mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-26 17:20:01 +00:00
eea6cc4433
* Add command line argument for draft model * Remove second context of draft model * Format print * print usage if parsing -draft fails --------- Co-authored-by: firecoperana <firecoperana>
5193 lines
209 KiB
C++
5193 lines
209 KiB
C++
#pragma warning(disable : 4996)
|
|
#include "chat.h"
|
|
#include "utils.hpp"
|
|
|
|
#include "common.h"
|
|
#include "speculative.h"
|
|
#include "mtmd.h"
|
|
#include "sampling.h"
|
|
#include "json-schema-to-grammar.h"
|
|
#include "llama.h"
|
|
#include "grammar-parser.h"
|
|
#include "llama-vocab.h"
|
|
|
|
#ifndef NDEBUG
|
|
// crash the server in debug mode, otherwise send an http 500 error
|
|
#define CPPHTTPLIB_NO_EXCEPTIONS 1
|
|
#endif
|
|
|
|
#include <nlohmann/json.hpp>
|
|
#include "index.html.gz.hpp"
|
|
#include "index_llamacpp.html.gz.hpp"
|
|
#include "loading.html.hpp"
|
|
|
|
#include <atomic>
|
|
#include <chrono>
|
|
#include <condition_variable>
|
|
#include <cstddef>
|
|
#include <set>
|
|
#include <mutex>
|
|
#include <thread>
|
|
#include <signal.h>
|
|
#include <memory>
|
|
#include <random>
|
|
#include <algorithm>
|
|
#include <src/llama-impl.h>
|
|
#ifdef SQLITE3_MODERN_CPP_SUPPORT
|
|
#include <sqlite_modern_cpp.h>
|
|
|
|
struct DatabaseHandle {
|
|
sqlite::database db;
|
|
|
|
DatabaseHandle(const std::string& path) : db(path) {
|
|
db << "CREATE TABLE IF NOT EXISTS sessions (key TEXT PRIMARY KEY, data TEXT)";
|
|
db << "CREATE TABLE IF NOT EXISTS templates (key TEXT PRIMARY KEY, data TEXT)";
|
|
db << "CREATE TABLE IF NOT EXISTS names (key TEXT PRIMARY KEY, data TEXT)";
|
|
}
|
|
};
|
|
#endif
|
|
|
|
using json = nlohmann::ordered_json;
|
|
|
|
bool server_verbose = false;
|
|
bool server_log_json = true;
|
|
|
|
|
|
|
|
enum stop_type {
|
|
STOP_TYPE_NONE,
|
|
STOP_TYPE_EOS,
|
|
STOP_TYPE_WORD,
|
|
STOP_TYPE_LIMIT,
|
|
};
|
|
enum slot_state {
|
|
SLOT_STATE_IDLE,
|
|
SLOT_STATE_PROCESSING,
|
|
};
|
|
|
|
enum slot_command {
|
|
SLOT_COMMAND_NONE,
|
|
SLOT_COMMAND_LOAD_PROMPT,
|
|
SLOT_COMMAND_RELEASE,
|
|
};
|
|
|
|
enum server_state {
|
|
SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet
|
|
SERVER_STATE_READY, // Server is ready and model is loaded
|
|
SERVER_STATE_ERROR // An error occurred, load_model failed
|
|
};
|
|
|
|
enum server_task_type {
|
|
SERVER_TASK_TYPE_COMPLETION,
|
|
SERVER_TASK_TYPE_EMBEDDING,
|
|
SERVER_TASK_TYPE_RERANK,
|
|
SERVER_TASK_TYPE_INFILL,
|
|
SERVER_TASK_TYPE_CANCEL,
|
|
SERVER_TASK_TYPE_NEXT_RESPONSE,
|
|
SERVER_TASK_TYPE_METRICS,
|
|
SERVER_TASK_TYPE_SLOT_SAVE,
|
|
SERVER_TASK_TYPE_SLOT_RESTORE,
|
|
SERVER_TASK_TYPE_SLOT_ERASE,
|
|
SERVER_TASK_TYPE_SET_LORA,
|
|
};
|
|
|
|
enum oaicompat_type {
|
|
OAICOMPAT_TYPE_NONE,
|
|
OAICOMPAT_TYPE_CHAT,
|
|
OAICOMPAT_TYPE_COMPLETION,
|
|
OAICOMPAT_TYPE_EMBEDDING,
|
|
};
|
|
|
|
struct result_timings {
|
|
int32_t prompt_n = -1;
|
|
double prompt_ms;
|
|
double prompt_per_token_ms;
|
|
double prompt_per_second;
|
|
|
|
int32_t predicted_n = -1;
|
|
double predicted_ms;
|
|
double predicted_per_token_ms;
|
|
double predicted_per_second;
|
|
|
|
// Optional speculative metrics - only included when > 0
|
|
int32_t draft_n = 0;
|
|
int32_t draft_n_accepted = 0;
|
|
|
|
json to_json() const {
|
|
json base = {
|
|
{"prompt_n", prompt_n},
|
|
{"prompt_ms", prompt_ms},
|
|
{"prompt_per_token_ms", prompt_per_token_ms},
|
|
{"prompt_per_second", prompt_per_second},
|
|
|
|
{"predicted_n", predicted_n},
|
|
{"predicted_ms", predicted_ms},
|
|
{"predicted_per_token_ms", predicted_per_token_ms},
|
|
{"predicted_per_second", predicted_per_second},
|
|
};
|
|
|
|
if (draft_n > 0) {
|
|
base["draft_n"] = draft_n;
|
|
base["draft_n_accepted"] = draft_n_accepted;
|
|
}
|
|
|
|
return base;
|
|
}
|
|
};
|
|
|
|
struct server_task {
|
|
int id = -1; // to be filled by server_queue
|
|
int id_multi = -1;
|
|
int id_target = -1;
|
|
|
|
// used by SERVER_TASK_TYPE_INFERENCE
|
|
server_tokens tokens;
|
|
|
|
server_task_type type;
|
|
json data;
|
|
|
|
bool infill = false;
|
|
bool embedding = false;
|
|
};
|
|
|
|
struct server_task_result {
|
|
int id = -1;
|
|
int id_multi = -1;
|
|
|
|
json data;
|
|
|
|
bool stop;
|
|
bool error;
|
|
bool final_result = false;
|
|
result_timings timings;
|
|
// OAI-compat fields
|
|
//bool verbose = false;
|
|
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
|
|
std::string oaicompat_model;
|
|
std::string oaicompat_cmpl_id;
|
|
common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
|
common_chat_msg oaicompat_msg;
|
|
std::vector<common_chat_msg_diff> oaicompat_msg_diffs;
|
|
|
|
int index = 0;
|
|
|
|
std::string content;
|
|
std::vector<llama_token> tokens;
|
|
|
|
bool stream;
|
|
bool include_usage;
|
|
std::string prompt;
|
|
//slot_params generation_params;
|
|
|
|
bool truncated;
|
|
int32_t n_decoded;
|
|
int32_t n_prompt_tokens;
|
|
int32_t n_tokens_cached;
|
|
bool has_new_line;
|
|
std::string stopping_word;
|
|
|
|
bool post_sampling_probs = false;
|
|
std::vector<completion_token_output> probs_output;
|
|
std::vector<std::string> response_fields;
|
|
|
|
//slot_params generation_params;
|
|
|
|
bool verbose = false;
|
|
|
|
|
|
int get_index() {
|
|
return index;
|
|
}
|
|
|
|
bool is_stop() {
|
|
return true; // in stream mode, final responses are considered stop
|
|
}
|
|
|
|
json to_json_final() {
|
|
switch (oaicompat) {
|
|
case OAICOMPAT_TYPE_NONE:
|
|
return to_json_non_oaicompat_final();
|
|
case OAICOMPAT_TYPE_COMPLETION:
|
|
return to_json_oaicompat_final();
|
|
case OAICOMPAT_TYPE_CHAT:
|
|
return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat_final();
|
|
default:
|
|
GGML_ASSERT(false && "Invalid oaicompat_type");
|
|
}
|
|
}
|
|
|
|
json to_json_partial() {
|
|
switch (oaicompat) {
|
|
case OAICOMPAT_TYPE_NONE:
|
|
return to_json_non_oaicompat_partial();
|
|
case OAICOMPAT_TYPE_COMPLETION:
|
|
return to_json_oaicompat_partial();
|
|
case OAICOMPAT_TYPE_CHAT:
|
|
return to_json_oaicompat_chat_partial();
|
|
default:
|
|
GGML_ASSERT(false && "Invalid oaicompat_type");
|
|
}
|
|
}
|
|
|
|
json to_json_non_oaicompat_partial() {
|
|
// non-OAI-compat JSON
|
|
json res = json{
|
|
{"index", index},
|
|
{"content", content},
|
|
{"tokens", tokens},
|
|
{"stop", false},
|
|
{"id_slot", id_multi},
|
|
{"tokens_predicted", n_decoded},
|
|
{"tokens_evaluated", n_prompt_tokens},
|
|
};
|
|
// populate the timings object when needed (usually for the last response or with timings_per_token enabled)
|
|
if (timings.prompt_n > 0) {
|
|
res.push_back({ "timings", timings.to_json() });
|
|
}
|
|
if (!probs_output.empty()) {
|
|
res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs);
|
|
}
|
|
return res;
|
|
}
|
|
|
|
json to_json_non_oaicompat_final() {
|
|
json res = json{
|
|
{"index", index},
|
|
{"content", stream ? "" : content}, // in stream mode, content is already in last partial chunk
|
|
{"tokens", stream ? std::vector<llama_token> {} : tokens},
|
|
{"id_slot", id_multi},
|
|
{"stop", true},
|
|
{"model", oaicompat_model},
|
|
{"tokens_predicted", n_decoded},
|
|
{"tokens_evaluated", n_prompt_tokens},
|
|
//{"generation_settings", default_generation_settings_for_props.to_json()},
|
|
{"prompt", prompt},
|
|
{"has_new_line", has_new_line},
|
|
{"truncated", truncated},
|
|
//{"stop_type", stop_type_to_str(STOP_TYPE_EOS)},
|
|
{"stopping_word", stopping_word},
|
|
{"tokens_cached", n_tokens_cached},
|
|
{"timings", timings.to_json()},
|
|
};
|
|
if (!stream && !probs_output.empty()) {
|
|
res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs);
|
|
}
|
|
return response_fields.empty() ? res : json_get_nested_values(response_fields, res);
|
|
}
|
|
|
|
json to_json_oaicompat_partial() {
|
|
std::time_t t = std::time(0);
|
|
json logprobs = json(nullptr); // OAI default to null
|
|
if (probs_output.size() > 0) {
|
|
logprobs = json{
|
|
{"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
|
|
};
|
|
}
|
|
json res = json{
|
|
{"choices", json::array({
|
|
json{
|
|
{"text", content},
|
|
{"index", index},
|
|
{"logprobs", logprobs},
|
|
{"finish_reason", nullptr},
|
|
}
|
|
})},
|
|
{"created", t},
|
|
{"model", oaicompat_model},
|
|
{"object", "text_completion"},
|
|
{"usage", json {
|
|
{"completion_tokens", n_decoded},
|
|
{"prompt_tokens", n_prompt_tokens},
|
|
{"total_tokens", n_decoded + n_prompt_tokens}
|
|
}},
|
|
{"id", oaicompat_cmpl_id}
|
|
};
|
|
|
|
// extra fields for debugging purposes
|
|
if (verbose) {
|
|
res["__verbose"] = to_json_non_oaicompat_partial();
|
|
}
|
|
if (timings.prompt_n >= 0) {
|
|
res.push_back({ "timings", timings.to_json() });
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
json to_json_oaicompat_final() {
|
|
std::time_t t = std::time(0);
|
|
json logprobs = json(nullptr); // OAI default to null
|
|
if (!stream && probs_output.size() > 0) {
|
|
logprobs = json{
|
|
{"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
|
|
};
|
|
}
|
|
json finish_reason = "length";
|
|
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
|
|
finish_reason = "stop";
|
|
}
|
|
json res = json{
|
|
{"choices", json::array({
|
|
json{
|
|
{"text", stream ? "" : content}, // in stream mode, content is already in last partial chunk
|
|
{"index", index},
|
|
{"logprobs", logprobs},
|
|
{"finish_reason", finish_reason},
|
|
}
|
|
})},
|
|
{"created", t},
|
|
{"model", oaicompat_model},
|
|
{"object", "text_completion"},
|
|
{"usage", json {
|
|
{"completion_tokens", n_decoded},
|
|
{"prompt_tokens", n_prompt_tokens},
|
|
{"total_tokens", n_decoded + n_prompt_tokens}
|
|
}},
|
|
{"id", oaicompat_cmpl_id}
|
|
};
|
|
|
|
// extra fields for debugging purposes
|
|
if (verbose) {
|
|
res["__verbose"] = to_json_non_oaicompat_final();
|
|
}
|
|
if (timings.prompt_n >= 0) {
|
|
res.push_back({ "timings", timings.to_json() });
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
json to_json_oaicompat_chat_partial() {
|
|
bool first = n_decoded == 1;
|
|
std::time_t t = std::time(0);
|
|
json choices;
|
|
|
|
std::vector<json> deltas;
|
|
auto add_delta = [&](const json& delta) {
|
|
deltas.push_back({
|
|
{"choices", json::array({
|
|
json {
|
|
{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta", delta},
|
|
},
|
|
})},
|
|
{"created", t},
|
|
{"id", oaicompat_cmpl_id},
|
|
{"model", oaicompat_model},
|
|
{"object", "chat.completion.chunk"},
|
|
{"usage", json {
|
|
{"completion_tokens", n_decoded},
|
|
{"prompt_tokens", n_prompt_tokens},
|
|
{"total_tokens", n_decoded + n_prompt_tokens},
|
|
}},
|
|
});
|
|
};
|
|
// We have to send an initial update to conform to openai behavior
|
|
if (first) {
|
|
add_delta({
|
|
{"role", "assistant"},
|
|
{"content", nullptr},
|
|
});
|
|
}
|
|
|
|
for (const auto& diff : oaicompat_msg_diffs) {
|
|
add_delta(common_chat_msg_diff_to_json_oaicompat<json>(diff));
|
|
}
|
|
|
|
if (!deltas.empty()) {
|
|
GGML_ASSERT(deltas[deltas.size() - 1].at("choices").size() >= 1);
|
|
|
|
if (probs_output.size() > 0) {
|
|
deltas[deltas.size() - 1].at("choices").at(0)["logprobs"] = json{
|
|
{"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
|
|
};
|
|
}
|
|
|
|
if (timings.prompt_n >= 0) {
|
|
deltas[deltas.size() - 1].push_back({ "timings", timings.to_json() });
|
|
}
|
|
}
|
|
|
|
return deltas;
|
|
}
|
|
|
|
json to_json_oaicompat_chat_final() {
|
|
std::string finish_reason = "length";
|
|
common_chat_msg msg;
|
|
if (!oaicompat_msg.empty()) {
|
|
msg = oaicompat_msg;
|
|
}
|
|
else {
|
|
msg.role = "assistant";
|
|
msg.content = content;
|
|
}
|
|
if (stop) {
|
|
finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
|
|
}
|
|
|
|
|
|
json choice{
|
|
{"finish_reason", finish_reason},
|
|
{"index", 0},
|
|
{"message", msg.to_json_oaicompat<json>()},
|
|
};
|
|
|
|
if (!stream && probs_output.size() > 0) {
|
|
choice["logprobs"] = json{
|
|
{"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
|
|
};
|
|
}
|
|
|
|
std::time_t t = std::time(0);
|
|
|
|
json res = json{
|
|
{"choices", json::array({choice})},
|
|
{"created", t},
|
|
{"model", oaicompat_model},
|
|
{"object", "chat.completion"},
|
|
{"usage", json {
|
|
{"completion_tokens", n_decoded},
|
|
{"prompt_tokens", n_prompt_tokens},
|
|
{"total_tokens", n_decoded + n_prompt_tokens}
|
|
}},
|
|
{"id", oaicompat_cmpl_id}
|
|
};
|
|
|
|
// extra fields for debugging purposes
|
|
if (verbose) {
|
|
res["__verbose"] = to_json_non_oaicompat_final();
|
|
}
|
|
if (timings.prompt_n >= 0) {
|
|
res.push_back({ "timings", timings.to_json() });
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
json to_json_oaicompat_chat_stream() {
|
|
std::time_t t = std::time(0);
|
|
std::string finish_reason = "length";
|
|
if (stop) {
|
|
//if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
|
|
finish_reason = oaicompat_msg.tool_calls.empty() ? "stop" : "tool_calls";
|
|
}
|
|
|
|
json deltas = json::array();
|
|
for (const auto& diff : oaicompat_msg_diffs) {
|
|
deltas.push_back({
|
|
{"choices", json::array({
|
|
json {
|
|
{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta", common_chat_msg_diff_to_json_oaicompat<json>(diff)},
|
|
},
|
|
})},
|
|
{"created", t},
|
|
{"id", oaicompat_cmpl_id},
|
|
{"model", oaicompat_model},
|
|
{"object", "chat.completion.chunk"},
|
|
});
|
|
}
|
|
|
|
deltas.push_back({
|
|
{"choices", json::array({
|
|
json {
|
|
{"finish_reason", finish_reason},
|
|
{"index", 0},
|
|
{"delta", json::object()},
|
|
},
|
|
})},
|
|
{"created", t},
|
|
{"id", oaicompat_cmpl_id},
|
|
{"model", oaicompat_model},
|
|
{"object", "chat.completion.chunk"},
|
|
});
|
|
if (include_usage) {
|
|
// OpenAI API spec for chat.completion.chunks specifies an empty `choices` array for the last chunk when including usage
|
|
// https://platform.openai.com/docs/api-reference/chat_streaming/streaming#chat_streaming/streaming-choices
|
|
deltas.push_back({
|
|
{"choices", json::array()},
|
|
{"created", t},
|
|
{"id", oaicompat_cmpl_id},
|
|
{"model", oaicompat_model},
|
|
{"object", "chat.completion.chunk"},
|
|
{"usage", json {
|
|
{"completion_tokens", n_decoded},
|
|
{"prompt_tokens", n_prompt_tokens},
|
|
{"total_tokens", n_decoded + n_prompt_tokens},
|
|
}},
|
|
});
|
|
}
|
|
if (timings.prompt_n >= 0) {
|
|
deltas.back().push_back({ "timings", timings.to_json() });
|
|
}
|
|
// extra fields for debugging purposes
|
|
if (verbose && !deltas.empty()) {
|
|
deltas.front()["__verbose"] = to_json_non_oaicompat_final();
|
|
}
|
|
|
|
return deltas;
|
|
}
|
|
};
|
|
|
|
inline std::string stop_type_to_str(stop_type type) {
|
|
switch (type) {
|
|
case STOP_TYPE_EOS: return "eos";
|
|
case STOP_TYPE_WORD: return "word";
|
|
case STOP_TYPE_LIMIT: return "limit";
|
|
default: return "none";
|
|
}
|
|
}
|
|
|
|
|
|
struct server_task_multi {
|
|
int id = -1;
|
|
|
|
std::set<int> subtasks_remaining;
|
|
std::vector<server_task_result> results;
|
|
};
|
|
|
|
struct slot_params {
|
|
bool stream = true;
|
|
bool include_usage = false;
|
|
bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt
|
|
|
|
int32_t n_keep = 0; // number of tokens to keep from initial prompt
|
|
int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
|
|
int32_t n_predict = -1; // new tokens to predict
|
|
|
|
std::vector<std::string> antiprompt;
|
|
|
|
bool timings_per_token = false;
|
|
bool post_sampling_probs = false;
|
|
json input_prefix;
|
|
json input_suffix;
|
|
|
|
// speculative decoding parameters
|
|
struct {
|
|
int n_max = 16; // max drafted tokens
|
|
int n_min = 0; // min drafted tokens to accept
|
|
float p_min = 0.75f; // min probability required to accept a token in the draft
|
|
} speculative;
|
|
|
|
// OAI-compat fields
|
|
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
|
|
std::string oaicompat_model;
|
|
std::string oaicompat_cmpl_id;
|
|
common_chat_syntax oaicompat_chat_syntax;
|
|
|
|
};
|
|
|
|
struct server_slot {
|
|
int id;
|
|
int id_task = -1;
|
|
int id_multi = -1;
|
|
|
|
struct slot_params params;
|
|
|
|
slot_state state = SLOT_STATE_IDLE;
|
|
slot_command command = SLOT_COMMAND_NONE;
|
|
|
|
// used to determine the slot that has been used the longest
|
|
int64_t t_last_used = -1;
|
|
|
|
// generation props
|
|
int32_t n_ctx = 0; // context size per slot
|
|
int32_t n_past = 0;
|
|
int32_t n_decoded = 0;
|
|
int32_t n_remaining = -1;
|
|
int32_t i_batch = -1;
|
|
int32_t n_predict = -1; // TODO: disambiguate from params.n_predict
|
|
|
|
int32_t n_prompt_tokens = 0;
|
|
int32_t n_prompt_tokens_processed = 0;
|
|
|
|
json prompt; // can be either a string, array of strings or array of token ids
|
|
|
|
// when a task is submitted, we first tokenize the prompt and store it here
|
|
server_tokens prompt_tokens;
|
|
server_tokens cache_tokens;
|
|
|
|
std::string generated_text;
|
|
|
|
std::vector<completion_token_output> generated_token_probs;
|
|
common_chat_msg chat_msg;
|
|
|
|
bool infill = false;
|
|
bool embedding = false;
|
|
bool has_next_token = true;
|
|
bool truncated = false;
|
|
bool stopped_eos = false;
|
|
bool stopped_word = false;
|
|
bool stopped_limit = false;
|
|
|
|
bool oaicompat = false;
|
|
|
|
std::string oaicompat_model;
|
|
std::string stopping_word;
|
|
stop_type stop;
|
|
// sampling
|
|
llama_token sampled;
|
|
struct llama_sampling_params sparams;
|
|
llama_sampling_context * ctx_sampling = nullptr;
|
|
json json_schema;
|
|
|
|
common_chat_format chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
|
std::vector<std::string> generated_tool_call_ids;
|
|
|
|
int32_t ga_i = 0; // group-attention state
|
|
int32_t ga_n = 1; // group-attention factor
|
|
int32_t ga_w = 512; // group-attention width
|
|
|
|
// multimodal
|
|
mtmd_context * mctx = nullptr;
|
|
|
|
// speculative decoding
|
|
struct llama_speculative * spec = nullptr;
|
|
llama_context * ctx_dft = nullptr;
|
|
llama_batch batch_spec = {};
|
|
|
|
// speculative decoding stats
|
|
int32_t n_draft_total = 0; // Total draft tokens generated
|
|
int32_t n_draft_accepted = 0; // Draft tokens actually accepted
|
|
|
|
int32_t n_past_se = 0; // self-extend
|
|
|
|
// stats
|
|
size_t n_sent_text = 0; // number of sent text character
|
|
size_t n_sent_token_probs = 0;
|
|
|
|
int64_t t_start_process_prompt;
|
|
int64_t t_start_generation;
|
|
|
|
double t_prompt_processing; // ms
|
|
double t_token_generation; // ms
|
|
|
|
void reset() {
|
|
n_prompt_tokens = 0;
|
|
generated_text = "";
|
|
truncated = false;
|
|
stopped_eos = false;
|
|
stopped_word = false;
|
|
stopped_limit = false;
|
|
stopping_word = "";
|
|
n_past = 0;
|
|
n_sent_text = 0;
|
|
n_sent_token_probs = 0;
|
|
infill = false;
|
|
ga_i = 0;
|
|
n_past_se = 0;
|
|
chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
|
|
|
generated_token_probs.clear();
|
|
|
|
|
|
// Reset speculative decoding stats
|
|
n_draft_total = 0;
|
|
n_draft_accepted = 0;
|
|
chat_msg = {};
|
|
json_schema = json();
|
|
generated_tool_call_ids.clear();
|
|
}
|
|
|
|
bool has_budget(gpt_params &global_params) {
|
|
if (params.n_predict == -1 && global_params.n_predict == -1) {
|
|
return true; // limitless
|
|
}
|
|
|
|
n_remaining = -1;
|
|
|
|
if (params.n_predict != -1) {
|
|
n_remaining = params.n_predict - n_decoded;
|
|
} else if (global_params.n_predict != -1) {
|
|
n_remaining = global_params.n_predict - n_decoded;
|
|
}
|
|
|
|
return n_remaining > 0; // no budget
|
|
}
|
|
|
|
bool available() const {
|
|
return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE;
|
|
}
|
|
|
|
bool is_processing() const {
|
|
return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING;
|
|
}
|
|
|
|
void add_token_string(const completion_token_output & token) {
|
|
if (command == SLOT_COMMAND_RELEASE) {
|
|
return;
|
|
}
|
|
generated_token_probs.push_back(token);
|
|
}
|
|
|
|
void release() {
|
|
if (state == SLOT_STATE_PROCESSING) {
|
|
t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
|
|
command = SLOT_COMMAND_RELEASE;
|
|
}
|
|
}
|
|
|
|
json get_formated_timings() const {
|
|
return json {
|
|
{"prompt_n", n_prompt_tokens_processed},
|
|
{"prompt_ms", t_prompt_processing},
|
|
{"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed},
|
|
{"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed},
|
|
|
|
{"predicted_n", n_decoded},
|
|
{"predicted_ms", t_token_generation},
|
|
{"predicted_per_token_ms", t_token_generation / n_decoded},
|
|
{"predicted_per_second", 1e3 / t_token_generation * n_decoded},
|
|
};
|
|
}
|
|
|
|
result_timings get_timings() const {
|
|
result_timings timings;
|
|
timings.prompt_n = n_prompt_tokens_processed;
|
|
timings.prompt_ms = t_prompt_processing;
|
|
timings.prompt_per_token_ms = t_prompt_processing / n_prompt_tokens_processed;
|
|
timings.prompt_per_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
|
|
|
|
timings.predicted_n = n_decoded;
|
|
timings.predicted_ms = t_token_generation;
|
|
timings.predicted_per_token_ms = t_token_generation / n_decoded;
|
|
timings.predicted_per_second = 1e3 / t_token_generation * n_decoded;
|
|
|
|
// Add speculative metrics
|
|
if (n_draft_total > 0) {
|
|
timings.draft_n = n_draft_total;
|
|
timings.draft_n_accepted = n_draft_accepted;
|
|
}
|
|
|
|
return timings;
|
|
}
|
|
|
|
const common_chat_msg& update_chat_msg(std::vector<common_chat_msg_diff>& diffs) {
|
|
auto previous_msg = chat_msg;
