+#include "utils.hpp"
+
#include "common.h"
#include "llama.h"
#include "grammar-parser.h"
-#include "utils.hpp"
-#include "oai.hpp"
-
-#include "../llava/clip.h"
-#include "../llava/llava.h"
-
-#include "stb_image.h"
#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
#include "completion.js.hpp"
#include "json-schema-to-grammar.mjs.hpp"
-#include <cstddef>
-#include <thread>
+#include <atomic>
#include <chrono>
#include <condition_variable>
-#include <atomic>
+#include <cstddef>
+#include <set>
+#include <mutex>
+#include <thread>
#include <signal.h>
using json = nlohmann::json;
-struct server_params {
- std::string hostname = "127.0.0.1";
- std::vector<std::string> api_keys;
- std::string public_path = "examples/server/public";
- std::string chat_template = "";
- int32_t port = 8080;
- int32_t read_timeout = 600;
- int32_t write_timeout = 600;
- bool slots_endpoint = true;
- bool metrics_endpoint = false;
- int n_threads_http = -1;
-};
-
bool server_verbose = false;
bool server_log_json = true;
enum stop_type {
- STOP_FULL,
- STOP_PARTIAL,
+ STOP_TYPE_FULL,
+ STOP_TYPE_PARTIAL,
};
-// TODO: can become bool if we can't find use of more states
enum slot_state {
- IDLE,
- PROCESSING,
+ SLOT_STATE_IDLE,
+ SLOT_STATE_PROCESSING,
};
enum slot_command {
- NONE,
- LOAD_PROMPT,
- RELEASE,
+ 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 {
json input_suffix;
};
-struct slot_image {
- int32_t id;
+struct server_params {
+ int32_t port = 8080;
+ int32_t read_timeout = 600;
+ int32_t write_timeout = 600;
+ int32_t n_threads_http = -1;
- bool request_encode_image = false;
- float * image_embedding = nullptr;
- int32_t image_tokens = 0;
+ std::string hostname = "127.0.0.1";
+ std::string public_path = "examples/server/public";
+ std::string chat_template = "";
+ std::string system_prompt = "";
- clip_image_u8 * img_data;
+ std::vector<std::string> api_keys;
- std::string prefix_prompt; // before of this image
+ bool slots_endpoint = true;
+ bool metrics_endpoint = false;
};
struct server_slot {
int id;
- int task_id = -1;
+ int id_task = -1;
+ int id_multi = -1;
struct slot_params params;
- slot_state state = IDLE;
- slot_command command = NONE;
+ 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;
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;
- llama_token sampled;
std::vector<llama_token> cache_tokens;
std::vector<completion_token_output> generated_token_probs;
- bool infill = false;
- bool embedding = false;
+ 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 truncated = false;
+ bool stopped_eos = false;
+ bool stopped_word = false;
+ bool stopped_limit = false;
bool oaicompat = false;
- std::string oaicompat_model;
+ std::string oaicompat_model;
std::string stopping_word;
// sampling
+ llama_token sampled;
struct llama_sampling_params sparams;
- llama_sampling_context *ctx_sampling = nullptr;
+ llama_sampling_context * ctx_sampling = nullptr;
int32_t ga_i = 0; // group-attention state
int32_t ga_n = 1; // group-attention factor
int32_t n_past_se = 0; // self-extend
- // multimodal
- std::vector<slot_image> images;
-
// 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_genereration;
+ int64_t t_start_generation;
double t_prompt_processing; // ms
double t_token_generation; // ms
- // multitasks
- int multitask_id = -1;
-
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;
+ 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();
-
- for (slot_image & img : images) {
- free(img.image_embedding);
- if (img.img_data) {
- clip_image_u8_free(img.img_data);
- }
- img.prefix_prompt = "";
- }
-
- images.clear();
}
bool has_budget(gpt_params &global_params) {
}
bool available() const {
- return state == IDLE && command == NONE;
+ return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE;
}
bool is_processing() const {
- return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
+ 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 == RELEASE) {
+ void add_token_string(const completion_token_output & token) {
+ if (command == SLOT_COMMAND_RELEASE) {
return;
}
- cache_tokens.push_back(token.tok);
generated_token_probs.push_back(token);
}
void release() {
- if (state == PROCESSING)
- {
- t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
- command = RELEASE;
+ if (state == SLOT_STATE_PROCESSING) {
+ t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
+ command = SLOT_COMMAND_RELEASE;
}
}
- json get_formated_timings() {
- return json
- {
+ 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},
};
}
+ 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];
+ 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;
- sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
+
+ 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, {
- {"slot_id", id},
- {"task_id", task_id},
+ {"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},
t_token = t_token_generation / n_decoded;
n_tokens_second = 1e3 / t_token_generation * n_decoded;
- sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
+
+ 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, {
- {"slot_id", id},
- {"task_id", task_id},
+ {"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},
});
- sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
+ snprintf(buffer, 512, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
+
LOG_INFO(buffer, {
- {"slot_id", id},
- {"task_id", task_id},
+ {"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},
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;
-
+ uint64_t n_tokens_predicted = 0;
+ uint64_t t_tokens_generation = 0;
void on_prompt_eval(const server_slot &slot) {
n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
}
};
-struct llama_server_context
-{
- llama_model *model = nullptr;
- llama_context *ctx = nullptr;
+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_run_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_run_slots(std::function<void(void)> callback) {
+ callback_run_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
+ * - Run 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_run_slots", {});
+
+ callback_run_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;
- clip_ctx *clp_ctx = nullptr;
+ // 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 multimodal = false;
- bool clean_kv_cache = true;
- bool all_slots_are_idle = false;
- bool add_bos_token = true;
+ bool clean_kv_cache = true;
+ bool add_bos_token = true;
- int32_t n_ctx; // total context for all clients / slots
+ int32_t n_ctx; // total context for all clients / slots
// system prompt
bool system_need_update = false;
std::vector<server_slot> slots;
json default_generation_settings_for_props;
- llama_server_queue queue_tasks;
- llama_server_response queue_results;
+ server_queue queue_tasks;
+ server_response queue_results;
server_metrics metrics;
- ~llama_server_context()
- {
- if (ctx)
- {
+ ~server_context() {
+ if (ctx) {
llama_free(ctx);
ctx = nullptr;
}
- if (model)
- {
+
+ if (model) {
llama_free_model(model);
model = nullptr;
}
}
- bool load_model(const gpt_params ¶ms_)
- {
+ bool load_model(const gpt_params & params_) {
params = params_;
- if (!params.mmproj.empty()) {
- multimodal = true;
- LOG_INFO("Multi Modal Mode Enabled", {});
- clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
- if(clp_ctx == nullptr) {
- LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
- return false;
- }
-
- if (params.n_ctx < 2048) { // request larger context for the image embedding
- params.n_ctx = 2048;
- }
- }
std::tie(model, ctx) = llama_init_from_gpt_params(params);
- if (model == nullptr)
- {
+ if (model == nullptr) {
LOG_ERROR("unable to load model", {{"model", params.model}});
return false;
}
- if (multimodal) {
- const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
- const int n_embd_llm = llama_n_embd(model);
- if (n_embd_clip != n_embd_llm) {
- LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
- llama_free(ctx);
- llama_free_model(model);
- return false;
- }
- }
-
n_ctx = llama_n_ctx(ctx);
add_bos_token = llama_should_add_bos_token(model);
return true;
}
- void validate_model_chat_template(server_params & sparams) {
+ bool validate_model_chat_template() const {
llama_chat_message chat[] = {{"user", "test"}};
- std::vector<char> buf(1);
- int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size());
- if (res < 0) {
- LOG_ERROR("The chat template 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";
- }
+
+ const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0);
+
+ return res > 0;
}
void initialize() {
- // create slots
- all_slots_are_idle = true;
-
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++)
- {
+ for (int i = 0; i < params.n_parallel; i++) {
server_slot slot;
slot.id = i;
slot.n_predict = params.n_predict;
LOG_INFO("new slot", {
- {"slot_id", slot.id},
+ {"id_slot", slot.id},
{"n_ctx_slot", slot.n_ctx}
});
//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", {
- {"slot_id", slot.id},
- {"ga_n", ga_n},
- {"ga_w", ga_w}
+ {"id_slot", slot.id},
+ {"ga_n", ga_n},
+ {"ga_w", ga_w}
});
}
batch = llama_batch_init(n_ctx, 0, params.n_parallel);
}
- std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
- {
+ 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
// or the first element of the json_prompt array is a string.
