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server.cpp
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server.cpp
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#include "common.h"
#include "llama.h"
#include "grammar-parser.h"
#include "utils.hpp"
#include "oai.hpp"
#include "../llava/clip.h"
#include "stb_image.h"
#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
#define CPPHTTPLIB_NO_EXCEPTIONS 1
#endif
// increase max payload length to allow use of larger context size
#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
#include "httplib.h"
#include "json.hpp"
// auto generated files (update with ./deps.sh)
#include "index.html.hpp"
#include "index.js.hpp"
#include "completion.js.hpp"
#include "json-schema-to-grammar.mjs.hpp"
#include <cstddef>
#include <thread>
#include <chrono>
#include <condition_variable>
#include <atomic>
using json = nlohmann::json;
struct server_params
{
std::string hostname = "127.0.0.1";
std::vector<std::string> api_keys;
std::string public_path = "examples/server/public";
int32_t port = 8080;
int32_t read_timeout = 600;
int32_t write_timeout = 600;
};
bool server_verbose = false;
static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
{
size_t i;
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++)
{
}
return i;
}
enum stop_type
{
STOP_FULL,
STOP_PARTIAL,
};
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 += llama_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 == -1 ? "" : llama_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;
}
// convert a vector of completion_token_output to json
static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> &probs)
{
json out = json::array();
for (const auto &prob : probs)
{
json probs_for_token = json::array();
for (const auto &p : prob.probs)
{
std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
probs_for_token.push_back(json
{
{"tok_str", tok_str},
{"prob", p.prob},
});
}
std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
out.push_back(json{
{"content", tok_str},
{"probs", probs_for_token},
});
}
return out;
}
struct llama_client_slot
{
int id;
int task_id = -1;
struct slot_params params;
slot_state state = IDLE;
slot_command 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 num_prompt_tokens = 0;
int32_t num_prompt_tokens_processed = 0;
json prompt;
std::string generated_text;
llama_token sampled;
std::vector<llama_token> cache_tokens;
std::vector<completion_token_output> generated_token_probs;
bool infill = false;
bool embedding = false;
bool 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;
// sampling
struct llama_sampling_params sparams;
llama_sampling_context *ctx_sampling = nullptr;
// multimodal
std::vector<slot_image> images;
// stats
size_t sent_count = 0;
size_t sent_token_probs_index = 0;
int64_t t_start_process_prompt;
int64_t t_start_genereration;
double t_prompt_processing; // ms
double t_token_generation; // ms
// multitasks
int multitask_id = -1;
void reset() {
num_prompt_tokens = 0;
generated_text = "";
truncated = false;
stopped_eos = false;
stopped_word = false;
stopped_limit = false;
stopping_word = "";
n_past = 0;
sent_count = 0;
sent_token_probs_index = 0;
infill = false;
generated_token_probs.clear();
for (slot_image & img : images)
{
free(img.image_embedding);
if (img.img_data) {
clip_image_u8_free(img.img_data);
}
img.prefix_prompt = "";
}
images.clear();
}
bool has_budget(gpt_params &global_params) {
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 == IDLE && command == NONE;
}
bool is_processing() const {
return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
}
void add_token_string(const completion_token_output &token) {
if (command == RELEASE)
{
return;
}
cache_tokens.push_back(token.tok);
generated_token_probs.push_back(token);
}
void release() {
if (state == PROCESSING)
{
t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
command = RELEASE;
}
}
json get_formated_timings() {
return json
{
{"prompt_n", num_prompt_tokens_processed},
{"prompt_ms", t_prompt_processing},
{"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed},
{"prompt_per_second", 1e3 / t_prompt_processing * num_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},
};
}
void print_timings() const {
LOG_TEE("\n");
LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
LOG_TEE("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded);
LOG_TEE("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation);
}
};
struct llama_server_context
{
llama_model *model = nullptr;
llama_context *ctx = nullptr;
clip_ctx *clp_ctx = nullptr;
gpt_params params;
llama_batch batch;
bool multimodal = false;
bool clean_kv_cache = true;
bool all_slots_are_idle = false;
bool add_bos_token = true;
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;
std::string name_user; // this should be the antiprompt
std::string name_assistant;
// slots / clients
