3272 lines
123 KiB
C++
Vendored
3272 lines
123 KiB
C++
Vendored
// MIT License
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// Copyright (c) 2023 Georgi Gerganov
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// Permission is hereby granted, free of charge, to any person obtaining a copy
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// of this software and associated documentation files (the "Software"), to deal
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// in the Software without restriction, including without limitation the rights
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// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the Software is
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// furnished to do so, subject to the following conditions:
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// The above copyright notice and this permission notice shall be included in all
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// copies or substantial portions of the Software.
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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// SOFTWARE.
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#include "common.h"
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#include "llama.h"
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#include "grammar-parser.h"
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#include "utils.hpp"
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#include "../llava/clip.h"
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#include "../llava/llava.h"
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#include "stb_image.h"
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#ifndef NDEBUG
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// crash the server in debug mode, otherwise send an http 500 error
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#define CPPHTTPLIB_NO_EXCEPTIONS 1
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#endif
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// increase max payload length to allow use of larger context size
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#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
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#include "httplib.h"
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#include "json.hpp"
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#if defined(_WIN32)
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#include <windows.h>
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#include <errhandlingapi.h>
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#endif
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#include <algorithm>
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#include <cstddef>
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#include <thread>
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#include <chrono>
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#include <condition_variable>
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#include <atomic>
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#include <signal.h>
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using json = nlohmann::json;
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struct server_params {
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std::string hostname = "127.0.0.1";
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std::vector<std::string> api_keys;
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std::string public_path = "examples/server/public";
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int32_t port = 8080;
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int32_t read_timeout = 600;
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int32_t write_timeout = 600;
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bool slots_endpoint = true;
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bool metrics_endpoint = false;
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int n_threads_http = -1;
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};
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bool server_verbose = false;
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bool server_log_json = false;
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enum stop_type {
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STOP_FULL,
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STOP_PARTIAL,
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};
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// TODO: can become bool if we can't find use of more states
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enum slot_state {
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IDLE,
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PROCESSING,
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};
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enum slot_command {
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NONE,
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LOAD_PROMPT,
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RELEASE,
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};
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struct slot_params {
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bool stream = true;
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bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
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uint32_t seed = -1; // RNG seed
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_predict = -1; // new tokens to predict
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std::vector<std::string> antiprompt;
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json input_prefix;
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json input_suffix;
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};
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struct slot_image {
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int32_t id;
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bool request_encode_image = false;
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float * image_embedding = nullptr;
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int32_t image_tokens = 0;
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clip_image_u8 * img_data;
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std::string prefix_prompt; // before of this image
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};
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struct server_slot {
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int id;
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int task_id = -1;
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struct slot_params params;
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slot_state state = IDLE;
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slot_command command = NONE;
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// used to determine the slot that has been used the longest
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int64_t t_last_used = -1;
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// generation props
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int32_t n_ctx = 0; // context size per slot
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int32_t n_past = 0;
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int32_t n_decoded = 0;
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int32_t n_remaining = -1;
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int32_t i_batch = -1;
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int32_t n_predict = -1;
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int32_t n_prompt_tokens = 0;
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int32_t n_prompt_tokens_processed = 0;
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json prompt;
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std::string generated_text;
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llama_token sampled;
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std::vector<llama_token> cache_tokens;
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std::vector<completion_token_output> generated_token_probs;
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bool embedding = false;
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bool has_next_token = true;
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bool truncated = false;
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bool stopped_eos = false;
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bool stopped_word = false;
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bool stopped_limit = false;
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std::string stopping_word;
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// sampling
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struct llama_sampling_params sparams;
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llama_sampling_context *ctx_sampling = nullptr;
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int32_t ga_i = 0; // group-attention state
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int32_t ga_n = 1; // group-attention factor
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int32_t ga_w = 512; // group-attention width
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int32_t n_past_se = 0; // self-extend
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// multimodal
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std::vector<slot_image> images;
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// stats
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size_t n_sent_text = 0; // number of sent text character
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size_t n_sent_token_probs = 0;
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int64_t t_start_process_prompt;
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int64_t t_start_genereration;
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double t_prompt_processing; // ms
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double t_token_generation; // ms
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// multitasks
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int multitask_id = -1;
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void reset() {
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n_prompt_tokens = 0;
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generated_text = "";
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truncated = false;
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stopped_eos = false;
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stopped_word = false;
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stopped_limit = false;
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stopping_word = "";
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n_past = 0;
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n_sent_text = 0;
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n_sent_token_probs = 0;
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ga_i = 0;
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n_past_se = 0;
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generated_token_probs.clear();
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for (slot_image & img : images) {
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free(img.image_embedding);
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if (img.img_data) {
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clip_image_u8_free(img.img_data);
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}
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img.prefix_prompt = "";
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}
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images.clear();
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}
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bool has_budget(gpt_params &global_params) {
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if (params.n_predict == -1 && global_params.n_predict == -1) {
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return true; // limitless
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}
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n_remaining = -1;
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if (params.n_predict != -1) {
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n_remaining = params.n_predict - n_decoded;
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} else if (global_params.n_predict != -1) {
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n_remaining = global_params.n_predict - n_decoded;
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}
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return n_remaining > 0; // no budget
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}
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bool available() const {
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return state == IDLE && command == NONE;
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}
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bool is_processing() const {
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return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
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}
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void add_token_string(const completion_token_output &token) {
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if (command == RELEASE) {
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return;
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}
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cache_tokens.push_back(token.tok);
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generated_token_probs.push_back(token);
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}
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void release() {
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if (state == PROCESSING)
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{
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t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
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command = RELEASE;
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}
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}
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json get_formated_timings() {
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return json
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{
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{"prompt_n", n_prompt_tokens_processed},
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{"prompt_ms", t_prompt_processing},
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{"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed},
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{"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed},
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{"predicted_n", n_decoded},
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{"predicted_ms", t_token_generation},
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{"predicted_per_token_ms", t_token_generation / n_decoded},
