
{"n_idle_slots", n_idle_slots},
{"n_processing_slots", n_processing_slots}
});
LOG_VERBOSE("slot data", {
{"id_task", task.id},
{"n_idle_slots", n_idle_slots},
{"n_processing_slots", n_processing_slots},
{"slots", slots_data}
});
server_task_result res;
res.id = task.id;
res.id_multi = task.id_multi;
res.stop = true;
res.error = false;
res.data = {
{ "idle", n_idle_slots },
{ "processing", n_processing_slots },
{ "deferred", queue_tasks.queue_tasks_deferred.size() },
{ "t_start", metrics.t_start},
{ "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
{ "t_tokens_generation_total", metrics.t_tokens_generation_total},
{ "n_tokens_predicted_total", metrics.n_tokens_predicted_total},
{ "t_prompt_processing_total", metrics.t_prompt_processing_total},
{ "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed},
{ "t_prompt_processing", metrics.t_prompt_processing},
{ "n_tokens_predicted", metrics.n_tokens_predicted},
{ "t_tokens_generation", metrics.t_tokens_generation},
{ "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)},
{ "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)},
{ "slots", slots_data },
};
if (json_value(task.data, "reset_bucket", false)) {
metrics.reset_bucket();
}
queue_results.send(res);
} break;
}
}
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);
}
void update_slots() {
if (system_need_update) {
system_prompt_update();
}
// release slots
for (auto & slot : slots) {
if (slot.command == SLOT_COMMAND_RELEASE) {
slot.state = SLOT_STATE_IDLE;
slot.command = SLOT_COMMAND_NONE;
slot.t_last_used = ggml_time_us();
LOG_INFO("slot released", {
{"id_slot", slot.id},
{"id_task", slot.id_task},
{"n_ctx", n_ctx},
{"n_past", slot.n_past},
{"n_system_tokens", system_tokens.size()},
{"n_cache_tokens", slot.cache_tokens.size()},
{"truncated", slot.truncated}
});
queue_tasks.notify_slot_changed();
}
}
// check if all slots are idle
{
bool all_idle = true;
for (auto & slot : slots) {
if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) {
all_idle = false;
break;
}
}
if (all_idle) {
LOG_INFO("all slots are idle", {});
if (system_prompt.empty() && clean_kv_cache) {
kv_cache_clear();
}
return;
}
}
{
LOG_VERBOSE("posting NEXT_RESPONSE", {});
server_task task;
task.type = SERVER_TASK_TYPE_NEXT_RESPONSE;
task.id_target = -1;
queue_tasks.post(task);
}
// apply context-shift if needed
// TODO: simplify and improve
for (server_slot & slot : slots) {
if (slot.ga_n == 1) {
if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
// Shift context
const int n_keep = slot.params.n_keep + add_bos_token;
const int n_left = (int) system_tokens.size() + slot.n_past - n_keep;
const int n_discard = n_left / 2;
LOG_INFO("slot context shift", {
{"id_slot", slot.id},
{"id_task", slot.id_task},
{"n_keep", n_keep},
{"n_left", n_left},
{"n_discard", n_discard},
{"n_ctx", n_ctx},
{"n_past", slot.n_past},
{"n_system_tokens", system_tokens.size()},
{"n_cache_tokens", slot.cache_tokens.size()}
});
llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep , n_keep + n_discard);
llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);
if (slot.params.cache_prompt) {
for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
}
slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
}
slot.n_past -= n_discard;
slot.truncated = true;
}
}
}
// start populating the batch for this iteration
llama_batch_clear(batch);
// frist, add sampled tokens from any ongoing sequences
for (auto & slot : slots) {
if (slot.state == SLOT_STATE_IDLE) {
continue;
}
slot.i_batch = batch.n_tokens;
const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
// TODO: we always have to take into account the "system_tokens"
// this is not great and needs to be improved somehow
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true);
slot.n_past += 1;
if (slot.params.cache_prompt) {
slot.cache_tokens.push_back(slot.sampled);
}
LOG_VERBOSE("slot decode token", {
{"id_slot", slot.id},
{"id_task", slot.id_task},
{"n_ctx", n_ctx},
{"n_past", slot.n_past},
{"n_system_tokens", system_tokens.size()},
{"n_cache_tokens", slot.cache_tokens.size()},
{"truncated", slot.truncated}
});
}
// process in chunks of params.n_batch
int32_t n_batch = llama_n_batch(ctx);
int32_t n_ubatch = llama_n_ubatch(ctx);
// next, batch any pending prompts without exceeding n_batch
if (params.cont_batching || batch.n_tokens == 0) {
for (auto & slot : slots) {
// this slot still has a prompt to be processed
if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) {
auto & prompt_tokens = slot.prompt_tokens;
// we haven't tokenized the prompt yet - do it now:
if (prompt_tokens.empty()) {
LOG_VERBOSE("tokenizing prompt", {
{"id_slot", slot.id},
{"id_task", slot.id_task}
});
slot.t_start_process_prompt = ggml_time_us();
slot.t_start_generation = 0;
if (slot.infill) {
bool suff_rm_leading_spc = true;
if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
params.input_suffix.erase(0, 1);