std::vector<llama_token> prompt_tokens;
- if (json_prompt.is_array())
- {
+ if (json_prompt.is_array()) {
bool first = true;
- for (const auto& p : json_prompt)
- {
- if (p.is_string())
- {
+ for (const auto & p : json_prompt) {
+ if (p.is_string()) {
auto s = p.template get<std::string>();
+
std::vector<llama_token> p;
- if (first)
- {
+ if (first) {
p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
first = false;
- }
- else
- {
+ } else {
p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
}
+
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
- }
- else
- {
- if (first)
- {
+ } else {
+ if (first) {
first = false;
}
+
prompt_tokens.push_back(p.template get<llama_token>());
}
}
- }
- else
- {
+ } 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) {
+ 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())
- {
+ server_slot * last_used = nullptr;
+
+ for (server_slot & slot : slots) {
+ if (slot.id == id && slot.available()) {
return &slot;
}
- if (slot.available() && slot.t_last_used < t_last)
- {
+ // among all available slots, find the one that has been least recently used
+ if (slot.available() && slot.t_last_used < t_last) {
last_used = &slot;
t_last = slot.t_last_used;
}
return last_used;
}
- bool launch_slot_with_data(server_slot* &slot, json data) {
+ bool launch_slot_with_data(server_slot & slot, json data) const {
slot_params default_params;
llama_sampling_params default_sparams;
if (data.count("__oaicompat") != 0) {
- slot->oaicompat = true;
- slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
+ 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->n_predict > 0 && slot->params.n_predict > slot->n_predict) {
+ 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},
+ {"params.n_predict", slot.params.n_predict},
+ {"slot.n_predict", slot.n_predict},
});
- slot->params.n_predict = slot->n_predict;
+ slot.params.n_predict = slot.n_predict;
}
// infill
- if (data.count("input_prefix") != 0)
- {
- slot->params.input_prefix = data["input_prefix"];
- }
- else
- {
- slot->params.input_prefix = "";
- }
+ slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
+ slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
+ slot.prompt = json_value(data, "prompt", std::string(""));
- if (data.count("input_suffix") != 0)
- {
- slot->params.input_suffix = data["input_suffix"];
- }
- else
+ // penalize user-provided tokens
{
- slot->params.input_suffix = "";
- }
+ slot.sparams.penalty_prompt_tokens.clear();
+ slot.sparams.use_penalty_prompt_tokens = false;
- if (data.count("prompt") != 0)
- {
- slot->prompt = data["prompt"];
- }
- else
- {
- slot->prompt = "";
- }
+ const auto & penalty_prompt = data.find("penalty_prompt");
- 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>();
- auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
- slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
- if (slot->params.n_predict > 0)
- {
- slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
+ 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},
+ });
}
- slot->sparams.use_penalty_prompt_tokens = true;
- }
- 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);
+ 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.use_penalty_prompt_tokens = true;
}
}
- slot->sparams.logit_bias.clear();
-
- if (json_value(data, "ignore_eos", false))
{
- slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
- }
+ slot.sparams.logit_bias.clear();
- const auto &logit_bias = data.find("logit_bias");
- if (logit_bias != data.end() && logit_bias->is_array())
- {
- const int n_vocab = llama_n_vocab(model);
- for (const auto &el : *logit_bias)
- {
- if (el.is_array() && el.size() == 2)
- {
- float bias;
- if (el[1].is_number())
- {
- bias = el[1].get<float>();
- }
- else if (el[1].is_boolean() && !el[1].get<bool>())
- {
- bias = -INFINITY;
- }
- else
- {
- continue;
- }
+ if (json_value(data, "ignore_eos", false)) {
+ slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
+ }
- 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;
+ const auto & logit_bias = data.find("logit_bias");
+ if (logit_bias != data.end() && logit_bias->is_array()) {
+ const int n_vocab = llama_n_vocab(model);
+ for (const auto & el : *logit_bias) {
+ if (el.is_array() && el.size() == 2) {
+ float bias;
+ if (el[1].is_number()) {
+ bias = el[1].get<float>();
+ } else if (el[1].is_boolean() && !el[1].get<bool>()) {
+ bias = -INFINITY;
+ } else {
+ continue;
}
- }
- 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;
+
+ 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);
- }
- }
- }
+ slot.params.antiprompt.clear();
- 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);
+ 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);
+ }
}
}
- slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
- }
- else
- {
- slot->sparams.samplers_sequence = default_sparams.samplers_sequence;
}
- if (multimodal)
{
- const auto &images_data = data.find("image_data");
- if (images_data != data.end() && images_data->is_array())
- {
- for (const auto &img : *images_data)
- {
- const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
-
- slot_image img_sl;
- img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
- img_sl.img_data = clip_image_u8_init();
- if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
- {
- LOG_ERROR("failed to load image", {
- {"slot_id", slot->id},
- {"img_sl_id", img_sl.id}
- });
- return false;
- }
- LOG_VERBOSE("image loaded", {
- {"slot_id", slot->id},
- {"img_sl_id", img_sl.id}
- });
- img_sl.request_encode_image = true;
- slot->images.push_back(img_sl);
- }
- // process prompt
- // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]}
- if (slot->images.size() > 0 && !slot->prompt.is_array())
- {
- std::string prompt = slot->prompt.get<std::string>();
- size_t pos = 0, begin_prefix = 0;
- std::string pattern = "[img-";
- while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
- size_t end_prefix = pos;
- pos += pattern.length();
- size_t end_pos = prompt.find(']', pos);
- if (end_pos != std::string::npos)
- {
- std::string image_id = prompt.substr(pos, end_pos - pos);
- try
- {
- int img_id = std::stoi(image_id);
- bool found = false;
- for (slot_image &img : slot->images)
- {
- if (img.id == img_id) {
- found = true;
- img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix);
- begin_prefix = end_pos + 1;
- break;
- }
- }
- if (!found) {
- LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id);
- slot->images.clear();
- return false;
- }
- } catch (const std::invalid_argument& e) {
- LOG_TEE("Invalid image number id in prompt\n");
- slot->images.clear();
- return false;
- }
- }
+ 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->prompt = "";
- slot->params.input_suffix = prompt.substr(begin_prefix);
- slot->params.cache_prompt = false; // multimodal doesn't support cache prompt
}
+ 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);
+ if (slot.ctx_sampling != nullptr) {
+ llama_sampling_free(slot.ctx_sampling);
+ }
+ slot.ctx_sampling = llama_sampling_init(slot.sparams);
+ llama_set_rng_seed(ctx, slot.params.seed);
}
- slot->ctx_sampling = llama_sampling_init(slot->sparams);
- llama_set_rng_seed(ctx, slot->params.seed);
- slot->command = LOAD_PROMPT;
- all_slots_are_idle = false;
+ slot.command = SLOT_COMMAND_LOAD_PROMPT;
+ slot.prompt_tokens.clear();
LOG_INFO("slot is processing task", {
- {"slot_id", slot->id},
- {"task_id", slot->task_id},
+ {"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();
llama_batch_clear(batch);
- for (int i = 0; i < (int)system_tokens.size(); ++i)
- {
+ for (int i = 0; i < (int)system_tokens.size(); ++i) {
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
}
- for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch)
- {
+ for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) {
const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i));
llama_batch batch_view = {
n_tokens,
batch.logits + i,
0, 0, 0, // unused
};
- if (llama_decode(ctx, batch_view) != 0)
- {
+
+ 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)
- {
+ for (int32_t i = 1; i < params.n_parallel; ++i) {
llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
}
}
- LOG_TEE("system prompt updated\n");
system_need_update = false;
}
- void system_prompt_notify() {
- // release all slots
- for (server_slot &slot : slots)
- {
- slot.release();
- }
-
- system_need_update = true;
- }
-
- void system_prompt_process(const json &sys_props) {
+ 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},
+ });
- system_prompt_notify();
- }
-
- static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
- const stop_type type, server_slot &slot)
- {
- size_t stop_pos = std::string::npos;
-
- for (const std::string &word : slot.params.antiprompt)
- {
- size_t pos;
- if (type == STOP_FULL)
- {
- const size_t tmp = word.size() + last_token_size;
- const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
- pos = text.find(word, from_pos);
- }
- else
- {
- pos = find_partial_stop_string(word, text);
- }
- if (pos != std::string::npos &&
- (stop_pos == std::string::npos || pos < stop_pos))
- {
- if (type == STOP_FULL)
- {
- slot.stopped_word = true;
- slot.stopping_word = word;
- slot.has_next_token = false;
- }
- stop_pos = pos;
- }
+ // release all slots
+ for (server_slot & slot : slots) {
+ slot.release();
}
- return stop_pos;
+ system_need_update = true;
}
- bool process_token(completion_token_output &result, server_slot &slot) {
+ 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;
slot.generated_text += token_str;
slot.has_next_token = true;
- if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
- {
+ 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)
- {
+ 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)
- {
+ if ((c & 0xC0) == 0x80) {
// continuation byte: 10xxxxxx
continue;
}
- if ((c & 0xE0) == 0xC0)
- {
+ if ((c & 0xE0) == 0xC0) {
// 2-byte character: 110xxxxx ...
incomplete = i < 2;
- }
- else if ((c & 0xF0) == 0xE0)
- {
+ } else if ((c & 0xF0) == 0xE0) {
// 3-byte character: 1110xxxx ...
incomplete = i < 3;
- }
- else if ((c & 0xF8) == 0xF0)
- {
+ } else if ((c & 0xF8) == 0xF0) {
// 4-byte character: 11110xxx ...