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{"predicted_per_second", 1e3 / t_token_generation * n_decoded},
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};
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}
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void print_timings() const {
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char buffer[512];
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double t_token = t_prompt_processing / n_prompt_tokens_processed;
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double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
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snprintf(buffer, sizeof(buffer), "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
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t_prompt_processing, n_prompt_tokens_processed,
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t_token, n_tokens_second);
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LOG_DEBUG(buffer, {
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{"slot_id", id},
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{"task_id", task_id},
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{"t_prompt_processing", t_prompt_processing},
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{"n_prompt_tokens_processed", n_prompt_tokens_processed},
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{"t_token", t_token},
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{"n_tokens_second", n_tokens_second},
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});
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t_token = t_token_generation / n_decoded;
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n_tokens_second = 1e3 / t_token_generation * n_decoded;
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snprintf(buffer, sizeof(buffer), "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
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t_token_generation, n_decoded,
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t_token, n_tokens_second);
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LOG_DEBUG(buffer, {
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{"slot_id", id},
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{"task_id", task_id},
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{"t_token_generation", t_token_generation},
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{"n_decoded", n_decoded},
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{"t_token", t_token},
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{"n_tokens_second", n_tokens_second},
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});
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snprintf(buffer, sizeof(buffer), " total time = %10.2f ms", t_prompt_processing + t_token_generation);
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LOG_DEBUG(buffer, {
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{"slot_id", id},
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{"task_id", task_id},
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{"t_prompt_processing", t_prompt_processing},
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{"t_token_generation", t_token_generation},
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{"t_total", t_prompt_processing + t_token_generation},
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});
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}
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};
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struct server_metrics {
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uint64_t n_prompt_tokens_processed_total = 0;
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uint64_t n_tokens_predicted_total = 0;
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uint64_t n_prompt_tokens_processed = 0;
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uint64_t t_prompt_processing = 0;
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uint64_t n_tokens_predicted = 0;
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uint64_t t_tokens_generation = 0;
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void on_prompt_eval(const server_slot &slot) {
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n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
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n_prompt_tokens_processed += slot.n_prompt_tokens_processed;
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t_prompt_processing += slot.t_prompt_processing;
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}
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void on_prediction(const server_slot &slot) {
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n_tokens_predicted_total += slot.n_decoded;
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n_tokens_predicted += slot.n_decoded;
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t_tokens_generation += slot.t_token_generation;
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}
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void reset_bucket() {
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n_prompt_tokens_processed = 0;
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t_prompt_processing = 0;
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n_tokens_predicted = 0;
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t_tokens_generation = 0;
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}
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};
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struct llama_server_context
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{
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llama_model *model = nullptr;
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float modelProgress = 0.0;
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llama_context *ctx = nullptr;
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clip_ctx *clp_ctx = nullptr;
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gpt_params params;
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llama_batch batch;
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bool multimodal = false;
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bool clean_kv_cache = true;
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bool all_slots_are_idle = false;
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bool add_bos_token = true;
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int32_t n_ctx; // total context for all clients / slots
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// system prompt
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bool system_need_update = false;
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std::string system_prompt;
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std::vector<llama_token> system_tokens;
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std::string name_user; // this should be the antiprompt
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std::string name_assistant;
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// slots / clients
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std::vector<server_slot> slots;
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llama_server_queue queue_tasks;
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llama_server_response queue_results;
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server_metrics metrics;
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~llama_server_context()
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{
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if (clp_ctx)
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{
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LOG_DEBUG("freeing clip model", {});
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clip_free(clp_ctx);
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clp_ctx = nullptr;
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}
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if (ctx)
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{
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llama_free(ctx);
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ctx = nullptr;
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}
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if (model)
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{
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llama_free_model(model);
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model = nullptr;
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}
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}
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bool load_model(const gpt_params ¶ms_)
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{
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params = params_;
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if (!params.mmproj.empty()) {
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multimodal = true;
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LOG_DEBUG("Multi Modal Mode Enabled", {});
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clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
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if(clp_ctx == nullptr) {
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LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
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return false;
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}
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if (params.n_ctx < 2048) { // request larger context for the image embedding
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params.n_ctx = 2048;
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}
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}
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auto init_result = llama_init_from_gpt_params(params);
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model = init_result.model;
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ctx = init_result.context;
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if (model == nullptr)
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{
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LOG_ERROR("unable to load model", {{"model", params.model}});
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return false;
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}
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if (multimodal) {
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const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
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const int n_embd_llm = llama_n_embd(model);
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if (n_embd_clip != n_embd_llm) {
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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);
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llama_free(ctx);
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llama_free_model(model);
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return false;
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}
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}
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n_ctx = llama_n_ctx(ctx);
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add_bos_token = llama_add_bos_token(model);
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return true;
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}
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void initialize() {
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// create slots
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all_slots_are_idle = true;
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const int32_t n_ctx_slot = n_ctx / params.n_parallel;
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LOG_DEBUG("initializing slots", {{"n_slots", params.n_parallel}});
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for (int i = 0; i < params.n_parallel; i++)
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{
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server_slot slot;
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slot.id = i;
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slot.n_ctx = n_ctx_slot;
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slot.n_predict = params.n_predict;
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LOG_DEBUG("new slot", {
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{"slot_id", slot.id},
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{"n_ctx_slot", slot.n_ctx}
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});
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const int ga_n = params.grp_attn_n;
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const int ga_w = params.grp_attn_w;
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if (ga_n != 1) {
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GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT
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GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
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//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT
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//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
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LOG_DEBUG("slot self-extend", {
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{"slot_id", slot.id},
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{"ga_n", ga_n},
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{"ga_w", ga_w}
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});
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}
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slot.ga_i = 0;
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slot.ga_n = ga_n;
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slot.ga_w = ga_w;
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slot.reset();
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slots.push_back(slot);
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}
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batch = llama_batch_init(n_ctx, 0, params.n_parallel);
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}
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std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
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{
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// TODO: currently, we tokenize using special tokens by default
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// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
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// but it's better compared to completely ignoring ChatML and other chat templates
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const bool TMP_FORCE_SPECIAL = true;
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// If `add_bos` is true, we only add BOS, when json_prompt is a string,
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// or the first element of the json_prompt array is a string.
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std::vector<llama_token> prompt_tokens;
