dnrops
/
llama_cpp
mirror of https://github.com/ggml-org/llama.cpp
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
llama_cpp/tools/server/utils.hpp

1310 lines
47 KiB
C++
Raw Permalink Blame History

#pragma once
#include "common.h"
#include "log.h"
#include "llama.h"
#include "arg.h" // common_remote_get_content
#include "base64.hpp"
#include "mtmd.h"
// increase max payload length to allow use of larger context size
#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
// disable Nagle's algorithm
#define CPPHTTPLIB_TCP_NODELAY true
#include "httplib.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
#include "chat.h"
#include <random>
#include <sstream>
#include <string>
#include <vector>
#include <memory>
#include <cinttypes>
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo"
using json = nlohmann::ordered_json;
#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
using raw_buffer = std::vector<uint8_t>;
template <typename T>
static T json_value(const json & body, const std::string & key, const T & default_value) {
// Fallback null to default value
if (body.contains(key) && !body.at(key).is_null()) {
try {
return body.at(key);
} catch (NLOHMANN_JSON_NAMESPACE::detail::type_error const &) {
LOG_WRN("Wrong type supplied for parameter '%s'. Expected '%s', using default value\n", key.c_str(), json(default_value).type_name());
return default_value;
}
} else {
return default_value;
}
}
const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT);
// thin wrapper around common_grammar_trigger with (de)serialization functions
struct server_grammar_trigger {
common_grammar_trigger value;
server_grammar_trigger() = default;
server_grammar_trigger(const common_grammar_trigger & value) : value(value) {}
server_grammar_trigger(const json & in) {
value.type = (common_grammar_trigger_type) in.at("type").get<int>();
value.value = in.at("value").get<std::string>();
if (value.type == COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN) {
value.token = (llama_token) in.at("token").get<int>();
}
}
json to_json() const {
json out {
{"type", (int) value.type},
{"value", value.value},
};
if (value.type == COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN) {
out["token"] = (int) value.token;
}
return out;
}
};
//
// tokenizer and input processing utils
//
static bool json_is_array_of_numbers(const json & data) {
if (data.is_array()) {
for (const auto & e : data) {
if (!e.is_number_integer()) {
return false;
}
}
return true;
}
return false;
}
// is array having BOTH numbers & strings?
static bool json_is_array_of_mixed_numbers_strings(const json & data) {
bool seen_string = false;
bool seen_number = false;
if (data.is_array()) {
for (const auto & e : data) {
seen_string |= e.is_string();
seen_number |= e.is_number_integer();
if (seen_number && seen_string) {
return true;
}
}
}
return false;
}
// get value by path(key1 / key2)
static json json_get_nested_values(const std::vector<std::string> & paths, const json & js) {
json result = json::object();
for (const std::string & path : paths) {
json current = js;
const auto keys = string_split<std::string>(path, /*separator*/ '/');
bool valid_path = true;
for (const std::string & k : keys) {
if (valid_path && current.is_object() && current.contains(k)) {
current = current[k];
} else {
valid_path = false;
}
}
if (valid_path) {
result[path] = current;
}
}
return result;
}
/**
* this handles 2 cases:
* - only string, example: "string"
* - mixed string and tokens, example: [12, 34, "string", 56, 78]
*/
static llama_tokens tokenize_mixed(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) {
// 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.