incomplete = i < 4;
}
break;
}
- if (!incomplete)
- {
+ 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 = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot);
- if (stop_pos != std::string::npos)
- {
+
+ 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
- {
+ } else {
is_stop_full = false;
- stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot);
+ 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))
- {
+ 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)
- {
+ if (slot.params.stream) {
send_partial_response(slot, result);
}
}
- if (incomplete)
- {
+ 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;
+ 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},
+ {"n_decoded", slot.n_decoded},
+ {"n_predict", slot.params.n_predict},
+ });
}
- if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model))
- {
- slot.stopped_eos = true;
+ if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) {
+ slot.stopped_eos = true;
slot.has_next_token = false;
+
LOG_VERBOSE("eos token found", {});
}
LOG_VERBOSE("next token", {
- {"token", result.tok},
- {"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
- {"has_next_token", slot.has_next_token},
- {"n_remain", slot.n_remaining},
- {"num_tokens_predicted", slot.n_decoded},
- {"stopped_eos", slot.stopped_eos},
- {"stopped_word", slot.stopped_word},
- {"stopped_limit", slot.stopped_limit},
- {"stopping_word", slot.stopping_word},
- });
+ {"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
}
- bool process_images(server_slot &slot) const
- {
- for (slot_image &img : slot.images)
- {
- if (!img.request_encode_image)
- {
- continue;
- }
-
- if (!llava_image_embed_make_with_clip_img(clp_ctx, params.n_threads, img.img_data, &img.image_embedding, &img.image_tokens)) {
- LOG_TEE("Error processing the given image");
- return false;
- }
-
-
- img.request_encode_image = false;
- }
-
- return slot.images.size() > 0;
- }
-
- void send_error(task_server& task, const std::string &error)
- {
- LOG_TEE("task %i - error: %s\n", task.id, error.c_str());
- task_result res;
- res.id = task.id;
- res.multitask_id = task.multitask_id;
- res.stop = false;
- res.error = true;
- res.result_json = { { "content", error } };
- queue_results.send(res);
- }
-
- json get_formated_generation(server_slot &slot)
- {
+ 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);
+ 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;
- for (const auto &sampler_type : slot.sparams.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},
+ {"n_ctx", slot.n_ctx},
+ {"n_predict", slot.n_predict},
+ {"model", params.model_alias},
+ {"seed", slot.params.seed},
+ {"temperature", slot.sparams.temp},
+ {"dynatemp_range", slot.sparams.dynatemp_range},
+ {"dynatemp_exponent", slot.sparams.dynatemp_exponent},
+ {"top_k", slot.sparams.top_k},
+ {"top_p", slot.sparams.top_p},
+ {"min_p", slot.sparams.min_p},
+ {"tfs_z", slot.sparams.tfs_z},
+ {"typical_p", slot.sparams.typical_p},
+ {"repeat_last_n", slot.sparams.penalty_last_n},
+ {"repeat_penalty", slot.sparams.penalty_repeat},
+ {"presence_penalty", slot.sparams.penalty_present},
+ {"frequency_penalty", slot.sparams.penalty_freq},
+ {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
{"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
- {"mirostat", slot.sparams.mirostat},
- {"mirostat_tau", slot.sparams.mirostat_tau},
- {"mirostat_eta", slot.sparams.mirostat_eta},
- {"penalize_nl", slot.sparams.penalize_nl},
- {"stop", slot.params.antiprompt},
- {"n_predict", slot.params.n_predict},
- {"n_keep", params.n_keep},
- {"ignore_eos", ignore_eos},
- {"stream", slot.params.stream},
- {"logit_bias", slot.sparams.logit_bias},
- {"n_probs", slot.sparams.n_probs},
- {"min_keep", slot.sparams.min_keep},
- {"grammar", slot.sparams.grammar},
- {"samplers", samplers_sequence}
+ {"mirostat", slot.sparams.mirostat},
+ {"mirostat_tau", slot.sparams.mirostat_tau},
+ {"mirostat_eta", slot.sparams.mirostat_eta},
+ {"penalize_nl", slot.sparams.penalize_nl},
+ {"stop", slot.params.antiprompt},
+ {"n_predict", slot.params.n_predict},
+ {"n_keep", params.n_keep},
+ {"ignore_eos", ignore_eos},
+ {"stream", slot.params.stream},
+ {"logit_bias", slot.sparams.logit_bias},
+ {"n_probs", slot.sparams.n_probs},
+ {"min_keep", slot.sparams.min_keep},
+ {"grammar", slot.sparams.grammar},
+ {"samplers", samplers_sequence}
};
}
- void send_partial_response(server_slot &slot, completion_token_output tkn)
- {
- task_result res;
- res.id = slot.task_id;
- res.multitask_id = slot.multitask_id;
- res.error = false;
- res.stop = false;
+ void send_error(const server_task & task, const std::string & error) {
+ LOG_TEE("task %i - error: %s\n", task.id, error.c_str());
- res.result_json = json
- {
+ server_task_result res;
+ res.id = task.id;
+ res.id_multi = task.id_multi;
+ res.stop = false;
+ res.error = true;
+ res.data = { { "content", error } };
+
+ queue_results.send(res);
+ }
+
+ void send_partial_response(server_slot & slot, completion_token_output tkn) {
+ server_task_result res;
+ res.id = slot.id_task;
+ res.id_multi = slot.id_multi;
+ res.error = false;
+ res.stop = false;
+ res.data = json {
{"content", tkn.text_to_send},
{"stop", false},
- {"slot_id", slot.id},
- {"multimodal", multimodal}
+ {"id_slot", slot.id},
+ {"multimodal", false}
};
- if (slot.sparams.n_probs > 0)
- {
- std::vector<completion_token_output> probs_output = {};
+ if (slot.sparams.n_probs > 0) {
const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
- size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size());
- size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());
- 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);
+ 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.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
+
+ res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
}
- if (slot.oaicompat)
- {
- res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
- res.result_json["model"] = slot.oaicompat_model;
+ 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(server_slot &slot)
- {
- task_result res;
- res.id = slot.task_id;
- res.multitask_id = slot.multitask_id;
- res.error = false;
- res.stop = true;
-
- res.result_json = json
- {
+ 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 : ""},
- {"slot_id", slot.id},
+ {"id_slot", slot.id},
{"stop", true},
{"model", params.model_alias},
{"tokens_predicted", slot.n_decoded},
{"timings", slot.get_formated_timings()}
};
- if (slot.sparams.n_probs > 0)
- {
- std::vector<completion_token_output> probs = {};
- if (!slot.params.stream && slot.stopped_word)
- {
+ 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());
+ 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.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
+
+ res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs);
}
- if (slot.oaicompat)
- {
- res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
- res.result_json["model"] = slot.oaicompat_model;
+ if (slot.oaicompat) {
+ res.data["oaicompat_token_ctr"] = slot.n_decoded;
+ res.data["model"] = slot.oaicompat_model;
}
queue_results.send(res);
}
- void send_embedding(server_slot & slot, const llama_batch & batch)
- {
- task_result res;
- res.id = slot.task_id;
- res.multitask_id = slot.multitask_id;
- res.error = false;
- res.stop = true;
+ 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);
- if (!params.embedding)
- {
- LOG_WARNING("embedding disabled", {{"params.embedding", params.embedding}});
- res.result_json = json
- {
- {"embedding", std::vector<float>(n_embd, 0.0f)},
- };
- }
- else
- {
- for (int i = 0; i < batch.n_tokens; ++i) {
- if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
- continue;
- }
+ for (int i = 0; i < batch.n_tokens; ++i) {
+ if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
+ 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 for token", {{"token", batch.token[i]}, {"seq_id", batch.seq_id[i][0]}});
- res.result_json = json
- {
- {"embedding", std::vector<float>(n_embd, 0.0f)},
- };
- continue;
- }
- }
+ const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+ if (embd == NULL) {
+ embd = llama_get_embeddings_ith(ctx, i);
+ }
- res.result_json = json
- {
- {"embedding", std::vector<float>(embd, embd + n_embd)},
+ if (embd == NULL) {
+ LOG_ERROR("failed to get embeddings", {
+ {"token", batch.token [i]},
+ {"seq_id", batch.seq_id[i][0]}
+ });
+
+ res.data = json {
+ {"embedding", std::vector<float>(n_embd, 0.0f)},
};
+
+ continue;
}
+
+ res.data = json {
+ {"embedding", std::vector<float>(embd, embd + n_embd)},
+ };
}
+
queue_results.send(res);
}
- void request_completion(int task_id, json data, bool infill, bool embedding, int multitask_id)
- {
- task_server task;
- task.id = task_id;
- task.target_id = 0;
- task.data = std::move(data);
- task.infill_mode = infill;
- task.embedding_mode = embedding;
- task.type = TASK_TYPE_COMPLETION;
- task.multitask_id = multitask_id;
+ 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")) {
+ for (const auto & e : task.data.at("prompt")) {
if (e.is_number()) {
numbers = true;
break;
if (numbers) {
queue_tasks.post(task);
} else {
- split_multiprompt_task(task_id, task);
+ split_multiprompt_task(id_task, task);
}
} else {
- // an empty prompt can make slot become buggy
- if (task.data.contains("prompt") && task.data["prompt"].is_string() && task.data["prompt"].get<std::string>().empty()) {
- task.data["prompt"] = " "; // add a space so that we have one token
- }
queue_tasks.post(task);
}
}
- // for multiple images processing
- bool ingest_images(server_slot &slot, int n_batch)
- {
- int image_idx = 0;
-
- while (image_idx < (int) slot.images.size())
- {
- slot_image &img = slot.images[image_idx];
-
- // process prefix prompt
- for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
- {
- const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
- llama_batch batch_view = {
- n_tokens,
- batch.token + i,
- nullptr,
- batch.pos + i,
- batch.n_seq_id + i,
- batch.seq_id + i,
- batch.logits + i,
- 0, 0, 0, // unused
- };
- if (llama_decode(ctx, batch_view))
- {
- LOG_TEE("%s : failed to eval\n", __func__);
- return false;
- }
- }
-
- // process image with llm
- for (int i = 0; i < img.image_tokens; i += n_batch)
- {
- int n_eval = img.image_tokens - i;
- if (n_eval > n_batch)
- {
- n_eval = n_batch;
- }
-
- const int n_embd = llama_n_embd(model);
- llama_batch batch_img = {
- n_eval,
- nullptr,
- (img.image_embedding + i * n_embd),
- nullptr,
- nullptr,
- nullptr,
- nullptr,
- slot.n_past,
- 1, 0
- };
- if (llama_decode(ctx, batch_img))
- {
- LOG_TEE("%s : failed to eval image\n", __func__);
- return false;
- }
- slot.n_past += n_eval;
- }
- image_idx++;
-
- llama_batch_clear(batch);
-
- // append prefix of next image
- const auto json_prompt = (image_idx >= (int) slot.images.size()) ?