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if (json_prompt.is_array())
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{
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bool first = true;
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for (const auto& p : json_prompt)
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{
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if (p.is_string())
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{
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auto s = p.template get<std::string>();
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std::vector<llama_token> p;
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if (first)
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{
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p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
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first = false;
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}
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else
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{
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p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
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}
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prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
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}
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else
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{
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if (first)
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{
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first = false;
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}
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prompt_tokens.push_back(p.template get<llama_token>());
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}
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}
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}
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else
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{
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auto s = json_prompt.template get<std::string>();
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prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
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}
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return prompt_tokens;
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}
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server_slot* get_slot(int id) {
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int64_t t_last = ggml_time_us();
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server_slot *last_used = nullptr;
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|
|
for (server_slot & slot : slots)
|
|
{
|
|
if (slot.id == id && slot.available())
|
|
{
|
|
return &slot;
|
|
}
|
|
|
|
if (slot.available() && slot.t_last_used < t_last)
|
|
{
|
|
last_used = &slot;
|
|
t_last = slot.t_last_used;
|
|
}
|
|
}
|
|
|
|
return last_used;
|
|
}
|
|
|
|
bool launch_slot_with_data(server_slot* &slot, json data) {
|
|
slot_params default_params;
|
|
llama_sampling_params default_sparams;
|
|
|
|
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->sparams.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) {
|
|
// Might be better to reject the request with a 400 ?
|
|
LOG_WARNING("Max tokens to predict exceeds server configuration", {
|
|
{"params.n_predict", slot->params.n_predict},
|
|
{"slot.n_predict", slot->n_predict},
|
|
});
|
|
slot->params.n_predict = slot->n_predict;
|
|
}
|
|
|
|
if (data.count("input_suffix") != 0)
|
|
{
|
|
slot->params.input_suffix = data["input_suffix"];
|
|
}
|
|
else
|
|
{
|
|
slot->params.input_suffix = "";
|
|
}
|
|
|
|
if (data.count("prompt") != 0)
|
|
{
|
|
slot->prompt = data["prompt"];
|
|
}
|
|
else
|
|
{
|
|
slot->prompt = "";
|
|
}
|
|
|
|
slot->sparams.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);
|
|
}
|
|
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);
|
|
}
|
|
}
|
|
}
|
|
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;
|
|
}
|
|
|
|
const auto &logit_bias = data.find("logit_bias");
|
|
if (logit_bias != data.end() && logit_bias->is_array())
|
|
{
|
|
const int n_vocab = llama_n_vocab(model);
|
|
for (const auto &el : *logit_bias)
|
|
{
|
|
if (el.is_array() && el.size() == 2)
|
|
{
|
|
float bias;
|
|
if (el[1].is_number())
|
|
{
|
|
bias = el[1].get<float>();
|
|
}
|
|
else if (el[1].is_boolean() && !el[1].get<bool>())
|
|
{
|
|
bias = -INFINITY;
|
|
}
|
|
else
|
|
{
|
|
continue;
|
|
}
|
|
|
|
if (el[0].is_number_integer())
|
|
{
|
|
llama_token tok = el[0].get<llama_token>();
|
|
if (tok >= 0 && tok < n_vocab)
|
|
{
|
|
slot->sparams.logit_bias[tok] = bias;
|
|
}
|
|
}
|
|
else if (el[0].is_string())
|
|
{
|
|
auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
|
|
for (auto tok : toks)
|
|
{
|
|
slot->sparams.logit_bias[tok] = bias;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
slot->params.antiprompt.clear();
|
|
|
|
const auto &stop = data.find("stop");
|
|
if (stop != data.end() && stop->is_array())
|
|
{
|
|
for (const auto &word : *stop)
|
|
{
|
|
if (!word.empty())
|
|
{
|
|
slot->params.antiprompt.push_back(word);
|
|
}
|
|
}
|
|
}
|
|
|
|
const auto &samplers_sequence = data.find("samplers");
|
|
if (samplers_sequence != data.end() && samplers_sequence->is_array())
|
|
{
|
|
std::vector<std::string> sampler_names;
|
|
for (const auto &sampler_name : *samplers_sequence)
|
|
{
|
|
if (sampler_name.is_string())
|
|
{
|
|
sampler_names.emplace_back(sampler_name);
|
|
}
|
|
}
|
|
slot->sparams.samplers_sequence = llama_sampling_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;
|
|
}
|
|
}
|
|
}
|
|
slot->prompt = "";
|
|
slot->params.input_suffix = prompt.substr(begin_prefix);
|
|
slot->params.cache_prompt = false; // multimodal doesn't support cache prompt
|
|
}
|
|
}
|
|
}
|
|
|
|
if (slot->ctx_sampling != nullptr)
|
|
{
|
|
llama_sampling_free(slot->ctx_sampling);
|
|
}
|
|
slot->ctx_sampling = llama_sampling_init(slot->sparams);
|
|
slot->command = LOAD_PROMPT;
|
|
|
|
all_slots_are_idle = false;
|
|
|
|
LOG_DEBUG("slot is processing task", {
|
|
{"slot_id", slot->id},
|
|
{"task_id", slot->task_id},
|
|
});
|
|
|
|
return true;
|
|
}
|
|
|
|
void kv_cache_clear() {
|
|
// clear the entire KV cache
|
|
llama_kv_cache_clear(ctx);
|
|
clean_kv_cache = false;
|
|
}
|
|
|
|
void system_prompt_update() {
|
|
kv_cache_clear();
|
|
system_tokens.clear();
|
|
|
|
if (!system_prompt.empty()) {
|
|
system_tokens = ::llama_tokenize(ctx, system_prompt, true);
|
|
|
|
llama_batch_clear(batch);
|
|
|
|
for (int i = 0; i < (int)system_tokens.size(); ++i)
|
|
{
|
|
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
|
|
}
|
|
|
|
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch)
|
|
{
|
|
const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i));
|
|
llama_batch batch_view = {
|
|
n_tokens,
|
|
batch.token + i,
|
|
nullptr,
|
|
batch.pos + i,
|
|
batch.n_seq_id + i,
|
|
batch.seq_id + i,
|
|
batch.logits + i,
|
|
0, 0, 0, // unused
|
|
};
|
|
if (llama_decode(ctx, batch_view) != 0)
|
|
{
|
|
LOG_TEE("%s: llama_decode() failed\n", __func__);
|
|
return;
|
|
}
|
|
}
|
|
|
|
// assign the system KV cache to all parallel sequences
|
|
for (int32_t i = 1; i < params.n_parallel; ++i)
|
|
{
|
|
llama_kv_cache_seq_cp(ctx, 0, i, 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;
|
|
}
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
return stop_pos;
|
|
}
|
|
|
|
bool process_token(completion_token_output &result, server_slot &slot) {
|
|
// remember which tokens were sampled - used for repetition penalties during sampling
|
|
const std::string token_str = llama_token_to_piece(ctx, result.tok);
|
|
slot.sampled = result.tok;
|
|
|
|
// search stop word and delete it
|
|
slot.generated_text += token_str;
|
|
slot.has_next_token = true;
|
|
|
|
if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
|
|
{
|
|
// we can change penalty_prompt_tokens because it is always created from scratch each request
|
|
slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
|
|
}
|
|
|
|
// check if there is incomplete UTF-8 character at the end
|
|
bool incomplete = false;
|
|
for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
|
|
{
|
|
unsigned char c = slot.generated_text[slot.generated_text.size() - i];
|
|
if ((c & 0xC0) == 0x80)
|
|
{
|
|
// continuation byte: 10xxxxxx
|
|
continue;
|
|
}
|
|
if ((c & 0xE0) == 0xC0)
|
|
{
|
|
// 2-byte character: 110xxxxx ...
|
|
incomplete = i < 2;
|
|
}
|
|
else if ((c & 0xF0) == 0xE0)
|
|
{
|
|
// 3-byte character: 1110xxxx ...
|
|
incomplete = i < 3;
|
|
}
|
|
else if ((c & 0xF8) == 0xF0)
|
|
{
|
|
// 4-byte character: 11110xxx ...
|
|
incomplete = i < 4;
|
|
}
|
|
// else 1-byte character or invalid byte
|
|
break;
|
|
}
|
|
|
|
if (!incomplete)
|
|
{
|
|
size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());
|
|
const std::string str_test = slot.generated_text.substr(pos);
|
|
bool is_stop_full = false;
|
|
size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot);
|
|
if (stop_pos != std::string::npos)
|
|
{
|
|
is_stop_full = true;
|
|
slot.generated_text.erase(
|
|
slot.generated_text.begin() + pos + stop_pos,
|
|
slot.generated_text.end());
|
|
pos = std::min(slot.n_sent_text, slot.generated_text.size());
|
|
}
|
|
else
|
|
{
|
|
is_stop_full = false;
|
|
stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot);
|
|
}
|
|
|
|
// check if there is any token to predict
|
|
if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0))
|
|
{
|
|
// no send the stop word in the response
|
|
result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
|
|
slot.n_sent_text += result.text_to_send.size();
|
|
// add the token to slot queue and cache
|
|
}
|
|
|
|
if (slot.params.stream)
|
|
{
|
|
send_partial_response(slot, result);
|
|
}
|
|
}
|
|
|
|
slot.add_token_string(result);
|
|
|
|
if (incomplete)
|
|
{
|
|
slot.has_next_token = true;
|
|
}
|
|
|
|
// check the limits
|
|
if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
|
|
{
|
|
slot.stopped_limit = true;
|
|
slot.has_next_token = false;
|
|
}
|
|
|
|
if (!slot.cache_tokens.empty() && llama_token_is_eog(model, result.tok))
|
|
{
|
|
slot.stopped_eos = true;
|
|
slot.has_next_token = false;
|
|
LOG_VERBOSE("eos token found", {});
|
|
}
|
|
|
|
LOG_VERBOSE("next token", {
|
|
{"token", result.tok},
|
|
{"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
|
|
{"has_next_token", slot.has_next_token},
|
|
{"n_remain", slot.n_remaining},
|
|
{"num_tokens_predicted", slot.n_decoded},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
});
|
|
|
|
return slot.has_next_token; // continue
|
|
}
|
|
|
|
bool process_images(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.cpuparams.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)
|
|
{
|
|
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
|
|
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
|
|
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
|
|
std::vector<std::string> samplers_sequence;
|
|
for (const auto &sampler_type : slot.sparams.samplers_sequence)
|
|
{
|
|
samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type));
|
|
}
|
|
|
|
return json {
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_predict", slot.n_predict},
|
|
{"model", params.model_alias},
|
|
{"seed", slot.params.seed},
|
|
{"temperature", slot.sparams.temp},
|
|
{"dynatemp_range", slot.sparams.dynatemp_range},
|
|
{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
|
|
{"top_k", slot.sparams.top_k},
|
|
{"top_p", slot.sparams.top_p},
|
|
{"min_p", slot.sparams.min_p},
|
|
{"tfs_z", slot.sparams.tfs_z},
|
|
{"typical_p", slot.sparams.typical_p},
|
|
{"repeat_last_n", slot.sparams.penalty_last_n},
|
|
{"repeat_penalty", slot.sparams.penalty_repeat},
|
|
{"presence_penalty", slot.sparams.penalty_present},
|
|
{"frequency_penalty", slot.sparams.penalty_freq},
|
|
{"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
|
|
{"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
|
|
{"mirostat", slot.sparams.mirostat},
|
|
{"mirostat_tau", slot.sparams.mirostat_tau},
|
|
{"mirostat_eta", slot.sparams.mirostat_eta},
|
|
{"penalize_nl", slot.sparams.penalize_nl},
|
|
{"stop", slot.params.antiprompt},
|
|
{"n_predict", slot.params.n_predict},
|
|
{"n_keep", params.n_keep},
|
|
{"ignore_eos", ignore_eos},
|
|
{"stream", slot.params.stream},
|
|
{"logit_bias", slot.sparams.logit_bias},
|
|
{"n_probs", slot.sparams.n_probs},
|
|
{"min_keep", slot.sparams.min_keep},
|
|
{"grammar", slot.sparams.grammar},
|
|
{"samplers", samplers_sequence}
|
|
};
|
|
}
|
|
|
|
void send_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;
|
|
|
|
res.result_json = json
|
|
{
|
|
{"stop", false},
|
|
{"slot_id", slot.id},
|
|
{"multimodal", multimodal}
|
|
};
|
|
|
|
if (!llama_token_is_eog(model, tkn.tok)) {
|
|
res.result_json["content"] = tkn.text_to_send;
|
|
}
|
|
|
|
if (slot.sparams.n_probs > 0)
|
|
{
|
|
std::vector<completion_token_output> probs_output = {};
|
|
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);
|
|
}
|
|
slot.n_sent_token_probs = probs_stop_pos;
|
|
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
|
|
}
|
|
|
|
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
|
|
{
|
|
{"content", !slot.params.stream ? slot.generated_text : ""},
|
|
{"slot_id", slot.id},
|
|
{"stop", true},
|
|
{"model", params.model_alias},
|
|
{"tokens_predicted", slot.n_decoded},
|
|
{"tokens_evaluated", slot.n_prompt_tokens},
|
|
{"truncated", slot.truncated},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
{"tokens_cached", slot.n_past},
|
|
{"timings", slot.get_formated_timings()}
|
|
};
|
|
|
|
if (slot.sparams.n_probs > 0)
|
|
{
|
|
std::vector<completion_token_output> probs = {};
|
|
if (!slot.params.stream && slot.stopped_word)
|
|
{
|
|
const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
|
|
probs = std::vector<completion_token_output>(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size());
|
|
}
|
|
else
|
|
{
|
|
probs = std::vector<completion_token_output>(
|
|
slot.generated_token_probs.begin(),
|
|
slot.generated_token_probs.end());
|
|
}
|
|
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
|
|
}
|
|
|
|
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;
|
|
|
|
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;
|
|
}
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
res.result_json = json
|
|
{
|
|
{"embedding", std::vector<float>(embd, embd + n_embd)},
|
|
};
|
|
}
|
|
}
|
|
queue_results.send(res);
|
|
}
|
|
|
|
void request_completion(int task_id, json data, bool embedding, int multitask_id)
|
|
{
|
|
task_server task;
|
|
task.id = task_id;
|
|
task.target_id = 0;
|
|
task.data = std::move(data);
|
|
task.embedding_mode = embedding;
|
|
task.type = TASK_TYPE_COMPLETION;
|
|
task.multitask_id = multitask_id;
|
|
|
|
// when a completion task's prompt array is not a singleton, we split it into multiple requests
|
|
// otherwise, it's a single-prompt task, we actually queue it
|
|
// if there's numbers in the prompt array it will be treated as an array of tokens
|
|
if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
|
|
bool numbers = false;
|
|
for (const auto& e : task.data.at("prompt")) {
|
|
if (e.is_number()) {
|
|
numbers = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
|
|
// it will completely stall the server. I don't know where the bug for this is.