llama_tokens 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>();
llama_tokens p;
if (first) {
p = common_tokenize(vocab, s, add_special, parse_special);
first = false;
} else {
p = common_tokenize(vocab, s, false, parse_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 = common_tokenize(vocab, s, add_special, parse_special);
}
return prompt_tokens;
}
/**
* break the input "prompt" object into multiple prompt if needed, then tokenize them
* this supports these cases:
* - "prompt": "string"
* - "prompt": [12, 34, 56]
* - "prompt": [12, 34, "string", 56, 78]
* and multiple prompts (multi-tasks):
* - "prompt": ["string1", "string2"]
* - "prompt": ["string1", [12, 34, 56]]
* - "prompt": [[12, 34, 56], [78, 90, 12]]
* - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]]
*/
static std::vector<llama_tokens> tokenize_input_prompts(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) {
std::vector<llama_tokens> result;
if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) {
// string or mixed
result.push_back(tokenize_mixed(vocab, json_prompt, add_special, parse_special));
} else if (json_is_array_of_numbers(json_prompt)) {
// array of tokens
result.push_back(json_prompt.get<llama_tokens>());
} else if (json_prompt.is_array()) {
// array of prompts
result.reserve(json_prompt.size());
for (const auto & p : json_prompt) {
if (p.is_string() || json_is_array_of_mixed_numbers_strings(p)) {
result.push_back(tokenize_mixed(vocab, p, add_special, parse_special));
} else if (json_is_array_of_numbers(p)) {
// array of tokens
result.push_back(p.get<llama_tokens>());
} else {
throw std::runtime_error("element of \"prompt\" must be a string, an list of tokens, or a list of mixed strings & tokens");
}
}
} else {
throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts");
}
if (result.empty()) {
throw std::runtime_error("\"prompt\" must not be empty");
}
return result;
}
// return the last index of character that can form a valid string
// if the last character is potentially cut in half, return the index before the cut
// if validate_utf8(text) == text.size(), then the whole text is valid utf8
static size_t validate_utf8(const std::string& text) {
size_t len = text.size();
if (len == 0) return 0;
// Check the last few bytes to see if a multi-byte character is cut off
for (size_t i = 1; i <= 4 && i <= len; ++i) {
unsigned char c = text[len - i];
// Check for start of a multi-byte sequence from the end
if ((c & 0xE0) == 0xC0) {
// 2-byte character start: 110xxxxx
// Needs at least 2 bytes
if (i < 2) return len - i;
} else if ((c & 0xF0) == 0xE0) {
// 3-byte character start: 1110xxxx
// Needs at least 3 bytes
if (i < 3) return len - i;
} else if ((c & 0xF8) == 0xF0) {
// 4-byte character start: 11110xxx
// Needs at least 4 bytes
if (i < 4) return len - i;
}
}
// If no cut-off multi-byte character is found, return full length
return len;
}
//
// template utils
//
// format rerank task: [BOS]query[EOS][SEP]doc[EOS]
static llama_tokens format_rerank(const struct llama_vocab * vocab, const llama_tokens & query, const llama_tokens & doc) {
llama_tokens result;
result.reserve(doc.size() + query.size() + 4);
result.push_back(llama_vocab_bos(vocab));
result.insert(result.end(), query.begin(), query.end());
result.push_back(llama_vocab_eos(vocab));
result.push_back(llama_vocab_sep(vocab));
result.insert(result.end(), doc.begin(), doc.end());
result.push_back(llama_vocab_eos(vocab));
return result;
}
// format infill task
static llama_tokens format_infill(
const llama_vocab * vocab,
const json & input_prefix,
const json & input_suffix,
const json & input_extra,
const int n_batch,
const int n_predict,
const int n_ctx,
const bool spm_infill,
const llama_tokens & tokens_prompt
) {
// TODO: optimize this block by reducing memory allocations and movement
// use FIM repo-level pattern:
// ref: https://arxiv.org/pdf/2409.12186
//
// [FIM_REP]myproject
// [FIM_SEP]filename0
// extra chunk 0
// [FIM_SEP]filename1
// extra chunk 1
// ...
// [FIM_SEP]filename
// [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt
//
llama_tokens extra_tokens;
extra_tokens.reserve(n_ctx);
auto tokens_prefix = tokenize_mixed(vocab, input_prefix, false, false);
auto tokens_suffix = tokenize_mixed(vocab, input_suffix, false, false);
if (llama_vocab_fim_rep(vocab) != LLAMA_TOKEN_NULL) {
// TODO: make project name an input
static const auto k_fim_repo = common_tokenize(vocab, "myproject\n", false, false);
extra_tokens.push_back(llama_vocab_fim_rep(vocab));
extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end());
}
for (const auto & chunk : input_extra) {
// { "text": string, "filename": string }
const std::string text = json_value(chunk, "text", std::string());
const std::string filename = json_value(chunk, "filename", std::string("tmp"));
if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) {
const auto k_fim_file = common_tokenize(vocab, filename + "\n", false, false);
extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab));
extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
} else {
// chunk separator in binary form to avoid confusing the AI
static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00};
static const auto k_chunk_prefix_tokens = common_tokenize(vocab, k_chunk_prefix_str, false, false);
extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end());
}
const auto chunk_tokens = common_tokenize(vocab, text, false, false);
extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end());
}
if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) {
// TODO: current filename
static const auto k_fim_file = common_tokenize(vocab, "filename\n", false, false);
extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab));
extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
}
// for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?)
