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server.cpp
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#include "utils.hpp"
#include "arg.h"
#include "common.h"
#include "json-schema-to-grammar.h"
#include "llama.h"
#include "log.h"
#include "sampling.h"
#include "speculative.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
// mime type for sending response
#define MIMETYPE_JSON "application/json; charset=utf-8"
// auto generated files (see README.md for details)
#include "index.html.gz.hpp"
#include "loading.html.hpp"
#include <atomic>
#include <chrono>
#include <condition_variable>
#include <cstddef>
#include <cinttypes>
#include <deque>
#include <memory>
#include <mutex>
#include <signal.h>
#include <thread>
#include <unordered_map>
#include <unordered_set>
using json = nlohmann::ordered_json;
constexpr int HTTP_POLLING_SECONDS = 1;
enum stop_type {
STOP_TYPE_NONE,
STOP_TYPE_EOS,
STOP_TYPE_WORD,
STOP_TYPE_LIMIT,
};
// state diagram: https://github.com/ggml-org/llama.cpp/pull/9283
enum slot_state {
SLOT_STATE_IDLE,
SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future
SLOT_STATE_PROCESSING_PROMPT,
SLOT_STATE_DONE_PROMPT,
SLOT_STATE_GENERATING,
};
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
};
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,
};
// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
enum error_type {
ERROR_TYPE_INVALID_REQUEST,
ERROR_TYPE_AUTHENTICATION,
ERROR_TYPE_SERVER,
ERROR_TYPE_NOT_FOUND,
ERROR_TYPE_PERMISSION,
ERROR_TYPE_UNAVAILABLE, // custom error
ERROR_TYPE_NOT_SUPPORTED, // custom error
};
struct slot_params {
bool stream = true;
bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt
bool return_tokens = false;
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
int32_t n_indent = 0; // mininum line indentation for the generated text in number of whitespace characters
int64_t t_max_prompt_ms = -1; // TODO: implement
int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
std::vector<common_adapter_lora_info> lora;
std::vector<std::string> antiprompt;
std::vector<std::string> response_fields;
bool timings_per_token = false;
bool post_sampling_probs = false;
bool ignore_eos = false;
struct common_params_sampling sampling;
struct common_params_speculative speculative;
// 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;
json to_json() const {
std::vector<std::string> samplers;
samplers.reserve(sampling.samplers.size());
for (const auto & sampler : sampling.samplers) {
samplers.emplace_back(common_sampler_type_to_str(sampler));
}
json lora = json::array();
for (size_t i = 0; i < this->lora.size(); ++i) {
lora.push_back({{"id", i}, {"scale", this->lora[i].scale}});
}
auto grammar_triggers = json::array();
for (const auto & trigger : sampling.grammar_triggers) {
grammar_triggers.push_back(trigger.to_json<json>());
}
return json {
{"n_predict", n_predict}, // Server configured n_predict
{"seed", sampling.seed},
{"temperature", sampling.temp},
{"dynatemp_range", sampling.dynatemp_range},
{"dynatemp_exponent", sampling.dynatemp_exponent},
{"top_k", sampling.top_k},
{"top_p", sampling.top_p},
{"min_p", sampling.min_p},
{"xtc_probability", sampling.xtc_probability},
{"xtc_threshold", sampling.xtc_threshold},
{"typical_p", sampling.typ_p},
{"repeat_last_n", sampling.penalty_last_n},
{"repeat_penalty", sampling.penalty_repeat},
{"presence_penalty", sampling.penalty_present},
{"frequency_penalty", sampling.penalty_freq},
{"dry_multiplier", sampling.dry_multiplier},
{"dry_base", sampling.dry_base},
{"dry_allowed_length", sampling.dry_allowed_length},
{"dry_penalty_last_n", sampling.dry_penalty_last_n},
{"dry_sequence_breakers", sampling.dry_sequence_breakers},
{"mirostat", sampling.mirostat},
{"mirostat_tau", sampling.mirostat_tau},
{"mirostat_eta", sampling.mirostat_eta},
{"stop", antiprompt},
{"max_tokens", n_predict}, // User configured n_predict
{"n_keep", n_keep},
{"n_discard", n_discard},
{"ignore_eos", sampling.ignore_eos},
{"stream", stream},
{"logit_bias", format_logit_bias(sampling.logit_bias)},
{"n_probs", sampling.n_probs},
{"min_keep", sampling.min_keep},
{"grammar", sampling.grammar},
{"grammar_lazy", sampling.grammar_lazy},
{"grammar_triggers", grammar_triggers},
{"preserved_tokens", sampling.preserved_tokens},
{"chat_format", common_chat_format_name(oaicompat_chat_format)},
{"samplers", samplers},
{"speculative.n_max", speculative.n_max},
{"speculative.n_min", speculative.n_min},
{"speculative.p_min", speculative.p_min},
{"timings_per_token", timings_per_token},
{"post_sampling_probs", post_sampling_probs},
{"lora", lora},
};
}
};
struct server_task {