|
|
//
|
|
// if there are numbers, it needs to be treated like a single prompt,
|
|
// queue_tasks handles a mix of strings and numbers just fine.
|
|
if (numbers) {
|
|
queue_tasks.post(task);
|
|
} else {
|
|
split_multiprompt_task(task_id, 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 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();
|
|
if (prompt_count <= 1) {
|
|
send_error(multiprompt_task, "error while handling multiple prompts");
|
|
return;
|
|
}
|
|
|
|
// generate all the ID for subtask
|
|
std::vector<int> subtask_ids(prompt_count);
|
|
for (int i = 0; i < prompt_count; i++)
|
|
{
|
|
subtask_ids[i] = queue_tasks.get_new_id();
|
|
}
|
|
|
|
// queue up the multitask so we can track its subtask progression
|
|
queue_tasks.add_multitask(multitask_id, subtask_ids);
|
|
|
|
// add subtasks
|
|
for (int i = 0; i < prompt_count; i++)
|
|
{
|
|
json subtask_data = multiprompt_task.data;
|
|
subtask_data["prompt"] = subtask_data["prompt"][i];
|
|
|
|
// subtasks inherit everything else (embedding mode, etc.)
|
|
request_completion(subtask_ids[i], subtask_data, multiprompt_task.embedding_mode, multitask_id);
|
|
}
|
|
}
|
|
|
|
std::string common_prefix(const std::string& str1, const std::string& str2) {
|
|
auto mismatch_pair = std::mismatch(str1.begin(), str1.end(), str2.begin());
|
|
return std::string(str1.begin(), mismatch_pair.first);
|
|
}
|
|
|
|
// Find the slot that has the greatest common prefix
|
|
server_slot *prefix_slot(const json &prompt) {
|
|
if (!prompt.is_string()) {
|
|
return nullptr;
|
|
}
|
|
|
|
std::string prompt_str = prompt.get<std::string>();
|
|
server_slot *slot = nullptr;
|
|
size_t longest = 0;
|
|
|
|
for (server_slot &s : slots) {
|
|
if (s.available() && s.prompt.is_string()) {
|
|
std::string s_prompt = s.prompt.get<std::string>();
|
|
std::string prefix = common_prefix(s_prompt, prompt_str);
|
|
|
|
if (prefix.size() > longest) {
|
|
slot = &s;
|
|
longest = prefix.size();
|
|
}
|
|
}
|
|
}
|
|
|
|
if (!slot) {
|
|
return get_slot(-1);
|
|
}
|
|
|
|
LOG_DEBUG("slot with common prefix found", {{
|
|
"slot_id", slot->id,
|
|
"characters", longest
|
|
}});
|
|
return slot;
|
|
}
|
|
|
|
void process_single_task(task_server& task)
|
|
{
|
|
switch (task.type)
|
|
{
|
|
case TASK_TYPE_COMPLETION: {
|
|
server_slot *slot = nullptr;
|
|
if (task.embedding_mode) {
|
|
// Embedding seq_id (aka slot id) must always be <= token length, so always use slot 0
|
|
slot = slots[0].available() ? &slots[0] : nullptr;
|
|
} else {
|
|
slot = prefix_slot(task.data["prompt"]);
|
|
}
|
|
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;
|
|
}
|
|
|
|
slot->reset();
|
|
|
|
slot->embedding = task.embedding_mode;
|
|
slot->task_id = task.id;
|
|
slot->multitask_id = task.multitask_id;
|
|
|
|
if (!launch_slot_with_data(slot, task.data))
|
|
{
|
|
// send error result
|
|
send_error(task, "internal_error");
|
|
break;
|
|
}
|
|
} break;
|
|
case TASK_TYPE_CANCEL: { // release slot linked with the task id
|
|
for (auto & slot : slots)
|
|
{
|
|
if (slot.task_id == task.target_id)
|
|
{
|
|
slot.release();
|
|
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++;
|
|
}
|
|
slots_data.push_back(slot_data);
|
|
}
|
|
LOG_DEBUG("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 = {
|
|
{ "idle", n_idle_slots },
|
|
{ "processing", n_processing_slots },
|
|
{ "deferred", queue_tasks.queue_tasks_deferred.size() },
|
|
|
|
{ "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
|
|
{ "n_tokens_predicted_total", metrics.n_tokens_predicted_total},
|
|
|
|
{ "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed},
|
|
{ "t_prompt_processing", metrics.t_prompt_processing},
|
|
{ "n_tokens_predicted", metrics.n_tokens_predicted},
|
|
{ "t_tokens_generation", metrics.t_tokens_generation},
|
|
|
|
{ "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)},
|
|
{ "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)},
|
|
|
|
{ "slots", slots_data },
|
|
};
|
|
metrics.reset_bucket();
|
|
queue_results.send(res);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
void on_finish_multitask(task_multi& multitask)
|
|
{
|
|
// all subtasks done == multitask is done
|
|
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);
|
|
result.error = result.error && subres.error;
|
|
}
|
|
result.result_json = json{ { "results", result_jsons } };
|
|
queue_results.send(result);
|
|
}
|
|
|
|
bool update_slots() {
|
|
if (system_need_update)
|
|
{
|
|
LOG_DEBUG("updating system prompt", {});
|
|
system_prompt_update();
|
|
}
|
|
|
|
llama_batch_clear(batch);
|
|
|
|
if (all_slots_are_idle)
|
|
{
|
|
if (system_prompt.empty() && clean_kv_cache)
|
|
{
|
|
LOG_DEBUG("all slots are idle and system prompt is empty, clear the KV cache", {});
|
|
kv_cache_clear();
|
|
}
|
|
return true;
|
|
}
|
|
|
|
LOG_VERBOSE("posting NEXT_RESPONSE", {});
|
|
task_server task;
|
|
task.type = TASK_TYPE_NEXT_RESPONSE;
|
|
task.target_id = -1;
|
|
queue_tasks.post(task);
|
|
|
|
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)
|
|
{
|
|
// 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_DEBUG("slot context shift", {
|
|
{"slot_id", slot.id},
|
|
{"task_id", slot.task_id},
|
|
{"n_keep", n_keep},
|
|
{"n_left", n_left},
|
|
{"n_discard", n_discard},
|
|
{"n_ctx", n_ctx},
|
|
{"n_past", slot.n_past},
|
|
{"n_system_tokens", system_tokens.size()},
|
|
{"n_cache_tokens", slot.cache_tokens.size()}
|
|
});
|
|
llama_kv_cache_seq_rm (ctx, slot.id, 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];
|
|
}
|
|
|
|
slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
|
|
|
|
slot.n_past -= n_discard;
|
|
|
|
slot.truncated = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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();
|
|
|
|
LOG_DEBUG("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)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
slot.i_batch = batch.n_tokens;
|
|
|
|
const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
|
|
|
|
// TODO: we always have to take into account the "system_tokens"
|
|
// this is not great and needs to be improved somehow
|
|
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true);
|
|
slot.n_past += 1;
|
|
}
|
|
|
|
// process in chunks of params.n_batch
|
|
int32_t n_batch = params.n_batch;
|
|
|
|
// assign workload to the slots
|
|
if (params.cont_batching || batch.n_tokens == 0)
|
|
{
|
|
for (auto & slot : slots)
|
|
{
|
|
const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty()) || !slot.images.empty();
|
|
|
|
// empty prompt passed -> release the slot and send empty response
|
|
if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt)
|
|
{
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
continue;
|
|
}
|
|
|
|
// need process the prompt
|
|
if (slot.state == IDLE && slot.command == LOAD_PROMPT)
|
|
{
|
|
slot.state = PROCESSING;
|
|
slot.command = NONE;
|
|
std::vector<llama_token> prompt_tokens;
|
|
slot.t_start_process_prompt = ggml_time_us();
|
|
slot.t_start_genereration = 0;
|
|
|
|
prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
|
|
|
|
slot.n_prompt_tokens = prompt_tokens.size();
|
|
|
|
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_shift = n_left / 2;
|
|
const int n_erase = slot.n_prompt_tokens - slot.params.n_keep - n_shift;
|
|
|
|
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 + n_erase,
|
|
prompt_tokens.end());
|
|
|
|
LOG_INFO("input truncated", {
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_left", n_left},
|
|
{"n_shift", n_shift},
|
|
{"n_erase", n_erase},
|
|
});
|
|
slot.truncated = true;
|
|
prompt_tokens = new_tokens;
|
|
|
|
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);
|
|
|
|
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);
|
|
}
|
|
|
|
slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
|
|
|
|
// 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;
|
|
}
|
|
|
|
slot.n_prompt_tokens_processed = slot.n_prompt_tokens;
|
|
|
|
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;
|
|
}
|
|
slot_npast++;
|
|
}
|
|
slot.n_past_se = slot_npast;
|
|
slot.ga_i = ga_i;
|
|
}
|
|
|
|
LOG_DEBUG("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 }
|
|
});
|
|
}
|
|
|
|
slot.cache_tokens = prompt_tokens;
|
|
|
|
if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0)
|
|
{
|
|
// we have to evaluate at least 1 token to generate logits.
|
|
LOG_DEBUG("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--;
|
|
}
|
|
}
|
|
|
|