- slot.params.input_suffix : // no more images, then process suffix prompt
- (json)(slot.images[image_idx].prefix_prompt);
-
- std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image
- for (int i = 0; i < (int) append_tokens.size(); ++i)
- {
- llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true);
- slot.n_past += 1;
- }
- }
-
- return true;
- }
+ void request_cancel(int id_task) {
+ server_task task;
+ task.type = SERVER_TASK_TYPE_CANCEL;
+ task.id_target = id_task;
- void request_cancel(int task_id)
- {
- task_server task;
- task.type = TASK_TYPE_CANCEL;
- task.target_id = task_id;
queue_tasks.post(task);
}
- void split_multiprompt_task(int multitask_id, task_server& multiprompt_task)
- {
- int prompt_count = multiprompt_task.data.at("prompt").size();
+ 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++)
- {
+ 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(multitask_id, subtask_ids);
+ queue_tasks.add_multitask(id_multi, subtask_ids);
// add subtasks
- for (int i = 0; i < prompt_count; i++)
- {
+ 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], subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id);
+ request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding);
}
}
- void process_single_task(task_server& task)
- {
- switch (task.type)
- {
- case TASK_TYPE_COMPLETION: {
- server_slot *slot = get_slot(json_value(task.data, "slot_id", -1));
- if (slot == nullptr)
- {
- // if no slot is available, we defer this task for processing later
- LOG_VERBOSE("no slot is available", {{"task_id", task.id}});
- queue_tasks.defer(task);
- break;
- }
-
- if (task.data.contains("system_prompt"))
+ void process_single_task(const server_task & task) {
+ switch (task.type) {
+ case SERVER_TASK_TYPE_COMPLETION:
{
- if (!all_slots_are_idle) {
- send_error(task, "system prompt can only be updated when all slots are idle");
+ 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;
}
- system_prompt_process(task.data["system_prompt"]);
-
- // reset cache_tokens for all slots
- for (server_slot &slot : slots)
- {
- slot.cache_tokens.clear();
- slot.n_past = 0;
- slot.n_past_se = 0;
+
+ 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->reset();
- slot->infill = task.infill_mode;
- slot->embedding = task.embedding_mode;
- slot->task_id = task.id;
- slot->multitask_id = task.multitask_id;
+ slot->id_task = task.id;
+ slot->id_multi = task.id_multi;
+ slot->infill = task.infill;
+ slot->embedding = task.embedding;
- if (!launch_slot_with_data(slot, task.data))
- {
- // send error result
- send_error(task, "internal_error");
- break;
- }
- } break;
- case TASK_TYPE_CANCEL: { // release slot linked with the task id
- for (auto & slot : slots)
- {
- if (slot.task_id == task.target_id)
- {
- slot.release();
+ if (!launch_slot_with_data(*slot, task.data)) {
+ // send error result
+ send_error(task, "internal_error");
break;
}
- }
- } break;
- case TASK_TYPE_NEXT_RESPONSE: {
- // do nothing
- } break;
- case 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["task_id"] = slot.task_id;
- 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},
- {"num_tokens_predicted", slot.n_decoded},
- {"stopped_eos", slot.stopped_eos},
- {"stopped_word", slot.stopped_word},
- {"stopped_limit", slot.stopped_limit},
- {"stopping_word", slot.stopping_word},
- };
- if (slot_data["state"] == IDLE) {
- n_idle_slots++;
- } else {
- n_processing_slots++;
+ } 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;
+ }
}
- slots_data.push_back(slot_data);
- }
- LOG_INFO("slot data", {
- {"task_id", task.id},
- {"n_idle_slots", n_idle_slots},
- {"n_processing_slots", n_processing_slots}
- });
- LOG_VERBOSE("slot data", {
- {"task_id", task.id},
- {"n_idle_slots", n_idle_slots},
- {"n_processing_slots", n_processing_slots},
- {"slots", slots_data}
- });
- task_result res;
- res.id = task.id;
- res.multitask_id = task.multitask_id;
- res.stop = true;
- res.error = false;
- res.result_json = {
+ } 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() },
{ "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)},
{ "slots", slots_data },
- };
- metrics.reset_bucket();
- queue_results.send(res);
- } break;
+ };
+
+ metrics.reset_bucket();
+ queue_results.send(res);
+ } break;
}
}
- void on_finish_multitask(task_multi& multitask)
- {
+ void on_finish_multitask(const server_task_multi & multitask) {
// all subtasks done == multitask is done
- task_result result;
- result.id = multitask.id;
- result.stop = true;
+ 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 (auto& subres : multitask.results)
- {
- result_jsons.push_back(subres.result_json);
+ for (const auto & subres : multitask.results) {
+ result_jsons.push_back(subres.data);
result.error = result.error && subres.error;
}
- result.result_json = json{ { "results", result_jsons } };
+ result.data = json {
+ { "results", result_jsons }
+ };
+
queue_results.send(result);
}
bool update_slots() {
- if (system_need_update)
- {
- LOG_INFO("updating system prompt", {});
+ if (system_need_update) {
system_prompt_update();
}
- llama_batch_clear(batch);
+ // 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();
- if (all_slots_are_idle)
- {
- if (system_prompt.empty() && clean_kv_cache)
- {
- LOG_INFO("all slots are idle and system prompt is empty, clear the KV cache", {});
- kv_cache_clear();
+ 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();
}
- return true;
}
- LOG_VERBOSE("posting NEXT_RESPONSE", {});
- task_server task;
- task.type = TASK_TYPE_NEXT_RESPONSE;
- task.target_id = -1;
- queue_tasks.post(task);
+ // check if all slots are idle
+ {
+ bool all_idle = true;
+
+ for (auto & slot : slots) {
+ if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) {
+ all_idle = false;
+ break;
+ }
+ }
+
+ if (all_idle) {
+ LOG_INFO("all slots are idle", {});
+ if (system_prompt.empty() && clean_kv_cache) {
+ kv_cache_clear();
+ }
+
+ return true;
+ }
+ }
- for (server_slot &slot : slots)
{
- if (slot.ga_n == 1)
- {
- if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx)
- {
+ 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", {
- {"slot_id", slot.id},
- {"task_id", slot.task_id},
+ {"id_slot", slot.id},
+ {"id_task", slot.id_task},
{"n_keep", n_keep},
{"n_left", n_left},
{"n_discard", n_discard},
{"n_system_tokens", system_tokens.size()},
{"n_cache_tokens", slot.cache_tokens.size()}
});
+
llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard);
llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);
- for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++)
- {
- slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
- }
+ 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.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
+ }
slot.n_past -= n_discard;
}
}
- // decode any currently ongoing sequences
- LOG_VERBOSE("decoding ongoing sequences", {});
- for (auto & slot : slots)
- {
- // release the slot
- if (slot.command == RELEASE)
- {
- slot.state = IDLE;
- slot.command = NONE;
- slot.t_last_used = ggml_time_us();
+ // start populating the batch for this iteration
+ llama_batch_clear(batch);
- LOG_INFO("slot released", {
- {"slot_id", slot.id},
- {"task_id", slot.task_id},
- {"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();
-
- continue;
- }
-
- if (slot.state == IDLE)
- {
+ // frist, add sampled tokens from any ongoing sequences
+ for (auto & slot : slots) {
+ if (slot.state == SLOT_STATE_IDLE) {
continue;
}
// 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 }, true);
+
slot.n_past += 1;
+
+ if (slot.params.cache_prompt) {
+ slot.cache_tokens.push_back(slot.sampled);
+ }
+
+ LOG_VERBOSE("slot decode token", {
+ {"id_slot", slot.id},
+ {"id_task", slot.id_task},
+ {"n_ctx", n_ctx},
+ {"n_past", slot.n_past},
+ {"n_system_tokens", system_tokens.size()},
+ {"n_cache_tokens", slot.cache_tokens.size()},
+ {"truncated", slot.truncated}
+ });
}
// process in chunks of params.n_batch
int32_t n_batch = params.n_batch;
- // assign workload to the slots
- if (params.cont_batching || batch.n_tokens == 0)
- {
- for (auto & slot : slots)
- {
- const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty()) || !slot.images.empty();
+ // next, batch any pending prompts without exceeding n_batch
+ if (params.cont_batching || batch.n_tokens == 0) {
+ for (auto & slot : slots) {
+ const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty());
// empty prompt passed -> release the slot and send empty response
// note: infill mode allows empty prompt
- if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt && !slot.infill)
- {
+ if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT && !has_prompt && !slot.infill) {
+ slot.state = SLOT_STATE_PROCESSING;
+ slot.command = SLOT_COMMAND_NONE;
slot.release();
slot.print_timings();
send_final_response(slot);
continue;
}
- // need process the prompt
- if (slot.state == IDLE && slot.command == LOAD_PROMPT)
- {
- slot.state = PROCESSING;
- slot.command = NONE;
- std::vector<llama_token> prompt_tokens;
- slot.t_start_process_prompt = ggml_time_us();
- slot.t_start_genereration = 0;
-
- if (slot.infill)
- {
- bool suff_rm_leading_spc = true;
- if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1)
- {
- params.input_suffix.erase(0, 1);
- suff_rm_leading_spc = false;
- }
- auto prefix_tokens = tokenize(slot.params.input_prefix, false);
- auto suffix_tokens = tokenize(slot.params.input_suffix, false);
+ // 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;
- 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());
- }
+ // 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}
+ });
- 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.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;
+ }
- slot.n_prompt_tokens = prompt_tokens.size();
+ auto prefix_tokens = tokenize(slot.params.input_prefix, false);
+ auto suffix_tokens = tokenize(slot.params.input_suffix, false);
- 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());
-
- LOG_VERBOSE("input truncated", {
- {"n_ctx", slot.n_ctx},
- {"n_keep", slot.params.n_keep},
- {"n_left", n_left},
- {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
- });
- slot.truncated = true;
- prompt_tokens = new_tokens;
+ 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();
- GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
- }
- if (!slot.params.cache_prompt)
- {
- llama_sampling_reset(slot.ctx_sampling);
+ if (slot.embedding) {
+ // this prompt is too large to process - discard it
+ if (slot.n_prompt_tokens > n_batch) {
+ slot.state = SLOT_STATE_PROCESSING;
+ slot.command = SLOT_COMMAND_NONE;
+ slot.release();
+ slot.print_timings();
+ send_final_response(slot);
+ continue;
+ }
+ } else {
+ if (slot.params.n_keep < 0) {
+ slot.params.n_keep = slot.n_prompt_tokens;
+ }
+ slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
- slot.n_past = 0;
- slot.n_past_se = 0;
- slot.ga_i = 0;
- slot.n_prompt_tokens_processed = slot.n_prompt_tokens;
- }
- else
- {
- // push the prompt into the sampling context (do not apply grammar)
- for (auto &token : prompt_tokens)
- {
- llama_sampling_accept(slot.ctx_sampling, ctx, token, false);
- }
+ // 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;
- slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
+ 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;
- // the last token of the cache is not in the KV cache until the next call to llama_decode
- // (it was sampled, pushed into the "cache_tokens", but not yet put in the context)
- if (slot.n_past > 0 && slot.n_past == (int32_t) slot.cache_tokens.size())
- {
- slot.n_past -= 1;
- }
+ std::vector<llama_token> new_tokens(
+ prompt_tokens.begin(),
+ prompt_tokens.begin() + slot.params.n_keep);
- slot.n_prompt_tokens_processed = slot.n_prompt_tokens - slot.n_past;
-
- if (slot.ga_n != 1)
- {
- int ga_i = 0;