|
|
auto new_msg = common_chat_parse(
|
|
generated_text,
|
|
/* is_partial= */ stop != STOP_TYPE_EOS,
|
|
params.oaicompat_chat_syntax);
|
|
if (!new_msg.empty()) {
|
|
new_msg.ensure_tool_call_ids_set(generated_tool_call_ids, gen_tool_call_id);
|
|
chat_msg = new_msg;
|
|
diffs = common_chat_msg_diff::compute_diffs(previous_msg, new_msg.empty() ? previous_msg : new_msg);
|
|
}
|
|
//LLAMA_LOG_DEBUG("Parsing chat message: %s\n", generated_text.c_str());
|
|
//LLAMA_LOG_DEBUG("Parsing chat message: %s\n", chat_msg.reasoning_content.c_str());
|
|
//LLAMA_LOG_DEBUG("Parsing chat message: %s\n", chat_msg.content.c_str());
|
|
return chat_msg;
|
|
}
|
|
|
|
|
|
size_t find_stopping_strings(const std::string & text, const size_t last_token_size, bool is_full_stop) {
|
|
size_t stop_pos = std::string::npos;
|
|
|
|
for (const std::string & word : params.antiprompt) {
|
|
size_t pos;
|
|
|
|
if (is_full_stop) {
|
|
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 = string_find_partial_stop(text, word);
|
|
}
|
|
|
|
if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) {
|
|
if (is_full_stop) {
|
|
stopped_word = true;
|
|
stopping_word = word;
|
|
has_next_token = false;
|
|
}
|
|
stop_pos = pos;
|
|
}
|
|
}
|
|
|
|
return stop_pos;
|
|
}
|
|
|
|
void print_timings() const {
|
|
char buffer[512];
|
|
|
|
double t_token = t_prompt_processing / n_prompt_tokens_processed;
|
|
double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
|
|
|
|
snprintf(buffer, 512, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
|
|
t_prompt_processing, n_prompt_tokens_processed,
|
|
t_token, n_tokens_second);
|
|
|
|
LOG_INFO(buffer, {
|
|
{"id_slot", id},
|
|
{"id_task", id_task},
|
|
{"t_prompt_processing", t_prompt_processing},
|
|
{"n_prompt_tokens_processed", n_prompt_tokens_processed},
|
|
{"t_token", t_token},
|
|
{"n_tokens_second", n_tokens_second},
|
|
});
|
|
|
|
t_token = t_token_generation / n_decoded;
|
|
n_tokens_second = 1e3 / t_token_generation * n_decoded;
|
|
|
|
snprintf(buffer, 512, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
|
|
t_token_generation, n_decoded,
|
|
t_token, n_tokens_second);
|
|
|
|
LOG_INFO(buffer, {
|
|
{"id_slot", id},
|
|
{"id_task", id_task},
|
|
{"t_token_generation", t_token_generation},
|
|
{"n_decoded", n_decoded},
|
|
{"t_token", t_token},
|
|
{"n_tokens_second", n_tokens_second},
|
|
});
|
|
|
|
snprintf(buffer, 512, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
|
|
|
|
LOG_INFO(buffer, {
|
|
{"id_slot", id},
|
|
{"id_task", id_task},
|
|
{"t_prompt_processing", t_prompt_processing},
|
|
{"t_token_generation", t_token_generation},
|
|
{"t_total", t_prompt_processing + t_token_generation},
|
|
});
|
|
}
|
|
};
|
|
|
|
struct server_metrics {
|
|
int64_t t_start = 0;
|
|
|
|
uint64_t n_prompt_tokens_processed_total = 0;
|
|
uint64_t t_prompt_processing_total = 0;
|
|
uint64_t n_tokens_predicted_total = 0;
|
|
uint64_t t_tokens_generation_total = 0;
|
|
|
|
uint64_t n_prompt_tokens_processed = 0;
|
|
uint64_t t_prompt_processing = 0;
|
|
|
|
uint64_t n_tokens_predicted = 0;
|
|
uint64_t t_tokens_generation = 0;
|
|
|
|
void init() {
|
|
t_start = ggml_time_us();
|
|
}
|
|
|
|
void on_prompt_eval(const server_slot & slot) {
|
|
n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
|
|
n_prompt_tokens_processed += slot.n_prompt_tokens_processed;
|
|
t_prompt_processing += slot.t_prompt_processing;
|
|
t_prompt_processing_total += slot.t_prompt_processing;
|
|
}
|
|
|
|
void on_prediction(const server_slot & slot) {
|
|
n_tokens_predicted_total += slot.n_decoded;
|
|
n_tokens_predicted += slot.n_decoded;
|
|
t_tokens_generation += slot.t_token_generation;
|
|
t_tokens_generation_total += slot.t_token_generation;
|
|
}
|
|
|
|
void reset_bucket() {
|
|
n_prompt_tokens_processed = 0;
|
|
t_prompt_processing = 0;
|
|
n_tokens_predicted = 0;
|
|
t_tokens_generation = 0;
|
|
}
|
|
};
|
|
|
|
struct server_queue {
|
|
int id = 0;
|
|
bool running;
|
|
|
|
// queues
|
|
std::vector<server_task> queue_tasks;
|
|
std::vector<server_task> queue_tasks_deferred;
|
|
|
|
std::vector<server_task_multi> queue_multitasks;
|
|
|
|
std::mutex mutex_tasks;
|
|
std::condition_variable condition_tasks;
|
|
|
|
// callback functions
|
|
std::function<void(server_task &&)> callback_new_task;
|
|
std::function<void(server_task_multi &)> callback_finish_multitask;
|
|
std::function<void(void)> callback_update_slots;
|
|
|
|
// Add a new task to the end of the queue
|
|
int post(server_task task) {
|
|
std::unique_lock<std::mutex> lock(mutex_tasks);
|
|
if (task.id == -1) {
|
|
task.id = id++;
|
|
LOG_VERBOSE("new task id", {{"new_id", task.id}});
|
|
}
|
|
queue_tasks.push_back(std::move(task));
|
|
condition_tasks.notify_one();
|
|
return task.id;
|
|
}
|
|
|
|
// Add a new task, but defer until one slot is available
|
|
void defer(server_task && task) {
|
|
std::unique_lock<std::mutex> lock(mutex_tasks);
|
|
queue_tasks_deferred.push_back(std::move(task));
|
|
}
|
|
|
|
// Get the next id for creating anew task
|
|
int get_new_id() {
|
|
std::unique_lock<std::mutex> lock(mutex_tasks);
|
|
int new_id = id++;
|
|
LOG_VERBOSE("new task id", {{"new_id", new_id}});
|
|
return new_id;
|
|
}
|
|
|
|
// Register function to process a new task
|
|
void on_new_task(std::function<void(server_task &&)> callback) {
|
|
callback_new_task = std::move(callback);
|
|
}
|
|
|
|
// Register function to process a multitask when it is finished
|
|
void on_finish_multitask(std::function<void(server_task_multi&)> callback) {
|
|
callback_finish_multitask = std::move(callback);
|
|
}
|
|
|
|
// Register the function to be called when all slots data is ready to be processed
|
|
void on_update_slots(std::function<void(void)> callback) {
|
|
callback_update_slots = std::move(callback);
|
|
}
|
|
|
|
// Call when the state of one slot is changed
|
|
void notify_slot_changed() {
|
|
// move deferred tasks back to main loop
|
|
std::unique_lock<std::mutex> lock(mutex_tasks);
|
|
for (auto & task : queue_tasks_deferred) {
|
|
queue_tasks.push_back(std::move(task));
|
|
}
|
|
queue_tasks_deferred.clear();
|
|
}
|
|
|
|
// end the start_loop routine
|
|
void terminate() {
|
|
std::unique_lock<std::mutex> lock(mutex_tasks);
|
|
running = false;
|
|
condition_tasks.notify_all();
|
|
}
|
|
|
|
/**
|
|
* Main loop consists of these steps:
|
|
* - Wait until a new task arrives
|
|
* - Process the task (i.e. maybe copy data into slot)
|
|
* - Check if multitask is finished
|
|
* - Update all slots
|
|
*/
|
|
void start_loop() {
|
|
running = true;
|
|
|
|
while (true) {
|
|
LOG_VERBOSE("new task may arrive", {});
|
|
|
|
while (true) {
|
|
std::unique_lock<std::mutex> lock(mutex_tasks);
|
|
if (queue_tasks.empty()) {
|
|
lock.unlock();
|
|
break;
|
|
}
|
|
server_task task = std::move(queue_tasks.front());
|
|
queue_tasks.erase(queue_tasks.begin());
|
|
lock.unlock();
|
|
LOG_VERBOSE("callback_new_task", {{"id_task", task.id}});
|
|
callback_new_task(std::move(task));
|
|
}
|
|
|
|
LOG_VERBOSE("update_multitasks", {});
|
|
|
|
// check if we have any finished multitasks
|
|
auto queue_iterator = queue_multitasks.begin();
|
|
while (queue_iterator != queue_multitasks.end()) {
|
|
if (queue_iterator->subtasks_remaining.empty()) {
|
|
// all subtasks done == multitask is done
|
|
server_task_multi current_multitask = *queue_iterator;
|
|
callback_finish_multitask(current_multitask);
|
|
// remove this multitask
|
|
queue_iterator = queue_multitasks.erase(queue_iterator);
|
|
} else {
|
|
++queue_iterator;
|
|
}
|
|
}
|
|
|
|
// all tasks in the current loop is processed, slots data is now ready
|
|
LOG_VERBOSE("callback_update_slots", {});
|
|
|
|
callback_update_slots();
|
|
|
|
LOG_VERBOSE("wait for new task", {});
|
|
{
|
|
std::unique_lock<std::mutex> lock(mutex_tasks);
|
|
if (queue_tasks.empty()) {
|
|
if (!running) {
|
|
LOG_VERBOSE("ending start_loop", {});
|
|
return;
|
|
}
|
|
condition_tasks.wait(lock, [&]{
|
|
return (!queue_tasks.empty() || !running);
|
|
});
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
//
|
|
// functions to manage multitasks
|
|
//
|
|
|
|
// add a multitask by specifying the id of all subtask (subtask is a server_task)
|
|
void add_multitask(int id_multi, std::vector<int> & sub_ids) {
|
|
std::lock_guard<std::mutex> lock(mutex_tasks);
|
|
server_task_multi multi;
|
|
multi.id = id_multi;
|
|
std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
|
|
queue_multitasks.push_back(multi);
|
|
}
|
|
|
|
// updatethe remaining subtasks, while appending results to multitask
|
|
void update_multitask(int id_multi, int id_sub, server_task_result & result) {
|
|
std::lock_guard<std::mutex> lock(mutex_tasks);
|
|
for (auto & multitask : queue_multitasks) {
|
|
if (multitask.id == id_multi) {
|
|
multitask.subtasks_remaining.erase(id_sub);
|
|
multitask.results.push_back(result);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
struct server_response {
|
|
typedef std::function<void(int, int, server_task_result &)> callback_multitask_t;
|
|
callback_multitask_t callback_update_multitask;
|
|
|
|
// for keeping track of all tasks waiting for the result
|
|
std::set<int> waiting_task_ids;
|
|
|
|
// the main result queue
|
|
std::vector<server_task_result> queue_results;
|
|
|
|
std::mutex mutex_results;
|
|
std::condition_variable condition_results;
|
|
|
|
// add the id_task to the list of tasks waiting for response
|
|
void add_waiting_task_id(int id_task) {
|
|
LOG_VERBOSE("waiting for task id", {{"id_task", id_task}});
|
|
|
|
std::unique_lock<std::mutex> lock(mutex_results);
|
|
waiting_task_ids.insert(id_task);
|
|
}
|
|
|
|
// when the request is finished, we can remove task associated with it
|
|
void remove_waiting_task_id(int id_task) {
|
|
LOG_VERBOSE("remove waiting for task id", {{"id_task", id_task}});
|
|
|
|
std::unique_lock<std::mutex> lock(mutex_results);
|
|
waiting_task_ids.erase(id_task);
|
|
}
|
|
|
|
// This function blocks the thread until there is a response for this id_task
|
|
server_task_result recv(int id_task) {
|
|
while (true) {
|
|
std::unique_lock<std::mutex> lock(mutex_results);
|
|
condition_results.wait(lock, [&]{
|
|
return !queue_results.empty();
|
|
});
|
|
|
|
for (int i = 0; i < (int) queue_results.size(); i++) {
|
|
if (queue_results[i].id == id_task) {
|
|
assert(queue_results[i].id_multi == -1);
|
|
server_task_result res = queue_results[i];
|
|
queue_results.erase(queue_results.begin() + i);
|
|
return res;
|
|
}
|
|
}
|
|
}
|
|
|
|
// should never reach here
|
|
}
|
|
|
|
// Register the function to update multitask
|
|
void on_multitask_update(callback_multitask_t callback) {
|
|
callback_update_multitask = std::move(callback);
|
|
}
|
|
|
|
// Send a new result to a waiting id_task
|
|
void send(server_task_result result) {
|
|
LOG_VERBOSE("send new result", {{"id_task", result.id}});
|
|
|
|
std::unique_lock<std::mutex> lock(mutex_results);
|
|
for (const auto & id_task : waiting_task_ids) {
|
|
// LOG_TEE("waiting task id %i \n", id_task);
|
|
// for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
|
|
if (result.id_multi == id_task) {
|
|
LOG_VERBOSE("callback_update_multitask", {{"id_task", id_task}});
|
|
callback_update_multitask(id_task, result.id, result);
|
|
continue;
|
|
}
|
|
|
|
if (result.id == id_task) {
|
|
LOG_VERBOSE("queue_results.push_back", {{"id_task", id_task}});
|
|
queue_results.push_back(result);
|
|
condition_results.notify_all();
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
struct server_context {
|
|
llama_model * model = nullptr;
|
|
llama_context * ctx = nullptr;
|
|
std::vector<llama_lora_adapter_container> lora_adapters;
|
|
|
|
gpt_params params;
|
|
|
|
llama_batch batch;
|
|
|
|
bool clean_kv_cache = true;
|
|
bool add_bos_token = true;
|
|
bool has_eos_token = false;
|
|
|
|
// multimodal
|
|
mtmd_context * mctx = nullptr;
|
|
|
|
// For speculative decoding
|
|
llama_model * model_draft = nullptr;
|
|
llama_context * ctx_draft = nullptr;
|
|
llama_context_params cparams_dft;
|
|
|
|
int32_t n_ctx; // total context for all clients / slots
|
|
|
|
// system prompt
|
|
bool system_need_update = false;
|
|
|
|
std::string system_prompt;
|
|
std::vector<llama_token> system_tokens;
|
|
|
|
// slots / clients
|
|
std::vector<server_slot> slots;
|
|
json default_generation_settings_for_props;
|
|
|
|
server_queue queue_tasks;
|
|
server_response queue_results;
|
|
|
|
server_metrics metrics;
|
|
|
|
common_chat_templates_ptr chat_templates;
|
|
oaicompat_parser_options oai_parser_opt;
|
|
// Necessary similarity of prompt for slot selection
|
|
float slot_prompt_similarity = 0.0f;
|
|
|
|
~server_context() {
|
|
if (ctx) {
|
|
llama_free(ctx);
|
|
ctx = nullptr;
|
|
}
|
|
|
|
if (model) {
|
|
llama_free_model(model);
|
|
model = nullptr;
|
|
}
|
|
// Free multimodal
|
|
mtmd_free(mctx);
|
|
// Free draft model and context if they exist
|
|
if (ctx_draft) {
|
|
llama_free(ctx_draft);
|
|
ctx_draft = nullptr;
|
|
}
|
|
if (model_draft) {
|
|
llama_free_model(model_draft);
|
|
model_draft = nullptr;
|
|
}
|
|
|
|
// Clear any sampling context
|
|
for (server_slot & slot : slots) {
|
|
if (slot.ctx_sampling != nullptr) {
|
|
llama_sampling_free(slot.ctx_sampling);
|
|
}
|
|
if (slot.ctx_dft) {
|
|
llama_free(slot.ctx_dft);
|
|
}
|
|
if (slot.spec) {
|
|
llama_speculative_free(slot.spec);
|
|
}
|
|
llama_batch_free(slot.batch_spec);
|
|
}
|
|
|
|
llama_batch_free(batch);
|
|
}
|
|
|
|
bool load_model(const gpt_params & params_) {
|
|
params = params_;
|
|
|
|
llama_init_result llama_init = llama_init_from_gpt_params(params);
|
|
|
|
model = llama_init.model;
|
|
ctx = llama_init.context;
|
|
lora_adapters = llama_init.lora_adapters;
|
|
|
|
if (model == nullptr) {
|
|
LOG_ERROR("unable to load model", {{"model", params.model}});
|
|
return false;
|
|
}
|
|
|
|
n_ctx = llama_n_ctx(ctx);
|
|
|
|
add_bos_token = llama_should_add_bos_token(model);
|
|
has_eos_token = llama_add_eos_token(model) != 1;
|
|
|
|
chat_templates = common_chat_templates_init(model, params.chat_template);
|
|
try {
|
|
common_chat_format_example(chat_templates.get(), params.use_jinja, {});
|
|
}
|
|
catch (const std::exception& e) {
|
|
LOG_WARNING("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
|
|
chat_templates = common_chat_templates_init(model, "chatml");
|
|
}
|
|
|
|
bool has_draft_model = !params.model_draft.empty() || !params.draft_params.empty();
|
|
std::string & mmproj_path = params.mmproj.path;
|
|
if (!mmproj_path.empty()) {
|
|
mtmd_context_params mparams = mtmd_context_params_default();
|
|
mparams.use_gpu = params.mmproj_use_gpu;
|
|
mparams.print_timings = false;
|
|
mparams.n_threads = params.n_threads;
|
|
mparams.verbosity = params.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO;
|
|
mctx = mtmd_init_from_file(mmproj_path.c_str(), model, mparams);
|
|
if (mctx == nullptr) {
|
|
LOG_ERROR("failed to load multimodal model, '%s'\n", mmproj_path.c_str());
|
|
return false;
|
|
}
|
|
LOG_INFO("loaded multimodal model, '%s'\n", mmproj_path.c_str());
|
|
|
|
if (params.ctx_shift) {
|
|
params.ctx_shift = false;
|
|
LOG_WARNING("%s\n", "ctx_shift is not supported by multimodal, it will be disabled");
|
|
}
|
|
|
|
//if (params.n_cache_reuse) {
|
|
// params_base.n_cache_reuse = 0;
|
|
// SRV_WRN("%s\n", "cache_reuse is not supported by multimodal, it will be disabled");
|
|
//}
|
|
|
|
if (has_draft_model) {
|
|
LOG_ERROR("%s\n", "err: speculative decode is not supported by multimodal");
|
|
return false;
|
|
}
|
|
}
|
|
// Load draft model for speculative decoding if specified
|
|
if (has_draft_model) {
|
|
LLAMA_LOG_INFO("\n\n==================================loading DRAFT model==================================\n\n");
|
|
|
|
gpt_params params_dft;
|
|
params_dft.devices = params.devices_draft;
|
|
params_dft.model = params.model_draft;
|
|
params_dft.n_gpu_layers = params.n_gpu_layers_draft;
|
|
params_dft.cache_type_k = params.cache_type_k_draft.empty() ? params.cache_type_k : params.cache_type_k_draft;
|
|
params_dft.cache_type_v = params.cache_type_v_draft.empty() ? params.cache_type_v : params.cache_type_v_draft;
|
|
params_dft.flash_attn = params.flash_attn;
|
|
if (!params.draft_params.empty()) {
|
|
auto [argc, argv] = parse_command_line("llama-server "+params.draft_params);
|
|
if (!gpt_params_parse(argc, argv, params_dft)) {
|
|
gpt_params_print_usage(argc, argv, params_dft);
|
|
free_command_line(argc, argv);
|
|
return false;
|
|
};
|
|
free_command_line(argc, argv);
|
|
}
|
|
LOG_INFO("", { {"model", params_dft.model} });
|
|
if (params_dft.n_ctx == 0) {
|
|
params_dft.n_ctx = params.n_ctx_draft;
|
|
}
|
|
params_dft.n_ctx = params_dft.n_ctx == 0 ? params.n_ctx / params.n_parallel : params_dft.n_ctx;
|
|
params_dft.n_parallel = 1;
|
|
|
|
llama_init_result llama_init_dft = llama_init_from_gpt_params(params_dft);
|
|
|
|
llama_model * model_dft = llama_init_dft.model;
|
|
if (model_dft == nullptr) {
|
|
LOG_ERROR("failed to load draft model", {{"model", params.model_draft}});
|
|
return false;
|
|
}
|
|
|
|
if (!llama_speculative_are_compatible(ctx, llama_init_dft.context)) {
|
|
LOG_INFO("the draft model is not compatible with the target model. tokens will be translated between the draft and target models.", {{}});
|
|
}
|
|
|
|
const int n_ctx_dft = llama_n_ctx(llama_init_dft.context);
|
|
|
|
cparams_dft = llama_context_params_from_gpt_params(params_dft);
|
|
cparams_dft.n_batch = n_ctx_dft;
|
|
|
|
model_draft = llama_init_dft.model;
|
|
ctx_draft = llama_init_dft.context;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
|
|
void init() {
|
|
const int32_t n_ctx_slot = n_ctx / params.n_parallel;
|
|
|
|
LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}});
|
|
|
|
for (int i = 0; i < params.n_parallel; i++) {
|
|
server_slot slot;
|
|
|
|
slot.id = i;
|
|
slot.n_ctx = n_ctx_slot;
|
|
slot.n_predict = params.n_predict;
|
|
slot.mctx = mctx;
|
|
slot.cache_tokens.has_mtmd = mctx != nullptr;
|
|
|
|
LOG_INFO("new slot", {
|
|
{"id_slot", slot.id},
|
|
{"n_ctx_slot", slot.n_ctx}
|
|
});
|
|
|
|
const int ga_n = params.grp_attn_n;
|
|
const int ga_w = params.grp_attn_w;
|
|
|
|
if (ga_n != 1) {
|
|
GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT
|
|
GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
|
|
//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT
|
|
//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
|
|
|
|
LOG_INFO("slot self-extend", {
|
|
{"id_slot", slot.id},
|
|
{"ga_n", ga_n},
|
|
{"ga_w", ga_w}
|
|
});
|
|
}
|
|
|
|
slot.ga_i = 0;
|
|
slot.ga_n = ga_n;
|
|
slot.ga_w = ga_w;
|
|
|
|
slot.sparams = params.sparams;
|
|
|
|
// Initialize speculative decoding if a draft model is loaded
|
|
if (ctx_draft) {
|
|
slot.batch_spec = llama_batch_init(slot.params.speculative.n_max + 1, 0, 1);
|
|
// slot.ctx_dft = llama_new_context_with_model(model_draft, cparams_dft); // initialized twice
|
|
slot.ctx_dft = ctx_draft;
|
|
if (slot.ctx_dft == nullptr) {
|
|
LOG_ERROR("failed to create draft context", {});
|
|
return;
|
|
}
|
|
|
|
slot.spec = llama_speculative_init(ctx, slot.ctx_dft);
|
|
if (slot.spec == nullptr) {
|
|
LOG_ERROR("failed to create speculator", {});
|
|
return;
|
|
}
|
|
for (auto & pair : params.replacements_draft) {
|
|
llama_speculative_add_replacement_tgt_dft(slot.spec, pair.first.c_str(), pair.second.c_str());
|
|
}
|
|
|
|
}
|
|
|
|
slot.reset();
|
|
|
|
slots.push_back(std::move(slot));
|
|
}
|
|
|
|
default_generation_settings_for_props = get_formated_generation(slots.front());
|
|
default_generation_settings_for_props["seed"] = -1;
|
|
|
|
// the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens
|
|
// note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
|
|
{
|
|
const int32_t n_batch = llama_n_batch(ctx);
|
|
|
|
// only a single seq_id per token is needed
|
|
batch = llama_batch_init(std::max(n_batch, params.n_parallel), 0, 1);
|
|
}
|
|
|
|
metrics.init();
|
|
|
|
// thinking is enabled if:
|
|
// 1. It's not explicitly disabled (reasoning_budget == 0)
|
|
// 2. The chat template supports it
|
|
const bool enable_thinking = params.use_jinja && params.reasoning_budget != 0 && common_chat_templates_support_enable_thinking(chat_templates.get());
|
|
//LLAMA_LOG_INFO("Enable thinking? %d\n", enable_thinking);
|
|
|
|
oai_parser_opt = {
|
|
/* use_jinja */ params.use_jinja,
|
|
/* prefill_assistant */ params.prefill_assistant,
|
|
/* reasoning_format */ params.reasoning_format,
|
|
/* chat_template_kwargs */ params.default_template_kwargs,
|
|
/* common_chat_templates */ chat_templates.get(),
|
|
/* allow_image */ mctx ? mtmd_support_vision(mctx) : false,
|
|
/* allow_audio */ mctx ? mtmd_support_audio(mctx) : false,
|
|
/* enable_thinking */ enable_thinking,
|
|
};
|
|
}
|
|
|
|
std::vector<llama_token> tokenize(const json & json_prompt, bool add_special) const {
|
|
// TODO: currently, we tokenize using special tokens by default
|
|
// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
|
|
// but it's better compared to completely ignoring ChatML and other chat templates
|
|
const bool TMP_FORCE_SPECIAL = true;
|
|
|
|
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
|
|
// or the first element of the json_prompt array is a string.