int p0 = (int) system_tokens.size() + slot.n_past;
|
|
LOG_DEBUG("kv cache rm [p0, end)", {
|
|
{ "slot_id", slot.id },
|
|
{ "task_id", slot.task_id },
|
|
{ "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_i = slot.ga_i;
|
|
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)
|
|
{
|
|
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);
|
|
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;
|
|
}
|
|
|
|
// extract the logits only for the last token
|
|
if (batch.n_tokens > 0)
|
|
{
|
|
batch.logits[batch.n_tokens - 1] = true;
|
|
}
|
|
|
|
slot.n_decoded = 0;
|
|
slot.i_batch = batch.n_tokens - 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (batch.n_tokens == 0)
|
|
{
|
|
all_slots_are_idle = true;
|
|
return true;
|
|
}
|
|
|
|
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
|
|
{
|
|
const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
|
|
|
|
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)
|
|
{
|
|
const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
|
|
const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
|
|
const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
|
|
|
|
LOG_TEE("\n");
|
|
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
|
|
LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
|
|
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
|
|
|
|
llama_kv_cache_seq_add(ctx, slot.id, 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);
|
|
|
|
slot.n_past_se -= bd;
|
|
|
|
slot.ga_i += slot.ga_w / slot.ga_n;
|
|
|
|
LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
|
|
}
|
|
slot.n_past_se += n_tokens;
|
|
}
|
|
}
|
|
|
|
llama_batch batch_view =
|
|
{
|
|
n_tokens,
|
|
batch.token + i,
|
|
nullptr,
|
|
batch.pos + i,
|
|
batch.n_seq_id + i,
|
|
batch.seq_id + i,
|
|
batch.logits + i,
|
|
0, 0, 0, // unused
|
|
};
|
|
|
|
const int ret = llama_decode(ctx, batch_view);
|
|
|
|
if (ret != 0)
|
|
{
|
|
if (n_batch == 1 || ret < 0)
|
|
{
|
|
// if you get here, it means the KV cache is full - try increasing it via the context size
|
|
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
|
|
return false;
|
|
}
|
|
|
|
LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
|
|
|
|
// retry with half the batch size to try to find a free slot in the KV cache
|
|
n_batch /= 2;
|
|
i -= n_batch;
|
|
continue;
|
|
}
|
|
|
|
for (auto & slot : slots)
|
|
{
|
|
if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens))
|
|
{
|
|
continue;
|
|
}
|
|
|
|
// prompt evaluated for embedding
|
|
if (slot.embedding)
|
|
{
|
|
send_embedding(slot, batch_view);
|
|
slot.release();
|
|
slot.i_batch = -1;
|
|
continue;
|
|
}
|
|
|
|
completion_token_output result;
|
|
const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i);
|
|
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
|
|
|
|
slot.n_decoded += 1;
|
|
if (slot.n_decoded == 1)
|
|
{
|
|
slot.t_start_genereration = ggml_time_us();
|
|
slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3;
|
|
metrics.on_prompt_eval(slot);
|
|
}
|
|
|
|
llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
|
|
result.tok = id;
|
|
|
|
const int32_t n_probs = slot.sparams.n_probs;
|
|
if (slot.sparams.temp <= 0 && n_probs > 0)
|
|
{
|
|
// for llama_sample_token_greedy we need to sort candidates
|
|
llama_sample_softmax(ctx, &cur_p);
|
|
}
|
|
|
|
for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i)
|
|
{
|
|
result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p});
|
|
}
|
|
|
|
if (!process_token(result, slot))
|
|
{
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
metrics.on_prediction(slot);
|
|
}
|
|
|
|
slot.i_batch = -1;
|
|
}
|
|
}
|
|
|
|
LOG_VERBOSE("slots updated", {});
|
|
return true;
|
|
}
|
|
|
|
json model_meta() {
|
|
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)
|
|
{
|
|
printf("usage: %s [options]\n", argv0);
|
|
printf("\n");
|
|
printf("options:\n");
|
|
printf(" -h, --help show this help message and exit\n");
|
|
printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
|
|
printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.cpuparams.n_threads);
|
|
printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n");
|
|
printf(" --threads-http N number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n");
|
|
printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
|
printf(" --rope-scaling {none,linear,yarn}\n");
|
|
printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n");
|
|
printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n");
|
|
printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n");
|
|
printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
|
|
printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
|
|
printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
|
|
printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
|
|
printf(" --pooling {none,mean,cls}\n");
|
|
printf(" pooling type for embeddings, use model default if unspecified\n");
|
|
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
|
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
|
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
|
|
if (llama_supports_mlock())
|
|
{
|
|
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
|
}
|
|
if (llama_supports_mmap())
|
|
{
|
|
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
|
}
|
|
printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n");
|
|
printf(" - distribute: spread execution evenly over all nodes\n");
|
|
printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n");
|
|
printf(" - numactl: use the CPU map provided my numactl\n");
|
|
if (llama_supports_gpu_offload()) {
|
|
printf(" -ngl N, --n-gpu-layers N\n");
|
|
printf(" number of layers to store in VRAM\n");
|
|
printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
|
|
printf(" how to split the model across multiple GPUs, one of:\n");
|
|
printf(" - none: use one GPU only\n");
|
|
printf(" - layer (default): split layers and KV across GPUs\n");
|
|
printf(" - row: split rows across GPUs\n");
|
|
printf(" -ts SPLIT --tensor-split SPLIT\n");
|
|
printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
|
|
printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n");
|
|
printf(" or for intermediate results and KV (with split-mode = row)\n");
|
|
}
|
|
printf(" -m FNAME, --model FNAME\n");
|
|
printf(" model path (default: %s)\n", params.model.c_str());
|
|
printf(" -a ALIAS, --alias ALIAS\n");
|
|
printf(" set an alias for the model, will be added as `model` field in completion response\n");
|
|
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
|
printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
|
|
printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
|
|
printf(" --port PORT port to listen (default (default: %d)\n", sparams.port);
|
|
printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
|
|
printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n");
|
|
printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
|
|
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
|
|
printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
|
|
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
|
|
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
|
|
printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
|
|
printf(" -spf FNAME, --system-prompt-file FNAME\n");
|
|
printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
|
|
printf(" -ctk TYPE, --cache-type-k TYPE\n");
|
|
printf(" KV cache data type for K (default: f16)\n");
|
|
printf(" -ctv TYPE, --cache-type-v TYPE\n");
|
|
printf(" KV cache data type for V (default: f16)\n");
|
|
printf(" --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(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled");
|
|
printf("\n");
|
|
printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict);
|
|
printf(" --override-kv KEY=TYPE:VALUE\n");
|
|
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
|
|
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
|
|
printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
|
|
printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
|
|
printf(" --chat-template JINJA_TEMPLATE\n");
|
|
printf(" set custom jinja chat template (default: template taken from model's metadata)\n");
|
|
printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n");
|
|
printf("\n");
|
|
}
|
|
|
|
static void server_params_parse(int argc, char **argv, server_params &sparams, gpt_params ¶ms)
|
|
{
|
|