- int32_t ga_n = slot.ga_n;
- int32_t ga_w = slot.ga_w;
- int32_t slot_npast = 0;
- for (int k = 0; k < slot.n_past; ++k)
- {
- 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;
+ 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", {
+ {"n_ctx", slot.n_ctx},
+ {"n_keep", slot.params.n_keep},
+ {"n_left", n_left},
+ {"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);
+
+ // remove the non-common part from the cache
+ slot.cache_tokens.resize(slot.n_past);
+
+ // 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);
}
- slot_npast++;
}
- slot.n_past_se = slot_npast;
- slot.ga_i = ga_i;
}
- LOG_INFO("slot progression", {
- { "slot_id", slot.id },
- { "task_id", slot.task_id },
- { "n_past", slot.n_past },
- { "n_past_se", slot.n_past_se },
- { "ga_i", slot.ga_i },
- { "n_prompt_tokens_processed", slot.n_prompt_tokens_processed }
- });
- }
+ 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.cache_tokens = prompt_tokens;
+ slot.n_prompt_tokens_processed = 0;
+ }
- 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", {
- { "slot_id", slot.id },
- { "task_id", slot.task_id }
- });
- slot.n_past--;
- if (slot.ga_i > 0)
- {
- slot.n_past_se--;
+ 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;
}
}
- int p0 = (int) system_tokens.size() + slot.n_past;
+ const int p0 = (int) system_tokens.size() + slot.n_past;
+ llama_kv_cache_seq_rm(ctx, slot.id, p0, -1);
+
LOG_INFO("kv cache rm [p0, end)", {
- { "slot_id", slot.id },
- { "task_id", slot.task_id },
+ { "id_slot", slot.id },
+ { "id_task", slot.id_task },
{ "p0", p0 }
});
- llama_kv_cache_seq_rm(ctx, slot.id, p0, -1);
-
- LOG_VERBOSE("prompt ingested", {
- {"n_past", slot.n_past},
- {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)},
- {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())},
- });
-
- const bool has_images = process_images(slot);
-
- // process the prefix of first image
- std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
int32_t ga_n = slot.ga_n;
int32_t ga_w = slot.ga_w;
- for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
- {
- if (slot.ga_n != 1)
- {
+ // 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, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false);
+
+ llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false);
+
+ if (slot.params.cache_prompt) {
+ slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
+ }
+
+ slot.n_prompt_tokens_processed++;
slot_npast++;
}
- if (has_images && !ingest_images(slot, n_batch))
- {
- LOG_ERROR("failed processing images", {
- {"slot_id", slot.id},
- {"task_id", slot.task_id},
- });
- // FIXME @phymbert: to be properly tested
- // early returning without changing the slot state will block the slot for ever
- // no one at the moment is checking the return value
- return false;
- }
+ 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;
- // extract the logits only for the last token
- if (batch.n_tokens > 0)
- {
+ 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},
+ });
}
+ }
- slot.n_decoded = 0;
- slot.i_batch = batch.n_tokens - 1;
+ if (batch.n_tokens >= n_batch) {
+ break;
}
}
}
- if (batch.n_tokens == 0)
- {
- all_slots_are_idle = true;
+ if (batch.n_tokens == 0) {
+ LOG_VERBOSE("no tokens to decode", {});
+
return true;
}
- for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
- {
+ 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)
- {
+ for (auto & slot : slots) {
+ if (slot.ga_n != 1) {
// context extension via Self-Extend
- while (slot.n_past_se >= slot.ga_i + slot.ga_w)
- {
+ // 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("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, slot.ga_i, slot.n_past_se, ib * bd);
- llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n);
- llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
+ llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n);
+ llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd);
slot.n_past_se -= bd;
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 =
- {
+ llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
const int ret = llama_decode(ctx, batch_view);
- if (ret != 0)
- {
- if (n_batch == 1 || ret < 0)
- {
+ if (ret != 0) {
+ if (n_batch == 1 || ret < 0) {
// if you get here, it means the KV cache is full - try increasing it via the context size
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
return false;
// retry with half the batch size to try to find a free slot in the KV cache
n_batch /= 2;
i -= n_batch;
+
continue;
}
- for (auto & slot : slots)
- {
- if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens))
- {
+ for (auto & slot : slots) {
+ if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
continue;
}
// prompt evaluated for embedding
- if (slot.embedding)
- {
+ if (slot.embedding) {
send_embedding(slot, batch_view);
slot.release();
slot.i_batch = -1;
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
slot.n_decoded += 1;
- if (slot.n_decoded == 1)
- {
- slot.t_start_genereration = ggml_time_us();
- slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3;
+ 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);
}
result.tok = id;
const int32_t n_probs = slot.sparams.n_probs;
- if (slot.sparams.temp <= 0 && n_probs > 0)
- {
+ 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});
+ 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))
- {
+ if (!process_token(result, slot)) {
slot.release();
slot.print_timings();
send_final_response(slot);
}
LOG_VERBOSE("slots updated", {});
+
return true;
}
- json model_meta() {
- 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)},
+ 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 ¶ms,
- const server_params &sparams)
-{
+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(" --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}\n");
- printf(" pooling type for embeddings, use model default if unspecified\n");
+ printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
- if (llama_supports_mlock())
- {
+ if (llama_supports_mlock()) {
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
}
- if (llama_supports_mmap())
- {
+ 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(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n");
printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
- printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
+ 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(" 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(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\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("\n");
}
-static void server_params_parse(int argc, char **argv, server_params &sparams,
- gpt_params ¶ms, llama_server_context& llama)
-{
- gpt_params default_params;
+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++)
- {
+ for (int i = 1; i < argc; i++) {
arg = argv[i];
- if (arg == "--port")
- {
- if (++i >= argc)
- {
+ if (arg == "--port") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
sparams.port = std::stoi(argv[i]);
- }
- else if (arg == "--host")
- {
- if (++i >= argc)
- {
+ } else if (arg == "--host") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
sparams.hostname = argv[i];
- }
- else if (arg == "--path")
- {
- if (++i >= argc)
- {
+ } else if (arg == "--path") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
sparams.public_path = argv[i];
- }
- else if (arg == "--api-key")
- {
- if (++i >= argc)
- {
+ } else if (arg == "--api-key") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
sparams.api_keys.emplace_back(argv[i]);
- }
- else if (arg == "--api-key-file")
- {
- if (++i >= argc)
- {
+ } else if (arg == "--api-key-file") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
}
}
key_file.close();
- }
- else if (arg == "--timeout" || arg == "-to")
- {
- if (++i >= argc)
- {
+ } 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)
- {
+ } 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)
- {
+ } else if (arg == "-a" || arg == "--alias") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
params.model_alias = argv[i];
- }
- else if (arg == "-h" || arg == "--help")
- {
+ } 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)
- {
+ } 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)
- {
+ } else if (arg == "--rope-scaling") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
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)
- {
+ } 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)
- {
+ } 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")
- {
+ } 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")
- {
+ 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")
- {
+ } 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")
- {
+ } 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")
- {
+ } else if (arg == "--pooling") {
if (++i >= argc) {
invalid_param = true;
break;
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
else { invalid_param = true; break; }
- }
- else if (arg == "--threads" || arg == "-t")
- {
+ } 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")
- {
+ } 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)
- {
+ } 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)
- {
+ } 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)
- {
+ } 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)
- {
+ } else if (arg == "-b" || arg == "--batch-size") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
params.n_batch = std::stoi(argv[i]);
- }
- else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
- {
- if (++i >= argc)
- {
+ } 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}});
+ 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")
- {
+ } else if (arg == "--split-mode" || arg == "-sm") {
if (++i >= argc) {
invalid_param = true;
break;
}
std::string arg_next = argv[i];
- if (arg_next == "none")
- {
+ if (arg_next == "none") {
params.split_mode = LLAMA_SPLIT_MODE_NONE;
- }
- else if (arg_next == "layer")
- {
+ } else if (arg_next == "layer") {
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
- }
- else if (arg_next == "row")
- {
+ } else if (arg_next == "row") {
params.split_mode = LLAMA_SPLIT_MODE_ROW;
- }
- else {
+ } 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)
- {
+ } else if (arg == "--tensor-split" || arg == "-ts") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
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())
- {
+ 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
- {
+ } 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)
- {
+ } else if (arg == "--main-gpu" || arg == "-mg") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
#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)
- {
+ } 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)
- {
+ } else if (arg == "--lora-scaled") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
const char * lora_adapter = argv[i];
- if (++i >= argc)
- {
+ 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)
- {
+ } else if (arg == "--lora-base") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
params.lora_base = argv[i];
- }
- else if (arg == "-v" || arg == "--verbose")
- {
+ } 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")
- {
+ } else if (arg == "--mlock") {
params.use_mlock = true;
- }
- else if (arg == "--no-mmap")
- {
+ } else if (arg == "--no-mmap") {
params.use_mmap = false;
- }
- else if (arg == "--numa") {
+ } 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 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")
- {
+ } else if (arg == "--embedding" || arg == "--embeddings") {
params.embedding = true;
- }
- else if (arg == "-cb" || arg == "--cont-batching")
- {
+ } else if (arg == "-cb" || arg == "--cont-batching") {
params.cont_batching = true;
- }
- else if (arg == "-np" || arg == "--parallel")
- {
- if (++i >= argc)
