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llama.cpp/examples/server/server.cpp

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#include "common.h"
#include "llama.h"
#include "build-info.h"
#include "grammar-parser.h"
#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
#define CPPHTTPLIB_NO_EXCEPTIONS 1
#endif
#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>
#ifndef SERVER_VERBOSE
#define SERVER_VERBOSE 1
#endif
using namespace httplib;
using json = nlohmann::json;
struct server_params
{
std::string hostname = "127.0.0.1";
std::string public_path = "examples/server/public";
int32_t port = 8040;
int32_t read_timeout = 600;
int32_t write_timeout = 600;
};
static bool server_verbose = false;
#if SERVER_VERBOSE != 1
#define LOG_VERBOSE(MSG, ...)
#else
#define LOG_VERBOSE(MSG, ...) \
do \
{ \
if (server_verbose) \
{ \
server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
} \
} while (0)
#endif
#define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
// parallel
enum slot_state
{
IDLE,
SLEEPING,
PROCESSING
};
enum slot_command {
NONE,
LOAD_PROMPT,
RELEASE
};
struct slot_params {
bool stream = true;
uint32_t seed = -1; // RNG seed
int32_t n_predict = 128; // new tokens to predict
// sampler params
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // 1.0 = disabled
float repeat_penalty = 1.10f; // 1.0 = disabled
int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float frequency_penalty = 0.00f; // 0.0 = disabled
float presence_penalty = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
int n_probs = 0;
bool penalize_nl = false;
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
std::string grammar = ""; // optional BNF-like grammar to constrain sampling
bool remember_generation = false; // remember a part of the prompt to avoid reprocessing all prompt
std::vector<std::string> antiprompt;
};
// completion token output with probabilities
struct completion_token_output
{
struct token_prob
{
llama_token tok;
float prob;
};
std::vector<token_prob> probs;
llama_token tok;
};
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 void slot_params_to_gpt_params(const slot_params &src, gpt_params & dst)
{
dst.frequency_penalty = src.frequency_penalty;
dst.temp = src.temp;
dst.top_k = src.top_k;
dst.top_p = src.top_p;
dst.grammar = src.grammar;
dst.logit_bias = src.logit_bias;
dst.mirostat = src.mirostat;
dst.mirostat_eta = src.mirostat_eta;
dst.mirostat_tau = src.mirostat_tau;
dst.typical_p = src.typical_p;
dst.repeat_penalty = src.repeat_penalty;
dst.repeat_last_n = src.repeat_last_n;
dst.presence_penalty = src.presence_penalty;
}
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;
}
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;
}
static void server_log(const char *level, const char *function, int line,
const char *message, const nlohmann::ordered_json &extra)
{
nlohmann::ordered_json log{
{"timestamp", time(nullptr)},
{"level", level},
{"function", function},
{"line", line},
{"message", message},
};
if (!extra.empty())
{
log.merge_patch(extra);
}
const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
printf("%.*s\n", (int)str.size(), str.data());
fflush(stdout);
}
// 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;
// generation props
int32_t num_prompt_tokens = 0;
int32_t n_decoded = 0;
int32_t i_batch = -1;
size_t n_past = 0;
json prompt;
std::string generated_text = "";
int num_tokens_predicted = 0;
llama_token sampled;
std::vector<llama_token> context_tokens;
std::vector<llama_token> last_n_tokens;
std::vector<completion_token_output> generated_token_probs;
int sent_tokens = 0;
slot_state state = IDLE;
slot_command command = NONE;
bool truncated = false;
bool stopped_eos = false;
bool stopped_word = false;
bool stopped_limit = false;
std::string stopping_word;
int32_t multibyte_pending = 0;
slot_params params;
// grammar props
grammar_parser::parse_state parsed_grammar;
llama_grammar *grammar = nullptr;
void reset() {
state = IDLE;
command = NONE;
num_prompt_tokens = 0;
num_tokens_predicted = 0;
generated_text = "";
generated_token_probs.clear();
truncated = false;
stopped_eos = false;
stopped_word = false;
stopped_limit = false;
stopping_word = "";
multibyte_pending = 0;
n_past = 0;
if (grammar != nullptr) {
llama_grammar_free(grammar);
grammar = nullptr;
}
// llama_set_rng_seed(ctx, params.seed); in batched the seed matter???????