gpt_params default_params;
|
|
server_params default_sparams;
|
|
std::string arg;
|
|
bool invalid_param = false;
|
|
|
|
for (int i = 1; i < argc; i++)
|
|
{
|
|
arg = argv[i];
|
|
if (arg == "--port")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.port = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "--host")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.hostname = argv[i];
|
|
}
|
|
else if (arg == "--path")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.public_path = argv[i];
|
|
}
|
|
else if (arg == "--api-key")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.api_keys.emplace_back(argv[i]);
|
|
}
|
|
else if (arg == "--api-key-file")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::ifstream key_file(argv[i]);
|
|
if (!key_file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string key;
|
|
while (std::getline(key_file, key)) {
|
|
if (key.size() > 0) {
|
|
sparams.api_keys.push_back(key);
|
|
}
|
|
}
|
|
key_file.close();
|
|
}
|
|
else if (arg == "--timeout" || arg == "-to")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.read_timeout = std::stoi(argv[i]);
|
|
sparams.write_timeout = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "-m" || arg == "--model")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.model = argv[i];
|
|
}
|
|
else if (arg == "-a" || arg == "--alias")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.model_alias = argv[i];
|
|
}
|
|
else if (arg == "-h" || arg == "--help")
|
|
{
|
|
server_print_usage(argv[0], default_params, default_sparams);
|
|
exit(0);
|
|
}
|
|
else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_ctx = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "--rope-scaling")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
|
|
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
|
|
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
|
|
else { invalid_param = true; break; }
|
|
}
|
|
else if (arg == "--rope-freq-base")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rope_freq_base = std::stof(argv[i]);
|
|
}
|
|
else if (arg == "--rope-freq-scale")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rope_freq_scale = std::stof(argv[i]);
|
|
}
|
|
else if (arg == "--yarn-ext-factor")
|
|
{
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_ext_factor = std::stof(argv[i]);
|
|
}
|
|
else if (arg == "--yarn-attn-factor")
|
|
{
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_attn_factor = std::stof(argv[i]);
|
|
}
|
|
else if (arg == "--yarn-beta-fast")
|
|
{
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_beta_fast = std::stof(argv[i]);
|
|
}
|
|
else if (arg == "--yarn-beta-slow")
|
|
{
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_beta_slow = std::stof(argv[i]);
|
|
}
|
|
else if (arg == "--pooling")
|
|
{
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
|
|
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
|
|
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
|
|
else { invalid_param = true; break; }
|
|
}
|
|
else if (arg == "--threads" || arg == "-t")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.cpuparams.n_threads = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "--grp-attn-n" || arg == "-gan")
|
|
{
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
|
|
params.grp_attn_n = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "--grp-attn-w" || arg == "-gaw")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
|
|
params.grp_attn_w = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "--threads-batch" || arg == "-tb")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.cpuparams_batch.n_threads = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "--threads-http")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.n_threads_http = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "-b" || arg == "--batch-size")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_batch = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
if (llama_supports_gpu_offload()) {
|
|
params.n_gpu_layers = std::stoi(argv[i]);
|
|
} else {
|
|
LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
|
|
"See main README.md for information on enabling GPU BLAS support",
|
|
{{"n_gpu_layers", params.n_gpu_layers}});
|
|
}
|
|
}
|
|
else if (arg == "--split-mode" || arg == "-sm")
|
|
{
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string arg_next = argv[i];
|
|
if (arg_next == "none")
|
|
{
|
|
params.split_mode = LLAMA_SPLIT_MODE_NONE;
|
|
}
|
|
else if (arg_next == "layer")
|
|
{
|
|
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
|
|
}
|
|
else if (arg_next == "row")
|
|
{
|
|
params.split_mode = LLAMA_SPLIT_MODE_ROW;
|
|
}
|
|
else {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#ifndef GGML_USE_CUDA
|
|
fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n");
|
|
#endif // GGML_USE_CUDA
|
|
}
|
|
else if (arg == "--tensor-split" || arg == "-ts")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
|
|
std::string arg_next = argv[i];
|
|
|
|
// split string by , and /
|
|
const std::regex regex{R"([,/]+)"};
|
|
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
|
|
std::vector<std::string> split_arg{it, {}};
|
|
GGML_ASSERT(split_arg.size() <= llama_max_devices());
|
|
|
|
for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device)
|
|
{
|
|
if (i_device < split_arg.size())
|
|
{
|
|
params.tensor_split[i_device] = std::stof(split_arg[i_device]);
|
|
}
|
|
else
|
|
{
|
|
params.tensor_split[i_device] = 0.0f;
|
|
}
|
|
}
|
|
#else
|
|
LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {});
|
|
#endif // GGML_USE_CUDA
|
|
}
|
|
else if (arg == "--main-gpu" || arg == "-mg")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
|
|
params.main_gpu = std::stoi(argv[i]);
|
|
#else
|
|
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
|
|
#endif
|
|
}
|
|
else if (arg == "--lora")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_adapters.push_back({
|
|
std::string(argv[i]),
|
|
1.0,
|
|
});
|
|
params.use_mmap = false;
|
|
}
|
|
else if (arg == "--lora-scaled")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
const char * lora_adapter = argv[i];
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_adapters.push_back({
|
|
lora_adapter,
|
|
std::stof(argv[i])
|
|
});
|
|
params.use_mmap = false;
|
|
}
|
|
else if (arg == "-v" || arg == "--verbose")
|
|
{
|
|
server_verbose = true;
|
|
}
|
|
else if (arg == "--mlock")
|
|
{
|
|
params.use_mlock = true;
|
|
}
|
|
else if (arg == "--no-mmap")
|
|
{
|
|
params.use_mmap = false;
|
|
}
|
|
else if (arg == "--numa")
|
|
{
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
} else {
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
|
|
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
|
|
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
|
|
else { invalid_param = true; break; }
|
|
}
|
|
}
|
|
else if (arg == "--embedding")
|
|
{
|
|
params.embedding = true;
|
|
}
|
|
else if (arg == "-cb" || arg == "--cont-batching")
|
|
{
|
|
params.cont_batching = true;
|
|
}
|
|
else if (arg == "-fa" || arg == "--flash-attn")
|
|
{
|
|
params.flash_attn = true;
|
|
}
|
|
else if (arg == "-np" || arg == "--parallel")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_parallel = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "-n" || arg == "--n-predict")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_predict = std::stoi(argv[i]);
|
|
}
|
|
else if (arg == "-ctk" || arg == "--cache-type-k") {
|
|
params.cache_type_k = argv[++i];
|
|
}
|
|
else if (arg == "-ctv" || arg == "--cache-type-v") {
|
|
params.cache_type_v = argv[++i];
|
|
}
|
|
else if(arg == "--mmproj")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.mmproj = argv[i];
|
|
}
|
|
else if (arg == "--log-format")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
if (std::strcmp(argv[i], "json") == 0)
|
|
{
|
|
server_log_json = true;
|
|
}
|
|
else if (std::strcmp(argv[i], "text") == 0)
|
|
{
|
|
server_log_json = false;
|
|
}
|
|
else
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
}
|
|
else if (arg == "--log-disable")
|
|
{
|
|
log_set_target(stdout);
|
|
LOG_DEBUG("logging to file is disabled.", {});
|
|
}
|
|
else if (arg == "--slots-endpoint-disable")
|
|
{
|
|
sparams.slots_endpoint = false;
|
|
}
|
|
else if (arg == "--metrics")
|
|
{
|
|
sparams.metrics_endpoint = true;
|
|
}
|
|
else if (arg == "--chat-template")
|
|
{
|
|
if (++i >= argc)
|
|
{
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