- {
+ } 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)
- {
+ } 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)
- {
+ } else if (arg == "-spf" || arg == "--system-prompt-file") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
invalid_param = true;
break;
}
- std::string systm_content;
+ std::string system_prompt;
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
- std::back_inserter(systm_content)
+ std::back_inserter(system_prompt)
);
- llama.system_prompt_process(json::parse(systm_content));
- }
- else if (arg == "-ctk" || arg == "--cache-type-k") {
+ 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") {
+ } else if (arg == "-ctv" || arg == "--cache-type-v") {
params.cache_type_v = argv[++i];
- }
- else if(arg == "--mmproj")
- {
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
- params.mmproj = argv[i];
- }
- else if (arg == "--log-format")
- {
- if (++i >= argc)
- {
+ } else if (arg == "--log-format") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
- if (std::strcmp(argv[i], "json") == 0)
- {
+ if (std::strcmp(argv[i], "json") == 0) {
server_log_json = true;
- }
- else if (std::strcmp(argv[i], "text") == 0)
- {
+ } else if (std::strcmp(argv[i], "text") == 0) {
server_log_json = false;
- }
- else
- {
+ } else {
invalid_param = true;
break;
}
- }
- else if (arg == "--log-disable")
- {
+ } else if (arg == "--log-disable") {
log_set_target(stdout);
LOG_INFO("logging to file is disabled.", {});
- }
- else if (arg == "--slots-endpoint-disable")
- {
+ } else if (arg == "--slots-endpoint-disable") {
sparams.slots_endpoint = false;
- }
- else if (arg == "--metrics")
- {
+ } else if (arg == "--metrics") {
sparams.metrics_endpoint = true;
- }
- else if (arg == "--chat-template")
- {
- if (++i >= argc)
- {
+ } else if (arg == "--chat-template") {
+ if (++i >= argc) {
invalid_param = true;
break;
}
break;
}
sparams.chat_template = argv[i];
- }
- else if (arg == "--override-kv")
- {
+ } else if (arg == "--override-kv") {
if (++i >= argc) {
invalid_param = true;
break;
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;
break;
}
params.kv_overrides.push_back(kvo);
- }
- else
- {
+ } 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)
- {
+ 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);
}
}
-/* llama.cpp completion api semantics */
-static json format_partial_response(
- llama_server_context &llama, server_slot *slot, const std::string &content, const std::vector<completion_token_output> &probs
-) {
- json res = json
- {
- {"content", content },
- {"stop", false},
- {"slot_id", slot->id },
- {"multimodal", llama.multimodal }
- };
-
- if (slot->sparams.n_probs > 0)
- {
- res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
- }
-
- return res;
-}
-
-static json format_tokenizer_response(const std::vector<llama_token> &tokens)
-{
- return json {
- {"tokens", tokens}
- };
-}
-
-static json format_detokenized_response(std::string content)
-{
- return json {
- {"content", content}
- };
-}
-
-
-static void log_server_request(const httplib::Request &req, const httplib::Response &res)
-{
+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")
- {
+ if (req.path == "/v1/health" || req.path == "/v1/completions") {
return;
}
});
}
-static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, server_slot *slot)
-{
- auto & gtps = slot->generated_token_probs;
- auto translator = token_translator{llama.ctx};
- auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); };
- const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen);
- if (slot->generated_text.capacity() < slot->generated_text.size() + len)
- {
- slot->generated_text.reserve(slot->generated_text.size() + len);
- }
- for (const completion_token_output & cto : gtps)
- {
- slot->generated_text += translator(cto);
- }
-}
-
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
fprintf(stderr, "Received second interrupt, terminating immediately.\n");
exit(1);
}
+
shutdown_handler(signal);
}
-int main(int argc, char **argv)
-{
+int main(int argc, char ** argv) {
#if SERVER_VERBOSE != 1
log_disable();
#endif
// own arguments required by this example
- gpt_params params;
+ gpt_params params;
server_params sparams;
// struct that contains llama context and inference
- llama_server_context llama;
+ server_context ctx_server;
- server_params_parse(argc, argv, sparams, params, llama);
+ server_params_parse(argc, argv, sparams, params);
- if (params.model_alias == "unknown")
- {
+ 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("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()},
- });
+ {"n_threads", params.n_threads},
+ {"n_threads_batch", params.n_threads_batch},
+ {"total_threads", std::thread::hardware_concurrency()},
+ {"system_info", llama_print_system_info()},
+ });
httplib::Server svr;
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"));
+ 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", "*");
+ res.set_header("Access-Control-Allow-Methods", "POST");
+ res.set_header("Access-Control-Allow-Headers", "*");
});
- svr.Get("/health", [&](const httplib::Request& req, httplib::Response& res) {
+ svr.Get("/health", [&](const httplib::Request & req, httplib::Response & res) {
server_state current_state = state.load();
- switch(current_state) {
- case SERVER_STATE_READY: {
- // request slots data using task queue
- task_server task;
- task.id = llama.queue_tasks.get_new_id();
- task.type = TASK_TYPE_METRICS;
- task.target_id = -1;
+ 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;
- llama.queue_results.add_waiting_task_id(task.id);
- llama.queue_tasks.post(task);
+ ctx_server.queue_results.add_waiting_task_id(task.id);
+ ctx_server.queue_tasks.post(task);
- // get the result
- task_result result = llama.queue_results.recv(task.id);
- llama.queue_results.remove_waiting_task_id(task.id);
+ // get the result
+ server_task_result result = ctx_server.queue_results.recv(task.id);
+ ctx_server.queue_results.remove_waiting_task_id(task.id);
- int n_idle_slots = result.result_json["idle"];
- int n_processing_slots = result.result_json["processing"];
+ const int n_idle_slots = result.data["idle"];
+ const int n_processing_slots = result.data["processing"];
- json health = {
+ 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.result_json["slots"];
- }
+ {"slots_processing", n_processing_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.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;
}
- res.set_content(health.dump(), "application/json");
- break;
- }
case SERVER_STATE_LOADING_MODEL:
- res.set_content(R"({"status": "loading model"})", "application/json");
- res.status = 503; // HTTP Service Unavailable
- break;
+ {
+ res.set_content(R"({"status": "loading model"})", "application/json");
+ res.status = 503; // HTTP Service Unavailable
+ } break;
case SERVER_STATE_ERROR:
- res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json");
- res.status = 500; // HTTP Internal Server Error
- break;
+ {
+ res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json");
+ res.status = 500; // HTTP Internal Server Error
+ } break;
}
});
if (sparams.slots_endpoint) {
- svr.Get("/slots", [&](const httplib::Request&, httplib::Response& res) {
+ svr.Get("/slots", [&](const httplib::Request &, httplib::Response & res) {
// request slots data using task queue
- task_server task;
- task.id = llama.queue_tasks.get_new_id();
- task.type = TASK_TYPE_METRICS;
- task.target_id = -1;
+ 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;
- llama.queue_results.add_waiting_task_id(task.id);
- llama.queue_tasks.post(task);
+ ctx_server.queue_results.add_waiting_task_id(task.id);
+ ctx_server.queue_tasks.post(task);
// get the result
- task_result result = llama.queue_results.recv(task.id);
- llama.queue_results.remove_waiting_task_id(task.id);
+ 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.result_json["slots"].dump(), "application/json");
+ res.set_content(result.data["slots"].dump(), "application/json");
res.status = 200; // HTTP OK
});
}
if (sparams.metrics_endpoint) {
- svr.Get("/metrics", [&](const httplib::Request&, httplib::Response& res) {
+ svr.Get("/metrics", [&](const httplib::Request &, httplib::Response & res) {
// request slots data using task queue
- task_server task;
- task.id = llama.queue_tasks.get_new_id();
- task.type = TASK_TYPE_METRICS;
- task.target_id = -1;
+ 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;
- llama.queue_results.add_waiting_task_id(task.id);
- llama.queue_tasks.post(task);
+ ctx_server.queue_results.add_waiting_task_id(task.id);
+ ctx_server.queue_tasks.post(task);
// get the result
- task_result result = llama.queue_results.recv(task.id);
- llama.queue_results.remove_waiting_task_id(task.id);
+ server_task_result result = ctx_server.queue_results.recv(task.id);
+ ctx_server.queue_results.remove_waiting_task_id(task.id);
- json data = result.result_json;
+ json data = result.data;
- uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"];
- uint64_t t_prompt_processing = data["t_prompt_processing"];
+ const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"];
+ const uint64_t t_prompt_processing = data["t_prompt_processing"];
- uint64_t n_tokens_predicted = data["n_tokens_predicted"];
- uint64_t t_tokens_generation = data["t_tokens_generation"];
+ const uint64_t n_tokens_predicted = data["n_tokens_predicted"];
+ const uint64_t t_tokens_generation = data["t_tokens_generation"];
- int32_t kv_cache_used_cells = data["kv_cache_used_cells"];
+ 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", data["n_prompt_tokens_processed_total"]}
- }, {
- {"name", "tokens_predicted_total"},
- {"help", "Number of generation tokens processed."},
- {"value", data["n_tokens_predicted_total"]}
- }}},
- {"gauge", {{
- {"name", "prompt_tokens_seconds"},
- {"help", "Average prompt throughput in tokens/s."},
- {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0}
- },{
- {"name", "predicted_tokens_seconds"},
- {"help", "Average generation throughput in tokens/s."},
- {"value", n_tokens_predicted ? 1e3 / 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", data["kv_cache_tokens_count"]}
- },{
- {"name", "requests_processing"},
- {"help", "Number of request processing."},
- {"value", data["processing"]}
- },{
- {"name", "requests_deferred"},
- {"help", "Number of request deferred."},
- {"value", data["deferred"]}
- }}}
+ {"counter", {{
+ {"name", "prompt_tokens_total"},
+ {"help", "Number of prompt tokens processed."},
+ {"value", data["n_prompt_tokens_processed_total"]}
+ }, {
+ {"name", "tokens_predicted_total"},
+ {"help", "Number of generation tokens processed."},
+ {"value", data["n_tokens_predicted_total"]}
+ }}},
+ {"gauge", {{
+ {"name", "prompt_tokens_seconds"},
+ {"help", "Average prompt throughput in tokens/s."},
+ {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0}
+ },{
+ {"name", "predicted_tokens_seconds"},
+ {"help", "Average generation throughput in tokens/s."},
+ {"value", n_tokens_predicted ? 1e3 / 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", data["kv_cache_tokens_count"]}
+ },{
+ {"name", "requests_processing"},
+ {"help", "Number of request processing."},
+ {"value", data["processing"]}
+ },{
+ {"name", "requests_deferred"},
+ {"help", "Number of request deferred."},
+ {"value", 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) {
- std::string name = metric_def["name"];
- std::string help = metric_def["help"];
+
+ 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"
svr.set_logger(log_server_request);
- svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep)
- {
- const char fmt[] = "500 Internal Server Error\n%s";
- char buf[BUFSIZ];
- try
- {
- std::rethrow_exception(std::move(ep));
- }
- catch (std::exception &e)
- {
- snprintf(buf, sizeof(buf), fmt, e.what());
- }
- catch (...)