const int n_prefix_take = std::min<int>(tokens_prefix.size(), 3*(n_batch/4));
const int n_suffix_take = std::min<int>(tokens_suffix.size(), std::max<int>(0, (n_batch/4) - (2 + tokens_prompt.size())));
SRV_DBG("n_prefix_take = %d, n_suffix_take = %d, total = %d\n", n_prefix_take, n_suffix_take, (n_prefix_take + n_suffix_take));
// fill the rest of the context with extra chunks
const int n_extra_take = std::min<int>(std::max<int>(0, n_ctx - (n_batch) - 2*n_predict), extra_tokens.size());
tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take);
tokens_suffix.resize(n_suffix_take);
tokens_prefix.insert(tokens_prefix.begin(), llama_vocab_fim_pre(vocab));
tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end());
tokens_suffix.insert(tokens_suffix.begin(), llama_vocab_fim_suf(vocab));
auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix;
auto embd_end = spm_infill ? tokens_prefix : tokens_suffix;
if (llama_vocab_get_add_bos(vocab)) {
embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab));
}
SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size());
// put the extra context before the FIM prefix
embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end());
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
embd_inp.push_back(llama_vocab_fim_mid(vocab));
return embd_inp;
}
//
// base64 utils (TODO: move to common in the future)
//
static const std::string base64_chars =
"ABCDEFGHIJKLMNOPQRSTUVWXYZ"
"abcdefghijklmnopqrstuvwxyz"
"0123456789+/";
static inline bool is_base64(uint8_t c) {
return (isalnum(c) || (c == '+') || (c == '/'));
}
static inline raw_buffer base64_decode(const std::string & encoded_string) {
int i = 0;
int j = 0;
int in_ = 0;
int in_len = encoded_string.size();
uint8_t char_array_4[4];
uint8_t char_array_3[3];
raw_buffer ret;
while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) {
char_array_4[i++] = encoded_string[in_]; in_++;
if (i == 4) {
for (i = 0; i < 4; i++) {
char_array_4[i] = base64_chars.find(char_array_4[i]);
}
char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
for (i = 0; (i < 3); i++) {
ret.push_back(char_array_3[i]);
}
i = 0;
}
}
if (i) {
for (j = i; j < 4; j++) {
char_array_4[j] = 0;
}
for (j = 0; j < 4; j++) {
char_array_4[j] = base64_chars.find(char_array_4[j]);
}
char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
for (j = 0; j < i - 1; j++) {
ret.push_back(char_array_3[j]);
}
}
return ret;
}
//
// random string / id
//
static std::string random_string() {
static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
std::random_device rd;
std::mt19937 generator(rd());
std::string result(32, ' ');
for (int i = 0; i < 32; ++i) {
result[i] = str[generator() % str.size()];
}
return result;
}
static std::string gen_chatcmplid() {
return "chatcmpl-" + random_string();
}
static std::string gen_tool_call_id() {
return random_string();
}
//
// other common utils
//
static bool ends_with(const std::string & str, const std::string & suffix) {
return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
}
static size_t find_partial_stop_string(const std::string &stop, const std::string &text) {
if (!text.empty() && !stop.empty()) {
const char text_last_char = text.back();
for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
if (stop[char_index] == text_last_char) {
const std::string current_partial = stop.substr(0, char_index + 1);
if (ends_with(text, current_partial)) {
return text.size() - char_index - 1;
}
}
}
}
return std::string::npos;
}
// TODO: reuse llama_detokenize
template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret;
for (; begin != end; ++begin) {
ret += common_token_to_piece(ctx, *begin);
}
return ret;
}
// format incomplete utf-8 multibyte character for output
static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
std::string out = token == LLAMA_TOKEN_NULL ? "" : common_token_to_piece(ctx, token);
// if the size is 1 and first bit is 1, meaning it's a partial character
// (size > 1 meaning it's already a known token)
if (out.size() == 1 && (out[0] & 0x80) == 0x80) {
std::stringstream ss;
ss << std::hex << (out[0] & 0xff);
std::string res(ss.str());
out = "byte: \\x" + res;
}
return out;
}
static bool server_sent_event(httplib::DataSink & sink, const char * event, const json & data) {
const std::string str =
std::string(event) + ": " +
data.dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n"; // required by RFC 8895 - A message is terminated by a blank line (two line terminators in a row).