suff_rm_leading_spc = false;
}
auto prefix_tokens = tokenize(slot.params.input_prefix, false);
auto suffix_tokens = tokenize(slot.params.input_suffix, false);
const int space_token = 29871; // TODO: this should not be hardcoded
if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
suffix_tokens.erase(suffix_tokens.begin());
}
prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model));
prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end());
prefix_tokens.push_back(llama_token_middle(model));
prompt_tokens = prefix_tokens;
} else {
prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_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())},
});
// empty prompt passed -> release the slot and send empty response
if (prompt_tokens.empty()) {
LOG_INFO("empty prompt - releasing slot", {
{"id_slot", slot.id},
{"id_task", slot.id_task}
});
slot.state = SLOT_STATE_PROCESSING;
slot.command = SLOT_COMMAND_NONE;
slot.release();
slot.print_timings();
send_final_response(slot);
continue;
}
if (slot.embedding) {
// this prompt is too large to process - discard it
if (slot.n_prompt_tokens > n_ubatch) {
slot.state = SLOT_STATE_PROCESSING;
slot.command = SLOT_COMMAND_NONE;
slot.release();
slot.print_timings();
send_final_response(slot);
continue;
}
} else {
if (slot.params.n_keep < 0) {
slot.params.n_keep = slot.n_prompt_tokens;
}
slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
// if input prompt is too big, truncate it (if group attention self-extend is disabled)
if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) {
const int n_left = slot.n_ctx - slot.params.n_keep;
const int n_block_size = n_left / 2;
const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
std::vector<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;
}
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);
for (auto & slot : slots) {
slot.state = SLOT_STATE_PROCESSING;
slot.command = SLOT_COMMAND_NONE;
slot.release();
send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size.");
}
break; // break loop of n_batch
}
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; // continue loop of n_batch
}
for (auto & slot : slots) {
if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
continue; // continue loop of slots
}
// prompt evaluated for embedding
if (slot.embedding) {
send_embedding(slot, batch_view);
slot.release();
slot.i_batch = -1;
continue; // continue loop of slots
}
completion_token_output result;
const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i);
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
slot.n_decoded += 1;
if (slot.n_decoded == 1) {
slot.t_start_generation = ggml_time_us();
slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
metrics.on_prompt_eval(slot);
}
llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
result.tok = id;
const 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("run slots completed", {});
}
json model_meta() const {
return json {
{"vocab_type", llama_vocab_type (model)},
{"n_vocab", llama_n_vocab (model)},
{"n_ctx_train", llama_n_ctx_train (model)},
{"n_embd", llama_n_embd (model)},
{"n_params", llama_model_n_params(model)},
{"size", llama_model_size (model)},
};
}
};
static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) {
printf("usage: %s [options]\n", argv0);
printf("\n");
printf("options:\n");
printf(" -h, --help show this help message and exit\n");
printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n");
printf(" --threads-http N number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n");
printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
printf(" --rope-scaling {none,linear,yarn}\n");
printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n");
printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n");
printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n");
printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
printf(" -dt N, --defrag-thold N\n");
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch);
printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch);
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: disabled)\n");
printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n");
printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
printf(" --ssl-key-file FNAME path to file a PEM-encoded SSL private key\n");
printf(" --ssl-cert-file FNAME path to file a PEM-encoded SSL certificate\n");
#endif
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: 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(" only commonly used templates are accepted:\n");
printf(" https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n");
printf("\n");
}
static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) {
gpt_params default_params;
server_params default_sparams;
std::string arg;
bool invalid_param = false;