if (!verify_custom_template(argv[i])) {
|
|
fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
|
|
fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
}
|
|
else if (arg == "--override-kv")
|
|
{
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
char * sep = strchr(argv[i], '=');
|
|
if (sep == nullptr || sep - argv[i] >= 128) {
|
|
fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
struct llama_model_kv_override kvo;
|
|
std::strncpy(kvo.key, argv[i], sep - argv[i]);
|
|
kvo.key[sep - argv[i]] = 0;
|
|
sep++;
|
|
if (strncmp(sep, "int:", 4) == 0) {
|
|
sep += 4;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
|
|
kvo.val_i64 = std::atol(sep);
|
|
} else if (strncmp(sep, "float:", 6) == 0) {
|
|
sep += 6;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
|
|
kvo.val_f64 = std::atof(sep);
|
|
} else if (strncmp(sep, "bool:", 5) == 0) {
|
|
sep += 5;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
|
|
if (std::strcmp(sep, "true") == 0) {
|
|
kvo.val_bool = true;
|
|
} else if (std::strcmp(sep, "false") == 0) {
|
|
kvo.val_bool = false;
|
|
} else {
|
|
fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
} else {
|
|
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.kv_overrides.push_back(kvo);
|
|
}
|
|
else
|
|
{
|
|
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
|
server_print_usage(argv[0], default_params, default_sparams);
|
|
exit(1);
|
|
}
|
|
}
|
|
if (!params.kv_overrides.empty()) {
|
|
params.kv_overrides.emplace_back();
|
|
params.kv_overrides.back().key[0] = 0;
|
|
}
|
|
|
|
postprocess_cpu_params(params.cpuparams, nullptr);
|
|
postprocess_cpu_params(params.cpuparams_batch, ¶ms.cpuparams);
|
|
postprocess_cpu_params(params.draft_cpuparams, ¶ms.cpuparams);
|
|
postprocess_cpu_params(params.draft_cpuparams_batch, ¶ms.cpuparams_batch);
|
|
|
|
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)
|
|
{
|
|
// skip GH copilot requests when using default port
|
|
if (req.path == "/health" || req.path == "/v1/health" || req.path == "/v1/completions")
|
|
{
|
|
return;
|
|
}
|
|
|
|
LOG_DEBUG("request", {
|
|
{"remote_addr", req.remote_addr},
|
|
{"remote_port", req.remote_port},
|
|
{"status", res.status},
|
|
{"method", req.method},
|
|
{"path", req.path},
|
|
{"params", req.params},
|
|
});
|
|
|
|
LOG_VERBOSE("request", {
|
|
{"request", req.body},
|
|
{"response", res.body},
|
|
});
|
|
}
|
|
|
|
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
|
|
// this is for better developer experience, we can remove when the server is stable enough
|
|
fprintf(stderr, "Received second interrupt, terminating immediately.\n");
|
|
exit(1);
|
|
}
|
|
shutdown_handler(signal);
|
|
}
|
|
|
|
static bool update_load_progress(float progress, void *data)
|
|
{
|
|
((llama_server_context*)data)->modelProgress = progress;
|
|
return true;
|
|
}
|
|
|
|
#if defined(_WIN32)
|
|
char* wchar_to_char(const wchar_t* wstr) {
|
|
if (wstr == nullptr) return nullptr;
|
|
|
|
// Determine the number of bytes needed for the UTF-8 string
|
|
int bytes = WideCharToMultiByte(CP_UTF8, 0, wstr, -1, nullptr, 0, nullptr, nullptr);
|
|
char* str = new char[bytes];
|
|
|
|
// Convert the wide-character string to a UTF-8 string
|
|
WideCharToMultiByte(CP_UTF8, 0, wstr, -1, str, bytes, nullptr, nullptr);
|
|
return str;
|
|
}
|
|
|
|
int wmain(int argc, wchar_t **wargv) {
|
|
char** argv = new char*[argc];
|
|
for (int i = 0; i < argc; ++i) {
|
|
argv[i] = wchar_to_char(wargv[i]);
|
|
}
|
|
|
|
// Adjust error mode to avoid error dialog after we start.
|
|
SetErrorMode(SEM_FAILCRITICALERRORS);
|
|
#else
|
|
int main(int argc, char **argv) {
|
|
#endif
|
|
|
|
#if SERVER_VERBOSE != 1
|
|
log_disable();
|
|
#endif
|
|
// own arguments required by this example
|
|
gpt_params params;
|
|
server_params sparams;
|
|
|
|
// struct that contains llama context and inference
|
|
llama_server_context llama;
|
|
|
|
server_params_parse(argc, argv, sparams, params);
|
|
|
|
if (params.model_alias == "unknown")
|
|
{
|
|
params.model_alias = params.model;
|
|
}
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER},
|
|
{"commit", LLAMA_COMMIT}});
|
|
|
|
LOG_INFO("system info", {
|
|
{"n_threads", params.cpuparams.n_threads},
|
|
{"n_threads_batch", params.cpuparams_batch.n_threads},
|
|
{"total_threads", std::thread::hardware_concurrency()},
|
|
{"system_info", llama_print_system_info()},
|
|
});
|
|
|
|
httplib::Server svr;
|
|
|
|
std::atomic<server_state> state{SERVER_STATE_LOADING_MODEL};
|
|
|
|
svr.set_default_headers({{"Server", "llama.cpp"}});
|
|
|
|
// CORS preflight
|
|
svr.Options(R"(.*)", [](const httplib::Request &req, httplib::Response &res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
res.set_header("Access-Control-Allow-Credentials", "true");
|
|
res.set_header("Access-Control-Allow-Methods", "POST");
|
|
res.set_header("Access-Control-Allow-Headers", "*");
|
|
});
|
|
|
|
svr.Get("/health", [&](const httplib::Request& req, httplib::Response& res) {
|
|
server_state current_state = state.load();
|
|
switch(current_state) {
|
|
case SERVER_STATE_READY: {
|
|
// request slots data using task queue
|
|
task_server task;
|
|
task.id = llama.queue_tasks.get_new_id();
|
|
task.type = TASK_TYPE_METRICS;
|
|
task.target_id = -1;
|
|
|
|
llama.queue_results.add_waiting_task_id(task.id);
|
|
llama.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);
|
|
|
|
int n_idle_slots = result.result_json["idle"];
|
|
int n_processing_slots = result.result_json["processing"];
|
|
|
|
json health = {
|
|
{"status", "ok"},
|
|
{"slots_idle", n_idle_slots},
|
|
{"slots_processing", n_processing_slots}};
|
|
res.status = 200; // HTTP OK
|
|
if (sparams.slots_endpoint && req.has_param("include_slots")) {
|
|
health["slots"] = result.result_json["slots"];
|
|
}
|
|
|
|
if (n_idle_slots == 0) {
|
|
health["status"] = "no slot available";
|
|
if (req.has_param("fail_on_no_slot")) {
|
|
res.status = 503; // HTTP Service Unavailable
|
|
}
|
|
}
|
|
res.set_content(health.dump(), "application/json");
|
|
break;
|
|
}
|
|
case SERVER_STATE_LOADING_MODEL:
|
|
char buf[128];
|
|
snprintf(&buf[0], 128, R"({"status": "loading model", "progress": %0.2f})", llama.modelProgress);
|
|
res.set_content(buf, "application/json");
|
|
res.status = 503; // HTTP Service Unavailable
|
|
break;
|
|
case SERVER_STATE_ERROR:
|
|
res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json");
|
|
res.status = 500; // HTTP Internal Server Error
|
|
break;
|
|
}
|
|
});
|
|
|
|
if (sparams.slots_endpoint) {
|
|
svr.Get("/slots", [&](const httplib::Request&, httplib::Response& res) {
|
|
// request slots data using task queue
|
|
task_server task;
|
|
task.id = llama.queue_tasks.get_new_id();
|
|
task.type = TASK_TYPE_METRICS;
|
|
task.target_id = -1;
|
|
|
|
llama.queue_results.add_waiting_task_id(task.id);
|
|
llama.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);
|
|
|
|
res.set_content(result.result_json["slots"].dump(), "application/json");
|
|
res.status = 200; // HTTP OK
|
|
});
|
|
}
|
|
|
|
if (sparams.metrics_endpoint) {
|
|
svr.Get("/metrics", [&](const httplib::Request&, httplib::Response& res) {
|
|
// request slots data using task queue
|
|
task_server task;
|
|
task.id = llama.queue_tasks.get_new_id();
|
|
task.type = TASK_TYPE_METRICS;
|
|
task.target_id = -1;
|
|
|
|
llama.queue_results.add_waiting_task_id(task.id);
|
|
llama.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);
|
|
|
|
json data = result.result_json;
|
|
|
|
uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"];
|
|
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"];
|
|
|
|
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"]}
|
|
}}}
|
|
};
|
|
|
|
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"];
|
|
auto value = json_value(metric_def, "value", 0);
|
|
prometheus << "# HELP llamacpp:" << name << " " << help << "\n"
|
|
<< "# TYPE llamacpp:" << name << " " << type << "\n"
|
|
<< "llamacpp:" << name << " " << value << "\n";
|
|
}
|
|
}
|
|
|
|
res.set_content(prometheus.str(), "text/plain; version=0.0.4");
|
|
res.status = 200; // HTTP OK
|
|
});
|
|
}
|
|
|
|
svr.set_logger(log_server_request);
|
|
|
|
svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep)
|
|
{
|
|
const char fmt[] = "500 Internal Server Error\n%s";
|
|
char buf[BUFSIZ];
|
|
try
|
|
{
|
|
std::rethrow_exception(std::move(ep));
|
|
}
|
|
catch (std::exception &e)
|
|
{
|
|
snprintf(buf, sizeof(buf), fmt, e.what());
|
|
}
|
|
catch (...)