LOG_DBG("data stream, to_send: %s", str.c_str());
return sink.write(str.c_str(), str.size());
}
//
// OAI utils
//
static json oaicompat_completion_params_parse(const json & body) {
json llama_params;
if (!body.contains("prompt")) {
throw std::runtime_error("\"prompt\" is required");
}
// Handle "stop" field
if (body.contains("stop") && body.at("stop").is_string()) {
llama_params["stop"] = json::array({body.at("stop").get<std::string>()});
} else {
llama_params["stop"] = json_value(body, "stop", json::array());
}
// Handle "n" field
int n_choices = json_value(body, "n", 1);
if (n_choices != 1) {
throw std::runtime_error("Only one completion choice is allowed");
}
// Handle "echo" field
if (json_value(body, "echo", false)) {
throw std::runtime_error("Only no echo is supported");
}
// Params supported by OAI but unsupported by llama.cpp
static const std::vector<std::string> unsupported_params { "best_of", "suffix" };
for (const auto & param : unsupported_params) {
if (body.contains(param)) {
throw std::runtime_error("Unsupported param: " + param);
}
}
// Copy remaining properties to llama_params
for (const auto & item : body.items()) {
// Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens"
if (!llama_params.contains(item.key()) || item.key() == "n_predict") {
llama_params[item.key()] = item.value();
}
}
return llama_params;
}
static json oaicompat_completion_params_parse(
const json & body, /* openai api json semantics */
bool use_jinja,
bool prefill_assistant,
common_reasoning_format reasoning_format,
const struct common_chat_templates * tmpls,
bool allow_non_text,
std::vector<raw_buffer> & out_files)
{
json llama_params;
auto tools = json_value(body, "tools", json());
auto stream = json_value(body, "stream", false);
if (tools.is_array() && !tools.empty()) {
if (stream) {
throw std::runtime_error("Cannot use tools with stream");
}
if (!use_jinja) {
throw std::runtime_error("tools param requires --jinja flag");
}
}
if (!use_jinja) {
if (body.contains("tool_choice") && !body.at("tool_choice").is_null()) {
throw std::runtime_error("Unsupported param: tool_choice");
}
}
// Handle "stop" field
if (body.contains("stop") && body.at("stop").is_string()) {
llama_params["stop"] = json::array({body.at("stop").get<std::string>()});
} else {
llama_params["stop"] = json_value(body, "stop", json::array());
}
auto json_schema = json_value(body, "json_schema", json());
auto grammar = json_value(body, "grammar", std::string());
if (!json_schema.is_null() && !grammar.empty()) {
throw std::runtime_error("Cannot use both json_schema and grammar");
}
// Handle "response_format" field
if (body.contains("response_format")) {
json response_format = json_value(body, "response_format", json::object());
std::string response_type = json_value(response_format, "type", std::string());
if (response_type == "json_object") {
json_schema = json_value(response_format, "schema", json::object());
} else if (response_type == "json_schema") {
auto schema_wrapper = json_value(response_format, "json_schema", json::object());
json_schema = json_value(schema_wrapper, "schema", json::object());
} else if (!response_type.empty() && response_type != "text") {
throw std::runtime_error("response_format type must be one of \"text\" or \"json_object\", but got: " + response_type);
}
}
// get input files
if (!body.contains("messages")) {
throw std::runtime_error("'messages' is required");
}
json messages = body.at("messages");
if (!messages.is_array()) {
throw std::runtime_error("Expected 'messages' to be an array");
}
for (auto & msg : messages) {
std::string role = json_value(msg, "role", std::string());
if (role != "assistant" && !msg.contains("content")) {
throw std::runtime_error("All non-assistant messages must contain 'content'");
}
if (role == "assistant") {
if (!msg.contains("content") && !msg.contains("tool_calls")) {
throw std::runtime_error("Assistant message must contain either 'content' or 'tool_calls'!");
}
if (!msg.contains("content")) {
continue; // avoid errors with no content
}
}
json & content = msg.at("content");
if (content.is_string() || content.is_null()) {
continue;
}
if (!content.is_array()) {
throw std::runtime_error("Expected 'content' to be a string or an array");
}
for (auto & p : content) {
std::string type = json_value(p, "type", std::string());
json image_url = json_value(p, "image_url", json::object());
if (type == "image_url") {
if (!allow_non_text) {
throw std::runtime_error("image input is not supported by this server");
}
std::string url = json_value(image_url, "url", std::string());