|
|
std::vector<llama_token> prompt_tokens;
|
|
|
|
if (json_prompt.is_array()) {
|
|
bool first = true;
|
|
for (const auto & p : json_prompt) {
|
|
if (p.is_string()) {
|
|
auto s = p.template get<std::string>();
|
|
|
|
std::vector<llama_token> p;
|
|
if (first) {
|
|
p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
|
|
first = false;
|
|
} else {
|
|
p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
|
|
}
|
|
|
|
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
|
|
} else {
|
|
if (first) {
|
|
first = false;
|
|
}
|
|
|
|
prompt_tokens.push_back(p.template get<llama_token>());
|
|
}
|
|
}
|
|
} else {
|
|
auto s = json_prompt.template get<std::string>();
|
|
prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
|
|
}
|
|
|
|
return prompt_tokens;
|
|
}
|
|
|
|
server_slot * get_slot_by_id(int id) {
|
|
for (server_slot & slot : slots) {
|
|
if (slot.id == id) {
|
|
return &slot;
|
|
}
|
|
}
|
|
|
|
return nullptr;
|
|
}
|
|
|
|
server_slot * get_available_slot(const server_task & task) {
|
|
server_slot * ret = nullptr;
|
|
|
|
// find the slot that has at least n% prompt similarity
|
|
if (ret == nullptr && slot_prompt_similarity != 0.0f) {
|
|
int max_lcp_len = 0;
|
|
float similarity = 0;
|
|
|
|
for (server_slot & slot : slots) {
|
|
// skip the slot if it is not available
|
|
if (!slot.available()) {
|
|
continue;
|
|
}
|
|
const auto & cache_tokens = slot.cache_tokens;
|
|
// skip the slot if it does not contains prompt
|
|
if (cache_tokens.empty()) {
|
|
continue;
|
|
}
|
|
|
|
// length of the Longest Common Prefix between the current slot's prompt and the input prompt
|
|
int lcp_len = cache_tokens.get_common_prefix(task.tokens);
|
|
// fraction of the common substring length compared to the current slot's prompt length
|
|
const float similarity = float(lcp_len) / task.tokens.size();
|
|
// select the current slot if the criteria match
|
|
if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity) {
|
|
max_lcp_len = lcp_len;
|
|
ret = &slot;
|
|
}
|
|
}
|
|
|
|
if (ret != nullptr) {
|
|
LOG_VERBOSE("selected slot by lcp similarity", {
|
|
{"id_slot", ret->id},
|
|
{"max_lcp_len", max_lcp_len},
|
|
{"similarity", similarity},
|
|
});
|
|
}
|
|
}
|
|
|
|
// find the slot that has been least recently used
|
|
if (ret == nullptr) {
|
|
int64_t t_last = ggml_time_us();
|
|
for (server_slot & slot : slots) {
|
|
// skip the slot if it is not available
|
|
if (!slot.available()) {
|
|
continue;
|
|
}
|
|
|
|
// select the current slot if the criteria match
|
|
if (slot.t_last_used < t_last) {
|
|
t_last = slot.t_last_used;
|
|
ret = &slot;
|
|
}
|
|
}
|
|
|
|
if (ret != nullptr) {
|
|
LOG_VERBOSE("selected slot by lru", {
|
|
{"id_slot", ret->id},
|
|
{"t_last", t_last},
|
|
});
|
|
}
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
bool launch_slot_with_task(server_slot & slot, server_task & task) {
|
|
slot_params default_params;
|
|
// Sampling parameter defaults are loaded from the global server context (but individual requests can still override them)
|
|
llama_sampling_params default_sparams = params.sparams;
|
|
auto & data = task.data;
|
|
|
|
if (data.count("__oaicompat") != 0) {
|
|
slot.oaicompat = true;
|
|
slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
|
|
} else {
|
|
slot.oaicompat = false;
|
|
slot.oaicompat_model = "";
|
|
}
|
|
slot.params.timings_per_token = json_value(data, "timings_per_token", false);
|
|
slot.params.stream = json_value(data, "stream", false);
|
|
auto stream_opt = json_value(data, "stream_options", json::object());
|
|
slot.params.include_usage = json_value(stream_opt, "include_usage", false);
|
|
slot.params.cache_prompt = json_value(data, "cache_prompt", true);
|
|
slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", 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.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability);
|
|
slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold);
|
|
slot.sparams.top_n_sigma = json_value(data, "top_n_sigma", default_sparams.top_n_sigma);
|
|
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.dry_multiplier = json_value(data, "dry_multiplier", default_sparams.dry_multiplier);
|
|
slot.sparams.dry_base = json_value(data, "dry_base", default_sparams.dry_base);
|
|
slot.sparams.dry_allowed_length = json_value(data, "dry_allowed_length", default_sparams.dry_allowed_length);
|
|
slot.sparams.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", default_sparams.dry_penalty_last_n);
|
|
slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
|
|
slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
|
|
slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
|
|
slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
|
|
slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
|
|
slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard);
|
|
slot.sparams.seed = json_value(data, "seed", default_sparams.seed);
|
|
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);
|
|
|
|
slot.params.post_sampling_probs = json_value(data, "post_sampling_probs", default_params.post_sampling_probs);
|
|
|
|
// speculative decoding parameters
|
|
slot.params.speculative.n_max = json_value(data, "speculative.n_max", params.n_draft);
|
|
slot.params.speculative.n_min = json_value(data, "speculative.n_min", params.n_draft_min);
|
|
slot.params.speculative.p_min = json_value(data, "speculative.p_min", params.p_draft_min);
|
|
|
|
// Clamp speculative parameters
|
|
slot.params.speculative.n_min = std::min(slot.params.speculative.n_max, slot.params.speculative.n_min);
|
|
slot.params.speculative.n_min = std::max(slot.params.speculative.n_min, 0);
|
|
slot.params.speculative.n_max = std::max(slot.params.speculative.n_max, 0);
|
|
|
|
if (slot.sparams.penalty_last_n < -1) {
|
|
throw std::runtime_error("Error: repeat_last_n must be >= -1");
|
|
}
|
|
|
|
if (slot.sparams.dry_penalty_last_n < -1) {
|
|
throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
|
|
}
|
|
|
|
if (slot.sparams.penalty_last_n == -1) {
|
|
// note: should be the slot's context and not the full context, but it's ok
|
|
slot.sparams.penalty_last_n = llama_n_ctx(ctx);
|
|
}
|
|
|
|
if (slot.sparams.dry_penalty_last_n == -1) {
|
|
slot.sparams.dry_penalty_last_n = llama_n_ctx(ctx);
|
|
|
|
}
|
|
if (slot.sparams.dry_base < 1.0f)
|
|
{
|
|
slot.sparams.dry_base = default_sparams.dry_base;
|
|
}
|
|
|
|
// sequence breakers for DRY
|
|
{
|
|
// Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
|
|
// Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39
|
|
|
|
if (data.contains("dry_sequence_breakers")) {
|
|
slot.sparams.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
|
|
if (slot.sparams.dry_sequence_breakers.empty()) {
|
|
send_error(task, "Error: dry_sequence_breakers must be a non-empty array of strings", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
// process "json_schema" and "grammar"
|
|
if (data.contains("json_schema") && !data.contains("grammar")) {
|
|
try {
|
|
auto schema = json_value(data, "json_schema", json::object());
|
|
LLAMA_LOG_DEBUG("JSON schema: %s\n", schema.dump(2).c_str());
|
|
slot.sparams.grammar = json_schema_to_grammar(schema);
|
|
LLAMA_LOG_DEBUG("Converted grammar: %s\n", slot.sparams.grammar.c_str());
|
|
}
|
|
catch (const std::exception& e) {
|
|
throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
|
|
}
|
|
}
|
|
else {
|
|
slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
|
|
LLAMA_LOG_DEBUG("Grammar: %s\n", slot.sparams.grammar.c_str());
|
|
slot.sparams.grammar_lazy = json_value(data, "grammar_lazy", default_sparams.grammar_lazy);
|
|
LLAMA_LOG_DEBUG("Grammar lazy: %s\n", slot.sparams.grammar_lazy ? "true" : "false");
|
|
}
|
|
|
|
if (slot.params.cache_prompt && slot.ga_n != 1) {
|
|
LOG_WARNING("cache_prompt is not supported with group-attention", {});
|
|
slot.params.cache_prompt = false;
|
|
}
|
|
|
|
if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
|
|
// Might be better to reject the request with a 400 ?
|
|
LOG_WARNING("Max tokens to predict exceeds server configuration", {
|
|
{"params.n_predict", slot.params.n_predict},
|
|
{"slot.n_predict", slot.n_predict},
|
|
});
|
|
slot.params.n_predict = slot.n_predict;
|
|
}
|
|
|
|
// infill
|
|
slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
|
|
slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
|
|
|
|
// get prompt
|
|
if (!task.infill) {
|
|
// maybe not needed since prompt has been tokenized?
|
|
const auto & prompt = data.find("prompt");
|
|
if (!slot.prompt_tokens.validate(ctx)) {
|
|
send_error(task, "Prompt contains invalid tokens", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
if (prompt == data.end()) {
|
|
send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
|
|
if ((prompt->is_string()) ||
|
|
(prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_string()) ||
|
|
(prompt->is_array() && !prompt->empty() && prompt->at(0).is_number_integer())) {
|
|
slot.prompt = *prompt;
|
|
} else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) {
|
|
slot.prompt = prompt->at(0);
|
|
} else {
|
|
send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
slot.prompt_tokens = std::move(task.tokens);
|
|
}
|
|
|
|
// penalize user-provided tokens
|
|
{
|
|
slot.sparams.penalty_prompt_tokens.clear();
|
|
slot.sparams.use_penalty_prompt_tokens = false;
|
|
|
|
const auto & penalty_prompt = data.find("penalty_prompt");
|
|
|
|
if (penalty_prompt != data.end()) {
|
|
if (penalty_prompt->is_string()) {
|
|
const auto penalty_prompt_string = penalty_prompt->get<std::string>();
|
|
slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false);
|
|
|
|
if (slot.params.n_predict > 0) {
|
|
slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() + slot.params.n_predict);
|
|
}
|
|
slot.sparams.use_penalty_prompt_tokens = true;
|
|
|
|
LOG_VERBOSE("penalty_prompt_tokens", {
|
|
{"id_slot", slot.id},
|
|
{"tokens", slot.sparams.penalty_prompt_tokens},
|
|
});
|
|
}
|
|
else if (penalty_prompt->is_array()) {
|
|
const auto n_tokens = penalty_prompt->size();
|
|
slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict));
|
|
|
|
const int n_vocab = llama_n_vocab(model);
|
|
for (const auto & penalty_token : *penalty_prompt) {
|
|
if (penalty_token.is_number_integer()) {
|
|
const auto tok = penalty_token.get<llama_token>();
|
|
if (tok >= 0 && tok < n_vocab) {
|
|
slot.sparams.penalty_prompt_tokens.push_back(tok);
|
|
}
|
|
}
|
|
}
|
|
slot.sparams.use_penalty_prompt_tokens = true;
|
|
|
|
LOG_VERBOSE("penalty_prompt_tokens", {
|
|
{"id_slot", slot.id},
|
|
{"tokens", slot.sparams.penalty_prompt_tokens},
|
|
});
|
|
}
|
|
}
|
|
}
|
|
{
|
|
auto it = data.find("chat_format");
|
|
if (it != data.end()) {
|
|
slot.params.oaicompat_chat_syntax.format = static_cast<common_chat_format>(it->get<int>());
|
|
LLAMA_LOG_DEBUG("Chat format: %s\n", common_chat_format_name(slot.params.oaicompat_chat_syntax.format));
|
|
}
|
|
else {
|
|
slot.params.oaicompat_chat_syntax.format = default_params.oaicompat_chat_syntax.format;
|
|
}
|
|
common_reasoning_format reasoning_format = params.reasoning_format;
|
|
if (data.contains("reasoning_format")) {
|
|
reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
|
|
}
|
|
slot.params.oaicompat_chat_syntax.reasoning_format = reasoning_format;
|
|
slot.params.oaicompat_chat_syntax.reasoning_in_content = slot.params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
|
|
slot.params.oaicompat_chat_syntax.parse_tool_calls = json_value(data, "parse_tool_calls", false);
|
|
|
|
slot.params.oaicompat_chat_syntax.thinking_forced_open = json_value(data, "thinking_forced_open", false);
|
|
}
|
|
{
|
|
|
|
const auto preserved_tokens = data.find("preserved_tokens");
|
|
if (preserved_tokens != data.end()) {
|
|
for (const auto& t : *preserved_tokens) {
|
|
auto ids = llama_tokenize(model, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
|
|
if (ids.size() == 1) {
|
|
LOG("Preserved token: %d\n", ids[0]);
|
|
slot.sparams.preserved_tokens.insert(ids[0]);
|
|
}
|
|
else {
|
|
// This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
|
|
LOG("Not preserved because more than 1 token: %s\n", t.get<std::string>().c_str());
|
|
}
|
|
}
|
|
}
|
|
const auto grammar_triggers = data.find("grammar_triggers");
|
|
if (grammar_triggers != data.end()) {
|
|
for (const auto& t : *grammar_triggers) {
|
|
server_grammar_trigger ct(t);
|
|
if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
|
|
const auto& word = ct.value.value;
|
|
auto ids = llama_tokenize(model, word, /* add_special= */ false, /* parse_special= */ true);
|
|
if (ids.size() == 1) {
|
|
auto token = ids[0];
|
|
if (std::find(slot.sparams.preserved_tokens.begin(), slot.sparams.preserved_tokens.end(), (llama_token)token) == slot.sparams.preserved_tokens.end()) {
|
|
throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
|
|
}
|
|
LOG("Grammar trigger token: %d (`%s`)\n", token, word.c_str());
|
|
common_grammar_trigger trigger;
|
|
trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
|
|
trigger.value = word;
|
|
trigger.token = token;
|
|
slot.sparams.grammar_triggers.push_back(std::move(trigger));
|
|
}
|
|
else {
|
|
LOG("Grammar trigger word: `%s`\n", word.c_str());
|
|
slot.sparams.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word });
|
|
}
|
|
}
|
|
else {
|
|
//slot.sparams.grammar_triggers.push_back(ct);
|
|
if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN) {
|
|
LLAMA_LOG_DEBUG("Grammar trigger pattern: `%s`\n", ct.value.value.c_str());
|
|
}
|
|
else if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL) {
|
|
LLAMA_LOG_DEBUG("Grammar trigger pattern full: `%s`\n", ct.value.value.c_str());
|
|
}
|
|
else {
|
|
throw std::runtime_error("Unknown grammar trigger type");
|
|
}
|
|
slot.sparams.grammar_triggers.emplace_back(std::move(ct.value));
|
|
}
|
|
}
|
|
}
|
|
|
|
if (slot.sparams.grammar_lazy && slot.sparams.grammar_triggers.empty()) {
|
|
throw std::runtime_error("Error: no triggers set for lazy grammar!");
|
|
}
|
|
}
|
|
|
|
{
|
|
slot.sparams.logit_bias.clear();
|
|
|
|
if (json_value(data, "ignore_eos", false) && has_eos_token) {
|
|
slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
|
|
}
|
|
|
|
const auto & logit_bias = data.find("logit_bias");
|
|
if (logit_bias != data.end() && logit_bias->is_array()) {
|
|
const int n_vocab = llama_n_vocab(model);
|
|
for (const auto & el : *logit_bias) {
|
|
// TODO: we may want to throw errors here, in case "el" is incorrect
|
|
if (el.is_array() && el.size() == 2) {
|
|
float bias;
|
|
if (el[1].is_number()) {
|
|
bias = el[1].get<float>();
|
|
} else if (el[1].is_boolean() && !el[1].get<bool>()) {
|
|
bias = -INFINITY;
|
|
} else {
|
|
continue;
|
|
}
|
|
|
|
if (el[0].is_number_integer()) {
|
|
llama_token tok = el[0].get<llama_token>();
|
|
if (tok >= 0 && tok < n_vocab) {
|
|
slot.sparams.logit_bias[tok] = bias;
|
|
}
|
|
} else if (el[0].is_string()) {
|
|
auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
|
|
for (auto tok : toks) {
|
|
slot.sparams.logit_bias[tok] = bias;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
slot.params.antiprompt.clear();
|
|
|
|
const auto & stop = data.find("stop");
|
|
if (stop != data.end() && stop->is_array()) {
|
|
for (const auto & word : *stop) {
|
|
if (!word.empty()) {
|
|
slot.params.antiprompt.push_back(word);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
const auto samplers = data.find("samplers");
|
|
if (samplers != data.end()) {
|
|
if (samplers->is_array()) {
|
|
slot.sparams.samplers_sequence = llama_sampling_types_from_names(*samplers, false);
|
|
}
|
|
else if (samplers->is_string()) {
|
|
slot.sparams.samplers_sequence = llama_sampling_types_from_chars(samplers->get<std::string>());
|
|
}
|
|
else {
|
|
slot.sparams.samplers_sequence = default_sparams.samplers_sequence;
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
if (slot.ctx_sampling != nullptr) {
|
|
llama_sampling_free(slot.ctx_sampling);
|
|
}
|
|
slot.ctx_sampling = llama_sampling_init(llama_get_model_vocab(model),slot.sparams);
|
|
if (slot.ctx_sampling == nullptr) {
|
|
// for now, the only error that may happen here is invalid grammar
|
|
send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
slot.command = SLOT_COMMAND_LOAD_PROMPT;
|
|
// slot.prompt_tokens.clear();
|
|
|
|
LOG_INFO("slot is processing task", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
});
|
|
|
|
return true;
|
|
}
|
|
|
|
void kv_cache_clear() {
|
|
LOG_VERBOSE("clearing KV cache", {});
|
|
|
|
// clear the entire KV cache
|
|
llama_kv_cache_clear(ctx);
|
|
clean_kv_cache = false;
|
|
}
|
|
|
|
void system_prompt_update() {
|
|
LOG_VERBOSE("system prompt update", {
|
|
{"system_prompt", system_prompt},
|
|
});
|
|
|
|
kv_cache_clear();
|
|
system_tokens.clear();
|
|
|
|
if (!system_prompt.empty()) {
|
|
system_tokens = ::llama_tokenize(ctx, system_prompt, true);
|
|
|
|
const int32_t n_batch = llama_n_batch(ctx);
|
|
const int32_t n_tokens_prompt = system_tokens.size();
|
|
|
|
for (int32_t i = 0; i < n_tokens_prompt; i += n_batch) {
|
|
const int32_t n_tokens = std::min(n_batch, n_tokens_prompt - i);
|
|
|
|
llama_batch_clear(batch);
|
|
|
|
for (int32_t j = 0; j < n_tokens; ++j) {
|
|
llama_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false);
|
|
}
|
|
|
|
if (llama_decode(ctx, batch) != 0) {
|
|
LOG_ERROR("llama_decode() failed", {});
|
|
return;
|
|
}
|
|
}
|
|
|
|
// assign the system KV cache to all parallel sequences
|
|
for (int32_t i = 1; i <= params.n_parallel; ++i) {
|
|
llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
|
|
}
|
|
}
|
|
|
|
system_need_update = false;
|
|
}
|
|
|
|
bool system_prompt_set(const std::string & sys_prompt) {
|
|
system_prompt = sys_prompt;
|
|
|
|
LOG_VERBOSE("system prompt process", {
|
|
{"system_prompt", system_prompt},
|
|
});
|
|
|
|
// release all slots
|
|
for (server_slot & slot : slots) {
|
|
slot.release();
|
|
}
|
|
|
|
system_need_update = true;
|
|
return true;
|
|
}
|
|
|
|
bool process_token(completion_token_output & result, server_slot & slot) {
|
|
// remember which tokens were sampled - used for repetition penalties during sampling
|
|
const std::string token_str = result.text_to_send;
|
|
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 = validate_utf8(slot.generated_text) < slot.generated_text.size();
|
|
|
|
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 send_text = true;
|
|
|
|
size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), true);
|
|
if (stop_pos != std::string::npos) {
|
|
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 if (slot.has_next_token && !llama_token_is_eog(model, result.tok)) {
|
|
stop_pos = slot.find_stopping_strings(str_test, token_str.size(), false);
|
|
send_text = stop_pos == std::string::npos;
|
|
}
|
|
|
|
// check if there is any token to predict
|
|
if (send_text) {
|
|
// 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
|
|
} else {
|
|
result.text_to_send = "";
|
|
}
|
|
|
|
slot.add_token_string(result);
|
|
if (slot.params.stream) {
|
|
send_partial_response(slot, result);
|
|
}
|
|
}
|
|
|
|
if (incomplete) {
|
|
slot.has_next_token = true;
|
|
}
|
|
|
|
// check the limits
|
|
if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) {
|
|
slot.stopped_limit = true;
|
|
slot.has_next_token = false;
|
|
|
|
LOG_VERBOSE("stopped by limit", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_decoded", slot.n_decoded},
|
|
{"n_predict", slot.params.n_predict},
|
|
});
|
|
}
|
|
|
|
if (llama_token_is_eog(model, result.tok)) {
|
|
slot.stopped_eos = true;
|
|
slot.has_next_token = false;
|
|
|
|
LOG_VERBOSE("eos token found", {});
|
|
}
|
|
|
|
auto n_ctx_train = llama_n_ctx_train(model);
|
|
if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1
|
|
&& slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
|
|
LOG_WARNING("n_predict is not set and self-context extend is disabled."