}
bool loadGrammar()
{
if (!params.grammar.empty()) {
parsed_grammar = grammar_parser::parse(params.grammar.c_str());
// will be empty (default) if there are parse errors
if (parsed_grammar.rules.empty()) {
LOG_ERROR("grammar parse error", {{"grammar", params.grammar}});
return false;
}
grammar_parser::print_grammar(stderr, parsed_grammar);
// TODO: fix this comment
// {
// auto it = params.logit_bias.find(llama_token_eos(ctx));
// if (it != params.logit_bias.end() && it->second == -INFINITY) {
// LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {});
// }
// }
std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
return true;
}
bool hasNewToken() {
return generated_token_probs.size() > sent_tokens;
}
bool available() {
return state == IDLE &&
command == NONE && !params.remember_generation;
}
bool isProcessing() {
return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
}
completion_token_output next() {
completion_token_output tkn = generated_token_probs.at(sent_tokens);
sent_tokens++;
return tkn;
}
void addTokenString(completion_token_output token) {
if(command == RELEASE) {
generated_token_probs.clear();
sent_tokens = 0;
return;
}
context_tokens.push_back(token.tok);
generated_token_probs.push_back(token);
num_tokens_predicted++;
}
void release() {
if(state == PROCESSING) {
command = RELEASE;
}
}
};
struct llama_server_context
{
std::vector<llama_client_slot> slots;
// system prompt
std::string system_prompt = "";
bool update_system_prompt = false;
std::vector<llama_token> tokens_system;
int32_t n_tokens_system = 0;
// broadcast to all clients to keep the same prompt format
std::string user_name = ""; // this should be the anti prompt
std::string assistant_name = ""; // this is for generate the prompt
llama_model *model = nullptr;
llama_context *ctx = nullptr;
llama_batch batch;
std::vector<llama_token_data> candidates;
bool all_slots_are_idle = false;
gpt_params params;
llama_sampling_context ctx_sampling;
int n_ctx;
int n_vocab;
bool clean_kv_cache = true;
std::mutex mutex;
std::unique_lock<std::mutex> lock()
{
return std::unique_lock<std::mutex>(mutex);
}
~llama_server_context()
{
if (ctx)
{
llama_free(ctx);
ctx = nullptr;
}
if (model)
{
llama_free_model(model);
model = nullptr;
}
}
void rewind()
{
params.antiprompt.clear();
params.grammar.clear();
num_prompt_tokens = 0;
num_tokens_predicted = 0;
generated_text = "";
generated_text.reserve(n_ctx);
generated_token_probs.clear();
truncated = false;
stopped_eos = false;
stopped_word = false;
stopped_limit = false;
stopping_word = "";
multibyte_pending = 0;
n_remain = 0;
n_past = 0;
if (grammar != nullptr) {
llama_grammar_free(grammar);
grammar = nullptr;
}
}
bool loadModel(const gpt_params &params_)
{
params = params_;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == nullptr)
{
LOG_ERROR("unable to load model", {{"model", params_.model}});
return false;
}
n_ctx = llama_n_ctx(ctx);
n_vocab = llama_n_vocab(model);
candidates.reserve(n_vocab);
return true;
}
void initialize() {
// create slots
LOG_TEE("Available slots:\n");
for (int i = 0; i < params.n_parallel; i++)
{
llama_client_slot slot;
slot.id = i;
slot.last_n_tokens.resize(params.n_predict); // max prediction per slot
slot.reset();
std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0);
LOG_TEE(" - slot %i\n", slot.id);
slots.push_back(slot);
}
batch = llama_batch_init(params.n_ctx, 0);
// empty system prompt
system_prompt = "";
all_slots_are_idle = true;
}
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
{
// 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);
first = false;
}
else
{
p = ::llama_tokenize(ctx, s, false);
}
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>();
printf("----------------------\nprompt:\n%s-----------------------\n", s.c_str());
prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
}
return prompt_tokens;
}
bool loadGrammar()
{
if (!params.grammar.empty()) {
parsed_grammar = grammar_parser::parse(params.grammar.c_str());
// will be empty (default) if there are parse errors
if (parsed_grammar.rules.empty()) {