- {
- snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
- }
- res.set_content(buf, "text/plain; charset=utf-8");
- res.status = 500;
- });
+ svr.set_exception_handler([](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) {
+ const char fmt[] = "500 Internal Server Error\n%s";
- svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
- {
- if (res.status == 401)
- {
- res.set_content("Unauthorized", "text/plain; charset=utf-8");
- }
- if (res.status == 400)
- {
- res.set_content("Invalid request", "text/plain; charset=utf-8");
- }
- else if (res.status == 404)
- {
- res.set_content("File Not Found", "text/plain; charset=utf-8");
- res.status = 404;
- }
- });
+ char buf[BUFSIZ];
+ try {
+ std::rethrow_exception(std::move(ep));
+ } catch (std::exception &e) {
+ snprintf(buf, sizeof(buf), fmt, e.what());
+ } catch (...) {
+ snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
+ }
+
+ res.set_content(buf, "text/plain; charset=utf-8");
+ res.status = 500;
+ });
+
+ svr.set_error_handler([](const httplib::Request &, httplib::Response & res) {
+ if (res.status == 401) {
+ res.set_content("Unauthorized", "text/plain; charset=utf-8");
+ }
+ if (res.status == 400) {
+ res.set_content("Invalid request", "text/plain; charset=utf-8");
+ }
+ if (res.status == 404) {
+ res.set_content("File Not Found", "text/plain; charset=utf-8");
+ }
+ });
// set timeouts and change hostname and port
svr.set_read_timeout (sparams.read_timeout);
svr.set_write_timeout(sparams.write_timeout);
- if (!svr.bind_to_port(sparams.hostname, sparams.port))
- {
+ 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;
}
svr.set_base_dir(sparams.public_path);
std::unordered_map<std::string, std::string> log_data;
+
log_data["hostname"] = sparams.hostname;
- log_data["port"] = std::to_string(sparams.port);
+ log_data["port"] = std::to_string(sparams.port);
if (sparams.api_keys.size() == 1) {
log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4);
}
// load the model
- if (!llama.load_model(params))
- {
+ if (!ctx_server.load_model(params)) {
state.store(SERVER_STATE_ERROR);
return 1;
} else {
- llama.initialize();
+ ctx_server.initialize();
state.store(SERVER_STATE_READY);
- LOG_INFO("model loaded", {});
}
- const auto model_meta = llama.model_meta();
+
+ LOG_INFO("model loaded", {});
+
+ const auto model_meta = ctx_server.model_meta();
if (sparams.chat_template.empty()) { // custom chat template is not supplied
- // check if the template comes with the model is supported by us
- llama.validate_model_chat_template(sparams);
+ if (!ctx_server.validate_model_chat_template()) {
+ LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
+ sparams.chat_template = "chatml";
+ }
}
// Middleware for API key validation
// 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());
};
// this is only called if no index.html is found in the public --path
- svr.Get("/", [](const httplib::Request &, httplib::Response &res)
- {
- res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html; charset=utf-8");
- return false;
- });
+ svr.Get("/", [](const httplib::Request &, httplib::Response & res) {
+ res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html; charset=utf-8");
+ return false;
+ });
// this is only called if no index.js is found in the public --path
- svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res)
- {
- res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript; charset=utf-8");
- return false;
- });
+ svr.Get("/index.js", [](const httplib::Request &, httplib::Response & res) {
+ res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript; charset=utf-8");
+ return false;
+ });
// this is only called if no index.html is found in the public --path
- svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res)
- {
- res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript; charset=utf-8");
- return false;
- });
+ svr.Get("/completion.js", [](const httplib::Request &, httplib::Response & res) {
+ res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript; charset=utf-8");
+ return false;
+ });
// this is only called if no index.html is found in the public --path
- svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res)
- {
- res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8");
- return false;
- });
+ svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response & res) {
+ res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8");
+ return false;
+ });
- svr.Get("/props", [&llama](const httplib::Request & req, httplib::Response &res)
- {
- res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
- json data = {
- { "user_name", llama.name_user.c_str() },
- { "assistant_name", llama.name_assistant.c_str() },
- { "default_generation_settings", llama.default_generation_settings_for_props },
- { "total_slots", llama.params.n_parallel }
- };
- res.set_content(data.dump(), "application/json; charset=utf-8");
- });
+ svr.Get("/props", [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+ res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+ json data = {
+ { "user_name", ctx_server.name_user.c_str() },
+ { "assistant_name", ctx_server.name_assistant.c_str() },
+ { "default_generation_settings", ctx_server.default_generation_settings_for_props },
+ { "total_slots", ctx_server.params.n_parallel }
+ };
- svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
- {
- res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
- if (!validate_api_key(req, res)) {
- return;
- }
- json data = json::parse(req.body);
- const int task_id = llama.queue_tasks.get_new_id();
- llama.queue_results.add_waiting_task_id(task_id);
- llama.request_completion(task_id, data, false, false, -1);
- if (!json_value(data, "stream", false)) {
- std::string completion_text;
- task_result result = llama.queue_results.recv(task_id);
- if (!result.error && result.stop) {
- res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
- }
- else
- {
- res.status = 404;
- res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
- }
- llama.queue_results.remove_waiting_task_id(task_id);
- } else {
- const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink)
- {
- while (true)
- {
- task_result result = llama.queue_results.recv(task_id);
- if (!result.error) {
- const std::string str =
- "data: " +
- result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
- "\n\n";
- LOG_VERBOSE("data stream", {
- { "to_send", str }
- });
- if (!sink.write(str.c_str(), str.size()))
- {
- llama.queue_results.remove_waiting_task_id(task_id);
- return false;
- }
- if (result.stop) {
- break;
- }
- } else {
- const std::string str =
- "error: " +
- result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
- "\n\n";
- LOG_VERBOSE("data stream", {
- { "to_send", str }
- });
- if (!sink.write(str.c_str(), str.size()))
- {
- llama.queue_results.remove_waiting_task_id(task_id);
- return false;
- }
- break;
- }
+ res.set_content(data.dump(), "application/json; charset=utf-8");
+ });
+
+ svr.Post("/completion", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) {
+ res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+ if (!validate_api_key(req, res)) {
+ return;
+ }
+
+ json data = json::parse(req.body);
+
+ const int id_task = ctx_server.queue_tasks.get_new_id();
+
+ ctx_server.queue_results.add_waiting_task_id(id_task);
+ ctx_server.request_completion(id_task, -1, data, false, false);
+
+ if (!json_value(data, "stream", false)) {
+ server_task_result result = ctx_server.queue_results.recv(id_task);
+ if (!result.error && result.stop) {
+ res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+ } else {
+ res.status = 500;
+ res.set_content(result.data["content"], "text/plain; charset=utf-8");
+ }
+
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+ } else {
+ const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
+ while (true) {
+ server_task_result result = ctx_server.queue_results.recv(id_task);
+ if (!result.error) {
+ const std::string str =
+ "data: " +
+ result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+ "\n\n";
+
+ LOG_VERBOSE("data stream", {
+ { "to_send", str }
+ });
+
+ if (!sink.write(str.c_str(), str.size())) {
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+ return false;
}
- llama.queue_results.remove_waiting_task_id(task_id);
- sink.done();
- return true;
- };
+ if (result.stop) {
+ break;
+ }
+ } else {
+ const std::string str =
+ "error: " +
+ result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+ "\n\n";
- auto on_complete = [task_id, &llama] (bool)
- {
- // cancel
- llama.request_cancel(task_id);
- llama.queue_results.remove_waiting_task_id(task_id);
- };
+ LOG_VERBOSE("data stream", {
+ { "to_send", str }
+ });
- res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+ if (!sink.write(str.c_str(), str.size())) {
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+ return false;
+ }
+
+ break;
+ }
}
- });
- svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request& req, httplib::Response& res)
- {
- res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
- std::time_t t = std::time(0);
-
- json models = {
- {"object", "list"},
- {"data", {
- {
- {"id", params.model_alias},
- {"object", "model"},
- {"created", t},
- {"owned_by", "llamacpp"},
- {"meta", model_meta}
- },
- }}
- };
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+ sink.done();
- res.set_content(models.dump(), "application/json; charset=utf-8");
- });
+ return true;
+ };
- const auto chat_completions = [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res)
- {
+ auto on_complete = [id_task, &ctx_server] (bool) {
+ // cancel
+ ctx_server.request_cancel(id_task);
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+ };
+
+ res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+ }
+ });
+
+ svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) {
+ res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+ json models = {
+ {"object", "list"},
+ {"data", {
+ {
+ {"id", params.model_alias},
+ {"object", "model"},
+ {"created", std::time(0)},
+ {"owned_by", "llamacpp"},
+ {"meta", model_meta}
+ },
+ }}
+ };
+
+ res.set_content(models.dump(), "application/json; charset=utf-8");
+ });
+
+ const auto chat_completions = [&ctx_server, &validate_api_key, &sparams](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
if (!validate_api_key(req, res)) {
return;
}
- json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template);
- const int task_id = llama.queue_tasks.get_new_id();
- llama.queue_results.add_waiting_task_id(task_id);
- llama.request_completion(task_id, data, false, false, -1);
+ json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template);
+
+ const int id_task = ctx_server.queue_tasks.get_new_id();
+
+ ctx_server.queue_results.add_waiting_task_id(id_task);
+ ctx_server.request_completion(id_task, -1, data, false, false);
if (!json_value(data, "stream", false)) {
- std::string completion_text;
- task_result result = llama.queue_results.recv(task_id);
+ server_task_result result = ctx_server.queue_results.recv(id_task);
if (!result.error && result.stop) {
- json oaicompat_result = format_final_response_oaicompat(data, result);
+ json result_oai = format_final_response_oaicompat(data, result.data);
- res.set_content(oaicompat_result.dump(-1, ' ', false,
- json::error_handler_t::replace),
- "application/json; charset=utf-8");
+ res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
} else {
res.status = 500;
- res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
+ res.set_content(result.data["content"], "text/plain; charset=utf-8");
}
- llama.queue_results.remove_waiting_task_id(task_id);
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
} else {
- const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
+ const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
while (true) {
- task_result llama_result = llama.queue_results.recv(task_id);
- if (!llama_result.error) {
- std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
+ server_task_result result = ctx_server.queue_results.recv(id_task);
+ if (!result.error) {
+ std::vector<json> result_array = format_partial_response_oaicompat(result.data);
- for (auto it = result_array.begin(); it != result_array.end(); ++it)
- {
+ for (auto it = result_array.begin(); it != result_array.end(); ++it) {
if (!it->empty()) {
const std::string str =
"data: " +
"\n\n";
LOG_VERBOSE("data stream", {{"to_send", str}});
if (!sink.write(str.c_str(), str.size())) {
- llama.queue_results.remove_waiting_task_id(task_id);
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
return false;
}
}
}
- if (llama_result.stop) {
+ if (result.stop) {
break;
}
} else {