|
|
" Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop", {
|
|
{ "id_slot", slot.id },
|
|
{ "params.n_predict", slot.params.n_predict },
|
|
{ "slot.n_prompt_tokens", slot.n_prompt_tokens },
|
|
{ "slot.n_decoded", slot.n_decoded },
|
|
{ "slot.n_predict", slot.n_predict },
|
|
{ "n_slots", params.n_parallel },
|
|
{ "slot.n_ctx", slot.n_ctx },
|
|
{ "n_ctx", n_ctx },
|
|
{ "n_ctx_train", n_ctx_train },
|
|
{ "ga_n", slot.ga_n },
|
|
});
|
|
slot.truncated = true;
|
|
slot.stopped_limit = true;
|
|
slot.has_next_token = false; // stop prediction
|
|
}
|
|
|
|
LOG_VERBOSE("next token", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"token", result.tok},
|
|
{"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
|
|
{"has_next_token", slot.has_next_token},
|
|
{"n_remain", slot.n_remaining},
|
|
{"n_decoded", slot.n_decoded},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
});
|
|
|
|
return slot.has_next_token; // continue
|
|
}
|
|
|
|
void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) {
|
|
size_t n_probs = slot.sparams.n_probs;
|
|
size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
|
|
|
|
if (post_sampling) {
|
|
const auto * cur_p = llama_sampling_get_candidates(slot.ctx_sampling);
|
|
const size_t max_probs = cur_p->size;
|
|
|
|
// set probability for sampled token
|
|
for (size_t i = 0; i < max_probs; i++) {
|
|
if (cur_p->data[i].id == result.tok) {
|
|
result.prob = cur_p->data[i].p;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// set probability for top n_probs tokens
|
|
result.probs.reserve(max_probs);
|
|
for (size_t i = 0; i < std::min(max_probs, n_probs); i++) {
|
|
result.probs.push_back({
|
|
cur_p->data[i].id,
|
|
llama_detokenize(ctx, {cur_p->data[i].id}, special),
|
|
cur_p->data[i].p
|
|
});
|
|
}
|
|
} else {
|
|
auto&&[sampled_token_p, cur] = get_token_probabilities(ctx, idx, result.tok, n_probs);
|
|
|
|
// set probability for sampled token
|
|
result.prob = sampled_token_p;
|
|
|
|
// set probability for top n_probs tokens
|
|
result.probs.reserve(n_probs);
|
|
for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) {
|
|
result.probs.push_back({
|
|
cur[i].id,
|
|
llama_detokenize(ctx, {cur[i].id}, special),
|
|
cur[i].p
|
|
});
|
|
}
|
|
}
|
|
}
|
|
|
|
json get_formated_generation(const server_slot & slot) const {
|
|
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
|
|
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second);
|
|
|
|
std::vector<std::string> samplers_sequence;
|
|
samplers_sequence.reserve(slot.sparams.samplers_sequence.size());
|
|
for (const auto & sampler_type : slot.sparams.samplers_sequence) {
|
|
samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type));
|
|
}
|
|
|
|
auto grammar_triggers = json::array();
|
|
for (const auto& trigger : slot.sparams.grammar_triggers) {
|
|
grammar_triggers.push_back(trigger.to_json<json>());
|
|
}
|
|
|
|
return json {
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_predict", slot.n_predict}, // Server configured n_predict
|
|
{"model", params.model_alias},
|
|
{"seed", slot.sparams.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},
|
|
{"dry_multiplier", slot.sparams.dry_multiplier},
|
|
{"dry_base", slot.sparams.dry_base},
|
|
{"dry_allowed_length", slot.sparams.dry_allowed_length},
|
|
{"dry_penalty_last_n", slot.sparams.dry_penalty_last_n},
|
|
{"dry_sequence_breakers", slot.sparams.dry_sequence_breakers},
|
|
{"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},
|
|
{"max_tokens", slot.params.n_predict}, // User configured n_predict
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_discard", slot.params.n_discard},
|
|
{"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},
|
|
{"grammar_triggers", grammar_triggers},
|
|
{"preserved_tokens", slot.sparams.preserved_tokens},
|
|
{"chat_format", common_chat_format_name(slot.params.oaicompat_chat_syntax.format)},
|
|
{"reasoning_format", common_reasoning_format_name(slot.params.oaicompat_chat_syntax.reasoning_format)},
|
|
{"reasoning_in_content", slot.params.oaicompat_chat_syntax.reasoning_in_content},
|
|
{"thinking_forced_open", slot.params.oaicompat_chat_syntax.thinking_forced_open},
|
|
{"samplers", samplers_sequence}
|
|
};
|
|
}
|
|
|
|
void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
|
|
send_error(task.id, task.id_multi, error, type);
|
|
}
|
|
|
|
void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
|
|
send_error(slot.id_task, slot.id_multi, error, type);
|
|
}
|
|
|
|
void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
|
|
LOG_ERROR("task error", {
|
|
{"id_multi", id_multi},
|
|
{"id_task", id_task},
|
|
{"error", error},
|
|
});
|
|
|
|
server_task_result res;
|
|
res.id = id_task;
|
|
res.id_multi = id_multi;
|
|
res.stop = false;
|
|
res.error = true;
|
|
res.data = format_error_response(error, type);
|
|
|
|
queue_results.send(res);
|
|
}
|
|
|
|
// if multimodal is enabled, send an error and return false
|
|
bool ensure_no_mtmd(const int id_task) {
|
|
if (mctx) {
|
|
int id_multi = 0;
|
|
send_error(id_task, id_multi, "This feature is not supported by multimodal", ERROR_TYPE_NOT_SUPPORTED);
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void send_partial_response(server_slot & slot, completion_token_output tkn) {
|
|
server_task_result res;
|
|
res.final_result = false;
|
|
res.id = slot.id_task;
|
|
res.id_multi = slot.id_multi;
|
|
res.error = false;
|
|
res.stop = false;
|
|
res.stream = slot.params.stream;
|
|
res.content = tkn.text_to_send;
|
|
res.post_sampling_probs = slot.params.post_sampling_probs;
|
|
res.oaicompat = slot.params.oaicompat;
|
|
res.oaicompat_model = slot.params.oaicompat_model;
|
|
res.oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
|
|
res.n_decoded = slot.n_decoded;
|
|
res.n_prompt_tokens = slot.n_prompt_tokens;
|
|
res.data = json {
|
|
{"content", tkn.text_to_send},
|
|
{"stop", false},
|
|
{"id_slot", slot.id},
|
|
{"multimodal", false}
|
|
};
|
|
slot.update_chat_msg(res.oaicompat_msg_diffs);
|
|
|
|
// populate res.probs_output
|
|
if (slot.sparams.n_probs > 0) {
|
|
res.probs_output = {tkn}; // copy the token probs
|
|
res.data["completion_probabilities"] = probs_vector_to_json(ctx, res.probs_output);
|
|
}
|
|
|
|
if (slot.oaicompat) {
|
|
res.data["oaicompat_token_ctr"] = slot.n_decoded;
|
|
res.data["model"] = slot.oaicompat_model;
|
|
}
|
|
|
|
// populate timings if this is final response or timings_per_token is enabled
|
|
if (slot.params.timings_per_token) {
|
|
res.timings = slot.get_timings();
|
|
}
|
|
queue_results.send(std::move(res));
|
|
}
|
|
|
|
void send_final_response(server_slot& slot) {
|
|
server_task_result res;
|
|
res.final_result = true;
|
|
res.id = slot.id_task;
|
|
res.id_multi = slot.id_multi;
|
|
res.error = false;
|
|
res.stop = true; // to do: set value
|
|
res.stream = slot.params.stream;
|
|
res.include_usage = slot.params.include_usage;
|
|
res.content = slot.generated_text;
|
|
res.timings = slot.get_timings();
|
|
res.post_sampling_probs = slot.params.post_sampling_probs;
|
|
res.oaicompat = slot.params.oaicompat;
|
|
res.oaicompat_model = slot.params.oaicompat_model;
|
|
res.oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
|
|
res.oaicompat_msg = slot.update_chat_msg(res.oaicompat_msg_diffs);
|
|
res.n_decoded = slot.n_decoded;
|
|
res.n_prompt_tokens = slot.n_prompt_tokens;
|
|
res.oaicompat_model = slot.oaicompat_model;
|
|
res.data = json {
|
|
{"content", !slot.params.stream ? slot.generated_text : ""},
|
|
{"generated_text", slot.generated_text}, // Always include full text for finish_reason logic
|
|
{"id_slot", slot.id},
|
|
{"stop", true},
|
|
{"model", params.model_alias},
|
|
{"tokens_predicted", slot.n_decoded},
|
|
{"tokens_evaluated", slot.n_prompt_tokens},
|
|
{"generation_settings", get_formated_generation(slot)},
|
|
{"prompt", slot.prompt},
|
|
{"truncated", slot.truncated},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
{"tokens_cached", slot.n_past},
|
|
{"timings", slot.get_formated_timings()},
|
|
//{"oaicompat_chat_format", slot.params.oaicompat_chat_format},
|
|
};
|
|
|
|
// populate res.probs_output
|
|
if (slot.sparams.n_probs > 0) {
|
|
if (!slot.params.stream && slot.stopped_word) {
|
|
const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
|
|
|
|
size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size());
|
|
res.probs_output = std::vector<completion_token_output>(
|
|
slot.generated_token_probs.begin(),
|
|
slot.generated_token_probs.end() - safe_offset);
|
|
} else {
|
|
res.probs_output = std::vector<completion_token_output>(
|
|
slot.generated_token_probs.begin(),
|
|
slot.generated_token_probs.end());
|
|
}
|
|
res.data["completion_probabilities"] = probs_vector_to_json(ctx, res.probs_output);
|
|
}
|
|
|
|
if (slot.oaicompat) {
|
|
res.data["oaicompat_token_ctr"] = slot.n_decoded;
|
|
res.data["model"] = slot.oaicompat_model;
|
|
}
|
|
|
|
queue_results.send(std::move(res));
|
|
}
|
|
|
|
void send_embedding(const server_slot & slot, const llama_batch & batch) {
|
|
server_task_result res;
|
|
res.id = slot.id_task;
|
|
res.id_multi = slot.id_multi;
|
|
res.error = false;
|
|
res.stop = true;
|
|
|
|
const int n_embd = llama_n_embd(model);
|
|
|
|
std::vector<float> embd_res(n_embd, 0.0f);
|
|
|
|
for (int i = 0; i < batch.n_tokens; ++i) {
|
|
if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
|
|
continue;
|
|
}
|
|
|
|
const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
|
|
if (embd == NULL) {
|
|
embd = llama_get_embeddings_ith(ctx, i);
|
|
}
|
|
|
|
if (embd == NULL) {
|
|
LOG_ERROR("failed to get embeddings", {
|
|
{"token", batch.token [i]},
|
|
{"seq_id", batch.seq_id[i][0]}
|
|
});
|
|
|
|
res.data = json {
|
|
{"embedding", std::vector<float>(n_embd, 0.0f)},
|
|
{"tokens_evaluated", slot.n_prompt_tokens},
|
|
};
|
|
|
|
continue;
|
|
}
|
|
|
|
llama_embd_normalize(embd, embd_res.data(), n_embd);
|
|
|
|
res.data = json {
|
|
{"embedding", embd_res},
|
|
{"tokens_evaluated", slot.n_prompt_tokens},
|
|
};
|
|
}
|
|
|
|
queue_results.send(res);
|
|
}
|
|
|
|
void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding, server_tokens && inputs) {
|
|
server_task task;
|
|
task.id = id_task;
|
|
task.id_multi = id_multi;
|
|
task.id_target = 0;
|
|
task.data = std::move(data);
|
|
task.infill = infill;
|
|
task.embedding = embedding;
|
|
task.type = SERVER_TASK_TYPE_COMPLETION;
|
|
task.tokens = std::move(inputs);
|
|
// 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(std::move(task));
|
|
} else {
|
|
split_multiprompt_task(id_task, task);
|
|
}
|
|
} else {
|
|
queue_tasks.post(std::move(task));
|
|
}
|
|
}
|
|
|
|
void request_cancel(int id_task) {
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_CANCEL;
|
|
task.id_target = id_task;
|
|
|
|
queue_tasks.post(std::move(task));
|
|
}
|
|
|
|
void split_multiprompt_task(int id_multi, server_task & multiprompt_task) {
|
|
const int prompt_count = multiprompt_task.data.at("prompt").size();
|
|
if (prompt_count <= 1) {
|
|
send_error(multiprompt_task, "error while handling multiple prompts");
|
|
return;
|
|
}
|
|
|
|
// generate all the ID for subtask
|
|
std::vector<int> subtask_ids(prompt_count);
|
|
for (int i = 0; i < prompt_count; i++) {
|
|
subtask_ids[i] = queue_tasks.get_new_id();
|
|
}
|
|
|
|
// queue up the multitask so we can track its subtask progression
|
|
queue_tasks.add_multitask(id_multi, subtask_ids);
|
|
|
|
// add subtasks
|
|
for (int i = 0; i < prompt_count; i++) {
|
|
json subtask_data = multiprompt_task.data;
|
|
subtask_data["prompt"] = subtask_data.at("prompt")[i];
|
|
|
|
// subtasks inherit everything else (infill mode, embedding mode, etc.)