int id = -1; // to be filled by server_queue
int index = -1; // used when there are multiple prompts (batch request)
server_task_type type;
// used by SERVER_TASK_TYPE_CANCEL
int id_target = -1;
// used by SERVER_TASK_TYPE_INFERENCE
slot_params params;
llama_tokens prompt_tokens;
int id_selected_slot = -1;
// used by SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE
struct slot_action {
int slot_id;
std::string filename;
std::string filepath;
};
slot_action slot_action;
// used by SERVER_TASK_TYPE_METRICS
bool metrics_reset_bucket = false;
// used by SERVER_TASK_TYPE_SET_LORA
std::vector<common_adapter_lora_info> set_lora;
server_task(server_task_type type) : type(type) {}
static slot_params params_from_json_cmpl(
const llama_context * ctx,
const common_params & params_base,
const json & data) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
slot_params params;
// Sampling parameter defaults are loaded from the global server context (but individual requests can still override them)
slot_params defaults;
defaults.sampling = params_base.sampling;
defaults.speculative = params_base.speculative;
// enabling this will output extra debug information in the HTTP responses from the server
params.verbose = params_base.verbosity > 9;
params.timings_per_token = json_value(data, "timings_per_token", false);
params.stream = json_value(data, "stream", false);
params.cache_prompt = json_value(data, "cache_prompt", true);
params.return_tokens = json_value(data, "return_tokens", false);
params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", defaults.n_predict));
params.n_indent = json_value(data, "n_indent", defaults.n_indent);
params.n_keep = json_value(data, "n_keep", defaults.n_keep);
params.n_discard = json_value(data, "n_discard", defaults.n_discard);
//params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement
params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms);
params.response_fields = json_value(data, "response_fields", std::vector<std::string>());
params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k);
params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p);
params.sampling.min_p = json_value(data, "min_p", defaults.sampling.min_p);
params.sampling.xtc_probability = json_value(data, "xtc_probability", defaults.sampling.xtc_probability);
params.sampling.xtc_threshold = json_value(data, "xtc_threshold", defaults.sampling.xtc_threshold);
params.sampling.typ_p = json_value(data, "typical_p", defaults.sampling.typ_p);
params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp);
params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range);
params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent);
params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n);
params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat);
params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq);
params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present);
params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier);
params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base);
params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length);
params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n);
params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat);
params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau);
params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta);
params.sampling.seed = json_value(data, "seed", defaults.sampling.seed);
params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs);
params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep);
params.post_sampling_probs = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);
params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min);
params.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min);
params.speculative.n_min = std::max(params.speculative.n_min, 0);
params.speculative.n_max = std::max(params.speculative.n_max, 0);
// Use OpenAI API logprobs only if n_probs wasn't provided
if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){
params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs);
}
if (data.contains("lora")) {
if (data.at("lora").is_array()) {
params.lora = parse_lora_request(params_base.lora_adapters, data.at("lora"));
} else {
throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
}
} else {
params.lora = params_base.lora_adapters;
}
// TODO: add more sanity checks for the input parameters
if (params.sampling.penalty_last_n < -1) {
throw std::runtime_error("Error: repeat_last_n must be >= -1");
}
if (params.sampling.dry_penalty_last_n < -1) {
throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
}
if (params.sampling.penalty_last_n == -1) {
// note: should be the slot's context and not the full context, but it's ok
params.sampling.penalty_last_n = llama_n_ctx(ctx);
}
if (params.sampling.dry_penalty_last_n == -1) {
params.sampling.dry_penalty_last_n = llama_n_ctx(ctx);
}
if (params.sampling.dry_base < 1.0f) {
params.sampling.dry_base = defaults.sampling.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")) {
params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
if (params.sampling.dry_sequence_breakers.empty()) {
throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings");
}
}
}
// process "json_schema" and "grammar"
if (data.contains("json_schema") && !data.contains("grammar")) {
try {
auto schema = json_value(data, "json_schema", json::object());
SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
params.sampling.grammar = json_schema_to_grammar(schema);
SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
} catch (const std::exception & e) {
throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
}
} else {
params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar);
SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
}
{
auto it = data.find("chat_format");
if (it != data.end()) {
params.oaicompat_chat_format = static_cast<common_chat_format>(it->get<int>());
SRV_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
} else {
params.oaicompat_chat_format = defaults.oaicompat_chat_format;
}
}
{
const auto preserved_tokens = data.find("preserved_tokens");
if (preserved_tokens != data.end()) {
for (const auto & t : *preserved_tokens) {
auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
if (ids.size() == 1) {
SRV_DBG("Preserved token: %d\n", ids[0]);
params.sampling.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.
SRV_DBG("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) {
auto ct = common_grammar_trigger::from_json(t);
if (ct.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
const auto & word = ct.value;
auto ids = common_tokenize(vocab, word, /* add_special= */ false, /* parse_special= */ true);
if (ids.size() == 1) {
auto token = ids[0];
if (std::find(params.sampling.preserved_tokens.begin(), params.sampling.preserved_tokens.end(), (llama_token) token) == params.sampling.preserved_tokens.end()) {
throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
}
SRV_DBG("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;
params.sampling.grammar_triggers.push_back(std::move(trigger));
} else {
SRV_DBG("Grammar trigger word: `%s`\n", word.c_str());
params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
}
} else {
params.sampling.grammar_triggers.push_back(ct);
}
}
}
if (params.sampling.grammar_lazy && params.sampling.grammar_triggers.empty()) {
throw std::runtime_error("Error: no triggers set for lazy grammar!");
}
}
{
params.sampling.logit_bias.clear();
params.ignore_eos = json_value(data, "ignore_eos", false);
const auto & logit_bias = data.find("logit_bias");
if (logit_bias != data.end() && logit_bias->is_array()) {
const int n_vocab = llama_vocab_n_tokens(vocab);
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) {
params.sampling.logit_bias.push_back({tok, bias});
}
} else if (el[0].is_string()) {
auto toks = common_tokenize(vocab, el[0].get<std::string>(), false);
for (auto tok : toks) {
params.sampling.logit_bias.push_back({tok, bias});
}
}
}
}
}
}
{
params.antiprompt.clear();
const auto & stop = data.find("stop");
if (stop != data.end() && stop->is_array()) {
for (const auto & word : *stop) {
if (!word.empty()) {
params.antiprompt.push_back(word);
}
}
}
}
{
const auto samplers = data.find("samplers");
if (samplers != data.end()) {
if (samplers->is_array()) {
params.sampling.samplers = common_sampler_types_from_names(*samplers, false);
} else if (samplers->is_string()){
params.sampling.samplers = common_sampler_types_from_chars(samplers->get<std::string>());
}
} else {
params.sampling.samplers = defaults.sampling.samplers;
}
}
std::string model_name = params_base.model_alias.empty() ? DEFAULT_OAICOMPAT_MODEL : params_base.model_alias;
params.oaicompat_model = json_value(data, "model", model_name);
return params;
}
// utility function
static std::unordered_set<int> get_list_id(const std::vector<server_task> & tasks) {
std::unordered_set<int> ids(tasks.size());
for (size_t i = 0; i < tasks.size(); i++) {
ids.insert(tasks[i].id);
}
return ids;
}
};
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_result {
int id = -1;
int id_slot = -1;
virtual bool is_error() {
// only used by server_task_result_error
return false;
}