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg == "--port") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.port = std::stoi(argv[i]);
} else if (arg == "--host") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.hostname = argv[i];
} else if (arg == "--path") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.public_path = argv[i];
} else if (arg == "--api-key") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.api_keys.push_back(argv[i]);
} else if (arg == "--api-key-file") {
if (++i >= argc) {
invalid_param = true;
break;
}
std::ifstream key_file(argv[i]);
if (!key_file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
break;
}
std::string key;
while (std::getline(key_file, key)) {
if (key.size() > 0) {
sparams.api_keys.push_back(key);
}
}
key_file.close();
}
#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
else if (arg == "--ssl-key-file") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.ssl_key_file = argv[i];
} else if (arg == "--ssl-cert-file") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.ssl_cert_file = argv[i];
}
#endif
else if (arg == "--timeout" || arg == "-to") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.read_timeout = std::stoi(argv[i]);
sparams.write_timeout = std::stoi(argv[i]);
} else if (arg == "-m" || arg == "--model") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.model = argv[i];
} else if (arg == "-a" || arg == "--alias") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.model_alias = argv[i];
} else if (arg == "-h" || arg == "--help") {
server_print_usage(argv[0], default_params, default_sparams);
exit(0);
} else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_ctx = std::stoi(argv[i]);
} else if (arg == "--rope-scaling") {
if (++i >= argc) {
invalid_param = true;
break;
}
std::string value(argv[i]);
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
else { invalid_param = true; break; }
} else if (arg == "--rope-freq-base") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rope_freq_base = std::stof(argv[i]);
} else if (arg == "--rope-freq-scale") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rope_freq_scale = std::stof(argv[i]);
} else if (arg == "--yarn-ext-factor") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.yarn_ext_factor = std::stof(argv[i]);
}
else if (arg == "--yarn-attn-factor") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.yarn_attn_factor = std::stof(argv[i]);
} else if (arg == "--yarn-beta-fast") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.yarn_beta_fast = std::stof(argv[i]);
} else if (arg == "--yarn-beta-slow") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.yarn_beta_slow = std::stof(argv[i]);
} else if (arg == "--pooling") {
if (++i >= argc) {
invalid_param = true;
break;
}
std::string value(argv[i]);
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
else { invalid_param = true; break; }
} else if (arg == "--defrag-thold" || arg == "-dt") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.defrag_thold = std::stof(argv[i]);
} else if (arg == "--threads" || arg == "-t") {
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_threads = std::stoi(argv[i]);
} else if (arg == "--grp-attn-n" || arg == "-gan") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.grp_attn_n = std::stoi(argv[i]);
} else if (arg == "--grp-attn-w" || arg == "-gaw") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.grp_attn_w = std::stoi(argv[i]);
} else if (arg == "--threads-batch" || arg == "-tb") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_threads_batch = std::stoi(argv[i]);
} else if (arg == "--threads-http") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.n_threads_http = std::stoi(argv[i]);
} else if (arg == "-b" || arg == "--batch-size") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_batch = std::stoi(argv[i]);
} else if (arg == "-ub" || arg == "--ubatch-size") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_ubatch = std::stoi(argv[i]);
} else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
if (++i >= argc) {
invalid_param = true;
break;
}
if (llama_supports_gpu_offload()) {
params.n_gpu_layers = std::stoi(argv[i]);
} else {
LOG_WARNING(
"Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
"See main README.md for information on enabling GPU BLAS support",
{{"n_gpu_layers", params.n_gpu_layers}});
}
} else if (arg == "--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()},
});
std::unique_ptr<httplib::Server> svr;
#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") {
LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}});
svr.reset(
new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str())
);
} else {
LOG_INFO("Running without SSL", {});
svr.reset(new httplib::Server());
}
#else
svr.reset(new httplib::Server());
#endif
std::atomic<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", "*");
return res.set_content("", "application/json; charset=utf-8");
});
svr->set_logger(log_server_request);