|
|
request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding,
|
|
std::move(multiprompt_task.tokens));
|
|
}
|
|
}
|
|
|
|
void process_single_task(server_task && task) {
|
|
switch (task.type) {
|
|
case SERVER_TASK_TYPE_COMPLETION:
|
|
{
|
|
const int id_slot = json_value(task.data, "id_slot", -1);
|
|
|
|
server_slot * slot;
|
|
|
|
if (id_slot != -1) {
|
|
slot = get_slot_by_id(id_slot);
|
|
} else {
|
|
slot = get_available_slot(task);
|
|
}
|
|
|
|
if (slot == nullptr) {
|
|
// if no slot is available, we defer this task for processing later
|
|
LOG_VERBOSE("no slot is available", {{"id_task", task.id}});
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
if (!slot->available()) {
|
|
// if requested slot is unavailable, we defer this task for processing later
|
|
LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
|
|
if (task.data.contains("system_prompt")) {
|
|
std::string sys_prompt = json_value(task.data, "system_prompt", std::string());
|
|
system_prompt_set(sys_prompt);
|
|
|
|
for (server_slot & slot : slots) {
|
|
slot.n_past = 0;
|
|
slot.n_past_se = 0;
|
|
}
|
|
}
|
|
|
|
slot->reset();
|
|
|
|
slot->id_task = task.id;
|
|
slot->id_multi = task.id_multi;
|
|
slot->infill = task.infill;
|
|
slot->embedding = task.embedding;
|
|
|
|
if (!launch_slot_with_task(*slot, task)) {
|
|
LOG_ERROR("error while launching slot", task.data);
|
|
break;
|
|
}
|
|
} break;
|
|
case SERVER_TASK_TYPE_CANCEL:
|
|
{
|
|
// release slot linked with the task id
|
|
for (auto & slot : slots) {
|
|
if (slot.id_task == task.id_target) {
|
|
slot.release();
|
|
break;
|
|
}
|
|
}
|
|
} break;
|
|
case SERVER_TASK_TYPE_NEXT_RESPONSE:
|
|
{
|
|
// do nothing
|
|
} break;
|
|
case SERVER_TASK_TYPE_METRICS:
|
|
{
|
|
json slots_data = json::array();
|
|
|
|
int n_idle_slots = 0;
|
|
int n_processing_slots = 0;
|
|
|
|
for (server_slot & slot : slots) {
|
|
json slot_data = get_formated_generation(slot);
|
|
slot_data["id"] = slot.id;
|
|
slot_data["id_task"] = slot.id_task;
|
|
slot_data["state"] = slot.state;
|
|
slot_data["prompt"] = slot.prompt;
|
|
slot_data["next_token"] = {
|
|
{"has_next_token", slot.has_next_token},
|
|
{"n_remain", slot.n_remaining},
|
|
{"n_decoded", slot.n_decoded},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
};
|
|
|
|
if (slot_data["state"] == SLOT_STATE_IDLE) {
|
|
n_idle_slots++;
|
|
} else {
|
|
n_processing_slots++;
|
|
}
|
|
|
|
slots_data.push_back(slot_data);
|
|
}
|
|
LOG_INFO("slot data", {
|
|
{"id_task", task.id},
|
|
{"n_idle_slots", n_idle_slots},
|
|
{"n_processing_slots", n_processing_slots}
|
|
});
|
|
|
|
LOG_VERBOSE("slot data", {
|
|
{"id_task", task.id},
|
|
{"n_idle_slots", n_idle_slots},
|
|
{"n_processing_slots", n_processing_slots},
|
|
{"slots", slots_data}
|
|
});
|
|
|
|
server_task_result res;
|
|
res.id = task.id;
|
|
res.id_multi = task.id_multi;
|
|
res.stop = true;
|
|
res.error = false;
|
|
res.data = {
|
|
{ "idle", n_idle_slots },
|
|
{ "processing", n_processing_slots },
|
|
{ "deferred", queue_tasks.queue_tasks_deferred.size() },
|
|
{ "t_start", metrics.t_start},
|
|
|
|
{ "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
|
|
{ "t_tokens_generation_total", metrics.t_tokens_generation_total},
|
|
{ "n_tokens_predicted_total", metrics.n_tokens_predicted_total},
|
|
{ "t_prompt_processing_total", metrics.t_prompt_processing_total},
|
|
|
|
{ "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed},
|
|
{ "t_prompt_processing", metrics.t_prompt_processing},
|
|
{ "n_tokens_predicted", metrics.n_tokens_predicted},
|
|
{ "t_tokens_generation", metrics.t_tokens_generation},
|
|
|
|
{ "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)},
|
|
{ "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)},
|
|
|
|
{ "slots", slots_data },
|
|
};
|
|
|
|
if (json_value(task.data, "reset_bucket", false)) {
|
|
metrics.reset_bucket();
|
|
}
|
|
queue_results.send(res);
|
|
} break;
|
|
case SERVER_TASK_TYPE_SLOT_SAVE:
|
|
{
|
|
if (!ensure_no_mtmd(task.id)) {
|
|
break;
|
|
}
|
|
int id_slot = task.data.at("id_slot");
|
|
server_slot * slot = get_slot_by_id(id_slot);
|
|
if (slot == nullptr) {
|
|
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
|
|
break;
|
|
}
|
|
if (!slot->available()) {
|
|
// if requested slot is unavailable, we defer this task for processing later
|
|
LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
|
|
const size_t token_count = slot->cache_tokens.size();
|
|
const int64_t t_start = ggml_time_us();
|
|
|
|
std::string filename = task.data.at("filename");
|
|
std::string filepath = task.data.at("filepath");
|
|
|
|
const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), token_count);
|
|
|
|
const int64_t t_end = ggml_time_us();
|
|
const double t_save_ms = (t_end - t_start) / 1000.0;
|
|
|
|
server_task_result result;
|
|
result.id = task.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
result.data = json {
|
|
{ "id_slot", id_slot },
|
|
{ "filename", filename },
|
|
{ "n_saved", token_count }, // tokens saved
|
|
{ "n_written", nwrite }, // bytes written
|
|
{ "timings", {
|
|
{ "save_ms", t_save_ms }
|
|
} }
|
|
};
|
|
queue_results.send(result);
|
|
} break;
|
|
case SERVER_TASK_TYPE_SLOT_RESTORE:
|
|
{
|
|
if (!ensure_no_mtmd(task.id)) break;
|
|
int id_slot = task.data.at("id_slot");
|
|
server_slot * slot = get_slot_by_id(id_slot);
|
|
if (slot == nullptr) {
|
|
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
|
|
break;
|
|
}
|
|
if (!slot->available()) {
|
|
// if requested slot is unavailable, we defer this task for processing later
|
|
LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
|
|
const int64_t t_start = ggml_time_us();
|
|
|
|
std::string filename = task.data.at("filename");
|
|
std::string filepath = task.data.at("filepath");
|
|
|
|
slot->cache_tokens.resize(slot->n_ctx);
|
|
size_t token_count = 0;
|
|
size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count);
|
|
if (nread == 0) {
|
|
slot->cache_tokens.resize(0);
|
|
send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST);
|
|
break;
|
|
}
|
|
slot->cache_tokens.resize(token_count);
|
|
|
|
const int64_t t_end = ggml_time_us();
|
|
const double t_restore_ms = (t_end - t_start) / 1000.0;
|
|
|
|
server_task_result result;
|
|
result.id = task.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
result.data = json {
|
|
{ "id_slot", id_slot },
|
|
{ "filename", filename },
|
|
{ "n_restored", token_count }, // tokens restored
|
|
{ "n_read", nread }, // bytes read
|
|
{ "timings", {
|
|
{ "restore_ms", t_restore_ms }
|
|
} }
|
|
};
|
|
queue_results.send(result);
|
|
} break;
|
|
case SERVER_TASK_TYPE_SLOT_ERASE:
|
|
{
|
|
if (!ensure_no_mtmd(task.id)) break;
|
|
int id_slot = task.data.at("id_slot");
|
|
server_slot * slot = get_slot_by_id(id_slot);
|
|
if (slot == nullptr) {
|
|
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
|
|
break;
|
|
}
|
|
if (!slot->available()) {
|
|
// if requested slot is unavailable, we defer this task for processing later
|
|
LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
|
|
// Erase token cache
|
|
const size_t n_erased = slot->cache_tokens.size();
|
|
llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1);
|
|
slot->cache_tokens.clear();
|
|
|
|
server_task_result result;
|
|
result.id = task.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
result.data = json {
|
|
{ "id_slot", id_slot },
|
|
{ "n_erased", n_erased }
|
|
};
|
|
queue_results.send(result);
|
|
} break;
|
|
case SERVER_TASK_TYPE_SET_LORA:
|
|
{
|
|
llama_lora_adapters_apply(ctx, lora_adapters);
|
|
server_task_result result;
|
|
result.id = task.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
result.data = json{{ "success", true }};
|
|
queue_results.send(result);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
void on_finish_multitask(const server_task_multi & multitask) {
|
|
// all subtasks done == multitask is done
|
|
server_task_result result;
|
|
result.id = multitask.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
|
|
// collect json results into one json result
|
|
std::vector<json> result_jsons;
|
|
for (const auto & subres : multitask.results) {
|
|
result_jsons.push_back(subres.data);
|
|
result.error = result.error && subres.error;
|
|
}
|
|
result.data = json {
|
|
{ "results", result_jsons }
|
|
};
|
|
|
|
queue_results.send(result);
|
|
}
|
|
|
|
void update_slots() {
|
|
if (system_need_update) {
|
|
system_prompt_update();
|
|
}
|
|
|
|
// release slots
|
|
for (auto & slot : slots) {
|
|
if (slot.command == SLOT_COMMAND_RELEASE) {
|
|
slot.state = SLOT_STATE_IDLE;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.t_last_used = ggml_time_us();
|
|
|
|
LOG_INFO("slot released", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", n_ctx},
|
|
{"n_past", slot.n_past},
|
|
{"n_system_tokens", system_tokens.size()},
|
|
{"n_cache_tokens", slot.cache_tokens.size()},
|
|
{"truncated", slot.truncated}
|
|
});
|
|
|
|
queue_tasks.notify_slot_changed();
|
|
}
|
|
}
|
|
|
|
// check if all slots are idle
|
|
{
|
|
bool all_idle = true;
|
|
|
|
for (auto & slot : slots) {
|
|
if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) {
|
|
all_idle = false;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (all_idle) {
|
|
LOG_INFO("all slots are idle", {});
|
|
if (system_prompt.empty() && clean_kv_cache) {
|
|
kv_cache_clear();
|
|
}
|
|
|
|
return;
|
|
}
|
|
}
|
|
|
|
{
|
|
LOG_VERBOSE("posting NEXT_RESPONSE", {});
|
|
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_NEXT_RESPONSE;
|
|
task.id_target = -1;
|
|
|
|
queue_tasks.post(std::move(task));
|
|
}
|
|
|
|
// apply context-shift if needed
|
|
// TODO: simplify and improve
|
|
for (server_slot & slot : slots) {
|
|
if (slot.ga_n == 1) {
|
|
if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
|
|
if (!params.ctx_shift) {
|
|
// this check is redundant (for good)
|
|
// we should never get here, because generation should already stopped in process_token()
|
|
send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER);
|
|
slot.release();
|
|
continue;
|
|
}
|
|
if (mctx) {
|
|
// we should never reach this because params_base.ctx_shift is automatically disabled if mmproj is loaded
|
|
// we don't support ctx_shift because an image chunk may contains multiple tokens
|
|
GGML_ABORT("not supported by multimodal");
|
|
}
|
|
// 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 = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);
|
|
|
|
LOG_INFO("slot context shift", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_keep", n_keep},
|
|
{"n_left", n_left},
|
|
{"n_discard", n_discard},
|
|
{"n_ctx", n_ctx},
|
|
{"n_past", slot.n_past},
|
|
{"n_system_tokens", system_tokens.size()},
|
|
{"n_cache_tokens", slot.cache_tokens.size()}
|
|
});
|
|
|
|
llama_kv_cache_seq_rm (ctx, slot.id, 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);
|
|
|
|
if (slot.params.cache_prompt) {
|
|
llama_tokens new_tokens = slot.cache_tokens.get_text_tokens(); // copy
|
|
for (size_t i = n_keep + n_discard; i < new_tokens.size(); i++) {
|
|
new_tokens[i - n_discard] = new_tokens[i];
|
|
}
|
|
new_tokens.resize(slot.cache_tokens.size() - n_discard);
|
|
slot.cache_tokens.clear();
|
|
slot.cache_tokens.insert(new_tokens);
|
|
}
|
|
|
|
slot.n_past -= n_discard;
|
|
|
|
slot.truncated = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
// start populating the batch for this iteration
|
|
llama_batch_clear(batch);
|
|
|
|
auto accept_special_token = [&](server_slot& slot, llama_token token) {
|
|
return params.special || slot.sparams.preserved_tokens.find(token) != slot.sparams.preserved_tokens.end();
|
|
};
|
|
|
|
// frist, add sampled tokens from any ongoing sequences
|
|
for (auto & slot : slots) {
|
|
if (slot.state == SLOT_STATE_IDLE) {
|
|
continue;
|
|
}
|
|
|
|
slot.i_batch = batch.n_tokens;
|
|
|
|
const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
|
|
|
|
// TODO: we always have to take into account the "system_tokens"
|
|
// this is not great and needs to be improved somehow
|
|
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true);
|
|
|
|
slot.n_past += 1;
|
|
|
|
if (slot.params.cache_prompt) {
|
|
slot.cache_tokens.push_back(slot.sampled);
|
|
}
|
|
|
|
LOG_VERBOSE("slot decode token", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", n_ctx},
|
|
{"n_past", slot.n_past},
|
|
{"n_system_tokens", system_tokens.size()},
|
|
{"n_cache_tokens", slot.cache_tokens.size()},
|
|
{"truncated", slot.truncated}
|
|
});
|
|
}
|
|
|
|
// process in chunks of params.n_batch
|
|
int32_t n_batch = llama_n_batch(ctx);
|
|
int32_t n_ubatch = llama_n_ubatch(ctx);
|
|
|
|
// track if this is an embedding or non-embedding batch
|
|
// if we've added sampled tokens above, we are in non-embedding mode
|
|
// -1: none, 0: non-embedding, 1: embedding
|
|
int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;
|
|
|
|
// next, batch any pending prompts without exceeding n_batch
|
|
if (params.cont_batching || batch.n_tokens == 0) {
|
|
for (auto & slot : slots) {
|
|
// this slot still has a prompt to be processed
|
|
if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) {
|
|
auto & prompt_tokens = slot.prompt_tokens;
|
|
|
|
// we haven't tokenized the prompt yet - do it now:
|
|
if (prompt_tokens.empty() || slot.n_prompt_tokens==0 ) {
|
|
LOG_VERBOSE("tokenizing prompt", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task}
|
|
});
|
|
|
|
slot.t_start_process_prompt = ggml_time_us();
|
|
slot.t_start_generation = 0;
|
|
|
|
if (slot.infill) {
|
|
const bool add_bos = llama_should_add_bos_token(model);
|
|
bool suff_rm_leading_spc = true;
|
|
if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
|
|
params.input_suffix.erase(0, 1);
|
|
suff_rm_leading_spc = false;
|
|
}
|
|
|
|
auto prefix_tokens = tokenize(slot.params.input_prefix, false);
|
|
auto suffix_tokens = tokenize(slot.params.input_suffix, false);
|
|
|
|
const int space_token = 29871; // TODO: this should not be hardcoded
|
|
if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
|
|
suffix_tokens.erase(suffix_tokens.begin());
|
|
}
|
|
|
|
prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
|
|
suffix_tokens.insert(suffix_tokens.begin(), llama_token_suffix(model));
|
|
|
|
auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens;
|
|
auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens;
|
|
if (add_bos) {
|
|
embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
|
|
}
|
|
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
|
|
|
|
const llama_token middle_token = llama_token_middle(model);
|
|
if (middle_token >= 0) {
|
|
embd_inp.push_back(middle_token);
|
|
}
|
|
|
|
prompt_tokens = server_tokens(embd_inp, false);
|
|
} else {
|
|
// prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
|
|
}
|
|
|
|
slot.n_past = 0;
|
|
slot.n_prompt_tokens = prompt_tokens.size();
|
|
|
|
LOG_VERBOSE("prompt tokenized", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_prompt_tokens", slot.n_prompt_tokens},
|
|
{"prompt_tokens", prompt_tokens.detokenize(ctx, true)},
|
|
});
|
|
|
|
// empty prompt passed -> release the slot and send empty response
|
|
if (prompt_tokens.empty()) {
|
|
LOG_INFO("empty prompt - releasing slot", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task}
|
|
});
|
|
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
continue;
|
|
}
|
|
|
|
if (slot.embedding) {
|
|
// this prompt is too large to process - discard it
|
|
if (slot.n_prompt_tokens > n_ubatch) {
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.release();
|
|
send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER);
|
|
continue;
|
|
}
|
|
} else {
|
|
// if input prompt is too big, truncate it (if group attention self-extend is disabled)
|
|
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 (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) {
|
|
if (!params.ctx_shift) {
|
|
send_error(slot, "the request exceeds the available context size, try increasing it", ERROR_TYPE_SERVER);
|
|
slot.release();
|
|
continue;
|
|
}
|
|
const int n_left = slot.n_ctx - slot.params.n_keep;
|
|
|
|
const int n_block_size = n_left / 2;
|
|
const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
|
|
int n_keep = slot.params.n_keep;
|
|
int n_discard = erased_blocks * n_block_size;
|
|
llama_tokens new_tokens = prompt_tokens.get_text_tokens(); // copy
|
|
for (size_t i = n_keep + n_discard; i < new_tokens.size(); i++) {
|
|
new_tokens[i - n_discard] = new_tokens[i];
|
|
}
|
|
new_tokens.resize(prompt_tokens.size() - n_discard);
|
|
prompt_tokens.clear();
|
|
prompt_tokens.insert(new_tokens);
|
|
slot.truncated = true;
|
|
slot.n_prompt_tokens = prompt_tokens.size();
|
|
|
|
LOG_VERBOSE("input truncated", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_left", n_left},
|
|
{"n_prompt_tokens", slot.n_prompt_tokens},
|
|
{"prompt_tokens", prompt_tokens.detokenize(ctx, true)},
|
|
});
|
|
|
|
GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
|
|
}
|
|
|
|
llama_sampling_reset(llama_get_model_vocab(model), slot.ctx_sampling);
|
|
|
|
if (!slot.params.cache_prompt) {
|
|
slot.n_past_se = 0;
|
|
slot.ga_i = 0;
|
|
} else {
|
|
GGML_ASSERT(slot.ga_n == 1);
|
|
|
|
// reuse any previously computed tokens that are common with the new prompt
|
|
slot.n_past = slot.cache_tokens.get_common_prefix(prompt_tokens);
|
|
|
|
// push the prompt into the sampling context (do not apply grammar)
|
|
for (int i = 0; i < slot.n_past; ++i) {
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) {
|
|
// we have to evaluate at least 1 token to generate logits.
|
|
LOG_INFO("we have to evaluate at least 1 token to generate logits", {
|
|
{ "id_slot", slot.id },
|
|
{ "id_task", slot.id_task }
|
|
});
|
|
|
|
slot.n_past--;
|
|
if (slot.ga_i > 0) {
|
|
slot.n_past_se--;
|
|
}
|
|
}
|
|
|
|
slot.n_prompt_tokens_processed = 0;
|
|
}
|
|
|
|
if (slot.embedding) {
|
|
// cannot fit the prompt in the current batch - will try next iter
|
|
if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
|
|
continue;
|
|
}
|
|
}
|
|
|
|
// check that we are in the right batch_type, if not defer the slot
|
|
bool slot_type = slot.embedding ? 1 : 0;
|
|
if (batch_type == -1) {
|
|
batch_type = slot_type;
|
|
} else if (batch_type != slot_type) {
|
|
continue;
|
|
}
|
|
|
|
// keep only the common part
|
|
int p0 = (int) system_tokens.size() + slot.n_past;
|
|
if (!llama_kv_cache_seq_rm(ctx, slot.id, p0, -1)) {
|
|
// could not partially delete (likely using a non-Transformer model)
|
|
llama_kv_cache_seq_rm(ctx, slot.id, -1, -1);
|
|
|
|
p0 = (int) system_tokens.size();
|
|
if (p0 != 0) {
|
|
// copy over the system prompt when there is one
|
|
llama_kv_cache_seq_cp(ctx, 0, slot.id, -1, -1);
|
|
}
|
|
|
|
// there is no common part left (except for the system prompt)
|
|
slot.n_past = 0;
|
|
slot.n_past_se = 0;
|
|
slot.ga_i = 0;
|
|
// TODO: is the system prompt ever in the sampling context?
|
|
llama_sampling_reset(llama_get_model_vocab(model), slot.ctx_sampling);
|
|
}
|
|
|
|
// remove the non-common part from the cache
|
|
slot.cache_tokens.keep_first(slot.n_past);
|
|
|
|
LOG_INFO("kv cache rm [p0, end)", {
|
|
{ "id_slot", slot.id },
|
|
{ "id_task", slot.id_task },
|
|
{ "p0", p0 }
|
|
});
|
|
|
|
// check if we should process the image
|
|
if (slot.n_past < slot.n_prompt_tokens
|
|
&& slot.prompt_tokens[slot.n_past] == LLAMA_TOKEN_NULL) {
|
|
// process the image
|
|
int32_t new_n_past;
|
|
size_t new_n_tokens;
|
|
int32_t res = slot.prompt_tokens.process_chunk(ctx, mctx, slot.n_past, slot.id, new_n_past, new_n_tokens);
|
|
int32_t n_pos = new_n_past - slot.n_past;
|
|
if (res != 0) {
|
|
LLAMA_LOG_ERROR("failed to process image, res = %d\n", res);
|
|
slot.release();
|
|
send_error(slot, "failed to process image", ERROR_TYPE_SERVER);
|
|
continue;
|
|
}
|
|
|
|
// add the image chunk to cache
|
|
{
|
|
const auto& chunk = slot.prompt_tokens.find_chunk(slot.n_past);
|
|
slot.cache_tokens.push_back(chunk.get()); // copy
|
|
}
|
|
|
|
slot.n_past += n_pos;
|
|
slot.n_prompt_tokens_processed += new_n_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;
|
|
|
|
// add prompt tokens for processing in the current batch
|
|
// TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow
|
|
while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) {
|
|
// get next token to process
|
|
llama_token cur_tok = slot.prompt_tokens[slot.n_past];
|
|
if (cur_tok == LLAMA_TOKEN_NULL) {
|
|
break; // end of text chunk
|
|
}
|
|
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, cur_tok, system_tokens.size() + slot_npast, { slot.id }, false);
|
|
{
|
|
slot.cache_tokens.push_back(cur_tok);
|
|
}
|
|
|
|
slot.n_prompt_tokens_processed++;
|
|
slot_npast++;
|
|
slot.n_past++;
|
|
}
|
|
LOG_VERBOSE("prompt processing progress", {
|
|
{"id_slot", slot.id},
|
|
{"n_past", slot.n_past},
|
|
{"n_ctx", n_ctx},
|
|
{"n_tokens", batch.n_tokens},
|
|
{"progress", (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens},
|
|
});
|
|
|
|
// entire prompt has been processed - start decoding new tokens
|
|
if (slot.n_past == slot.n_prompt_tokens) {
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
|
|
GGML_ASSERT(batch.n_tokens > 0);
|
|
GGML_ASSERT((size_t)slot.n_prompt_tokens == slot.prompt_tokens.size());
|
|
llama_sampling_reset(llama_get_model_vocab(model), slot.ctx_sampling);
|
|
for (int i = 0; i < slot.n_prompt_tokens; ++i) {
|
|