LOG_ERROR("grammar parse error", {{"grammar", params.grammar}});
return false;
}
grammar_parser::print_grammar(stderr, parsed_grammar);
{
auto it = params.logit_bias.find(llama_token_eos(ctx));
if (it != params.logit_bias.end() && it->second == -INFINITY) {
LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {});
}
}
std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
return true;
}
void loadInfill()
{
// bool suff_rm_leading_spc = true;
// if (params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) {
// params.input_suffix.erase(0, 1);
// suff_rm_leading_spc = false;
// }
// auto prefix_tokens = tokenize(params.input_prefix, false);
// auto suffix_tokens = tokenize(params.input_suffix, false);
// const int space_token = 29871;
// if (suff_rm_leading_spc && suffix_tokens[0] == space_token) {
// suffix_tokens.erase(suffix_tokens.begin());
// }
// prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx));
// prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS
// prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx));
// prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end());
// prefix_tokens.push_back(llama_token_middle(ctx));
// auto prompt_tokens = prefix_tokens;
// num_prompt_tokens = prompt_tokens.size();
// if (params.n_keep < 0)
// {
// params.n_keep = (int)num_prompt_tokens;
// }
// params.n_keep = std::min(params.n_ctx - 4, params.n_keep);
// // if input prompt is too big, truncate like normal
// if (num_prompt_tokens >= (size_t)params.n_ctx)
// {
// printf("Input prompt is too big, truncating. Can only take %d tokens but got %zu\n", params.n_ctx, num_prompt_tokens);
// // todo we probably want to cut from both sides
// const int n_left = (params.n_ctx - params.n_keep) / 2;
// std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
// const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left;
// new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
// std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin());
// LOG_VERBOSE("input truncated", {
// {"n_ctx", params.n_ctx},
// {"n_keep", params.n_keep},
// {"n_left", n_left},
// {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
// });
// truncated = true;
// prompt_tokens = new_tokens;
// }
// else
// {
// const size_t ps = num_prompt_tokens;
// std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0);
// std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps);
// }
// compare the evaluated prompt with the new prompt
n_past = common_part(embd, prompt_tokens);
embd = prompt_tokens;
if (n_past == num_prompt_tokens)
{
// we have to evaluate at least 1 token to generate logits.
printf("we have to evaluate at least 1 token to generate logits\n");
n_past--;
}
// LOG_VERBOSE("prompt ingested", {
// {"n_past", n_past},
// {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)},
// {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
// });
// has_next_token = true;
}
void cleanKVCache() {
// clear the entire KV cache
for (int i = 0; i < params.n_parallel; ++i)
{
llama_kv_cache_seq_rm(ctx, i, 0, -1);
}
params.n_keep = std::min(n_ctx - 4, params.n_keep);
// if input prompt is too big, truncate like normal
if (num_prompt_tokens >= (size_t)n_ctx)
{
const int n_left = (n_ctx - params.n_keep) / 2;
std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left;
new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), last_n_tokens.begin());
LOG_VERBOSE("input truncated", {
{"n_ctx", n_ctx},
{"n_keep", params.n_keep},
{"n_left", n_left},
{"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
});
truncated = true;
prompt_tokens = new_tokens;
}
else
{
const size_t ps = num_prompt_tokens;
std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0);
std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps);
}
// compare the evaluated prompt with the new prompt
n_past = common_part(embd, prompt_tokens);
// since #3228 we now have to manually manage the KV cache
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
embd = prompt_tokens;
if (n_past == num_prompt_tokens)
{
// we have to evaluate at least 1 token to generate logits.