std::vector<llama_client_slot> slots;
llama_server_queue queue_tasks;
llama_server_response queue_results;
~llama_server_context()
{
if (ctx)
{
llama_free(ctx);
ctx = nullptr;
}
if (model)
{
llama_free_model(model);
model = nullptr;
}
}
bool load_model(const gpt_params ¶ms_)
{
params = params_;
if (!params.mmproj.empty()) {
multimodal = true;
LOG_TEE("Multi Modal Mode Enabled");
clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
if(clp_ctx == nullptr) {
LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
return false;
}
if (params.n_ctx < 2048) { // request larger context for the image embedding
params.n_ctx = 2048;
}
}
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == nullptr)
{
LOG_ERROR("unable to load model", {{"model", params.model}});
return false;
}
if (multimodal) {
const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
const int n_embd_llm = llama_n_embd(model);
if (n_embd_clip != n_embd_llm) {
LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
llama_free(ctx);
llama_free_model(model);
return false;
}
}
n_ctx = llama_n_ctx(ctx);
add_bos_token = llama_should_add_bos_token(model);
return true;
}
void initialize() {
// create slots
all_slots_are_idle = true;
const int32_t n_ctx_slot = n_ctx / params.n_parallel;
LOG_TEE("Available slots:\n");
for (int i = 0; i < params.n_parallel; i++)
{
llama_client_slot slot;
slot.id = i;
slot.n_ctx = n_ctx_slot;
slot.reset();
LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
slots.push_back(slot);
}
batch = llama_batch_init(n_ctx, 0, params.n_parallel);
// empty system prompt
system_prompt = "";
system_tokens.clear();
}
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
{
// TODO: currently, we tokenize using special tokens by default
// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
// but it's better compared to completely ignoring ChatML and other chat templates
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_bos, 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_bos, TMP_FORCE_SPECIAL);
}
return prompt_tokens;
}
llama_client_slot* get_slot(int id) {
int64_t t_last = ggml_time_us();
llama_client_slot *last_used = nullptr;
for (llama_client_slot & slot : slots)
{
if (slot.id == id && slot.available())
{
return &slot;
}
if (slot.available() && slot.t_last_used < t_last)
{
last_used = &slot;
t_last = slot.t_last_used;
}
}
return last_used;
}
bool launch_slot_with_data(llama_client_slot* &slot, json data) {
slot_params default_params;
llama_sampling_params default_sparams;
if (data.count("__oaicompat") != 0) {
slot->oaicompat = true;
slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
} else {
slot->oaicompat = false;
slot->oaicompat_model = "";
}
slot->params.stream = json_value(data, "stream", false);
slot->params.cache_prompt = json_value(data, "cache_prompt", false);
slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
slot->params.seed = json_value(data, "seed", default_params.seed);
slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
// infill
if (data.count("input_prefix") != 0)
{
slot->params.input_prefix = data["input_prefix"];
}
else
{
slot->params.input_prefix = "";
}
if (data.count("input_suffix") != 0)
{
slot->params.input_suffix = data["input_suffix"];
}
else
{
slot->params.input_suffix = "";
}
if (data.count("prompt") != 0)
{
slot->prompt = data["prompt"];
}
else
{
slot->prompt = "";
}
slot->sparams.penalty_prompt_tokens.clear();
slot->sparams.use_penalty_prompt_tokens = false;
const auto &penalty_prompt = data.find("penalty_prompt");
if (penalty_prompt != data.end())
{
if (penalty_prompt->is_string())
{
const auto penalty_prompt_string = penalty_prompt->get<std::string>();
auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
if (slot->params.n_predict > 0)
{
slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
}
slot->sparams.use_penalty_prompt_tokens = true;
}
else if (penalty_prompt->is_array())
{
const auto n_tokens = penalty_prompt->size();
slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict));
const int n_vocab = llama_n_vocab(model);
for (const auto &penalty_token : *penalty_prompt)
{
if (penalty_token.is_number_integer())
{
const auto tok = penalty_token.get<llama_token>();
if (tok >= 0 && tok < n_vocab)
{
slot->sparams.penalty_prompt_tokens.push_back(tok);
}
}
}
slot->sparams.use_penalty_prompt_tokens = true;
}
}
slot->sparams.logit_bias.clear();
if (json_value(data, "ignore_eos", false))
{
slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
}
const auto &logit_bias = data.find("logit_bias");
if (logit_bias != data.end() && logit_bias->is_array())
{
const int n_vocab = llama_n_vocab(model);
for (const auto &el : *logit_bias)
{
if (el.is_array() && el.size() == 2 && el[0].is_number_integer())
{
llama_token tok = el[0].get<llama_token>();
if (tok >= 0 && tok < n_vocab)
{
if (el[1].is_number())
{
slot->sparams.logit_bias[tok] = el[1].get<float>();
}