llama_token id = slot.prompt_tokens[i];
|
|
if (id != LLAMA_TOKEN_NULL) {
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, id, false);
|
|
}
|
|
}
|
|
|
|
// extract the logits only for the last token
|
|
batch.logits[batch.n_tokens - 1] = true;
|
|
|
|
slot.n_decoded = 0;
|
|
slot.i_batch = batch.n_tokens - 1;
|
|
|
|
LOG_VERBOSE("prompt done", {
|
|
{"id_slot", slot.id},
|
|
{"n_past", slot.n_past},
|
|
{"n_ctx", n_ctx},
|
|
{"n_tokens", batch.n_tokens},
|
|
});
|
|
}
|
|
}
|
|
|
|
if (batch.n_tokens >= n_batch) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (batch.n_tokens == 0) {
|
|
LOG_VERBOSE("no tokens to decode", {});
|
|
return;
|
|
}
|
|
|
|
LOG_VERBOSE("decoding batch", {
|
|
{"n_tokens", batch.n_tokens},
|
|
});
|
|
|
|
// make sure we're in the right embedding mode
|
|
llama_set_embeddings(ctx, batch_type == 1);
|
|
|
|
// process the created batch of tokens
|
|
for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
|
|
const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
|
|
|
|
for (auto & slot : slots) {
|
|
if (slot.ga_n != 1) {
|
|
// context extension via Self-Extend
|
|
// TODO: simplify and/or abstract this
|
|
while (slot.n_past_se >= slot.ga_i + slot.ga_w) {
|
|
const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
|
|
const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
|
|
const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
|
|
|
|
LOG_TEE("\n");
|
|
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
|
|
LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
|
|
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
|
|
|
|
llama_kv_cache_seq_add(ctx, slot.id, 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_ERROR("failed to decode the batch: KV cache is full - try increasing it via the context size", {
|
|
{"i", i},
|
|
{"n_batch", ret},
|
|
{"ret", ret},
|
|
});
|
|
for (auto & slot : slots) {
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.release();
|
|
send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size.");
|
|
}
|
|
break; // break loop of n_batch
|
|
}
|
|
|
|
// retry with half the batch size to try to find a free slot in the KV cache
|
|
n_batch /= 2;
|
|
i -= n_batch;
|
|
|
|
LOG_WARNING("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation", {
|
|
{"i", i},
|
|
{"n_batch", n_batch},
|
|
{"ret", ret},
|
|
});
|
|
|
|
continue; // continue loop of n_batch
|
|
}
|
|
|
|
for (auto & slot : slots) {
|
|
if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
|
|
continue; // continue loop of slots
|
|
}
|
|
|
|
// prompt evaluated for embedding
|
|
if (slot.embedding) {
|
|
send_embedding(slot, batch_view);
|
|
slot.release();
|
|
slot.i_batch = -1;
|
|
continue; // continue loop of slots
|
|
}
|
|
|
|
completion_token_output result;
|
|
const int tok_idx = slot.i_batch - i;
|
|
const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, tok_idx);
|
|
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
|
|
|
|
slot.n_decoded += 1;
|
|
|
|
const int64_t t_current = ggml_time_us();
|
|
|
|
if (slot.n_decoded == 1) {
|
|
slot.t_start_generation = ggml_time_us();
|
|
slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
|
|
metrics.on_prompt_eval(slot);
|
|
}
|
|
|
|
slot.t_token_generation = (t_current - slot.t_start_generation) / 1e3;
|
|
|
|
result.tok = id;
|
|
result.prob = 1.0f; // TODO: set it here instead of doing inside populate_token_probs
|
|
result.text_to_send = llama_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
|
|
|
|
if (slot.sparams.n_probs > 0) {
|
|
populate_token_probs(slot, result, slot.params.post_sampling_probs, params.special, tok_idx);
|
|
}
|
|
|
|
if (!process_token(result, slot)) {
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
metrics.on_prediction(slot);
|
|
}
|
|
|
|
slot.i_batch = -1;
|
|
}
|
|
|
|
// Do speculative decoding
|
|
for (auto & slot : slots) {
|
|
if (!slot.is_processing() || !slot.spec) {
|
|
continue;
|
|
}
|
|
|
|
if (slot.state != SLOT_STATE_PROCESSING) {
|
|
continue;
|
|
}
|
|
|
|
if (mctx) {
|
|
// we should never reach this, as speculative is automatically disabled if mmproj is loaded
|
|
GGML_ABORT("not supported by multimodal");
|
|
}
|
|
|
|
// determine the max draft that fits the current slot state
|
|
int n_draft_max = slot.params.speculative.n_max;
|
|
|
|
// note: n_past is not yet increased for the `id` token sampled above
|
|
// also, need to leave space for 1 extra token to allow context shifts
|
|
n_draft_max = std::min(n_draft_max, slot.n_ctx - slot.n_past - 2);
|
|
|
|
if (slot.n_predict > 0) {
|
|
n_draft_max = std::min(n_draft_max, slot.n_predict - slot.n_decoded - 1);
|
|
}
|
|
|
|
LOG_VERBOSE("max possible draft", {
|
|
{"id_slot", slot.id},
|
|
{"n_draft_max", n_draft_max}
|
|
});
|
|
|
|
if (n_draft_max < slot.params.speculative.n_min) {
|
|
LOG_VERBOSE("the max possible draft is too small", {
|
|
{"id_slot", slot.id},
|
|
{"n_draft_max", n_draft_max},
|
|
{"n_min", slot.params.speculative.n_min}
|
|
});
|
|
continue;
|
|
}
|
|
|
|
llama_token id = slot.sampled;
|
|
|
|
struct llama_speculative_params params_spec;
|
|
params_spec.n_draft = n_draft_max;
|
|
params_spec.n_reuse = cparams_dft.n_ctx - slot.params.speculative.n_max;
|
|
params_spec.p_min = slot.params.speculative.p_min;
|
|
|
|
const std::vector<llama_token> & cached_text_tokens = slot.cache_tokens.tokens_data();
|
|
std::vector<llama_token> draft = llama_speculative_gen_draft(slot.spec, params_spec, cached_text_tokens, id);
|
|
|
|
// ignore small drafts
|
|
if (slot.params.speculative.n_min > (int) draft.size()) {
|
|
LOG_VERBOSE("ignoring small draft", {
|
|
{"id_slot", slot.id},
|
|
{"draft_size", (int) draft.size()},
|
|
{"n_min", slot.params.speculative.n_min}
|
|
});
|
|
continue;
|
|
}
|
|
|
|
// keep track of total number of drafted tokens tested
|
|
slot.n_draft_total += draft.size();
|
|
|
|
// construct the speculation batch
|
|
llama_batch_clear(slot.batch_spec);
|
|
llama_batch_add(slot.batch_spec, id, slot.n_past, { slot.id }, true);
|
|
|
|
for (size_t i = 0; i < draft.size(); ++i) {
|
|
llama_batch_add(slot.batch_spec, draft[i], slot.n_past + 1 + i, { slot.id }, true);
|
|
}
|
|
|
|
LOG_VERBOSE("decoding speculative batch", {
|
|
{"id_slot", slot.id},
|
|
{"size", slot.batch_spec.n_tokens}
|
|
});
|
|
|
|
llama_decode(ctx, slot.batch_spec);
|
|
|
|
// the accepted tokens from the speculation
|
|
std::vector<llama_token> ids = llama_sampling_sample_and_accept_n(slot.ctx_sampling, ctx, draft);
|
|
|
|
slot.n_past += ids.size();
|
|
slot.n_decoded += ids.size();
|
|
|
|
// update how many tokens out of those tested were accepted
|
|
slot.n_draft_accepted += ids.size() - 1;
|
|
|
|
slot.cache_tokens.push_back(id);
|
|
slot.cache_tokens.insert({ ids.begin(), ids.end() - 1 });
|
|
|
|
llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1);
|
|
|
|
for (size_t i = 0; i < ids.size(); ++i) {
|
|
completion_token_output result;
|
|
|
|
result.tok = ids[i];
|
|
result.text_to_send = llama_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
|
|
result.prob = 1.0f; // set later
|
|
|
|
if (slot.sparams.n_probs > 0) {
|
|
populate_token_probs(slot, result, slot.params.post_sampling_probs, params.special, i);
|
|
}
|
|
|
|
if (!process_token(result, slot)) {
|
|
// release slot because of stop condition
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
metrics.on_prediction(slot);
|
|
break;
|
|
}
|
|
}
|
|
|
|
LOG_VERBOSE("speculative decoding result", {
|
|
{"id_slot", slot.id},
|
|
{"accepted", (int) ids.size() - 1},
|
|
{"total", (int) draft.size()},
|
|
{"new_n_past", slot.n_past}
|
|
});
|
|
}
|
|
}
|
|
|
|
LOG_VERBOSE("run slots completed", {});
|
|
}
|
|
|
|
json model_meta() const {
|
|
return json {
|
|
{"vocab_type", llama_vocab_type (model)},
|
|
{"n_vocab", llama_n_vocab (model)},
|
|
{"n_ctx_train", llama_n_ctx_train (model)},
|
|
{"n_embd", llama_n_embd (model)},
|
|
{"n_params", llama_model_n_params(model)},
|
|
{"size", llama_model_size (model)},
|
|
};
|
|
}
|
|
};
|
|
|
|
static json format_final_response_oaicompat(const json& request, json result, const std::string& completion_id, bool streaming = false) {
|
|
bool stopped_word = result.count("stopped_word") != 0;
|
|
bool stopped_eos = json_value(result, "stopped_eos", false);
|
|
int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
|
|
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
|
|
std::string content = json_value(result, "content", std::string(""));
|
|
|
|
std::string finish_reason = "length";
|
|
if (stopped_word || stopped_eos) {
|
|
finish_reason = "stop";
|
|
}
|
|
|
|
json choices =
|
|
streaming ? json::array({ json{{"finish_reason", finish_reason},
|
|
{"index", 0},
|
|
{"delta", json::object()}} })
|
|
: json::array({ json{{"finish_reason", finish_reason},
|
|
{"index", 0},
|
|
{"message", json{{"content", content},
|
|
{"role", "assistant"}}}} });
|
|
|
|
std::time_t t = std::time(0);
|
|
|
|
json res = json{
|
|
{"choices", choices},
|
|
{"created", t},
|
|
{"model",
|
|
json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
|
|
{"object", streaming ? "chat.completion.chunk" : "chat.completion"},
|
|
{"usage", json {
|
|
{"completion_tokens", num_tokens_predicted},
|
|
{"prompt_tokens", num_prompt_tokens},
|
|
{"total_tokens", num_tokens_predicted + num_prompt_tokens}
|
|
}},
|
|
{"id", completion_id}
|
|
};
|
|
|
|
if (server_verbose) {
|
|
res["__verbose"] = result;
|
|
}
|
|
|
|
if (result.contains("completion_probabilities")) {
|
|
res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
// return value is vector as there is one case where we might need to generate two responses
|
|
static std::vector<json> format_partial_response_oaicompat(server_task_result task_result, const std::string& completion_id) {
|
|
json result = task_result.data;
|
|
std::cout << result.dump(4) << std::endl;
|
|
if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
|
|
return std::vector<json>({ result });
|
|
}
|
|
|
|
bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
|
|
std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
|
|
|
|
bool stopped_word = json_value(result, "stopped_word", false);
|
|
bool stopped_eos = json_value(result, "stopped_eos", false);
|
|
bool stopped_limit = json_value(result, "stopped_limit", false);
|
|
std::string content = json_value(result, "content", std::string(""));
|
|
|
|
std::string finish_reason;
|
|
if (stopped_word || stopped_eos) {
|
|
finish_reason = "stop";
|
|
}
|
|
if (stopped_limit) {
|
|
finish_reason = "length";
|
|
}
|
|
|
|
std::time_t t = std::time(0);
|
|
|
|
json choices;
|
|
|
|
if (!finish_reason.empty()) {
|
|
choices = json::array({ json{{"finish_reason", finish_reason},
|
|
{"index", 0},
|
|
{"delta", json::object()}} });
|
|
}
|
|
else {
|
|
if (first) {
|
|
if (content.empty()) {
|
|
choices = json::array({ json{{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta", json{{"role", "assistant"}}}} });
|
|
}
|
|
else {
|
|
// We have to send this as two updates to conform to openai behavior
|
|
json initial_ret = json{ {"choices", json::array({json{
|
|
{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta", json{
|
|
{"role", "assistant"}
|
|
}}}})},
|
|
{"created", t},
|
|
{"id", completion_id},
|
|
{"model", modelname},
|
|
{"object", "chat.completion.chunk"} };
|
|
|
|
json second_ret = json{
|
|
{"choices", json::array({json{{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta", json{
|
|
{"content", content}}}
|
|
}})},
|
|
{"created", t},
|
|
{"id", completion_id},
|
|
{"model", modelname},
|
|
{"object", "chat.completion.chunk"} };
|
|
|
|
return std::vector<json>({ initial_ret, second_ret });
|
|
}
|
|
}
|
|
else {
|
|
// Some idiosyncrasy in task processing logic makes several trailing calls
|
|
// with empty content, we ignore these at the calee site.
|
|
if (content.empty()) {
|
|
return std::vector<json>({ json::object() });
|
|
}
|
|
|
|
choices = json::array({ json{
|
|
{"finish_reason", nullptr},
|
|
{"index", 0},
|
|
{"delta",
|
|
json{
|
|
{"content", content},
|
|
}},
|
|
} });
|
|
}
|
|
}
|
|
|
|
json ret = json{
|
|
{"choices", choices},
|
|
{"created", t},
|
|
{"id", completion_id},
|
|
{"model", modelname},
|
|
{"object", "chat.completion.chunk"}
|
|
};
|
|
|
|
if (task_result.timings.prompt_n != -1) {
|
|
ret.push_back({ "timings", task_result.timings.to_json() });
|
|
}
|
|
|
|
//
|
|
if (!finish_reason.empty()) {
|
|
int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
|
|
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
|
|
ret.push_back({ "usage", json {
|
|
{"completion_tokens", num_tokens_predicted},
|
|
{"prompt_tokens", num_prompt_tokens},
|
|
{"total_tokens", num_tokens_predicted + num_prompt_tokens}
|
|
} });
|
|
}
|
|
|
|
return std::vector<json>({ ret });
|
|
}
|
|
|
|
|
|
//static json format_embeddings_response_oaicompat(const json& request, const json& embeddings) {
|
|
// json data = json::array();
|
|
// int32_t n_tokens = 0;
|
|
// int i = 0;
|
|
// for (auto& elem : embeddings) {
|
|
// data.push_back(json{
|
|
// {"embedding", json_value(elem, "embedding", json::array())},
|
|
// {"index", i++},
|
|
// {"object", "embedding"}
|
|
// });
|
|
// }
|
|
//
|
|
// json res = json{
|
|
// {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
|
|
// {"object", "list"},
|
|
// {"usage", json {
|
|
// {"prompt_tokens", n_tokens},
|
|
// {"total_tokens", n_tokens}
|
|
// }},
|
|
// {"data", data}
|
|
// };
|
|
//
|
|
// return res;
|
|
//}
|
|
|
|
static json format_embeddings_response_oaicompat(const json& request, const json& embeddings, bool use_base64 = false) {
|
|
json data = json::array();
|
|
int32_t n_tokens = 0;
|
|
int i = 0;
|
|
for (const auto& elem : embeddings) {
|
|
json embedding_obj;
|
|
|
|
if (use_base64) {
|
|
const auto& vec = json_value(elem, "embedding", json::array()).get<std::vector<float>>();
|
|
const char* data_ptr = reinterpret_cast<const char*>(vec.data());
|
|
size_t data_size = vec.size() * sizeof(float);
|
|
embedding_obj = {
|
|
{"embedding", base64::encode(data_ptr, data_size)},
|
|
{"index", i++},
|
|
{"object", "embedding"},
|
|
{"encoding_format", "base64"}
|
|
};
|
|
}
|
|
else {
|
|
embedding_obj = {
|
|
{"embedding", json_value(elem, "embedding", json::array())},
|
|
{"index", i++},
|
|
{"object", "embedding"}
|
|
};
|
|
}
|
|
data.push_back(embedding_obj);
|
|
n_tokens += json_value(elem, "tokens_evaluated", 0);
|
|
}
|
|
json res = json{
|
|
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
|
|
{"object", "list"},
|
|
{"usage", json {
|
|
{"prompt_tokens", n_tokens},
|
|
{"total_tokens", n_tokens}
|
|
}},
|
|
{"data", data}
|
|
};
|
|
|
|
return res;
|
|
}
|
|
|
|
static void log_server_request(const httplib::Request & req, const httplib::Response & res) {
|
|
// skip GH copilot requests when using default port
|
|
if (req.path == "/v1/health" || req.path == "/v1/completions") {
|
|
return;
|
|
}
|
|
|
|
LOG_INFO("request", {
|
|
{"remote_addr", req.remote_addr},
|
|
{"remote_port", req.remote_port},
|
|
{"status", res.status},
|
|
{"method", req.method},
|
|
{"path", req.path},
|
|
{"params", req.params},
|
|
});
|
|
|
|
LOG_VERBOSE("request", {
|
|
{"request", req.body},
|
|
{"response", res.body},
|
|
});
|
|
}
|
|
|
|
std::function<void(int)> shutdown_handler;
|
|
std::atomic_flag is_terminating = ATOMIC_FLAG_INIT;
|
|
|
|
inline void signal_handler(int signal) {
|
|
if (is_terminating.test_and_set()) {
|
|
// in case it hangs, we can force terminate the server by hitting Ctrl+C twice
|
|
// this is for better developer experience, we can remove when the server is stable enough
|
|
fprintf(stderr, "Received second interrupt, terminating immediately.\n");
|
|
exit(1);
|
|
}
|
|
|
|
shutdown_handler(signal);
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
#if SERVER_VERBOSE != 1
|
|
log_disable();
|
|
#endif
|
|
// own arguments required by this example
|
|
gpt_params params;
|
|
|
|
if (!gpt_params_parse(argc, argv, params)) {
|
|
gpt_params_print_usage(argc, argv, params);
|
|
return 1;
|
|
}
|
|
|
|
// parse arguments from environment variables
|
|
gpt_params_parse_from_env(params);
|
|
|
|
// TODO: not great to use extern vars
|
|
server_log_json = params.log_json;
|
|
server_verbose = params.verbosity > 0;
|
|
|
|
|
|
// struct that contains llama context and inference
|
|
server_context ctx_server;
|
|
|
|
if (!params.system_prompt.empty()) {
|
|
ctx_server.system_prompt_set(params.system_prompt);
|
|
}
|
|
|
|
if (params.model_alias == "unknown") {
|
|
params.model_alias = params.model;
|
|
}
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
LOG_INFO("build info", {
|
|
{"build", LLAMA_BUILD_NUMBER},
|
|
{"commit", LLAMA_COMMIT}
|
|
});
|
|
|
|
LOG_INFO("system info", {
|
|
{"n_threads", params.n_threads},
|
|
{"n_threads_batch", params.n_threads_batch},
|
|
{"total_threads", std::thread::hardware_concurrency()},
|
|
{"system_info", llama_print_system_info()},
|
|
});
|
|
|
|
std::unique_ptr<httplib::Server> svr;
|
|
#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
|
|
if (params.ssl_file_key != "" && params.ssl_file_cert != "") {
|
|
LOG_INFO("Running with SSL", {{"key", params.ssl_file_key}, {"cert", params.ssl_file_cert}});
|
|
svr.reset(
|
|
new httplib::SSLServer(params.ssl_file_cert.c_str(), params.ssl_file_key.c_str())
|
|
);
|
|
} else {
|
|
LOG_INFO("Running without SSL", {});
|
|
svr.reset(new httplib::Server());
|
|
}
|
|
#else
|
|
svr.reset(new httplib::Server());
|
|
#endif
|
|
|
|
std::atomic<server_state> state{SERVER_STATE_LOADING_MODEL};
|
|
|
|
svr->set_default_headers({{"Server", "ik_llama.cpp"}});
|
|
|
|
svr->set_logger(log_server_request);
|
|
|
|
auto res_error = [](httplib::Response & res, json error_data) {
|
|
json final_response {{"error", error_data}};
|
|
res.set_content(final_response.dump(), "application/json; charset=utf-8");
|
|
res.status = json_value(error_data, "code", 500);
|
|
};
|
|
|
|
auto res_ok = [](httplib::Response& res, const json& data) {
|
|
res.set_content(data.dump(), "application/json; charset=utf-8");
|
|
res.status = 200;
|
|
};
|
|
|
|
svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) {
|
|
std::string message;
|
|
try {
|
|
std::rethrow_exception(std::move(ep));
|
|
} catch (std::exception & e) {
|
|
message = e.what();
|
|
} catch (...) {
|
|
message = "Unknown Exception";
|
|
}
|
|
|
|
json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
|
|
LOG_VERBOSE("Got exception", formatted_error);
|
|
res_error(res, formatted_error);
|
|
});
|
|
|
|
svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) {
|
|
if (res.status == 404) {
|
|
res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND));
|
|
}
|
|
// for other error codes, we skip processing here because it's already done by res_error()
|
|
});
|
|
|
|
// set timeouts and change hostname and port
|
|
svr->set_read_timeout (params.timeout_read);
|
|
svr->set_write_timeout(params.timeout_write);
|
|
|
|
if (!svr->bind_to_port(params.hostname, params.port)) {
|
|
fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", params.hostname.c_str(), params.port);
|
|
return 1;
|
|
}
|
|
|
|
std::unordered_map<std::string, std::string> log_data;
|
|
|
|
log_data["hostname"] = params.hostname;
|
|
log_data["port"] = std::to_string(params.port);
|
|
|
|
if (params.api_keys.size() == 1) {
|
|
auto key = params.api_keys[0];
|
|
log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0));
|
|
} else if (params.api_keys.size() > 1) {
|
|
log_data["api_key"] = "api_key: " + std::to_string(params.api_keys.size()) + " keys loaded";
|
|
}
|
|
|
|
// Necessary similarity of prompt for slot selection
|
|
ctx_server.slot_prompt_similarity = params.slot_prompt_similarity;
|
|
#ifdef SQLITE3_MODERN_CPP_SUPPORT
|
|
auto db_handle = std::make_shared<DatabaseHandle>(params.sql_save_file);
|
|
bool sqlite_extension_loaded = false;
|
|
if (!params.sqlite_zstd_ext_file.empty()) {
|
|
auto* conn = db_handle->db.connection().get();
|
|
sqlite3_enable_load_extension(conn, 1);
|
|
char* errmsg = nullptr;
|
|
const int rc = sqlite3_load_extension(
|
|
conn,
|
|
params.sqlite_zstd_ext_file.c_str(),
|
|
nullptr,
|
|
&errmsg
|
|
);
|
|
if(rc != SQLITE_OK) {
|
|
const std::string err = errmsg ? errmsg : "Unknown extension error";
|
|
sqlite3_free(errmsg);
|
|
LOG_WARNING("Failed to load extension", {{"err", err}});
|
|
}
|
|
else {
|
|
sqlite_extension_loaded = true;
|
|
}
|
|
sqlite3_enable_load_extension(conn, 0);
|
|
}
|
|
#else
|
|
auto db_handle = false;
|
|
#endif
|
|
// load the model
|
|
if (!ctx_server.load_model(params)) {
|
|
state.store(SERVER_STATE_ERROR);
|
|
return 1;
|
|
} else {
|
|
ctx_server.init();
|
|
state.store(SERVER_STATE_READY);
|
|
}
|
|
|
|
LOG_INFO("model loaded", {});
|
|
|
|
const auto model_meta = ctx_server.model_meta();
|
|
|
|
// print sample chat example to make it clear which template is used
|
|
|
|
LOG_INFO("chat template", {
|
|
{"chat_template", common_chat_templates_source(ctx_server.chat_templates.get())},
|
|
});
|
|
|
|
LOG_INFO("chat template", {
|
|
{"chat_example", common_chat_format_example(ctx_server.chat_templates.get(), ctx_server.params.use_jinja, {}).c_str()
|
|
},
|
|
{"built_in", params.chat_template.empty()},
|
|
});
|
|
//
|
|
// Middlewares
|
|
//
|
|
|
|
auto middleware_validate_api_key = [¶ms, &res_error](const httplib::Request & req, httplib::Response & res) {
|
|
// TODO: should we apply API key to all endpoints, including "/health" and "/models"?