n_past--;
}
LOG_VERBOSE("prompt ingested", {
{"n_past", n_past},
{"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)},
{"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
});
has_next_token = true;
}
void beginCompletion()
{
// number of tokens to keep when resetting context
n_remain = params.n_predict;
llama_set_rng_seed(ctx, params.seed);
}
completion_token_output nextToken()
{
completion_token_output result;
result.tok = -1;
if (embd.size() >= (size_t)n_ctx)
{
// Shift context
const int n_left = n_past - params.n_keep - 1;
const int n_discard = n_left/2;
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
for (size_t i = params.n_keep + 1 + n_discard; i < embd.size(); i++)
{
embd[i - n_discard] = embd[i];
}
embd.resize(embd.size() - n_discard);
n_past -= n_discard;
truncated = true;
LOG_VERBOSE("input truncated", {
{"n_ctx", n_ctx},
{"n_keep", params.n_keep},
{"n_left", n_left},
});
}
bool tg = true;
while (n_past < embd.size())
{
int n_eval = (int)embd.size() - n_past;
tg = n_eval == 1;
if (n_eval > params.n_batch)
{
n_eval = params.n_batch;
}
if (llama_decode(ctx, llama_batch_get_one(&embd[n_past], n_eval, n_past, 0)))
{
LOG_ERROR("failed to eval", {
{"n_eval", n_eval},
{"n_past", n_past},
{"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
});
has_next_token = false;
return result;
}
n_past += n_eval;
}
if (params.n_predict == 0)
{
has_next_token = false;
result.tok = llama_token_eos(ctx);
return result;
}
{
// out of user input, sample next token
std::vector<llama_token_data> candidates;
candidates.reserve(llama_n_vocab(model));
result.tok = llama_sample_token(ctx, NULL, grammar, params, last_n_tokens, candidates);
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
const int32_t n_probs = params.n_probs;
if (params.temp <= 0 && n_probs > 0)
{
// For llama_sample_token_greedy we need to sort candidates
llama_sample_softmax(ctx, &candidates_p);
}
for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i)
{
result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
}
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(result.tok);
if (tg) {
num_tokens_predicted++;
}
}
// add it to the context
embd.push_back(result.tok);
// decrement remaining sampling budget
--n_remain;
if (!embd.empty() && embd.back() == llama_token_eos(ctx))
{
// stopping_word = llama_token_to_piece(ctx, embd.back());
has_next_token = false;
stopped_eos = true;
LOG_VERBOSE("eos token found", {});
return result;
}
has_next_token = params.n_predict == -1 || n_remain != 0;
return result;
}
size_t findStoppingStrings(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 = slot.generated_text.size() > tmp ? slot.generated_text.size() - tmp : 0;
pos = slot.generated_text.find(word, from_pos);
}
else
{
pos = find_partial_stop_string(word, slot.generated_text);
}
if (pos != std::string::npos &&
(stop_pos == std::string::npos || pos < stop_pos))
{
if (type == STOP_FULL)
{
slot.stopping_word = word;
slot.stopped_word = true;
}
stop_pos = pos;
}
}
return stop_pos;
}
bool processToken(completion_token_output & result, llama_client_slot & slot) {
// remember which tokens were sampled - used for repetition penalties during sampling
slot.last_n_tokens.erase(slot.last_n_tokens.begin());
slot.last_n_tokens.push_back(result.tok);
const std::string token_str = llama_token_to_piece(ctx, result.tok);
printf("%s", token_str.c_str());
slot.sampled = result.tok;
size_t stop_pos =
findStoppingStrings(token_str.size(), STOP_FULL, slot);
slot.addTokenString(result);
slot.generated_text += token_str;
bool has_next_token = !(slot.n_decoded > 2 &&
(result.tok == llama_token_eos(ctx) ||
(slot.n_decoded + slot.n_past >=
params.n_predict) ||
stop_pos != std::string::npos));
if (params.n_probs > 0)
{
slot.generated_token_probs.push_back(result);
}
if (slot.multibyte_pending > 0)
{
slot.multibyte_pending -= token_str.size();
}
else if (token_str.size() == 1)
{
const char c = token_str[0];
// 2-byte characters: 110xxxxx 10xxxxxx
if ((c & 0xE0) == 0xC0)
{