virtual bool is_stop() {
// only used by server_task_result_cmpl_*
return false;
}
virtual int get_index() {
return -1;
}
virtual json to_json() = 0;
virtual ~server_task_result() = default;
};
// using shared_ptr for polymorphism of server_task_result
using server_task_result_ptr = std::unique_ptr<server_task_result>;
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 completion_token_output {
llama_token tok;
float prob;
std::string text_to_send;
struct prob_info {
llama_token tok;
std::string txt;
float prob;
};
std::vector<prob_info> probs;
json to_json(bool post_sampling_probs) const {
json probs_for_token = json::array();
for (const auto & p : probs) {
std::string txt(p.txt);
txt.resize(validate_utf8(txt));
probs_for_token.push_back(json {
{"id", p.tok},
{"token", txt},
{"bytes", str_to_bytes(p.txt)},
{
post_sampling_probs ? "prob" : "logprob",
post_sampling_probs ? p.prob : logarithm(p.prob)
},
});
}
return probs_for_token;
}
static json probs_vector_to_json(const std::vector<completion_token_output> & probs, bool post_sampling_probs) {
json out = json::array();
for (const auto & p : probs) {
std::string txt(p.text_to_send);
txt.resize(validate_utf8(txt));
out.push_back(json {
{"id", p.tok},
{"token", txt},
{"bytes", str_to_bytes(p.text_to_send)},
{
post_sampling_probs ? "prob" : "logprob",
post_sampling_probs ? p.prob : logarithm(p.prob)
},
{
post_sampling_probs ? "top_probs" : "top_logprobs",
p.to_json(post_sampling_probs)
},
});
}
return out;
}
static float logarithm(float x) {
// nlohmann::json converts -inf to null, so we need to prevent that
return x == 0.0f ? std::numeric_limits<float>::lowest() : std::log(x);
}
static std::vector<unsigned char> str_to_bytes(const std::string & str) {
std::vector<unsigned char> bytes;
for (unsigned char c : str) {
bytes.push_back(c);
}
return bytes;
}
};
struct server_task_result_cmpl_final : server_task_result {
int index = 0;
std::string content;
llama_tokens tokens;
bool stream;
result_timings timings;
std::string prompt;
bool truncated;
int32_t n_decoded;
int32_t n_prompt_tokens;
int32_t n_tokens_cached;
bool has_new_line;
std::string stopping_word;
stop_type stop = STOP_TYPE_NONE;
bool post_sampling_probs;
std::vector<completion_token_output> probs_output;
std::vector<std::string> response_fields;
slot_params generation_params;
// 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;
virtual int get_index() override {
return index;
}
virtual bool is_stop() override {
return true; // in stream mode, final responses are considered stop
}
virtual json to_json() override {
switch (oaicompat) {
case OAICOMPAT_TYPE_NONE:
return to_json_non_oaicompat();
case OAICOMPAT_TYPE_COMPLETION:
return to_json_oaicompat();
case OAICOMPAT_TYPE_CHAT:
return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat();
default:
GGML_ASSERT(false && "Invalid oaicompat_type");
}
}
json to_json_non_oaicompat() {
json res = json {
{"index", index},
{"content", stream ? "" : content}, // in stream mode, content is already in last partial chunk
{"tokens", stream ? llama_tokens {} : tokens},
{"id_slot", id_slot},
{"stop", true},
{"model", oaicompat_model},
{"tokens_predicted", n_decoded},
{"tokens_evaluated", n_prompt_tokens},
{"generation_settings", generation_params.to_json()},
{"prompt", prompt},
{"has_new_line", has_new_line},
{"truncated", truncated},
{"stop_type", stop_type_to_str(stop)},
{"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() {
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},
{"system_fingerprint", build_info},
{"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();
}
if (timings.prompt_n >= 0) {
res.push_back({"timings", timings.to_json()});
}
return res;
}
json to_json_oaicompat_chat() {
std::string finish_reason = "length";
common_chat_msg msg;
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
SRV_DBG("Parsing chat message: %s\n", content.c_str());
msg = common_chat_parse(content, oaicompat_chat_format);
finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
} else {
msg.content = content;
}
json message {
{"role", "assistant"},
};
if (!msg.reasoning_content.empty()) {
message["reasoning_content"] = msg.reasoning_content;
}
if (msg.content.empty() && !msg.tool_calls.empty()) {
message["content"] = json();
} else {
message["content"] = msg.content;
}
if (!msg.tool_calls.empty()) {
auto tool_calls = json::array();
for (const auto & tc : msg.tool_calls) {
tool_calls.push_back({
{"type", "function"},
{"function", {
{"name", tc.name},
{"arguments", tc.arguments},
}},
// Some templates generate and require an id (sometimes in a very specific format, e.g. Mistral Nemo).