auto res_error = [](httplib::Response & res, json error_data) {
json final_response {{"error", error_data}};
res.set_content(final_response.dump(), "application/json; charset=utf-8");
res.status = json_value(error_data, "code", 500);
};
svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) {
std::string message;
try {
std::rethrow_exception(std::move(ep));
} catch (std::exception & e) {
message = e.what();
} catch (...) {
message = "Unknown Exception";
}
json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
LOG_VERBOSE("Got exception", formatted_error);
res_error(res, formatted_error);
});
svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) {
if (res.status == 404) {
res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND));
}
// for other error codes, we skip processing here because it's already done by res_error()
});
// set timeouts and change hostname and port
svr->set_read_timeout (sparams.read_timeout);
svr->set_write_timeout(sparams.write_timeout);
if (!svr->bind_to_port(sparams.hostname, sparams.port)) {
fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
return 1;
}
std::unordered_map<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) {
auto key = sparams.api_keys[0];
log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0));
} else if (sparams.api_keys.size() > 1) {
log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded";
}
// load the model
if (!ctx_server.load_model(params)) {
state.store(SERVER_STATE_ERROR);
return 1;
} else {
ctx_server.init();
state.store(SERVER_STATE_READY);
}
LOG_INFO("model loaded", {});
const auto model_meta = ctx_server.model_meta();
// if a custom chat template is not supplied, we will use the one that comes with the model (if any)
if (sparams.chat_template.empty()) {
if (!ctx_server.validate_model_chat_template()) {
LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
sparams.chat_template = "chatml";
}
}
// print sample chat example to make it clear which template is used
{
json chat;
chat.push_back({{"role", "system"}, {"content", "You are a helpful assistant"}});
chat.push_back({{"role", "user"}, {"content", "Hello"}});
chat.push_back({{"role", "assistant"}, {"content", "Hi there"}});
chat.push_back({{"role", "user"}, {"content", "How are you?"}});
const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat);
LOG_INFO("chat template", {
{"chat_example", chat_example},
{"built_in", sparams.chat_template.empty()},
});
}
//
// Middlewares
//
auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
// TODO: should we apply API key to all endpoints, including "/health" and "/models"?
static const std::set<std::string> protected_endpoints = {
"/props",
"/completion",
"/completions",
"/v1/completions",
"/chat/completions",
"/v1/chat/completions",
"/infill",
"/tokenize",
"/detokenize",
"/embedding",
"/embeddings",
"/v1/embeddings",
};
// If API key is not set, skip validation
if (sparams.api_keys.empty()) {
return true;
}
// If path is not in protected_endpoints list, skip validation
if (protected_endpoints.find(req.path) == protected_endpoints.end()) {
return true;
}
// Check for API key in the header
auto auth_header = req.get_header_value("Authorization");
std::string prefix = "Bearer ";
if (auth_header.substr(0, prefix.size()) == prefix) {
std::string received_api_key = auth_header.substr(prefix.size());
if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) {
return true; // API key is valid
}
}
// API key is invalid or not provided
// TODO: make another middleware for CORS related logic
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION));
LOG_WARNING("Unauthorized: Invalid API Key", {});
return false;
};
// register server middlewares
svr->set_pre_routing_handler([&middleware_validate_api_key](const httplib::Request & req, httplib::Response & res) {
if (!middleware_validate_api_key(req, res)) {
return httplib::Server::HandlerResponse::Handled;
}
return httplib::Server::HandlerResponse::Unhandled;
});
//
// Route handlers (or controllers)
//
const auto handle_health = [&](const httplib::Request & req, httplib::Response & res) {
server_state current_state = state.load();
switch (current_state) {
case SERVER_STATE_READY:
{
// request slots data using task queue
server_task task;
task.id = ctx_server.queue_tasks.get_new_id();
task.type = SERVER_TASK_TYPE_METRICS;
task.id_target = -1;
ctx_server.queue_results.add_waiting_task_id(task.id);
ctx_server.queue_tasks.post(task);
// get the result
server_task_result result = ctx_server.queue_results.recv(task.id);
ctx_server.queue_results.remove_waiting_task_id(task.id);
const int n_idle_slots = result.data["idle"];
const int n_processing_slots = result.data["processing"];
json health = {
{"status", "ok"},
{"slots_idle", n_idle_slots},
{"slots_processing", n_processing_slots}
};
res.status = 200; // HTTP OK
if (sparams.slots_endpoint && req.has_param("include_slots")) {