slot.multibyte_pending = 1;
// 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
}
else if ((c & 0xF0) == 0xE0)
{
slot.multibyte_pending = 2;
// 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
}
else if ((c & 0xF8) == 0xF0)
{
slot.multibyte_pending = 3;
}
else
{
slot.multibyte_pending = 0;
}
}
if (slot.multibyte_pending > 0 && !has_next_token)
{
has_next_token = true;
}
if (!has_next_token && (slot.n_decoded + slot.n_past >= params.n_predict))
{
slot.stopped_limit = true;
}
if (!slot.context_tokens.empty() && result.tok == llama_token_eos(ctx)){
slot.stopped_eos = true;
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", has_next_token},
{"n_remain", (params.n_predict - slot.n_decoded + slot.n_past)},
{"num_tokens_predicted", slot.num_tokens_predicted},
{"stopped_eos", slot.stopped_eos},
{"stopped_word", slot.stopped_word},
{"stopped_limit", slot.stopped_limit},
{"stopping_word", slot.stopping_word},
});
return has_next_token; // continue
}
bool updateSlots() {
// update the system prompt wait until all slots are idle state
if(update_system_prompt) {
updateSystemPrompt();
}
batch.n_tokens = 0;
int kv_cache_free = (n_ctx - n_tokens_system);
if(all_slots_are_idle) {
if(system_prompt.empty() && clean_kv_cache) {
cleanKVCache();
}
// avoid 100% usage of cpu all time
std::this_thread::sleep_for(std::chrono::milliseconds(5));
}
// decode any currently ongoing sequences
for (auto & slot : slots) {
// release the slot
if (slot.state == PROCESSING && slot.command == RELEASE && !slot.hasNewToken())
{
LOG_TEE("slot %i released\n", slot.id);
if(!slot.params.remember_generation) {
llama_kv_cache_seq_rm(ctx, slot.id, n_tokens_system, n_ctx);
slot.state = IDLE;
slot.command = NONE;
slot.num_prompt_tokens = 0;
slot.num_tokens_predicted = 0;
} else {
slot.state = SLEEPING;
slot.command = NONE;
}
continue;
}
kv_cache_free -= slot.num_prompt_tokens;
if (slot.state == IDLE || slot.command == RELEASE) {
continue;
}
batch.token [batch.n_tokens] = slot.sampled;
batch.pos [batch.n_tokens] = n_tokens_system + slot.n_past + slot.n_decoded;
batch.seq_id[batch.n_tokens] = slot.id;
batch.logits[batch.n_tokens] = true;
slot.n_decoded += 1;
slot.i_batch = batch.n_tokens;
batch.n_tokens += 1;
}
// assign workload to the slots
if (params.cont_batching || batch.n_tokens == 0) {
for (auto & slot : slots) {
// need process the prompt
bool keep_gen = slot.state == SLEEPING; // remember generation
if ((slot.state == IDLE || keep_gen) && slot.command == LOAD_PROMPT) {
LOG_TEE("processing prompt\n");
slot.state = PROCESSING;
slot.command = NONE;
auto prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
slot.num_prompt_tokens = prompt_tokens.size();
slot.n_past = keep_gen ? common_part(slot.context_tokens, prompt_tokens) : 0;
slot.context_tokens = prompt_tokens;
LOG_VERBOSE("prompt ingested", {
{"n_past", slot.n_past},
{"cached", tokens_to_str(ctx, slot.context_tokens.cbegin(), slot.context_tokens.cbegin() + slot.n_past)},
{"to_eval", tokens_to_str(ctx, slot.context_tokens.cbegin() + slot.n_past, slot.context_tokens.cend())},
});
if(system_prompt.empty()) {
LOG_TEE("cleaning kv: %i\n", slot.n_past);
llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1);
}
std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end(), 0);
for (size_t i = slot.n_past; i < slot.context_tokens.size(); ++i) {
batch.token [batch.n_tokens] = slot.context_tokens[i];
batch.pos [batch.n_tokens] = i + n_tokens_system;
batch.seq_id[batch.n_tokens] = slot.id;
batch.logits[batch.n_tokens] = false;
batch.n_tokens += 1;
}
// extract the logits only for the last token
if (batch.n_tokens > 0) {
batch.logits[batch.n_tokens - 1] = true;
}
slot.n_decoded = 0;
slot.i_batch = batch.n_tokens - 1;
}
}
}
if (batch.n_tokens == 0) {
all_slots_are_idle = true;
return true;
}
// process in chunks of params.n_batch
int32_t n_batch = params.n_batch;
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
batch.pos + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};
const int ret = llama_decode(ctx, batch_view);