|
|
static const std::set<std::string> protected_endpoints = {
|
|
"/props",
|
|
"/completion",
|
|
"/completions",
|
|
"/v1/completions",
|
|
"/chat/completions",
|
|
"/v1/chat/completions",
|
|
"/infill",
|
|
"/tokenize",
|
|
"/detokenize",
|
|
"/embedding",
|
|
"/embeddings",
|
|
"/v1/embeddings",
|
|
};
|
|
|
|
// If API key is not set, skip validation
|
|
if (params.api_keys.empty()) {
|
|
return true;
|
|
}
|
|
|
|
// If path is not in protected_endpoints list, skip validation
|
|
if (protected_endpoints.find(req.path) == protected_endpoints.end()) {
|
|
return true;
|
|
}
|
|
|
|
// Check for API key in the header
|
|
auto auth_header = req.get_header_value("Authorization");
|
|
|
|
std::string prefix = "Bearer ";
|
|
if (auth_header.substr(0, prefix.size()) == prefix) {
|
|
std::string received_api_key = auth_header.substr(prefix.size());
|
|
if (std::find(params.api_keys.begin(), params.api_keys.end(), received_api_key) != params.api_keys.end()) {
|
|
return true; // API key is valid
|
|
}
|
|
}
|
|
|
|
// API key is invalid or not provided
|
|
res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION));
|
|
|
|
LOG_WARNING("Unauthorized: Invalid API Key", {});
|
|
|
|
return false;
|
|
};
|
|
|
|
auto middleware_server_state = [&res_error, &state](const httplib::Request& req, httplib::Response& res) {
|
|
server_state current_state = state.load();
|
|
if (current_state == SERVER_STATE_LOADING_MODEL) {
|
|
auto tmp = string_split<std::string>(req.path, '.');
|
|
if (req.path == "/" || tmp.back() == "html") {
|
|
res.set_content(reinterpret_cast<const char*>(loading_html), loading_html_len, "text/html; charset=utf-8");
|
|
res.status = 503;
|
|
}
|
|
else if (req.path == "/models" || req.path == "/v1/models" || req.path == "/api/tags") {
|
|
// allow the models endpoint to be accessed during loading
|
|
return true;
|
|
}
|
|
else {
|
|
res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
|
|
}
|
|
return false;
|
|
}
|
|
return true;
|
|
};
|
|
|
|
// register server middlewares
|
|
svr->set_pre_routing_handler([&middleware_validate_api_key, &middleware_server_state](const httplib::Request& req, httplib::Response& res) {
|
|
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
|
|
// If this is OPTIONS request, skip validation because browsers don't include Authorization header
|
|
if (req.method == "OPTIONS") {
|
|
res.set_header("Access-Control-Allow-Credentials", "true");
|
|
res.set_header("Access-Control-Allow-Methods", "GET, POST");
|
|
res.set_header("Access-Control-Allow-Headers", "*");
|
|
res.set_content("", "text/html"); // blank response, no data
|
|
return httplib::Server::HandlerResponse::Handled; // skip further processing
|
|
}
|
|
if (!middleware_server_state(req, res)) {
|
|
return httplib::Server::HandlerResponse::Handled;
|
|
}
|
|
if (!middleware_validate_api_key(req, res)) {
|
|
return httplib::Server::HandlerResponse::Handled;
|
|
}
|
|
return httplib::Server::HandlerResponse::Unhandled;
|
|
});
|
|
|
|
//
|
|
// Route handlers (or controllers)
|
|
//
|
|
|
|
const auto handle_health = [&](const httplib::Request & req, httplib::Response & res) {
|
|
server_state current_state = state.load();
|
|
switch (current_state) {
|
|
case SERVER_STATE_READY:
|
|
{
|
|
// request slots data using task queue
|
|
server_task task;
|
|
task.id = ctx_server.queue_tasks.get_new_id();
|
|
task.type = SERVER_TASK_TYPE_METRICS;
|
|
task.id_target = -1;
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(task.id);
|
|
ctx_server.queue_tasks.post(std::move(task));
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(task.id);
|
|
ctx_server.queue_results.remove_waiting_task_id(task.id);
|
|
|
|
const int n_idle_slots = result.data.at("idle");
|
|
const int n_processing_slots = result.data.at("processing");
|
|
|
|
json health = {
|
|
{"status", "ok"},
|
|
{"slots_idle", n_idle_slots},
|
|
{"slots_processing", n_processing_slots}
|
|
};
|
|
|
|
res.status = 200; // HTTP OK
|
|
if (params.endpoint_slots && req.has_param("include_slots")) {
|
|
health["slots"] = result.data.at("slots");
|
|
}
|
|
|
|
if (n_idle_slots == 0) {
|
|
health["status"] = "no slot available";
|
|
if (req.has_param("fail_on_no_slot")) {
|
|
res.status = 503; // HTTP Service Unavailable
|
|
}
|
|
}
|
|
|
|
res.set_content(health.dump(), "application/json");
|
|
break;
|
|
}
|
|
case SERVER_STATE_LOADING_MODEL:
|
|
{
|
|
res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
|
|
} break;
|
|
case SERVER_STATE_ERROR:
|
|
{
|
|
res_error(res, format_error_response("Model failed to load", ERROR_TYPE_SERVER));
|
|
} break;
|
|
}
|
|
};
|
|
|
|
const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) {
|
|
if (!params.endpoint_slots) {
|
|
res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED));
|
|
return;
|
|
}
|
|
|
|
// request slots data using task queue
|
|
server_task task;
|
|
task.id = ctx_server.queue_tasks.get_new_id();
|
|
task.id_multi = -1;
|
|
task.id_target = -1;
|
|
task.type = SERVER_TASK_TYPE_METRICS;
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(task.id);
|
|
ctx_server.queue_tasks.post(std::move(task));
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(task.id);
|
|
ctx_server.queue_results.remove_waiting_task_id(task.id);
|
|
|
|
res.set_content(result.data.at("slots").dump(), "application/json");
|
|
res.status = 200; // HTTP OK
|
|
};
|
|
|
|
const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) {
|
|
if (!params.endpoint_metrics) {
|
|
res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED));
|
|
return;
|
|
}
|
|
|
|
// request slots data using task queue
|
|
server_task task;
|
|
task.id = ctx_server.queue_tasks.get_new_id();
|
|
task.id_multi = -1;
|
|
task.id_target = -1;
|
|
task.type = SERVER_TASK_TYPE_METRICS;
|
|
task.data.push_back({{"reset_bucket", true}});
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(task.id);
|
|
ctx_server.queue_tasks.post(std::move(task));
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(task.id);
|
|
ctx_server.queue_results.remove_waiting_task_id(task.id);
|
|
|
|
json data = result.data;
|
|
|
|
const uint64_t n_prompt_tokens_processed = data.at("n_prompt_tokens_processed");
|
|
const uint64_t t_prompt_processing = data.at("t_prompt_processing");
|
|
|
|
const uint64_t n_tokens_predicted = data.at("n_tokens_predicted");
|
|
const uint64_t t_tokens_generation = data.at("t_tokens_generation");
|
|
|
|
const int32_t kv_cache_used_cells = data.at("kv_cache_used_cells");
|
|
|
|
// metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
|
|
json all_metrics_def = json {
|
|
{"counter", {{
|
|
{"name", "prompt_tokens_total"},
|
|
{"help", "Number of prompt tokens processed."},
|
|
{"value", (uint64_t) data.at("n_prompt_tokens_processed_total")}
|
|
}, {
|
|
{"name", "prompt_seconds_total"},
|
|
{"help", "Prompt process time"},
|
|
{"value", (uint64_t) data.at("t_prompt_processing_total") / 1.e3}
|
|
}, {
|
|
{"name", "tokens_predicted_total"},
|
|
{"help", "Number of generation tokens processed."},
|
|
{"value", (uint64_t) data.at("n_tokens_predicted_total")}
|
|
}, {
|
|
{"name", "tokens_predicted_seconds_total"},
|
|
{"help", "Predict process time"},
|
|
{"value", (uint64_t) data.at("t_tokens_generation_total") / 1.e3}
|
|
}}},
|
|
{"gauge", {{
|
|
{"name", "prompt_tokens_seconds"},
|
|
{"help", "Average prompt throughput in tokens/s."},
|
|
{"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.}
|
|
},{
|
|
{"name", "predicted_tokens_seconds"},
|
|
{"help", "Average generation throughput in tokens/s."},
|
|
{"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.}
|
|
},{
|
|
{"name", "kv_cache_usage_ratio"},
|
|
{"help", "KV-cache usage. 1 means 100 percent usage."},
|
|
{"value", 1. * kv_cache_used_cells / params.n_ctx}
|
|
},{
|
|
{"name", "kv_cache_tokens"},
|
|
{"help", "KV-cache tokens."},
|
|
{"value", (uint64_t) data.at("kv_cache_tokens_count")}
|
|
},{
|
|
{"name", "requests_processing"},
|
|
{"help", "Number of request processing."},
|
|
{"value", (uint64_t) data.at("processing")}
|
|
},{
|
|
{"name", "requests_deferred"},
|
|
{"help", "Number of request deferred."},
|
|
{"value", (uint64_t) data.at("deferred")}
|
|
}}}
|
|
};
|
|
|
|
std::stringstream prometheus;
|
|
|
|
for (const auto & el : all_metrics_def.items()) {
|
|
const auto & type = el.key();
|
|
const auto & metrics_def = el.value();
|
|
|
|
for (const auto & metric_def : metrics_def) {
|
|
const std::string name = metric_def.at("name");
|
|
const std::string help = metric_def.at("help");
|
|
|
|
auto value = json_value(metric_def, "value", 0.);
|
|
prometheus << "# HELP llamacpp:" << name << " " << help << "\n"
|
|
<< "# TYPE llamacpp:" << name << " " << type << "\n"
|
|
<< "llamacpp:" << name << " " << value << "\n";
|
|
}
|
|
}
|
|
|
|
const int64_t t_start = data.at("t_start");
|
|
res.set_header("Process-Start-Time-Unix", std::to_string(t_start));
|
|
|
|
res.set_content(prometheus.str(), "text/plain; version=0.0.4");
|
|
res.status = 200; // HTTP OK
|
|
};
|
|
|
|
const auto handle_slots_save = [&ctx_server, &res_error, ¶ms](const httplib::Request & req, httplib::Response & res, int id_slot) {
|
|
json request_data = json::parse(req.body);
|
|
std::string filename = request_data.at("filename");
|
|
if (!fs_validate_filename(filename)) {
|
|
res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
|
|
return;
|
|
}
|
|
std::string filepath = params.slot_save_path + filename;
|
|
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_SLOT_SAVE;
|
|
task.data = {
|
|
{ "id_slot", id_slot },
|
|
{ "filename", filename },
|
|
{ "filepath", filepath }
|
|
};
|
|
|
|
const int id_task = ctx_server.queue_tasks.post(std::move(task));
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
|
|
if (result.error) {
|
|
res_error(res, result.data);
|
|
} else {
|
|
res.set_content(result.data.dump(), "application/json");
|
|
}
|
|
};
|
|
|
|
const auto handle_slots_restore = [&ctx_server, &res_error, ¶ms](const httplib::Request & req, httplib::Response & res, int id_slot) {
|
|
json request_data = json::parse(req.body);
|
|
std::string filename = request_data.at("filename");
|
|
if (!fs_validate_filename(filename)) {
|
|
res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
|
|
return;
|
|
}
|
|
std::string filepath = params.slot_save_path + filename;
|
|
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_SLOT_RESTORE;
|
|
task.data = {
|
|
{ "id_slot", id_slot },
|
|
{ "filename", filename },
|
|
{ "filepath", filepath }
|
|
};
|
|
|
|
const int id_task = ctx_server.queue_tasks.post(std::move(task));
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
|
|
if (result.error) {
|
|
res_error(res, result.data);
|
|
} else {
|
|
res.set_content(result.data.dump(), "application/json");
|
|
}
|
|
};
|
|
|
|
const auto handle_slots_erase = [&ctx_server, &res_error](const httplib::Request & /* req */, httplib::Response & res, int id_slot) {
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_SLOT_ERASE;
|
|
task.data = {
|
|
{ "id_slot", id_slot },
|
|
};
|
|
|
|
const int id_task = ctx_server.queue_tasks.post(std::move(task));
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
|
|
if (result.error) {
|
|
res_error(res, result.data);
|
|
} else {
|
|
res.set_content(result.data.dump(), "application/json");
|
|
}
|
|
};
|
|
|
|
const auto handle_slots_action = [&res_error, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) {
|
|
std::string id_slot_str = req.path_params.at("id_slot");
|
|
int id_slot;
|
|
|
|
try {
|
|
id_slot = std::stoi(id_slot_str);
|
|
} catch (const std::exception &) {
|
|
res_error(res, format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST));
|
|
return;
|
|
}
|
|
|
|
std::string action = req.get_param_value("action");
|
|
|
|
if (action == "save") {
|
|
handle_slots_save(req, res, id_slot);
|
|
} else if (action == "restore") {
|
|
handle_slots_restore(req, res, id_slot);
|
|
} else if (action == "erase") {
|
|
handle_slots_erase(req, res, id_slot);
|
|
} else {
|
|
res_error(res, format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST));
|
|
}
|
|
};
|
|
|
|
const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
|
|
std::string template_key = "tokenizer.chat_template", curr_tmpl;
|
|
int32_t tlen = llama_model_meta_val_str(ctx_server.model, template_key.c_str(), nullptr, 0);
|
|
if (tlen > 0) {
|
|
std::vector<char> curr_tmpl_buf(tlen + 1, 0);
|
|
if (llama_model_meta_val_str(ctx_server.model, template_key.c_str(), curr_tmpl_buf.data(), curr_tmpl_buf.size()) == tlen) {
|
|
curr_tmpl = std::string(curr_tmpl_buf.data(), tlen);
|
|
}
|
|
}
|
|
json data = {
|
|
{ "system_prompt", ctx_server.system_prompt.c_str() },
|
|
{ "default_generation_settings", ctx_server.default_generation_settings_for_props },
|
|
{ "total_slots", ctx_server.params.n_parallel },
|
|
{ "chat_template", common_chat_templates_source(ctx_server.chat_templates.get()) },
|
|
{ "bos_token", llama_token_to_piece(ctx_server.ctx, llama_token_bos(ctx_server.model), /* special= */ true)},
|
|
{ "eos_token", llama_token_to_piece(ctx_server.ctx, llama_token_eos(ctx_server.model), /* special= */ true)},
|
|
{ "model_path", ctx_server.params.model },
|
|
{ "modalities", json {
|
|
{"vision", ctx_server.oai_parser_opt.allow_image},
|
|
{"audio", ctx_server.oai_parser_opt.allow_audio},
|
|
} },
|
|
{ "n_ctx", ctx_server.n_ctx }
|
|
|
|
};
|
|
|
|
if (ctx_server.params.use_jinja) {
|
|
if (auto tool_use_src = common_chat_templates_source(ctx_server.chat_templates.get(), "tool_use")) {
|
|
data["chat_template_tool_use"] = tool_use_src;
|
|
}
|
|
}
|
|
res.set_content(data.dump(), "application/json; charset=utf-8");
|
|
};
|
|
|
|
|
|
// handle completion-like requests (completion, chat, infill)
|
|
// we can optionally provide a custom format for partial results and final results
|
|
const auto handle_completions_impl = [&ctx_server, ¶ms, &res_error, &res_ok](
|
|
server_task_type type,
|
|
json& data,
|
|
const std::vector<raw_buffer>& files,
|
|
httplib::Response& res,
|
|
oaicompat_type oaicompat) -> void {
|
|
GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION);
|
|
if (ctx_server.params.embedding) {
|
|
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
|
|
return;
|
|
}
|
|
|
|
const auto& prompt = data.at("prompt");
|
|
|
|
// process prompt
|
|
std::vector<server_tokens> inputs;
|
|
|
|
if (oaicompat && ctx_server.mctx != nullptr) {
|
|
// This is the case used by OAI compatible chat path with MTMD. TODO It can be moved to the path below.
|
|
#ifndef NDEBUG
|
|
print_files_info(files);
|
|
#endif // !NDEBUG
|
|
inputs.push_back(process_mtmd_prompt(ctx_server.mctx, prompt.get<std::string>(), files));
|
|
}
|
|
else {
|
|
// Everything else, including multimodal completions.
|
|
inputs = tokenize_input_prompts(llama_get_vocab(ctx_server.ctx), ctx_server.mctx, prompt, true, true);
|
|
}
|
|
const auto completion_id = gen_chatcmplid();
|
|
const int id_task = ctx_server.queue_tasks.get_new_id();
|
|
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
ctx_server.request_completion(id_task, -1, data, false, false, std::move(inputs[0]));
|
|
bool stream = json_value(data, "stream", false);
|
|
if (!stream) {
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
result.oaicompat = oaicompat;
|
|
result.oaicompat_cmpl_id = completion_id;
|
|
json result_oai;
|
|
if (oaicompat) {
|
|
if (result.final_result) {
|
|
result_oai = result.to_json_final();
|
|
}
|
|
else {
|
|
result_oai = result.to_json_partial();
|
|
}
|
|
}
|
|
else {
|
|
// legacy completions
|
|
result_oai = result.data;
|
|
}
|
|
if (!result.error && result.stop) {
|
|
res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
|
|
}
|
|
else {
|
|
res_error(res, result_oai);
|
|
}
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
}
|
|
else {
|
|
const auto chunked_content_provider = [id_task, &ctx_server, completion_id, oaicompat, send_done = params.send_done](size_t, httplib::DataSink& sink) {
|
|
bool successful_completion = false;
|
|
while (true) {
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
if (!result.error) {
|
|
result.oaicompat = oaicompat;
|
|
result.oaicompat_cmpl_id = completion_id;
|
|
json res_json;
|
|
if (oaicompat) {
|
|
if (result.final_result) {
|
|
res_json = result.to_json_final();
|
|
}
|
|
else {
|
|
res_json = result.to_json_partial();
|
|
}
|
|
}
|
|
else {
|
|
// legacy completions
|
|
res_json = result.data;
|
|
}
|
|
if (res_json.is_array()) {
|
|
// chat completions and oai completions
|
|
for (const auto& res : res_json) {
|
|
if (!server_sent_event(sink, res)) {
|
|
// sending failed (HTTP connection closed), cancel the generation
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return false;
|
|
}
|
|
}
|
|
if (result.stop) {
|
|
successful_completion = true;
|
|
break;
|
|
}
|
|
}
|
|
else {
|
|
// legacy completions
|
|
if (!server_sent_event(sink, res_json)) {
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return false;
|
|
}
|
|
if (result.stop) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
if (!server_sent_event(sink, result.data)) {
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return false;
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
bool ok = true;
|
|
if (successful_completion) {
|
|
static const std::string done_message = "data: [DONE]\n\n";
|
|
LOG_VERBOSE("data stream", { {"to_send", done_message} });
|
|
if (!sink.write(done_message.c_str(), done_message.size())) {
|
|
// If writing [DONE] fails, the stream is likely already problematic.
|
|
ok = false;
|
|
}
|
|
}
|
|
sink.done();
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
return ok;
|
|
};
|
|
|
|
auto on_complete = [id_task, &ctx_server](bool) {
|
|
// cancel request
|
|
ctx_server.request_cancel(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
};
|
|
|
|
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
|
}
|
|
};
|
|
|
|
const auto handle_completions = [&handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
|
|
auto data = json::parse(req.body);
|
|
std::vector<raw_buffer> files; // dummy
|
|
handle_completions_impl(
|
|
SERVER_TASK_TYPE_COMPLETION,
|
|
data,
|
|
files,
|
|
res,
|
|
OAICOMPAT_TYPE_NONE);
|
|
};
|
|
|
|
const auto handle_completions_oai = [&handle_completions_impl](const httplib::Request& req, httplib::Response& res) {
|
|
auto body = json::parse(req.body);
|
|
json data = oaicompat_chat_params_parse(body);
|
|
std::vector<raw_buffer> files; // dummy
|
|
handle_completions_impl(
|
|
SERVER_TASK_TYPE_COMPLETION,
|
|
data,
|
|
files,
|
|
res,
|
|
OAICOMPAT_TYPE_COMPLETION);
|
|
};
|
|
|
|
const auto handle_models = [¶ms, &model_meta](const httplib::Request & req, httplib::Response & res) {
|
|
json models = {
|
|
{"object", "list"},
|
|
{"data", {
|
|
{
|
|
{"id", params.model_alias},
|
|
{"object", "model"},
|
|
{"created", std::time(0)},
|
|
{"owned_by", "llamacpp"},
|
|
{"meta", model_meta}
|
|
},
|
|
}}
|
|
};
|
|
|
|
res.set_content(models.dump(), "application/json; charset=utf-8");
|
|
};
|
|
|
|
|
|
|
|
const auto handle_chat_completions = [&ctx_server, ¶ms, &handle_completions_impl, &res_error](const httplib::Request & req, httplib::Response & res) {
|
|
auto body = json::parse(req.body);
|
|
std::vector<raw_buffer> files;
|
|
json data = oaicompat_chat_params_parse(ctx_server.model, body, ctx_server.oai_parser_opt, files);
|
|
handle_completions_impl(
|
|
SERVER_TASK_TYPE_COMPLETION,
|
|
data,
|
|
files,
|
|
res,
|
|
OAICOMPAT_TYPE_CHAT);
|
|
};
|
|
|
|
// same with handle_chat_completions, but without inference part
|
|
const auto handle_apply_template = [&ctx_server, ¶ms, &res_ok](const httplib::Request& req, httplib::Response& res) {
|
|
auto body = json::parse(req.body);
|
|
std::vector<raw_buffer> files; // dummy, unused
|
|
json data = oaicompat_chat_params_parse(ctx_server.model, body,ctx_server.oai_parser_opt, files);
|
|
res_ok(res, { { "prompt", std::move(data.at("prompt")) } });
|
|
};
|
|
|
|
const auto handle_infill = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
|
|
json data = json::parse(req.body);
|
|
const int id_task = ctx_server.queue_tasks.get_new_id();
|
|
server_tokens token; // dummy tokens
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
ctx_server.request_completion(id_task, -1, data, true, false, std::move(token));
|
|
std::vector<raw_buffer> files; // dummy
|
|
handle_completions_impl(
|
|
SERVER_TASK_TYPE_INFILL,
|
|
data,
|
|
files,
|
|
res,
|
|
OAICOMPAT_TYPE_NONE); // infill is not OAI compatible
|
|
};
|
|
|
|
const auto handle_tokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
|
|
const json body = json::parse(req.body);
|
|
|
|
std::vector<llama_token> tokens;
|
|
if (body.count("content") != 0) {
|
|
const bool add_special = json_value(body, "add_special", false);
|
|
tokens = ctx_server.tokenize(body.at("content"), add_special);
|
|
}
|
|
const json data = format_tokenizer_response(tokens);
|
|
return res.set_content(data.dump(), "application/json; charset=utf-8");
|
|
};
|
|
|
|
const auto handle_detokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
|
|
const json body = json::parse(req.body);
|
|
|
|
std::string content;
|
|
if (body.count("tokens") != 0) {
|
|
const std::vector<llama_token> tokens = body.at("tokens");
|
|
content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend());
|
|
}
|
|
|
|
const json data = format_detokenized_response(content);
|
|
return res.set_content(data.dump(), "application/json; charset=utf-8");
|
|
};
|
|
|
|
|
|
const auto handle_embeddings = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
|
|
const json body = json::parse(req.body);
|
|
bool is_openai = false;
|
|
|
|
// an input prompt can be a string or a list of tokens (integer)
|
|
json prompt;
|
|
if (body.count("input") != 0) {
|
|
is_openai = true;
|
|
prompt = body.at("input");
|
|
} else if (body.count("content") != 0) {
|
|
// with "content", we only support single prompt
|
|
prompt = std::vector<std::string>{body.at("content")};
|
|
} else {
|
|
res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
|
|
return;
|
|
}
|
|
|
|
// create and queue the task
|
|
json responses;
|
|
{
|
|
const int id_task = ctx_server.queue_tasks.get_new_id();
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
std::vector<server_tokens> inputs;
|
|