if (string_starts_with(url, "http")) {
// download remote image
// TODO @ngxson : maybe make these params configurable
common_remote_params params;
params.headers.push_back("User-Agent: llama.cpp/" + build_info);
params.max_size = 1024 * 1024 * 10; // 10MB
params.timeout = 10; // seconds
SRV_INF("downloading image from '%s'\n", url.c_str());
auto res = common_remote_get_content(url, params);
if (200 <= res.first && res.first < 300) {
SRV_INF("downloaded %ld bytes\n", res.second.size());
raw_buffer data;
data.insert(data.end(), res.second.begin(), res.second.end());
out_files.push_back(data);
} else {
throw std::runtime_error("Failed to download image");
}
} else {
// try to decode base64 image
std::vector<std::string> parts = string_split<std::string>(url, /*separator*/ ',');
if (parts.size() != 2) {
throw std::runtime_error("Invalid image_url.url value");
} else if (!string_starts_with(parts[0], "data:image/")) {
throw std::runtime_error("Invalid image_url.url format: " + parts[0]);
} else if (!string_ends_with(parts[0], "base64")) {
throw std::runtime_error("image_url.url must be base64 encoded");
} else {
auto base64_data = parts[1];
auto decoded_data = base64_decode(base64_data);
out_files.push_back(decoded_data);
}
}
// replace this chunk with a marker
p["type"] = "text";
p["text"] = MTMD_DEFAULT_IMAGE_MARKER;
p.erase("image_url");
}
}
}
common_chat_templates_inputs inputs;
inputs.messages = common_chat_msgs_parse_oaicompat(messages);
inputs.tools = common_chat_tools_parse_oaicompat(tools);
inputs.tool_choice = common_chat_tool_choice_parse_oaicompat(json_value(body, "tool_choice", std::string("auto")));
inputs.json_schema = json_schema.is_null() ? "" : json_schema.dump();
inputs.grammar = grammar;
inputs.add_generation_prompt = json_value(body, "add_generation_prompt", true);
inputs.use_jinja = use_jinja;
inputs.parallel_tool_calls = json_value(body, "parallel_tool_calls", false);
inputs.extract_reasoning = reasoning_format != COMMON_REASONING_FORMAT_NONE;
inputs.add_generation_prompt = json_value(body, "add_generation_prompt", true);
if (!inputs.tools.empty() && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && body.contains("grammar")) {
throw std::runtime_error("Cannot use custom grammar constraints with tools.");
}
// if the assistant message appears at the end of list, we do not add end-of-turn token
// for ex. this can be useful to modify the reasoning process in reasoning models
bool prefill_assistant_message = !inputs.messages.empty() && inputs.messages.back().role == "assistant" && prefill_assistant;
common_chat_msg last_message;
if (prefill_assistant_message) {
last_message = inputs.messages.back();
inputs.messages.pop_back();
/* sanity check, max one assistant message at the end of the list */
if (!inputs.messages.empty() && inputs.messages.back().role == "assistant"){
throw std::runtime_error("Cannot have 2 or more assistant messages at the end of the list.");
}
inputs.extract_reasoning = false;
inputs.add_generation_prompt = true;
}
// Apply chat template to the list of messages
auto chat_params = common_chat_templates_apply(tmpls, inputs);
/* Append assistant prefilled message */
if (prefill_assistant_message) {
chat_params.prompt += last_message.content;
}
llama_params["chat_format"] = static_cast<int>(chat_params.format);
llama_params["prompt"] = chat_params.prompt;
if (!chat_params.grammar.empty()) {
llama_params["grammar"] = chat_params.grammar;
}
llama_params["grammar_lazy"] = chat_params.grammar_lazy;
auto grammar_triggers = json::array();
for (const auto & trigger : chat_params.grammar_triggers) {
server_grammar_trigger ct(trigger);
grammar_triggers.push_back(ct.to_json());
}
llama_params["grammar_triggers"] = grammar_triggers;
llama_params["preserved_tokens"] = chat_params.preserved_tokens;
for (const auto & stop : chat_params.additional_stops) {
llama_params["stop"].push_back(stop);
}
// Handle "n" field
int n_choices = json_value(body, "n", 1);
if (n_choices != 1) {
throw std::runtime_error("Only one completion choice is allowed");
}
// Handle "logprobs" field
// TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future
if (json_value(body, "logprobs", false)) {
llama_params["n_probs"] = json_value(body, "top_logprobs", 20);
} else if (body.contains("top_logprobs") && !body.at("top_logprobs").is_null()) {
throw std::runtime_error("top_logprobs requires logprobs to be set to true");
}
// Copy remaining properties to llama_params
// This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint.