else if (el[1].is_boolean() && !el[1].get<bool>())
{
slot->sparams.logit_bias[tok] = -INFINITY;
}
}
}
}
}
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);
}
}
}
if (multimodal)
{
const auto &images_data = data.find("image_data");
if (images_data != data.end() && images_data->is_array())
{
for (const auto &img : *images_data)
{
const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
slot_image img_sl;
img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
img_sl.img_data = clip_image_u8_init();
if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
{
LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
return false;
}
LOG_TEE("slot %i - loaded image\n", slot->id);
img_sl.request_encode_image = true;
slot->images.push_back(img_sl);
}
// process prompt
// example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]}
if (slot->images.size() > 0 && !slot->prompt.is_array())
{
std::string prompt = slot->prompt.get<std::string>();
size_t pos = 0, begin_prefix = 0;
std::string pattern = "[img-";
while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
size_t end_prefix = pos;
pos += pattern.length();
size_t end_pos = prompt.find("]", pos);
if (end_pos != std::string::npos)
{
std::string image_id = prompt.substr(pos, end_pos - pos);
try
{
int img_id = std::stoi(image_id);
bool found = false;
for (slot_image &img : slot->images)
{
if (img.id == img_id) {
found = true;
img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix);
begin_prefix = end_pos + 1;
break;
}
}
if (!found) {
LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id);
slot->images.clear();
return false;
}
} catch (const std::invalid_argument& e) {
LOG_TEE("Invalid image number id in prompt\n");
slot->images.clear();
return false;
}
}
}
slot->prompt = "";
slot->params.input_suffix = prompt.substr(begin_prefix);
slot->params.cache_prompt = false; // multimodal doesn't support cache prompt
}
}
}
if (slot->ctx_sampling != nullptr)
{
llama_sampling_free(slot->ctx_sampling);
}
slot->ctx_sampling = llama_sampling_init(slot->sparams);
llama_set_rng_seed(ctx, slot->params.seed);
slot->command = LOAD_PROMPT;
all_slots_are_idle = false;
LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);
return true;
}
void kv_cache_clear() {
// clear the entire KV cache
llama_kv_cache_clear(ctx);
clean_kv_cache = false;
}
void update_system_prompt() {
system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
llama_batch_clear(batch);
kv_cache_clear();
for (int i = 0; i < (int) system_tokens.size(); ++i)
{
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
}
if (llama_decode(ctx, batch) != 0)
{
LOG_TEE("%s: llama_decode() failed\n", __func__);
return;
}
// assign the system KV cache to all parallel sequences
for (int32_t i = 1; i < params.n_parallel; ++i)
{
llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
}
LOG_TEE("system prompt updated\n");
system_need_update = false;
}
void notify_system_prompt_changed() {
// release all slots
for (llama_client_slot &slot : slots)
{
slot.release();
}
system_need_update = true;
}
void process_system_prompt_data(const json &sys_props) {
system_prompt = sys_props.value("prompt", "");
name_user = sys_props.value("anti_prompt", "");
name_assistant = sys_props.value("assistant_name", "");
if (slots.size() > 0)
{
notify_system_prompt_changed();
}
}
static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
const stop_type type, llama_client_slot &slot)
{
size_t stop_pos = std::string::npos;
for (const std::string &word : slot.params.antiprompt)
{
size_t pos;
if (type == STOP_FULL)
{
const size_t tmp = word.size() + last_token_size;
const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
pos = text.find(word, from_pos);
}
else
{
pos = find_partial_stop_string(word, text);
}
if (pos != std::string::npos &&
(stop_pos == std::string::npos || pos < stop_pos))
{
if (type == STOP_FULL)
{
slot.stopped_word = true;
slot.stopping_word = word;
slot.has_next_token = false;
}
stop_pos = pos;
}
}
return stop_pos;
}
bool process_token(completion_token_output &result, llama_client_slot &slot) {
// remember which tokens were sampled - used for repetition penalties during sampling
const std::string token_str = llama_token_to_piece(ctx, result.tok);
slot.sampled = result.tok;
// search stop word and delete it
slot.generated_text += token_str;
slot.has_next_token = true;
if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
{
// we can change penalty_prompt_tokens because it is always created from scratch each request
slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
}
// check if there is incomplete UTF-8 character at the end
bool incomplete = false;
for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
{
unsigned char c = slot.generated_text[slot.generated_text.size() - i];
if ((c & 0xC0) == 0x80)
{
// continuation byte: 10xxxxxx
continue;
}
if ((c & 0xE0) == 0xC0)
{
// 2-byte character: 110xxxxx ...