// We only generate a random id for the ones that don't generate one by themselves
// (they also won't get to see it as their template likely doesn't use it, so it's all for the client)
{"id", tc.id.empty() ? gen_tool_call_id() : tc.id},
});
}
message["tool_calls"] = tool_calls;
}
json choice {
{"finish_reason", finish_reason},
{"index", 0},
{"message", message},
};
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},
{"system_fingerprint", build_info},
{"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();
}
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 == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
finish_reason = "stop";
}
json choice = json {
{"finish_reason", finish_reason},
{"index", 0},
{"delta", json::object()}
};
json ret = json {
{"choices", json::array({choice})},
{"created", t},
{"id", oaicompat_cmpl_id},
{"model", oaicompat_model},
{"system_fingerprint", build_info},
{"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) {
ret.push_back({"timings", timings.to_json()});
}
// extra fields for debugging purposes
if (verbose) {
ret["__verbose"] = to_json_non_oaicompat();
}
return ret;
}
};
struct server_task_result_cmpl_partial : server_task_result {
int index = 0;
std::string content;
llama_tokens tokens;
int32_t n_decoded;
int32_t n_prompt_tokens;
bool post_sampling_probs;
completion_token_output prob_output;
result_timings timings;
// OAI-compat fields
bool verbose = false;
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
std::string oaicompat_model;
std::string oaicompat_cmpl_id;
virtual int get_index() override {
return index;
}
virtual bool is_stop() override {
return false; // in stream mode, partial responses are not considered stop
}
virtual json to_json() override {
switch (oaicompat) {
case OAICOMPAT_TYPE_NONE:
return to_json_non_oaicompat();
case OAICOMPAT_TYPE_COMPLETION:
return to_json_oaicompat();
case OAICOMPAT_TYPE_CHAT:
return to_json_oaicompat_chat();
default:
GGML_ASSERT(false && "Invalid oaicompat_type");
}
}
json to_json_non_oaicompat() {
// non-OAI-compat JSON
json res = json {
{"index", index},
{"content", content},
{"tokens", tokens},
{"stop", false},
{"id_slot", id_slot},
{"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 (!prob_output.probs.empty()) {
res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs);
}
return res;
}
json to_json_oaicompat() {
std::time_t t = std::time(0);
json logprobs = json(nullptr); // OAI default to null
if (prob_output.probs.size() > 0) {
logprobs = json{
{"content", completion_token_output::probs_vector_to_json({prob_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},
{"system_fingerprint", build_info},
{"object", "text_completion"},
{"id", oaicompat_cmpl_id}
};
// extra fields for debugging purposes
if (verbose) {
res["__verbose"] = to_json_non_oaicompat();
}
if (timings.prompt_n >= 0) {
res.push_back({"timings", timings.to_json()});
}
return res;
}
json to_json_oaicompat_chat() {
bool first = n_decoded == 0;
std::time_t t = std::time(0);
json choices;
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", oaicompat_cmpl_id},
{"model", oaicompat_model},
{"object", "chat.completion.chunk"}};
json second_ret = json{
{"choices", json::array({json{{"finish_reason", nullptr},
{"index", 0},
{"delta", json {
{"content", content}}}
}})},
{"created", t},
{"id", oaicompat_cmpl_id},
{"model", oaicompat_model},
{"object", "chat.completion.chunk"}};
return std::vector<json>({initial_ret, second_ret});
}
} else {
choices = json::array({json{
{"finish_reason", nullptr},
{"index", 0},
{"delta",
json {
{"content", content},
}},
}});
}
GGML_ASSERT(choices.size() >= 1);
if (prob_output.probs.size() > 0) {
choices[0]["logprobs"] = json{
{"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},