if (ret != 0) {
if (n_batch == 1 || ret < 0) {
// if you get here, it means the KV cache is full - try increasing it via the context size
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
return false;
}
LOG("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2);
// retry with half the batch size to try to find a free slot in the KV cache
n_batch /= 2;
i -= n_batch;
continue;
}
for (auto & slot : slots) {
if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
continue;
}
slot_params_to_gpt_params(slot.params, params);
completion_token_output result;
const llama_token id = llama_sample_token(ctx, NULL, NULL, params, slot.last_n_tokens, candidates, slot.i_batch - i);
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
result.tok = id;
const int32_t n_probs = params.n_probs;
if (params.temp <= 0 && n_probs > 0)
{
// For llama_sample_token_greedy we need to sort candidates
llama_sample_softmax(ctx, &candidates_p);
}
for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i)
{
result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
}
if (!processToken(result, slot)) {
slot.generated_text.clear();
slot.release();
}
kv_cache_free -= slot.num_tokens_predicted;
slot.i_batch = -1;
}
}
if(kv_cache_free < 0) {
LOG_TEE("\nError: kv cache is full, increase context size.");
return false;
}
return true;
}
std::vector<float> getEmbedding()
{
static const int n_embd = llama_n_embd(model);
if (!params.embedding)
{
LOG_WARNING("embedding disabled", {
{"params.embedding", params.embedding},
});
return std::vector<float>(n_embd, 0.0f);
}
const float *data = llama_get_embeddings(ctx);
std::vector<float> embedding(data, data + n_embd);
return embedding;
}
};
static void server_print_usage(const char *argv0, const gpt_params &params,
const server_params &sparams)
{
printf("usage: %s [options]\n", argv0);
printf("\n");
printf("options:\n");
printf(" -h, --help show this help message and exit\n");
printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n");
printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n");
printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n");
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
if (llama_mlock_supported())
{
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
}
if (llama_mmap_supported())
{
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
printf(" --numa attempt optimizations that help on some NUMA systems\n");
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
printf(" -ngl N, --n-gpu-layers N\n");
printf(" number of layers to store in VRAM\n");
printf(" -ts SPLIT --tensor-split SPLIT\n");
printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
printf(" -nommq, --no-mul-mat-q\n");
printf(" use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
printf(" Not recommended since this is both slower and uses more VRAM.\n");
#endif
printf(" -m FNAME, --model FNAME\n");
printf(" model path (default: %s)\n", params.model.c_str());
printf(" -a ALIAS, --alias ALIAS\n");
printf(" set an alias for the model, will be added as `model` field in completion response\n");
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
printf(" --port PORT port to listen (default (default: %d)\n", sparams.port);
printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
printf("\n");
}
static void server_params_parse(int argc, char **argv, server_params &sparams,
gpt_params &params)
{
gpt_params default_params;
server_params default_sparams;
std::string arg;
bool invalid_param = false;
for (int i = 1; i < argc; i++)
{
arg = argv[i];
if (arg == "--port")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.port = std::stoi(argv[i]);
}
else if (arg == "--host")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.hostname = argv[i];
}
else if (arg == "--path")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.public_path = argv[i];
}
else if (arg == "--timeout" || arg == "-to")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.read_timeout = std::stoi(argv[i]);