health["slots"] = result.data["slots"];
}
if (n_idle_slots == 0) {
health["status"] = "no slot available";
if (req.has_param("fail_on_no_slot")) {
res.status = 503; // HTTP Service Unavailable
}
}
res.set_content(health.dump(), "application/json");
break;
}
case SERVER_STATE_LOADING_MODEL:
{
res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
} break;
case SERVER_STATE_ERROR:
{
res_error(res, format_error_response("Model failed to load", ERROR_TYPE_SERVER));
} break;
}
};
const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) {
if (!sparams.slots_endpoint) {
res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED));
return;
}
// request slots data using task queue
server_task task;
task.id = ctx_server.queue_tasks.get_new_id();
task.id_multi = -1;
task.id_target = -1;
task.type = SERVER_TASK_TYPE_METRICS;
ctx_server.queue_results.add_waiting_task_id(task.id);
ctx_server.queue_tasks.post(task);
// get the result
server_task_result result = ctx_server.queue_results.recv(task.id);
ctx_server.queue_results.remove_waiting_task_id(task.id);
res.set_content(result.data["slots"].dump(), "application/json");
res.status = 200; // HTTP OK
};
const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) {
if (!sparams.metrics_endpoint) {
res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED));
return;
}
// request slots data using task queue
server_task task;
task.id = ctx_server.queue_tasks.get_new_id();
task.id_multi = -1;
task.id_target = -1;
task.type = SERVER_TASK_TYPE_METRICS;
task.data.push_back({{"reset_bucket", true}});
ctx_server.queue_results.add_waiting_task_id(task.id);
ctx_server.queue_tasks.post(task);
// get the result
server_task_result result = ctx_server.queue_results.recv(task.id);
ctx_server.queue_results.remove_waiting_task_id(task.id);
json data = result.data;
const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"];
const uint64_t t_prompt_processing = data["t_prompt_processing"];
const uint64_t n_tokens_predicted = data["n_tokens_predicted"];
const uint64_t t_tokens_generation = data["t_tokens_generation"];
const int32_t kv_cache_used_cells = data["kv_cache_used_cells"];
// metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
json all_metrics_def = json {
{"counter", {{
{"name", "prompt_tokens_total"},
{"help", "Number of prompt tokens processed."},
{"value", (uint64_t) data["n_prompt_tokens_processed_total"]}
}, {
{"name", "prompt_seconds_total"},
{"help", "Prompt process time"},
{"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3}
}, {
{"name", "tokens_predicted_total"},
{"help", "Number of generation tokens processed."},
{"value", (uint64_t) data["n_tokens_predicted_total"]}
}, {
{"name", "tokens_predicted_seconds_total"},
{"help", "Predict process time"},
{"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3}
}}},
{"gauge", {{
{"name", "prompt_tokens_seconds"},
{"help", "Average prompt throughput in tokens/s."},
{"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.}
},{
{"name", "predicted_tokens_seconds"},
{"help", "Average generation throughput in tokens/s."},
{"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.}
},{
{"name", "kv_cache_usage_ratio"},
{"help", "KV-cache usage. 1 means 100 percent usage."},
{"value", 1. * kv_cache_used_cells / params.n_ctx}
},{
{"name", "kv_cache_tokens"},
{"help", "KV-cache tokens."},
{"value", (uint64_t) data["kv_cache_tokens_count"]}
},{
{"name", "requests_processing"},
{"help", "Number of request processing."},
{"value", (uint64_t) data["processing"]}
},{
{"name", "requests_deferred"},
{"help", "Number of request deferred."},
{"value", (uint64_t) data["deferred"]}
}}}
};
std::stringstream prometheus;
for (const auto & el : all_metrics_def.items()) {
const auto & type = el.key();
const auto & metrics_def = el.value();
for (const auto & metric_def : metrics_def) {
const std::string name = metric_def["name"];
const std::string help = metric_def["help"];
auto value = json_value(metric_def, "value", 0.);
prometheus << "# HELP llamacpp:" << name << " " << help << "\n"
<< "# TYPE llamacpp:" << name << " " << type << "\n"
<< "llamacpp:" << name << " " << value << "\n";
}
}
const int64_t t_start = data["t_start"];
res.set_header("Process-Start-Time-Unix", std::to_string(t_start));
res.set_content(prometheus.str(), "text/plain; version=0.0.4");
res.status = 200; // HTTP OK
};
const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = {
{ "user_name", ctx_server.name_user.c_str() },
{ "assistant_name", ctx_server.name_assistant.c_str() },
{ "default_generation_settings", ctx_server.default_generation_settings_for_props },
{ "total_slots", ctx_server.params.n_parallel }
};
res.set_content(data.dump(), "application/json; charset=utf-8");
};
const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = json::parse(req.body);
const int id_task = ctx_server.queue_tasks.get_new_id();
ctx_server.queue_results.add_waiting_task_id(id_task);