// See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
for (const auto & item : body.items()) {
// Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens"
if (!llama_params.contains(item.key()) || item.key() == "n_predict") {
llama_params[item.key()] = item.value();
}
}
return llama_params;
}
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 json format_response_rerank(
const json & request,
const json & ranks,
bool is_tei_format,
std::vector<std::string> & texts) {
json res;
if (is_tei_format) {
// TEI response format
res = json::array();
bool return_text = json_value(request, "return_text", false);
for (const auto & rank : ranks) {
int index = json_value(rank, "index", 0);
json elem = json{
{"index", index},
{"score", json_value(rank, "score", 0.0)},
};
if (return_text) {
elem["text"] = std::move(texts[index]);
}
res.push_back(elem);
}
} else {
// Jina response format
json results = json::array();
int32_t n_tokens = 0;
for (const auto & rank : ranks) {
results.push_back(json{
{"index", json_value(rank, "index", 0)},
{"relevance_score", json_value(rank, "score", 0.0)},
});
n_tokens += json_value(rank, "tokens_evaluated", 0);
}
res = json{
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", "list"},
{"usage", json{
{"prompt_tokens", n_tokens},
{"total_tokens", n_tokens}
}},
{"results", results}
};
}
return res;
}
static bool is_valid_utf8(const std::string & str) {
const unsigned char* bytes = reinterpret_cast<const unsigned char*>(str.data());
const unsigned char* end = bytes + str.length();
while (bytes < end) {
if (*bytes <= 0x7F) {
// 1-byte sequence (0xxxxxxx)
bytes++;
} else if ((*bytes & 0xE0) == 0xC0) {
// 2-byte sequence (110xxxxx 10xxxxxx)
if (end - bytes < 2 || (bytes[1] & 0xC0) != 0x80)
return false;
bytes += 2;
} else if ((*bytes & 0xF0) == 0xE0) {
// 3-byte sequence (1110xxxx 10xxxxxx 10xxxxxx)
if (end - bytes < 3 || (bytes[1] & 0xC0) != 0x80 || (bytes[2] & 0xC0) != 0x80)
return false;
bytes += 3;
} else if ((*bytes & 0xF8) == 0xF0) {
// 4-byte sequence (11110xxx 10xxxxxx 10xxxxxx 10xxxxxx)
if (end - bytes < 4 || (bytes[1] & 0xC0) != 0x80 ||
(bytes[2] & 0xC0) != 0x80 || (bytes[3] & 0xC0) != 0x80)
return false;
bytes += 4;
} else {
// Invalid UTF-8 lead byte
return false;
}
}
return true;
}
static json format_tokenizer_response(const json & tokens) {
return json {
{"tokens", tokens}
};
}
static json format_detokenized_response(const std::string & content) {
return json {
{"content", content}
};
}
static json format_logit_bias(const std::vector<llama_logit_bias> & logit_bias) {
json data = json::array();
for (const auto & lb : logit_bias) {
data.push_back(json{
{"bias", lb.bias},
{"token", lb.token},
});
}
return data;
}
static std::string safe_json_to_str(const json & data) {
return data.dump(-1, ' ', false, json::error_handler_t::replace);
}
static std::vector<llama_token_data> get_token_probabilities(llama_context * ctx, int idx) {
std::vector<llama_token_data> cur;
const auto * logits = llama_get_logits_ith(ctx, idx);
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const int n_vocab = llama_vocab_n_tokens(vocab);
cur.resize(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
}
// sort tokens by logits
std::sort(cur.begin(), cur.end(), [](const llama_token_data & a, const llama_token_data & b) {
return a.logit > b.logit;
});
// apply softmax
float max_l = cur[0].logit;
float cum_sum = 0.0f;
for (size_t i = 0; i < cur.size(); ++i) {
float p = expf(cur[i].logit - max_l);
cur[i].p = p;
cum_sum += p;
}
for (size_t i = 0; i < cur.size(); ++i) {
cur[i].p /= cum_sum;
}
return cur;
}
static bool are_lora_equal(
const std::vector<common_adapter_lora_info> & l1,
const std::vector<common_adapter_lora_info> & l2) {
if (l1.size() != l2.size()) {
return false;
}
for (size_t i = 0; i < l1.size(); ++i) {
// we don't check lora.path to reduce the time complexity
if (l1[i].scale != l2[i].scale || l1[i].ptr != l2[i].ptr) {
return false;
}
}
return true;
}
// parse lora config from JSON request, returned a copy of lora_base with updated scale
static std::vector<common_adapter_lora_info> parse_lora_request(
const std::vector<common_adapter_lora_info> & lora_base,
const json & data) {
std::vector<common_adapter_lora_info> lora(lora_base);
int max_idx = lora.size();
// clear existing value
for (auto & entry : lora) {
entry.scale = 0.0f;
}
// set value
for (const auto & entry : data) {
int id = json_value(entry, "id", -1);
float scale = json_value(entry, "scale", 0.0f);
if (0 <= id && id < max_idx) {
lora[id].scale = scale;
} else {
throw std::runtime_error("invalid adapter id");
}
}
return lora;
}
//
// utils for interacting with libmtmd
// (may need to refactor in near future)
//
/**
* server_tokens is a helper to manage the input tokens and image for the server.