sparams.write_timeout = std::stoi(argv[i]);
}
else if (arg == "-m" || arg == "--model")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.model = argv[i];
}
else if (arg == "-a" || arg == "--alias")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.model_alias = argv[i];
}
else if (arg == "-h" || arg == "--help")
{
server_print_usage(argv[0], default_params, default_sparams);
exit(0);
}
else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_ctx = std::stoi(argv[i]);
}
else if (arg == "--rope-freq-base")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.rope_freq_base = std::stof(argv[i]);
}
else if (arg == "--rope-freq-scale")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.rope_freq_scale = std::stof(argv[i]);
}
else if (arg == "--memory-f32" || arg == "--memory_f32")
{
params.memory_f16 = false;
}
else if (arg == "--threads" || arg == "-t")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_threads = std::stoi(argv[i]);
}
else if (arg == "--threads-batch" || arg == "-tb")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_threads_batch = std::stoi(argv[i]);
}
else if (arg == "-b" || arg == "--batch-size")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_batch = std::stoi(argv[i]);
params.n_batch = std::min(512, params.n_batch);
}
else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
params.n_gpu_layers = std::stoi(argv[i]);
#else
LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
"See main README.md for information on enabling GPU BLAS support",
{{"n_gpu_layers", params.n_gpu_layers}});
#endif
}
else if (arg == "--tensor-split" || arg == "-ts")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
#ifdef GGML_USE_CUBLAS
std::string arg_next = argv[i];
// split string by , and /
const std::regex regex{R"([,/]+)"};
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
std::vector<std::string> split_arg{it, {}};
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device)
{
if (i_device < split_arg.size())
{
params.tensor_split[i_device] = std::stof(split_arg[i_device]);
}
else
{
params.tensor_split[i_device] = 0.0f;
}
}
#else
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--no-mul-mat-q" || arg == "-nommq")
{
#ifdef GGML_USE_CUBLAS
params.mul_mat_q = false;
#else
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--main-gpu" || arg == "-mg")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
#ifdef GGML_USE_CUBLAS
params.main_gpu = std::stoi(argv[i]);
#else
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
#endif
}
else if (arg == "--lora")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
params.use_mmap = false;
}
else if (arg == "--lora-scaled")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
const char * lora_adapter = argv[i];
if (++i >= argc)
{
invalid_param = true;
break;
}
params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
params.use_mmap = false;
}
else if (arg == "--lora-base")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.lora_base = argv[i];
}
else if (arg == "-v" || arg == "--verbose")
{
#if SERVER_VERBOSE != 1
LOG_WARNING("server.cpp is not built with verbose logging.", {});
#else
server_verbose = true;
#endif
}
else if (arg == "--mlock")
{
params.use_mlock = true;
}
else if (arg == "--no-mmap")
{
params.use_mmap = false;
}
else if (arg == "--numa")
{
params.numa = true;
}
else if (arg == "--embedding")
{
params.embedding = true;
} else if (arg == "-cb" || arg == "--cont-batching")
{
params.cont_batching = true;
}
else if (arg == "-np" || arg == "--parallel")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_parallel = std::stoi(argv[i]);
} else if (arg == "-n" || arg == "--n-predict")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_predict = std::stoi(argv[i]);
if(params.n_predict <= 128) { // this example don't support long prompts
params.n_predict = 128;
}
}
else
{
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
server_print_usage(argv[0], default_params, default_sparams);
exit(1);
}
}
if (invalid_param)
{