const std::string str =
"error: " +
- llama_result.result_json.dump(-1, ' ', false,
- json::error_handler_t::replace) +
+ 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())) {
- llama.queue_results.remove_waiting_task_id(task_id);
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
return false;
}
break;
}
}
sink.done();
- llama.queue_results.remove_waiting_task_id(task_id);
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
return true;
};
- auto on_complete = [task_id, &llama](bool) {
+ auto on_complete = [id_task, &ctx_server](bool) {
// cancel request
- llama.request_cancel(task_id);
- llama.queue_results.remove_waiting_task_id(task_id);
+ ctx_server.request_cancel(id_task);
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
}
};
- svr.Post("/chat/completions", chat_completions);
+ svr.Post("/chat/completions", chat_completions);
svr.Post("/v1/chat/completions", chat_completions);
- svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
- {
- res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
- if (!validate_api_key(req, res)) {
- return;
- }
- json data = json::parse(req.body);
- const int task_id = llama.queue_tasks.get_new_id();
- llama.queue_results.add_waiting_task_id(task_id);
- llama.request_completion(task_id, data, true, false, -1);
- if (!json_value(data, "stream", false)) {
- std::string completion_text;
- task_result result = llama.queue_results.recv(task_id);
- if (!result.error && result.stop)
- {
- res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
- }
- else
- {
- res.status = 404;
- res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
- }
- llama.queue_results.remove_waiting_task_id(task_id);
- } else {
- const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) {
- while (true)
- {
- task_result result = llama.queue_results.recv(task_id);
- if (!result.error) {
- const std::string str =
- "data: " +
- result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
- "\n\n";
- LOG_VERBOSE("data stream", {
- { "to_send", str }
- });
- if (!sink.write(str.c_str(), str.size()))
- {
- llama.queue_results.remove_waiting_task_id(task_id);
- return false;
- }
- if (result.stop)
- {
- break;
- }
- }
- else
- {
- break;
- }
- }
+ svr.Post("/infill", [&ctx_server, &validate_api_key](const httplib::Request & req, httplib::Response & res) {
+ res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+ if (!validate_api_key(req, res)) {
+ return;
+ }
- llama.queue_results.remove_waiting_task_id(task_id);
- sink.done();
- return true;
- };
+ json data = json::parse(req.body);
- auto on_complete = [task_id, &llama] (bool)
- {
- // cancel
- llama.request_cancel(task_id);
- };
+ const int id_task = ctx_server.queue_tasks.get_new_id();
- res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
- }
- });
+ ctx_server.queue_results.add_waiting_task_id(id_task);
+ ctx_server.request_completion(id_task, -1, data, true, false);
- svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
- { return res.set_content("", "application/json; charset=utf-8"); });
+ if (!json_value(data, "stream", false)) {
+ server_task_result result = ctx_server.queue_results.recv(id_task);
+ if (!result.error && result.stop) {
+ res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+ } else {
+ res.status = 404;
+ res.set_content(result.data["content"], "text/plain; charset=utf-8");
+ }
- svr.Post("/tokenize", [&llama](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 = llama.tokenize(body["content"], false);
- }
- const json data = format_tokenizer_response(tokens);
- return res.set_content(data.dump(), "application/json; charset=utf-8");
- });
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+ } else {
+ const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
+ while (true) {
+ server_task_result result = ctx_server.queue_results.recv(id_task);
+ if (!result.error) {
+ const std::string str =
+ "data: " +
+ result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+ "\n\n";
- svr.Post("/detokenize", [&llama](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(llama.ctx, tokens.cbegin(), tokens.cend());
- }
+ LOG_VERBOSE("data stream", {
+ { "to_send", str }
+ });
- const json data = format_detokenized_response(content);
- return res.set_content(data.dump(), "application/json; charset=utf-8");
- });
+ if (!sink.write(str.c_str(), str.size())) {
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+ return false;
+ }
- svr.Post("/embedding", [&llama](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);
- json prompt;
- if (body.count("content") != 0)
- {
- prompt = body["content"];
- }
- else
- {
- prompt = "";
+ if (result.stop) {
+ break;
+ }
+ } else {
+ break;
+ }
}
- json image_data;
- if (body.count("image_data") != 0) {
- image_data = body["image_data"];
- }
- else
- {
- image_data = "";
- }
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+ sink.done();
- // create and queue the task
- const int task_id = llama.queue_tasks.get_new_id();
- llama.queue_results.add_waiting_task_id(task_id);
- llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1);
+ return true;
+ };
- // get the result
- task_result result = llama.queue_results.recv(task_id);
- llama.queue_results.remove_waiting_task_id(task_id);
+ auto on_complete = [id_task, &ctx_server] (bool) {
+ ctx_server.request_cancel(id_task);
+ };
- // send the result
- return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
- });
+ res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+ }
+ });
- svr.Post("/v1/embeddings", [&llama](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);
+ svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) {
+ return res.set_content("", "application/json; charset=utf-8");
+ });
- json prompt;
- if (body.count("input") != 0)
- {
- prompt = body["input"];
- // batch
- if(prompt.is_array()) {
- json data = json::array();
- int i = 0;
- for (const json &elem : prompt) {
- const int task_id = llama.queue_tasks.get_new_id();
- llama.queue_results.add_waiting_task_id(task_id);
- llama.request_completion(task_id, { {"prompt", elem}, { "n_predict", 0} }, false, true, -1);
-
- // get the result
- task_result result = llama.queue_results.recv(task_id);
- llama.queue_results.remove_waiting_task_id(task_id);
-
- json embedding = json{
- {"embedding", json_value(result.result_json, "embedding", json::array())},
- {"index", i++},
- {"object", "embedding"}
- };
- data.push_back(embedding);
- }
- json result = format_embeddings_response_oaicompat(body, data);
- return res.set_content(result.dump(), "application/json; charset=utf-8");
- }
- }
- else
- {
- prompt = "";
- }
+ svr.Post("/tokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+ res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+ const json body = json::parse(req.body);
- // create and queue the task
- const int task_id = llama.queue_tasks.get_new_id();
- llama.queue_results.add_waiting_task_id(task_id);
- llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}}, false, true, -1);
+ 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");
+ });
- // get the result
- task_result result = llama.queue_results.recv(task_id);
- llama.queue_results.remove_waiting_task_id(task_id);
+ svr.Post("/detokenize", [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+ res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+ const json body = json::parse(req.body);
- json data = json::array({json{
- {"embedding", json_value(result.result_json, "embedding", json::array())},
- {"index", 0},
- {"object", "embedding"}
- }}
- );
+ 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());
+ }
- json root = format_embeddings_response_oaicompat(body, data);
+ const json data = format_detokenized_response(content);
+ return res.set_content(data.dump(), "application/json; charset=utf-8");
+ });
- // send the result
- return res.set_content(root.dump(), "application/json; charset=utf-8");
- });
+ svr.Post("/embedding", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) {
+ res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+ if (!params.embedding) {
+ res.status = 501;
+ res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
+ return;
+ }
- // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!?
- // "Bus error: 10" - this is on macOS, it does not crash on Linux
- //std::thread t2([&]()
- /*{
- bool running = true;
- while (running)
- {
- running = llama.update_slots();
+ const json body = json::parse(req.body);
+
+ json prompt;
+ if (body.count("content") != 0) {
+ prompt = body["content"];
+ } else {
+ prompt = "";
+ }
+
+ // create and queue the task
+ const int id_task = ctx_server.queue_tasks.get_new_id();
+
+ ctx_server.queue_results.add_waiting_task_id(id_task);
+ ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0} }, false, true);
+
+ // get the result
+ server_task_result result = ctx_server.queue_results.recv(id_task);
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+ // send the result
+ return res.set_content(result.data.dump(), "application/json; charset=utf-8");
+ });
+
+ svr.Post("/v1/embeddings", [¶ms, &ctx_server](const httplib::Request & req, httplib::Response & res) {
+ res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+ if (!params.embedding) {
+ res.status = 501;
+ res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
+ return;
+ }
+
+ const json body = json::parse(req.body);
+
+ json prompt;
+ if (body.count("input") != 0) {
+ prompt = body["input"];
+ if (prompt.is_array()) {
+ json data = json::array();
+
+ int i = 0;
+ for (const json & elem : prompt) {
+ const int id_task = ctx_server.queue_tasks.get_new_id();
+
+ ctx_server.queue_results.add_waiting_task_id(id_task);
+ ctx_server.request_completion(id_task, -1, { {"prompt", elem}, { "n_predict", 0} }, false, true);
+
+ // get the result
+ server_task_result result = ctx_server.queue_results.recv(id_task);
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+ json embedding = json{
+ {"embedding", json_value(result.data, "embedding", json::array())},
+ {"index", i++},
+ {"object", "embedding"}
+ };
+
+ data.push_back(embedding);
+ }
+
+ json result = format_embeddings_response_oaicompat(body, data);
+
+ return res.set_content(result.dump(), "application/json; charset=utf-8");
+ }
+ } else {
+ prompt = "";
}
- }*/
- //);
+
+ // create and queue the task
+ const int id_task = ctx_server.queue_tasks.get_new_id();
+
+ ctx_server.queue_results.add_waiting_task_id(id_task);
+ ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true);
+
+ // get the result
+ server_task_result result = ctx_server.queue_results.recv(id_task);
+ ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+ json data = json::array({json{
+ {"embedding", json_value(result.data, "embedding", json::array())},
+ {"index", 0},
+ {"object", "embedding"}
+ }}
+ );
+
+ json root = format_embeddings_response_oaicompat(body, data);
+
+ return res.set_content(root.dump(), "application/json; charset=utf-8");
+ });
if (sparams.n_threads_http < 1) {
// +2 threads for monitoring endpoints
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;
- }
+ std::thread t([&]() {
+ if (!svr.listen_after_bind()) {
+ state.store(SERVER_STATE_ERROR);
+ return 1;
+ }
- return 0;
- });
+ return 0;
+ });
- llama.queue_tasks.on_new_task(std::bind(
- &llama_server_context::process_single_task, &llama, std::placeholders::_1));
- llama.queue_tasks.on_finish_multitask(std::bind(
- &llama_server_context::on_finish_multitask, &llama, std::placeholders::_1));
- llama.queue_tasks.on_run_slots(std::bind(
- &llama_server_context::update_slots, &llama));
- llama.queue_results.on_multitask_update(std::bind(
- &llama_server_queue::update_multitask,
- &llama.queue_tasks,
+ ctx_server.queue_tasks.on_new_task(std::bind(
+ &server_context::process_single_task, &ctx_server, std::placeholders::_1));
+ ctx_server.queue_tasks.on_finish_multitask(std::bind(
+ &server_context::on_finish_multitask, &ctx_server, std::placeholders::_1));
+ ctx_server.queue_tasks.on_run_slots(std::bind(
+ &server_context::update_slots, &ctx_server));
+ ctx_server.queue_results.on_multitask_update(std::bind(
+ &server_queue::update_multitask,
+ &ctx_server.queue_tasks,
std::placeholders::_1,
std::placeholders::_2,
std::placeholders::_3
));
shutdown_handler = [&](int) {
- llama.queue_tasks.terminate();
+ ctx_server.queue_tasks.terminate();
};
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
};
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
- llama.queue_tasks.start_loop();
+
+ ctx_server.queue_tasks.start_loop();
+
svr.stop();
t.join();
llama_backend_free();
+
return 0;
}
overview / pkg / ggml / sources / llama.cpp / commitdiff