ctx_server.request_completion(id_task, -1, data, false, false);
if (!json_value(data, "stream", false)) {
server_task_result result = ctx_server.queue_results.recv(id_task);
if (!result.error && result.stop) {
res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
} else {
res_error(res, result.data);
}
ctx_server.queue_results.remove_waiting_task_id(id_task);
} else {
const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
while (true) {
server_task_result result = ctx_server.queue_results.recv(id_task);
if (!result.error) {
const std::string str =
"data: " +
result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {
{ "to_send", str }
});
if (!sink.write(str.c_str(), str.size())) {
ctx_server.queue_results.remove_waiting_task_id(id_task);
return false;
}
if (result.stop) {
break;
}
} else {
const std::string str =
"error: " +
result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {
{ "to_send", str }
});
if (!sink.write(str.c_str(), str.size())) {
ctx_server.queue_results.remove_waiting_task_id(id_task);
return false;
}
break;
}
}
ctx_server.queue_results.remove_waiting_task_id(id_task);
sink.done();
return true;
};
auto on_complete = [id_task, &ctx_server] (bool) {
// cancel
ctx_server.request_cancel(id_task);
ctx_server.queue_results.remove_waiting_task_id(id_task);
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
}
};
const auto handle_models = [&params, &model_meta](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json models = {
{"object", "list"},
{"data", {
{
{"id", params.model_alias},
{"object", "model"},
{"created", std::time(0)},
{"owned_by", "llamacpp"},
{"meta", model_meta}
},
}}
};
res.set_content(models.dump(), "application/json; charset=utf-8");
};
const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template);
const int id_task = ctx_server.queue_tasks.get_new_id();
ctx_server.queue_results.add_waiting_task_id(id_task);
ctx_server.request_completion(id_task, -1, data, false, false);
const auto completion_id = gen_chatcmplid();
if (!json_value(data, "stream", false)) {
server_task_result result = ctx_server.queue_results.recv(id_task);
if (!result.error && result.stop) {
json result_oai = format_final_response_oaicompat(data, result.data, completion_id);
res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
} else {
res_error(res, result.data);
}
ctx_server.queue_results.remove_waiting_task_id(id_task);
} else {
const auto chunked_content_provider = [id_task, &ctx_server, completion_id](size_t, httplib::DataSink & sink) {
while (true) {
server_task_result result = ctx_server.queue_results.recv(id_task);
if (!result.error) {
std::vector<json> result_array = format_partial_response_oaicompat(result.data, completion_id);
for (auto it = result_array.begin(); it != result_array.end(); ++it) {
if (!it->empty()) {
const std::string str =
"data: " +
it->dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {{"to_send", str}});
if (!sink.write(str.c_str(), str.size())) {
ctx_server.queue_results.remove_waiting_task_id(id_task);
return false;
}
}
}
if (result.stop) {
break;
}
} else {
const std::string str =
"error: " +
result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {{"to_send", str}});
if (!sink.write(str.c_str(), str.size())) {
ctx_server.queue_results.remove_waiting_task_id(id_task);
return false;
}
break;
}
}
sink.done();
ctx_server.queue_results.remove_waiting_task_id(id_task);
return true;
};
auto on_complete = [id_task, &ctx_server](bool) {
// cancel request
ctx_server.request_cancel(id_task);
ctx_server.queue_results.remove_waiting_task_id(id_task);
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
}
};
const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = json::parse(req.body);
const int id_task = ctx_server.queue_tasks.get_new_id();
ctx_server.queue_results.add_waiting_task_id(id_task);
ctx_server.request_completion(id_task, -1, data, true, false);
if (!json_value(data, "stream", false)) {
server_task_result result = ctx_server.queue_results.recv(id_task);
if (!result.error && result.stop) {
res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
} else {
res_error(res, result.data);
}
ctx_server.queue_results.remove_waiting_task_id(id_task);
} else {
const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
while (true) {
server_task_result result = ctx_server.queue_results.recv(id_task);
if (!result.error) {
const std::string str =
"data: " +
result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {
{ "to_send", str }
});
if (!sink.write(str.c_str(), str.size())) {
ctx_server.queue_results.remove_waiting_task_id(id_task);
return false;
}
if (result.stop) {
break;
}
} else {
break;
}
}
ctx_server.queue_results.remove_waiting_task_id(id_task);
sink.done();
return true;
};
auto on_complete = [id_task, &ctx_server] (bool) {
ctx_server.request_cancel(id_task);
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