* it is made this way to simplify the logic of KV cache management.
*/
struct server_tokens {
bool has_mtmd = false;
private: // disallow accessing these members directly, risking out-of-sync
// map a **start** position in tokens to the image chunk
std::unordered_map<llama_pos, mtmd::input_chunk_ptr> map_pos_to_image;
// list of tokens
// it can include LLAMA_TOKEN_NULL, which is used to indicate a token that is not a text token
// a mtmd_input_chunk can occupy multiple tokens, one llama_token per **position**
// important: for models using mrope, an image can contain multiple tokens but will use only one **position**
llama_tokens tokens;
// for ex. with input of 5 text tokens and 2 images:
// [0] [1] [2] [3] [4] [img0] [img0] [img0] [img1] [img1]
// pos 0 1 2 3 4 5 6 7 8 9
// map_pos_to_image will contain: {5, img0}, {8, img1}
public:
server_tokens() = default;
~server_tokens() = default;
// Prevent copying
server_tokens(const server_tokens&) = delete;
server_tokens& operator=(const server_tokens&) = delete;
// Allow moving (usually implicitly generated if members are movable)
server_tokens(server_tokens&&) = default;
server_tokens& operator=(server_tokens&&) = default;
// Allow accessing elements using [] operator
llama_token operator[](size_t index) { return tokens[index]; }
const llama_token& operator[](size_t index) const { return tokens[index]; }
server_tokens(mtmd::input_chunks & mtmd_chunks, bool has_mtmd) : has_mtmd(has_mtmd) {
for (size_t i = 0; i < mtmd_chunks.size(); ++i) {
push_back(mtmd_chunks[i]);
}
}
server_tokens(llama_tokens & tokens, bool has_mtmd) : has_mtmd(has_mtmd), tokens(tokens) {}
// for debugging
std::string str() const {
std::ostringstream oss;
oss << "tokens: ";
for (const auto & t : tokens) {
if (t == LLAMA_TOKEN_NULL) {
oss << "<embd> ";
} else {
oss << t << " ";
}
}
oss << "\n";
oss << "image pos: ";
for (const auto & it : map_pos_to_image) {
oss << it.first << ", ";
}
return oss.str();
}
const mtmd::input_chunk_ptr & find_chunk(llama_pos pos) const {
auto it = map_pos_to_image.find(pos);
if (it != map_pos_to_image.end()) {
return it->second;
} else {
throw std::runtime_error("Chunk not found");
}
}
void push_back(llama_token tok) {
if (tok == LLAMA_TOKEN_NULL) {
throw std::runtime_error("Invalid token");
}
tokens.emplace_back(tok);
}
// will create a copy of the chunk if it contains non-text data
void push_back(const mtmd_input_chunk * chunk) {
auto type = mtmd_input_chunk_get_type(chunk);
if (type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
GGML_ASSERT(has_mtmd);
auto img_tokens = mtmd_input_chunk_get_tokens_image(chunk);
const int n_pos = mtmd_image_tokens_get_n_pos(img_tokens);
llama_pos start_pos = tokens.size();
for (int i = 0; i < n_pos; ++i) {
tokens.emplace_back(LLAMA_TOKEN_NULL);
}
mtmd::input_chunk_ptr new_chunk(mtmd_input_chunk_copy(chunk));
map_pos_to_image[start_pos] = std::move(new_chunk);
} else if (type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
size_t n_tokens;
auto text_tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens);
for (size_t i = 0; i < n_tokens; ++i) {
push_back(text_tokens[i]);
}
} else {
GGML_ABORT("Invalid chunk type");
}
}
// for compatibility with context shift and prompt truncation
void insert(const llama_tokens & inp_tokens) {
GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled
tokens.insert(tokens.end(), inp_tokens.begin(), inp_tokens.end());
}
// for compatibility with speculative decoding, ctx shift, slot save/load
const llama_tokens & get_text_tokens() const {
GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled
return tokens;
}
// for compatibility with speculative decoding
void set_token(llama_pos pos, llama_token id) {
GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled
tokens[pos] = id;
}
size_t size() const {
return tokens.size();
}
bool empty() const {
return tokens.empty();
}
void clear() {
tokens.clear();
}
void keep_first(size_t n) {