incomplete = i < 2;
}
else if ((c & 0xF0) == 0xE0)
{
// 3-byte character: 1110xxxx ...
incomplete = i < 3;
}
else if ((c & 0xF8) == 0xF0)
{
// 4-byte character: 11110xxx ...
incomplete = i < 4;
}
// else 1-byte character or invalid byte
break;
}
if (!incomplete)
{
size_t pos = std::min(slot.sent_count, slot.generated_text.size());
const std::string str_test = slot.generated_text.substr(pos);
bool is_stop_full = false;
size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot);
if (stop_pos != std::string::npos)
{
is_stop_full = true;
slot.generated_text.erase(
slot.generated_text.begin() + pos + stop_pos,
slot.generated_text.end());
pos = std::min(slot.sent_count, slot.generated_text.size());
}
else
{
is_stop_full = false;
stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot);
}
// check if there is any token to predict
if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0))
{
// no send the stop word in the response
result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
slot.sent_count += result.text_to_send.size();
// add the token to slot queue and cache
}
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;
}
if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model))
{
slot.stopped_eos = true;
slot.has_next_token = false;
LOG_VERBOSE("eos token found", {});
}
LOG_VERBOSE("next token", {
{"token", result.tok},
{"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
{"has_next_token", slot.has_next_token},
{"n_remain", slot.n_remaining},
{"num_tokens_predicted", slot.n_decoded},
{"stopped_eos", slot.stopped_eos},
{"stopped_word", slot.stopped_word},
{"stopped_limit", slot.stopped_limit},
{"stopping_word", slot.stopping_word},
});
return slot.has_next_token; // continue
}
bool process_images(llama_client_slot &slot) const
{
for (slot_image &img : slot.images)
{
if (!img.request_encode_image)
{
continue;
}
clip_image_f32 * img_res = clip_image_f32_init();
if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true))
{
LOG_TEE("Error processing the given image");
clip_free(clp_ctx);
return false;
}
img.image_tokens = clip_n_patches(clp_ctx);
img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx));
if (!img.image_embedding)
{
LOG_TEE("Unable to allocate memory for image embeddings\n");
clip_free(clp_ctx);
return false;
}
LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
{
LOG_TEE("Unable to encode image\n");
return false;
}
clip_image_f32_free(img_res);
img.request_encode_image = false;
}
return slot.images.size() > 0;
}
void send_error(task_server& task, const std::string &error)
{
LOG_TEE("task %i - error: %s\n", task.id, error.c_str());
task_result res;
res.id = task.id;
res.multitask_id = task.multitask_id;
res.stop = false;
res.error = true;
res.result_json = { { "content", error } };
queue_results.send(res);
}
json get_model_props()
{
return get_formated_generation(slots[0]);
}
json get_formated_generation(llama_client_slot &slot)
{
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
return json {
{"n_ctx", slot.n_ctx},
{"model", params.model_alias},
{"seed", slot.params.seed},
{"temperature", slot.sparams.temp},
{"top_k", slot.sparams.top_k},
{"top_p", slot.sparams.top_p},
{"min_p", slot.sparams.min_p},
{"tfs_z", slot.sparams.tfs_z},
{"typical_p", slot.sparams.typical_p},
{"repeat_last_n", slot.sparams.penalty_last_n},
{"repeat_penalty", slot.sparams.penalty_repeat},
{"presence_penalty", slot.sparams.penalty_present},
{"frequency_penalty", slot.sparams.penalty_freq},
{"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
{"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
{"mirostat", slot.sparams.mirostat},
{"mirostat_tau", slot.sparams.mirostat_tau},
{"mirostat_eta", slot.sparams.mirostat_eta},
{"penalize_nl", slot.sparams.penalize_nl},
{"stop", slot.params.antiprompt},
{"n_predict", slot.params.n_predict},
{"n_keep", params.n_keep},
{"ignore_eos", ignore_eos},
{"stream", slot.params.stream},
{"logit_bias", slot.sparams.logit_bias},
{"n_probs", slot.sparams.n_probs},
{"grammar", slot.sparams.grammar},
};
}