|
|
{
|
|
snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
|
|
}
|
|
res.set_content(buf, "text/plain; charset=utf-8");
|
|
res.status = 500;
|
|
});
|
|
|
|
svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
|
|
{
|
|
if (res.status == 401)
|
|
{
|
|
res.set_content("Unauthorized", "text/plain; charset=utf-8");
|
|
}
|
|
if (res.status == 400)
|
|
{
|
|
res.set_content("Invalid request", "text/plain; charset=utf-8");
|
|
}
|
|
else if (res.status == 404)
|
|
{
|
|
res.set_content("File Not Found", "text/plain; charset=utf-8");
|
|
res.status = 404;
|
|
}
|
|
});
|
|
|
|
// set timeouts and change hostname and port
|
|
svr.set_read_timeout (sparams.read_timeout);
|
|
svr.set_write_timeout(sparams.write_timeout);
|
|
|
|
if (!svr.bind_to_port(sparams.hostname, sparams.port))
|
|
{
|
|
fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
|
|
return 1;
|
|
}
|
|
|
|
// Set the base directory for serving static files
|
|
svr.set_base_dir(sparams.public_path);
|
|
|
|
std::unordered_map<std::string, std::string> log_data;
|
|
log_data["hostname"] = sparams.hostname;
|
|
log_data["port"] = std::to_string(sparams.port);
|
|
|
|
if (sparams.api_keys.size() == 1) {
|
|
log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4);
|
|
} else if (sparams.api_keys.size() > 1) {
|
|
log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded";
|
|
}
|
|
|
|
if (sparams.n_threads_http < 1) {
|
|
// +2 threads for monitoring endpoints
|
|
sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1);
|
|
}
|
|
log_data["n_threads_http"] = std::to_string(sparams.n_threads_http);
|
|
svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); };
|
|
|
|
LOG_INFO("HTTP server listening", log_data);
|
|
// run the HTTP server in a thread - see comment below
|
|
std::thread t([&]()
|
|
{
|
|
if (!svr.listen_after_bind())
|
|
{
|
|
state.store(SERVER_STATE_ERROR);
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
});
|
|
|
|
// load the model
|
|
params.progress_callback = update_load_progress;
|
|
params.progress_callback_user_data = (void*)&llama;
|
|
|
|
if (!llama.load_model(params))
|
|
{
|
|
state.store(SERVER_STATE_ERROR);
|
|
return 1;
|
|
} else {
|
|
llama.initialize();
|
|
state.store(SERVER_STATE_READY);
|
|
LOG_INFO("model loaded", {});
|
|
}
|
|
const auto model_meta = llama.model_meta();
|
|
|
|
// Middleware for API key validation
|
|
auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
|
|
// If API key is not set, skip validation
|
|
if (sparams.api_keys.empty()) {
|
|
return true;
|
|
}
|
|
|
|
// Check for API key in the header
|
|
auto auth_header = req.get_header_value("Authorization");
|
|
std::string prefix = "Bearer ";
|
|
if (auth_header.substr(0, prefix.size()) == prefix) {
|
|
std::string received_api_key = auth_header.substr(prefix.size());
|
|
if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) {
|
|
return true; // API key is valid
|
|
}
|
|
}
|
|
|
|
// API key is invalid or not provided
|
|
res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8");
|
|
res.status = 401; // Unauthorized
|
|
|
|
LOG_WARNING("Unauthorized: Invalid API Key", {});
|
|
|
|
return false;
|
|
};
|
|
|
|
// this is only called if no index.html is found in the public --path
|
|
svr.Get("/", [](const httplib::Request &, httplib::Response &res)
|
|
{
|
|
res.set_content("server running", "text/plain; charset=utf-8");
|
|
res.status = 200; // Unauthorized
|
|
return true;
|
|
});
|
|
|
|
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, -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;
|
|
}
|
|
}
|
|
|
|
llama.queue_results.remove_waiting_task_id(task_id);
|
|
sink.done();
|
|
return true;
|
|
};
|
|
|
|
auto on_complete = [task_id, &llama] (bool)
|
|
{
|
|
// cancel
|
|
llama.request_cancel(task_id);
|
|
llama.queue_results.remove_waiting_task_id(task_id);
|
|
};
|
|
|
|
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
|
}
|
|
});
|
|
|
|
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");
|
|
});
|
|
|
|
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());
|
|
}
|
|
|
|
const json data = format_detokenized_response(content);
|
|
return res.set_content(data.dump(), "application/json; charset=utf-8");
|
|
});
|
|
|
|
svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res)
|
|
{
|
|
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 = "";
|
|
}
|
|
|
|
// 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}}, true, -1);
|
|
|
|
// get the result
|
|
task_result result = llama.queue_results.recv(task_id);
|
|
llama.queue_results.remove_waiting_task_id(task_id);
|
|
|
|
// send the result
|
|
return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
|
|
});
|
|
|
|
// 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();
|
|
}
|
|
}*/
|
|
//);
|
|
|
|
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,
|
|
std::placeholders::_1,
|
|
std::placeholders::_2,
|
|
std::placeholders::_3
|
|
));
|
|
|
|
shutdown_handler = [&](int) {
|
|
llama.queue_tasks.terminate();
|
|
};
|
|
|
|
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
|
|
struct sigaction sigint_action;
|
|
sigint_action.sa_handler = signal_handler;
|
|
sigemptyset (&sigint_action.sa_mask);
|
|
sigint_action.sa_flags = 0;
|
|
sigaction(SIGINT, &sigint_action, NULL);
|
|
#elif defined (_WIN32)
|
|
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
|
|
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
|
|
};
|
|
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
|
|
|
|
for (int i = 0; i < argc; ++i) {
|
|
delete[] argv[i];
|
|
}
|
|
delete[] argv;
|
|
#endif
|
|
llama.queue_tasks.start_loop();
|
|
svr.stop();
|
|
t.join();
|
|
|
|
llama_backend_free();
|
|
return 0;
|
|
}
|