inputs = tokenize_input_prompts(llama_get_vocab(ctx_server.ctx), ctx_server.mctx, prompt, true, true);
|
|
ctx_server.request_completion(id_task, -1, {{"prompt", prompt}}, false, true, std::move(inputs[0]));
|
|
|
|
// get the result
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
if (!result.error) {
|
|
if (result.data.count("results")) {
|
|
// result for multi-task
|
|
responses = result.data.at("results");
|
|
} else {
|
|
// result for single task
|
|
responses = std::vector<json>{ result.data };
|
|
}
|
|
} else {
|
|
// error received, ignore everything else
|
|
res_error(res, result.data);
|
|
return;
|
|
}
|
|
}
|
|
|
|
// write JSON response
|
|
json root = is_openai
|
|
? format_embeddings_response_oaicompat(body, responses, false)
|
|
: responses[0];
|
|
return res.set_content(root.dump(), "application/json; charset=utf-8");
|
|
};
|
|
|
|
const auto handle_lora_adapters_list = [&](const httplib::Request & req, httplib::Response & res) {
|
|
json result = json::array();
|
|
for (size_t i = 0; i < ctx_server.lora_adapters.size(); ++i) {
|
|
auto & la = ctx_server.lora_adapters[i];
|
|
result.push_back({
|
|
{"id", i},
|
|
{"path", la.path},
|
|
{"scale", la.scale},
|
|
});
|
|
}
|
|
res.set_content(result.dump(), "application/json");
|
|
res.status = 200; // HTTP OK
|
|
};
|
|
|
|
const auto handle_lora_adapters_apply = [&](const httplib::Request & req, httplib::Response & res) {
|
|
const std::vector<json> body = json::parse(req.body);
|
|
int max_idx = ctx_server.lora_adapters.size();
|
|
|
|
// clear existing value
|
|
for (auto & la : ctx_server.lora_adapters) {
|
|
la.scale = 0.0f;
|
|
}
|
|
|
|
// set value
|
|
for (auto entry : body) {
|
|
int id = entry.at("id");
|
|
float scale = entry.at("scale");
|
|
if (0 <= id && id < max_idx) {
|
|
ctx_server.lora_adapters[id].scale = scale;
|
|
} else {
|
|
throw std::runtime_error("invalid adapter id");
|
|
}
|
|
}
|
|
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_SET_LORA;
|
|
const int id_task = ctx_server.queue_tasks.post(std::move(task));
|
|
ctx_server.queue_results.add_waiting_task_id(id_task);
|
|
|
|
server_task_result result = ctx_server.queue_results.recv(id_task);
|
|
ctx_server.queue_results.remove_waiting_task_id(id_task);
|
|
|
|
res.set_content(result.data.dump(), "application/json");
|
|
res.status = 200; // HTTP OK
|
|
};
|
|
|
|
const auto list_saved_prompts = [&ctx_server, ¶ms](const httplib::Request& req, httplib::Response& res) {
|
|
json response = json::array();
|
|
namespace fs = std::filesystem;
|
|
|
|
try {
|
|
for (const auto& entry : fs::directory_iterator(params.slot_save_path)) {
|
|
if (!entry.is_regular_file() || entry.file_size() < 12) {
|
|
continue;
|
|
}
|
|
|
|
std::ifstream file(entry.path(), std::ios::binary);
|
|
if (!file) continue;
|
|
|
|
uint32_t magic, version, n_token_count;
|
|
file.read(reinterpret_cast<char*>(&magic), sizeof(magic));
|
|
file.read(reinterpret_cast<char*>(&version), sizeof(version));
|
|
file.read(reinterpret_cast<char*>(&n_token_count), sizeof(n_token_count));
|
|
|
|
if (magic != LLAMA_STATE_SEQ_MAGIC ||
|
|
version != LLAMA_STATE_SEQ_VERSION ||
|
|
entry.file_size() < (12 + (n_token_count * sizeof(llama_token)))) {
|
|
continue;
|
|
}
|
|
|
|
std::vector<llama_token> tokens(n_token_count);
|
|
file.read(reinterpret_cast<char*>(tokens.data()), tokens.size() * sizeof(llama_token));
|
|
|
|
//C++17 is not modern enough to have a nice and portable way to get the mtime of a file
|
|
//so the following seems to be needed
|
|
auto ftime = fs::last_write_time(entry.path());
|
|
auto system_time = std::chrono::time_point_cast<std::chrono::system_clock::duration>(
|
|
ftime - fs::file_time_type::clock::now() + std::chrono::system_clock::now()
|
|
);
|
|
std::time_t c_time = std::chrono::system_clock::to_time_t(system_time);
|
|
std::tm tm_struct;
|
|
#if defined(_WIN32)
|
|
localtime_s(&tm_struct, &c_time);
|
|
#else
|
|
localtime_r(&c_time, &tm_struct);
|
|
#endif
|
|
std::ostringstream oss;
|
|
oss << std::put_time(&tm_struct, "%Y-%m-%d %H:%M:%S");
|
|
auto str_time = oss.str();
|
|
|
|
|
|
response.push_back({
|
|
{"filename", entry.path().filename().string()},
|
|
{"filesize", entry.file_size()},
|
|
{"mtime", str_time},
|
|
{"token_count", n_token_count},
|
|
{"prompt", tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend())}
|
|
});
|
|
}
|
|
} catch (const std::exception& e) {
|
|
res.status = 500;
|
|
response = {{"error", e.what()}};
|
|
}
|
|
res.set_content(response.dump(), "application/json; charset=utf-8");
|
|
};
|
|
|
|
const auto list_slot_prompts = [&ctx_server, ¶ms](const httplib::Request& req, httplib::Response& res) {
|
|
json response = json::array();
|
|
for (server_slot & slot : ctx_server.slots) {
|
|
response.push_back({
|
|
{"slot_id", slot.id},
|
|
{"token_count", slot.cache_tokens.size()},
|
|
{"prompt", slot.cache_tokens.detokenize(ctx_server.ctx, true) }
|
|
});
|
|
}
|
|
res.set_content(response.dump(), "application/json; charset=utf-8");
|
|
};
|
|
|
|
|
|
const auto delete_saved_prompt = [&ctx_server, ¶ms](const httplib::Request& req, httplib::Response& res)-> void {
|
|
json response;
|
|
namespace fs = std::filesystem;
|
|
|
|
try {
|
|
const json body = json::parse(req.body);
|
|
const std::string filename_str = body.at("filename");
|
|
|
|
// prevent directory traversal attacks
|
|
if (filename_str.find("..") != std::string::npos || filename_str.find('/') != std::string::npos || filename_str.find('\\') != std::string::npos) {
|
|
res.status = 400;
|
|
response = {{"error", "Invalid filename format."}};
|
|
res.set_content(response.dump(), "application/json; charset=utf-8");
|
|
return;
|
|
}
|
|
|
|
const fs::path file_to_delete = fs::path(params.slot_save_path) / fs::path(filename_str);
|
|
|
|
if (!fs::exists(file_to_delete) || !fs::is_regular_file(file_to_delete)) {
|
|
res.status = 404;
|
|
response = {{"error", "File not found."}};
|
|
res.set_content(response.dump(), "application/json; charset=utf-8");
|
|
return;
|
|
}
|
|
|
|
if (fs::remove(file_to_delete)) {
|
|
response = {
|
|
{"status", "deleted"},
|
|
{"filename", filename_str}
|
|
};
|
|
} else {
|
|
res.status = 500;
|
|
response = {{"error", "Failed to delete the file."}};
|
|
}
|
|
} catch (const json::parse_error& e) {
|
|
res.status = 400;
|
|
response = {{"error", "Invalid JSON request body."}};
|
|
} catch (const json::out_of_range& e) {
|
|
res.status = 400;
|
|
response = {{"error", "Missing 'filename' key in request body."}};
|
|
} catch (const std::exception& e) {
|
|
res.status = 500;
|
|
response = {{"error", e.what()}};
|
|
}
|
|
res.set_content(response.dump(), "application/json; charset=utf-8");
|
|
};
|
|
|
|
const auto rename_saved_prompt = [&ctx_server, ¶ms](const httplib::Request& req, httplib::Response& res)-> void {
|
|
json response;
|
|
namespace fs = std::filesystem;
|
|
|
|
try {
|
|
const json body = json::parse(req.body);
|
|
const std::string old_filename_str = body.at("old_filename");
|
|
const std::string new_filename_str = body.at("new_filename");
|
|
|
|
if (old_filename_str.find("..") != std::string::npos || old_filename_str.find_first_of("/\\") != std::string::npos ||
|
|
new_filename_str.find("..") != std::string::npos || new_filename_str.find_first_of("/\\") != std::string::npos) {
|
|
res.status = 400;
|
|
response = {{"error", "Invalid filename format."}};
|
|
res.set_content(response.dump(), "application/json; charset=utf-8");
|
|
return;
|
|
}
|
|
|
|
const fs::path old_path = fs::path(params.slot_save_path) / old_filename_str;
|
|
const fs::path new_path = fs::path(params.slot_save_path) / new_filename_str;
|
|
|
|
if (!fs::exists(old_path) || !fs::is_regular_file(old_path)) {
|
|
res.status = 404;
|
|
response = {{"error", "Source file not found."}};
|
|
res.set_content(response.dump(), "application/json; charset=utf-8");
|
|
return;
|
|
}
|
|
|
|
if (fs::exists(new_path)) {
|
|
res.status = 409;
|
|
response = {{"error", "Destination filename already exists."}};
|
|
res.set_content(response.dump(), "application/json; charset=utf-8");
|
|
return;
|
|
}
|
|
|
|
std::error_code ec;
|
|
fs::rename(old_path, new_path, ec);
|
|
|
|
if (ec) {
|
|
res.status = 500;
|
|
response = {{"error", "Failed to rename file: " + ec.message()}};
|
|
} else {
|
|
response = {
|
|
{"status", "renamed"},
|
|
{"old_filename", old_filename_str},
|
|
{"new_filename", new_filename_str}
|
|
};
|
|
}
|
|
|
|
} catch (const json::parse_error& e) {
|
|
res.status = 400;
|
|
response = {{"error", "Invalid JSON request body."}};
|
|
} catch (const json::out_of_range& e) {
|
|
res.status = 400;
|
|
response = {{"error", "Missing 'old_filename' or 'new_filename' in request body."}};
|
|
} catch (const std::exception& e) {
|
|
res.status = 500;
|
|
response = {{"error", e.what()}};
|
|
}
|
|
|
|
res.set_content(response.dump(), "application/json; charset=utf-8");
|
|
};
|
|
|
|
auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) {
|
|
return [content, len, mime_type](const httplib::Request &, httplib::Response & res) {
|
|
res.set_content(reinterpret_cast<const char*>(content), len, mime_type);
|
|
return false;
|
|
};
|
|
};
|
|
#ifdef SQLITE3_MODERN_CPP_SUPPORT
|
|
const auto handle_version = [¶ms, sqlite_extension_loaded](const httplib::Request&, httplib::Response& res) {
|
|
res.set_content(
|
|
json{{"version", 4},
|
|
{"features", {{"sql", !params.sql_save_file.empty()}, {"zstd_compression", sqlite_extension_loaded}}}}.dump(),
|
|
"application/json"
|
|
);
|
|
};
|
|
#else
|
|
const auto handle_version = [](const httplib::Request&, httplib::Response& res)-> void {
|
|
res.set_content(
|
|
json{{"version", 4},
|
|
{"features", {{"sql", false}, {"zstd_compression", false}}}}.dump(),
|
|
"application/json"
|
|
);
|
|
};
|
|
#endif
|
|
|
|
#ifdef SQLITE3_MODERN_CPP_SUPPORT
|
|
auto db_handler = [db_handle](auto func) {
|
|
return [func, db_handle](const httplib::Request& req, httplib::Response& res) {
|
|
res.set_header("Access-Control-Allow-Origin", "*");
|
|
try {
|
|
const json body = !req.body.empty() ? json::parse(req.body) : json::object();
|
|
func(*db_handle, body, req, res);
|
|
} catch(const std::exception& e) {
|
|
res.status = 500;
|
|
res.set_content(
|
|
json{{"ok", false}, {"message", e.what()}}.dump(),
|
|
"application/json"
|
|
);
|
|
}
|
|
};
|
|
};
|
|
#else
|
|
auto db_handler = [db_handle](auto func) {
|
|
return [func, db_handle](const httplib::Request& req, httplib::Response& res) {
|
|
res.set_header("Access-Control-Allow-Origin", "*");
|
|
res.status = 500;
|
|
res.set_content(
|
|
json{{"ok", false}, {"message", "Sqlite3 support was not enabled. Recompile with '-DLLAMA_SERVER_SQLITE3=ON'"}}.dump(),
|
|
"application/json"
|
|
);
|
|
};
|
|
};
|
|
#endif
|
|
|
|
const auto normalize_store_name = [](const std::string& storeName) {
|
|
if(storeName.empty()) return std::string("sessions");
|
|
|
|
std::string normalized;
|
|
normalized.reserve(storeName.size());
|
|
|
|
for(char c : storeName) {
|
|
if(std::isalpha(static_cast<unsigned char>(c))) {
|
|
normalized.push_back(std::tolower(static_cast<unsigned char>(c)));
|
|
}
|
|
}
|
|
|
|
return normalized.empty() ? "sessions" : normalized;
|
|
};
|
|
|
|
const auto get_key_string = [](const json& j) {
|
|
return j.is_string() ? j.get<std::string>() : j.dump();
|
|
};
|
|
|
|
|
|
const auto handle_load = db_handler([normalize_store_name, get_key_string](auto& db, const json& body, auto&, auto& res) {
|
|
std::string data;
|
|
const std::string store = normalize_store_name(body["storeName"]);
|
|
db.db << "SELECT data FROM " + store + " WHERE key = ?" << get_key_string(body["key"]) >> data;
|
|
if(data.empty()) {
|
|
res.status = 404;
|
|
res.set_content(json{{"ok", false}, {"message", "Key not found"}}.dump(), "application/json");
|
|
} else {
|
|
json response{{"ok", true}};
|
|
response["result"] = (store == "names") ? json(data) : json::parse(data);
|
|
res.set_content(response.dump(), "application/json");
|
|
}
|
|
});
|
|
|
|
const auto handle_save = db_handler([normalize_store_name, get_key_string](auto& db, const json& body, auto&, auto& res) {
|
|
const std::string store = normalize_store_name(body["storeName"]);
|
|
const std::string data = (store == "names") ? body["data"].get<std::string>() : body["data"].dump();
|
|
db.db << "INSERT OR REPLACE INTO " + store + " (key, data) VALUES (?, ?)" << get_key_string(body["key"]) << data;
|
|
res.set_content(json{{"ok", true}, {"result", "Data saved successfully"}}.dump(), "application/json");
|
|
});
|
|
|
|
const auto handle_rename = db_handler([get_key_string](auto& db, const json& body, auto&, auto& res) {
|
|
db.db << "UPDATE names SET data = ? WHERE key = ?"
|
|
<< body["newName"].get<std::string>()
|
|
<< get_key_string(body["key"]);
|
|
res.set_content(json{{"ok", true}, {"result", "Session renamed successfully"}}.dump(), "application/json");
|
|
});
|
|
|
|
const auto handle_all = db_handler([normalize_store_name](auto& db, const json& body, auto&, auto& res) {
|
|
json result = json::object();
|
|
db.db << "SELECT key, data FROM " + normalize_store_name(body["storeName"]) >>
|
|
[&](const std::string& key, const std::string& data) {
|
|
result[key] = json::parse(data);
|
|
};
|
|
res.set_content(json{{"ok", true}, {"result", result}}.dump(), "application/json");
|
|
});
|
|
|
|
const auto handle_sessions = db_handler([](auto& db, const json& body, auto&, auto& res) {
|
|
json result = json::object();
|
|
db.db << "SELECT key, data FROM names" >> [&](const std::string& key, const std::string& data) {
|
|
result[key] = data;
|
|
};
|
|
res.set_content(json{{"ok", true}, {"result", result}}.dump(), "application/json");
|
|
});
|
|
|
|
const auto handle_delete = db_handler([normalize_store_name, get_key_string](auto& db, const json& body, auto&, auto& res) {
|
|
db.db << "DELETE FROM " + normalize_store_name(body["storeName"]) + " WHERE key = ?"
|
|
<< get_key_string(body["key"]);
|
|
res.set_content(json{{"ok", true}, {"result", "Session deleted successfully"}}.dump(), "application/json");
|
|
});
|
|
|
|
const auto handle_vacuum = db_handler([](auto& db, const json& body, auto&, auto& res) {
|
|
json result = json::object();
|
|
db.db << "VACUUM";
|
|
res.set_content(json{"ok", true}.dump(), "application/json");
|
|
});
|
|
|
|
const auto handle_zstd_get_configs = db_handler([](auto& db, const json& body, auto&, auto& res) {
|
|
json result = json::object();
|
|
db.db << "SELECT id, config FROM _zstd_configs" >> [&](const std::string id, const std::string& config) {
|
|
result[id] = config;
|
|
};
|
|
res.set_content(json{{"ok", true}, {"configs", result}}.dump(), "application/json");
|
|
});
|
|
|
|
const auto handle_zstd_maintenance = db_handler([](auto& db, const json& body, auto&, auto& res) {
|
|
std::string data;
|
|
if (body["duration"].is_null()) {
|
|
db.db << "select zstd_incremental_maintenance(?, ?)" << nullptr << body["db_load"].get<double>() >> data;
|
|
}
|
|
else {
|
|
db.db << "select zstd_incremental_maintenance(?, ?)" << body["duration"].get<double>() << body["db_load"].get<double>() >> data;
|
|
}
|
|
json response{{"ok", true}};
|
|
response["result"] = json::parse(data);
|
|
res.set_content(response.dump(), "application/json");
|
|
});
|
|
|
|
const auto handle_zstd_enable = db_handler([](auto& db, const json& body, auto&, auto& res) {
|
|
db.db << "select zstd_enable_transparent('{\"table\": \"" + body["table"].get<std::string>() + "\",\"column\": \"" + body["column"].get<std::string>() + "\", \"compression_level\": " + std::to_string(body["compression_level"].get<int>()) + ", \"dict_chooser\": \"''a''\", \"train_dict_samples_ratio\": " + std::to_string(body["train_dict_samples_ratio"].get<int>()) + "}')";
|
|
res.set_content(json{"ok", true}.dump(), "application/json");
|
|
});
|
|
|
|
const auto handle_zstd_config_update = db_handler([](auto& db, const json& body, auto&, auto& res) {
|
|
std::string patch_json = "{\"compression_level\": " + std::to_string(body["compression_level"].get<int>()) + ", \"train_dict_samples_ratio\": " + std::to_string(body["train_dict_samples_ratio"].get<int>()) + "}";
|
|
db.db << "update _zstd_configs set config = json_patch(config, '" + patch_json + "')";
|
|
res.set_content(json{{"ok", true}}.dump(), "application/json");
|
|
});
|
|
|
|
//
|
|
// Router
|
|
//
|
|
if (params.webui == COMMON_WEBUI_NONE) {
|
|
LLAMA_LOG_INFO("Web UI is disabled\n");
|
|
}
|
|
else {
|
|
// register static assets routes
|
|
if (!params.public_path.empty()) {
|
|
// Set the base directory for serving static files
|
|
svr->set_base_dir(params.public_path);
|
|
}
|
|
|
|
{
|
|
// register static assets routes
|
|
if (!params.public_path.empty()) {
|
|
// Set the base directory for serving static files
|
|
bool is_found = svr->set_mount_point("/", params.public_path);
|
|
if (!is_found) {
|
|
GGML_ABORT("%s: static assets path not found: %s\n", __func__, params.public_path.c_str());
|
|
return 1;
|
|
}
|
|
}
|
|
else {
|
|
|
|
// using embedded static index.html
|
|
svr->Get("/", [params](const httplib::Request& req, httplib::Response& res) {
|
|
if (req.get_header_value("Accept-Encoding").find("gzip") == std::string::npos) {
|
|
res.set_content("Error: gzip is not supported by this browser", "text/plain");
|
|
}
|
|
else {
|
|
res.set_header("Content-Encoding", "gzip");
|
|
// COEP and COOP headers, required by pyodide (python interpreter)
|
|
res.set_header("Cross-Origin-Embedder-Policy", "require-corp");
|
|
res.set_header("Cross-Origin-Opener-Policy", "same-origin");
|
|
if (params.webui == COMMON_WEBUI_AUTO) {
|
|
res.set_content(reinterpret_cast<const char*>(index_html_gz), index_html_gz_len, "text/html; charset=utf-8");
|
|
}
|
|
else if (params.webui == COMMON_WEBUI_LLAMACPP) {
|
|
res.set_content(reinterpret_cast<const char*>(index_llamacpp_html_gz), index_llamacpp_html_gz_len, "text/html; charset=utf-8");
|
|
}
|
|
else {
|
|
res.set_content(reinterpret_cast<const char*>(index_html_gz), index_html_gz_len, "text/html; charset=utf-8");
|
|
}
|
|
}
|
|
return false;
|
|
});
|
|
}
|
|
}
|
|
}
|
|
// register API routes
|
|
svr->Get ("/health", handle_health);
|
|
svr->Get ("/metrics", handle_metrics);
|
|
svr->Get ("/props", handle_props);
|
|
svr->Get ("/v1/models", handle_models);
|
|
svr->Post("/completion", handle_completions); // legacy
|
|
svr->Post("/completions", handle_completions); // legacy
|
|
svr->Post("/v1/completions", handle_completions_oai);
|
|
svr->Post("/chat/completions", handle_chat_completions);
|
|
svr->Post("/v1/chat/completions", handle_chat_completions);
|
|
svr->Post("/infill", handle_infill);
|
|
svr->Post("/embedding", handle_embeddings); // legacy
|
|
svr->Post("/embeddings", handle_embeddings);
|
|
svr->Post("/v1/embeddings", handle_embeddings);
|
|
svr->Post("/tokenize", handle_tokenize);
|
|
svr->Post("/detokenize", handle_detokenize);
|
|
// LoRA adapters hotswap
|
|
svr->Get ("/lora-adapters", handle_lora_adapters_list);
|
|
svr->Post("/lora-adapters", handle_lora_adapters_apply);
|
|
// Save & load slots
|
|
svr->Get ("/slots", handle_slots);
|
|
svr->Get ("/slots/list", list_slot_prompts);
|
|
if (!params.slot_save_path.empty()) {
|
|
// these endpoints rely on slot_save_path existing
|
|
svr->Post("/slots/:id_slot", handle_slots_action);
|
|
svr->Get ("/list", list_saved_prompts);
|
|
svr->Post("/delete_prompt", delete_saved_prompt);
|
|
svr->Post("/rename_prompt", rename_saved_prompt);
|
|
|
|
}
|
|
|
|
svr->Get ("/version", handle_version);
|
|
if (!params.sql_save_file.empty()) {
|
|
// these endpoints rely on sql_save_file existing
|
|
svr->Post("/load", handle_load);
|
|
svr->Post("/save", handle_save);
|
|
svr->Post("/rename", handle_rename);
|
|
svr->Post("/all", handle_all);
|
|
svr->Post("/sessions", handle_sessions);
|
|
svr->Get ("/sessions", handle_sessions);
|
|
svr->Post("/delete", handle_delete);
|
|
//VACUUM is there for the extension but does not require the extension
|
|
svr->Get ("/vacuum", handle_vacuum);
|
|
#ifdef SQLITE3_MODERN_CPP_SUPPORT
|
|
if (sqlite_extension_loaded) {
|
|
svr->Get ("/zstd_get_configs", handle_zstd_get_configs);
|
|
svr->Post("/zstd_incremental_maintenance", handle_zstd_maintenance);
|
|
svr->Post("/zstd_enable_transparent", handle_zstd_enable);
|
|
svr->Post("/zstd_update_transparent", handle_zstd_config_update);
|
|
}
|
|
#endif
|
|
}
|
|
//
|
|
// Start the server
|
|
//
|
|
if (params.n_threads_http < 1) {
|
|
// +2 threads for monitoring endpoints
|
|
params.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1);
|
|
}
|
|
log_data["n_threads_http"] = std::to_string(params.n_threads_http);
|
|
svr->new_task_queue = [¶ms] { return new httplib::ThreadPool(params.n_threads_http); };
|
|
|
|
LOG_INFO("HTTP server listening", log_data);
|
|
|
|
// run the HTTP server in a thread - see comment below
|
|
std::thread t([&]() {
|
|
if (!svr->listen_after_bind()) {
|
|
state.store(SERVER_STATE_ERROR);
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
});
|
|
|
|
ctx_server.queue_tasks.on_new_task([&ctx_server](server_task && task) {
|
|
ctx_server.process_single_task(std::move(task));
|
|
});
|
|
ctx_server.queue_tasks.on_finish_multitask(std::bind(
|
|
&server_context::on_finish_multitask, &ctx_server, std::placeholders::_1));
|
|
ctx_server.queue_tasks.on_update_slots(std::bind(
|
|
&server_context::update_slots, &ctx_server));
|
|
ctx_server.queue_results.on_multitask_update(std::bind(
|
|
&server_queue::update_multitask,
|
|
&ctx_server.queue_tasks,
|
|
std::placeholders::_1,
|
|
std::placeholders::_2,
|
|
std::placeholders::_3
|
|
));
|
|
|
|
shutdown_handler = [&](int) {
|
|
ctx_server.queue_tasks.terminate();
|
|
};
|
|
|
|
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
|
|
struct sigaction sigint_action;
|
|
sigint_action.sa_handler = signal_handler;
|
|
sigemptyset (&sigint_action.sa_mask);
|
|
sigint_action.sa_flags = 0;
|
|
sigaction(SIGINT, &sigint_action, NULL);
|
|
sigaction(SIGTERM, &sigint_action, NULL);
|
|
#elif defined (_WIN32)
|
|
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
|
|
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
|
|
};
|
|
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
|
|
#endif
|
|
|
|
ctx_server.queue_tasks.start_loop();
|
|
|
|
svr->stop();
|
|
t.join();
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|