GGML_ASSERT(n <= tokens.size());
if (has_mtmd) {
// we throw an error if we try to remove a token in the middle of an image
// for ex. with input of 5 text tokens and 2 images:
// [0] [1] [2] [3] [4] [img0] [img0] [img0] [img1] [img1]
// n 1 2 3 4 5 6 7 8 9 10
// allowed to resize ^ ^
// disallowed to resize ^ ^ ^
if (n > 0) {
llama_token last_token = tokens[n - 1];
// make sure we never remove tokens in the middle of an image
if (last_token == LLAMA_TOKEN_NULL) {
find_chunk(n - 1); // will throw an error if the token is not begin-of-chunk
}
}
// remove all image chunks that are not used anymore
for (auto it = map_pos_to_image.begin(); it != map_pos_to_image.end(); ) {
llama_pos pos = it->first;
if (pos >= (llama_pos)n) {
it = map_pos_to_image.erase(it);
} else {
++it;
}
}
}
tokens.resize(n);
}
std::string detokenize(const llama_context * ctx, bool special) const {
llama_tokens text_tokens;
text_tokens.reserve(tokens.size());
for (const auto & t : tokens) {
if (t != LLAMA_TOKEN_NULL) {
text_tokens.push_back(t);
}
}
return common_detokenize(ctx, text_tokens, special);
}
size_t get_common_prefix(const server_tokens & b) const {
size_t max_idx = std::min(tokens.size(), b.tokens.size());
for (size_t i = 0; i < max_idx; ++i) {
auto & ai = tokens[i];
auto & bi = b.tokens[i];
if (ai == LLAMA_TOKEN_NULL && bi == LLAMA_TOKEN_NULL) {
GGML_ASSERT(has_mtmd);
const auto & a_chunk = find_chunk(i);
const auto & b_chunk = b.find_chunk(i);
GGML_ASSERT(a_chunk && b_chunk);
const auto * a_img = mtmd_input_chunk_get_tokens_image(a_chunk.get());
const auto * b_img = mtmd_input_chunk_get_tokens_image(b_chunk.get());
std::string ai_id = mtmd_image_tokens_get_id(a_img);
std::string bi_id = mtmd_image_tokens_get_id(b_img);
size_t a_pos = mtmd_image_tokens_get_n_pos(a_img);
size_t b_pos = mtmd_image_tokens_get_n_pos(b_img);
if (ai_id == bi_id && a_pos == b_pos) {
GGML_ASSERT(a_pos > 0 && "Invalid image token"); // should never happen
i += a_pos - 1; // will be +1 by the for loop
continue;
} else {
return i;
}
} else if (ai == bi) {
continue;
} else {
return i;
}
}
return max_idx; // all tokens are equal
}
// make sure all text tokens are within the vocab range
bool validate(const struct llama_context * ctx) const {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const int32_t n_vocab = llama_vocab_n_tokens(vocab);
for (size_t i = 0; i < tokens.size(); ++i) {
auto & t = tokens[i];
if (t == LLAMA_TOKEN_NULL) {
try {
const auto & chunk = find_chunk(i);
const auto * img_tokens = mtmd_input_chunk_get_tokens_image(chunk.get());
size_t n_pos = mtmd_image_tokens_get_n_pos(img_tokens);
i += n_pos - 1; // will be +1 by the for loop
} catch (const std::exception & e) {
return false;
}
} else if (t < 0 || t >= n_vocab) {
return false;
}
}
return true;
}
// encode and decode the image chunk
int32_t process_chunk(
llama_context * ctx,
mtmd_context * mctx,
llama_pos n_past,
int32_t seq_id,
llama_pos & n_pos_out) {
auto it = map_pos_to_image.find(n_past);
if (it == map_pos_to_image.end()) {
throw std::runtime_error("Chunk not found");
}
SRV_INF("%s\n", "processing image...");
int32_t n_batch = llama_n_batch(ctx);
int64_t t0 = ggml_time_ms();
llama_pos new_n_past = n_past;
int32_t result = mtmd_helper_eval_chunk_single(mctx, ctx,
it->second.get(), // chunk
n_past,
seq_id,
n_batch,
true, // logits last
&new_n_past);
SRV_INF("image processed in %" PRId64 " ms\n", ggml_time_ms() - t0);
if (result != 0) {
LOG_ERR("mtmd_helper_eval failed with status %d", result);
n_pos_out = n_past;
return result;
}
n_pos_out = new_n_past;
return 0;
}
};
// Computes FNV-1a hash of the data
static std::string fnv_hash(const uint8_t * data, size_t len) {
const uint64_t fnv_prime = 0x100000001b3ULL;
uint64_t hash = 0xcbf29ce484222325ULL;
for (size_t i = 0; i < len; ++i) {
hash ^= data[i];
hash *= fnv_prime;
}
return std::to_string(hash);
}
Reference in New Issue View Git Blame Copy Permalink