}
};
const auto handle_tokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
const json body = json::parse(req.body);
std::vector<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");
};
const auto handle_detokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
const json body = json::parse(req.body);
std::string content;
if (body.count("tokens") != 0) {
const std::vector<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");
};
const auto handle_embeddings = [&params, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
if (!params.embedding) {
res.status = 501;
res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
return;
}
const json body = json::parse(req.body);
bool is_openai = false;
// an input prompt can be a string or a list of tokens (integer)
json prompt;
if (body.count("input") != 0) {
is_openai = true;
prompt = body["input"];
} else if (body.count("content") != 0) {
// with "content", we only support single prompt
prompt = std::vector<std::string>{body["content"]};
} else {
res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
return;
}
// create and queue the task
json responses;
{
const int id_task = ctx_server.queue_tasks.get_new_id();
ctx_server.queue_results.add_waiting_task_id(id_task);
ctx_server.request_completion(id_task, -1, {{"prompt", prompt}}, false, true);
// get the result
server_task_result result = ctx_server.queue_results.recv(id_task);
ctx_server.queue_results.remove_waiting_task_id(id_task);
if (!result.error) {
if (result.data.count("results")) {
// result for multi-task
responses = result.data["results"];
} else {
// result for single task
responses = std::vector<json>{result.data};
}
} else {
// error received, ignore everything else
res_error(res, result.data);
return;
}
}
// write JSON response
json root = is_openai
? format_embeddings_response_oaicompat(body, responses)
: responses[0];
return res.set_content(root.dump(), "application/json; charset=utf-8");
};
auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) {
return [content, len, mime_type](const httplib::Request &, httplib::Response & res) {
res.set_content(reinterpret_cast<const char*>(content), len, mime_type);
return false;
};
};
//
// Router
//
// register static assets routes
if (!sparams.public_path.empty()) {
// Set the base directory for serving static files
svr->set_base_dir(sparams.public_path);
}
// using embedded static files
svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8"));
svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8"));
svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8"));
svr->Get("/json-schema-to-grammar.mjs", handle_static_file(
json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8"));
// register API routes
svr->Get ("/health", handle_health);
svr->Get ("/slots", handle_slots);
svr->Get ("/metrics", handle_metrics);
svr->Get ("/props", handle_props);
svr->Get ("/v1/models", handle_models);
svr->Post("/completion", handle_completions); // legacy
svr->Post("/completions", handle_completions);
svr->Post("/v1/completions", handle_completions);
svr->Post("/chat/completions", handle_chat_completions);
svr->Post("/v1/chat/completions", handle_chat_completions);
svr->Post("/infill", handle_infill);
svr->Post("/embedding", handle_embeddings); // legacy
svr->Post("/embeddings", handle_embeddings);
svr->Post("/v1/embeddings", handle_embeddings);
svr->Post("/tokenize", handle_tokenize);
svr->Post("/detokenize", handle_detokenize);
//
// Start the server
//
if (sparams.n_threads_http < 1) {
// +2 threads for monitoring endpoints
sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1);
}
log_data["n_threads_http"] = std::to_string(sparams.n_threads_http);
svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); };
LOG_INFO("HTTP server listening", log_data);
// run the HTTP server in a thread - see comment below
std::thread t([&]() {
if (!svr->listen_after_bind()) {
state.store(SERVER_STATE_ERROR);
return 1;
}
return 0;
});
ctx_server.queue_tasks.on_new_task(std::bind(
&server_context::process_single_task, &ctx_server, std::placeholders::_1));
ctx_server.queue_tasks.on_finish_multitask(std::bind(
&server_context::on_finish_multitask, &ctx_server, std::placeholders::_1));
ctx_server.queue_tasks.on_update_slots(std::bind(
&server_context::update_slots, &ctx_server));
ctx_server.queue_results.on_multitask_update(std::bind(
&server_queue::update_multitask,
&ctx_server.queue_tasks,
std::placeholders::_1,
std::placeholders::_2,
std::placeholders::_3
));
shutdown_handler = [&](int) {
ctx_server.queue_tasks.terminate();
};
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
struct sigaction sigint_action;
sigint_action.sa_handler = signal_handler;
sigemptyset (&sigint_action.sa_mask);
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
#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;
}
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