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
server_print_usage(argv[0], default_params, default_sparams);
exit(1);
}
}
static json format_generation_settings(llama_server_context &llama, llama_client_slot* &slot)
{
const auto eos_bias = llama.params.logit_bias.find(llama_token_eos(llama.ctx));
const bool ignore_eos = eos_bias != llama.params.logit_bias.end() &&
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
return json{
{"n_ctx", llama.n_ctx},
{"model", llama.params.model_alias},
{"seed", llama.params.seed},
{"temp", llama.params.temp},
{"top_k", llama.params.top_k},
{"top_p", llama.params.top_p},
{"tfs_z", llama.params.tfs_z},
{"typical_p", llama.params.typical_p},
{"repeat_last_n", llama.params.repeat_last_n},
{"repeat_penalty", llama.params.repeat_penalty},
{"presence_penalty", llama.params.presence_penalty},
{"frequency_penalty", llama.params.frequency_penalty},
{"mirostat", llama.params.mirostat},
{"mirostat_tau", llama.params.mirostat_tau},
{"mirostat_eta", llama.params.mirostat_eta},
{"penalize_nl", llama.params.penalize_nl},
{"stop", llama.params.antiprompt},
{"n_predict", llama.params.n_predict},
{"n_keep", llama.params.n_keep},
{"ignore_eos", ignore_eos},
{"stream", llama.stream},
{"logit_bias", llama.params.logit_bias},
{"n_probs", llama.params.n_probs},
{"grammar", llama.params.grammar},
};
}
static json format_embedding_response(llama_server_context &llama)
{
return json{
{"embedding", llama.getEmbedding()},
};
}
static json format_timings(llama_server_context &llama)
{
const auto timings = llama_get_timings(llama.ctx);
return json{
{"prompt_n", timings.n_p_eval},
{"prompt_ms", timings.t_p_eval_ms},
{"prompt_per_token_ms", timings.t_p_eval_ms / timings.n_p_eval},
{"prompt_per_second", 1e3 / timings.t_p_eval_ms * timings.n_p_eval},
{"predicted_n", timings.n_eval},
{"predicted_ms", timings.t_eval_ms},
{"predicted_per_token_ms", timings.t_eval_ms / timings.n_eval},
{"predicted_per_second", 1e3 / timings.t_eval_ms * timings.n_eval},
};
}
static json format_final_response(llama_server_context &llama, llama_client_slot* &slot, const std::string &content, const std::vector<completion_token_output> &probs)
{
json res = json{
{"content", content},
{"stop", true},
{"model", llama.params.model_alias},
{"tokens_predicted", slot->num_tokens_predicted},
{"tokens_evaluated", slot->num_prompt_tokens},
{"generation_settings", format_generation_settings(llama, slot)},
{"prompt", slot->prompt},
{"truncated", slot->truncated},
{"stopped_eos", slot->stopped_eos},
{"stopped_word", slot->stopped_word},
{"stopped_limit", slot->stopped_limit},
{"stopping_word", slot->stopping_word},
{"tokens_cached", slot->n_past},
// {"timings", format_timings(llama)},
};
if (llama.params.n_probs > 0)
{
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
}
return res;
}
static json format_partial_response(
llama_server_context &llama, llama_client_slot* &slot, const std::string &content, const std::vector<completion_token_output> &probs
) {
json res = json{
{"content", content},
{"stop", false},
{ "slot_id", slot->id }
};
if (llama.params.n_probs > 0)
{
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
}
return res;
}
static json format_tokenizer_response(const std::vector<llama_token> &tokens)
{
return json{
{"tokens", tokens}};
}
static json format_detokenized_response(std::string content)
{
return json{
{"content", content}};
}
template <typename T>
static T json_value(const json &body, const std::string &key, const T &default_value)
{
// Fallback null to default value
return body.contains(key) && !body.at(key).is_null()
? body.value(key, default_value)
: default_value;
}
static void parse_options_completion(const json &body, llama_client_slot* &slot, llama_server_context &llama)
{
gpt_params default_params;
llama.stream = json_value(body, "stream", false);
llama.params.n_predict = json_value(body, "n_predict", default_params.n_predict);
llama.params.top_k = json_value(body, "top_k", default_params.top_k);
llama.params.top_p = json_value(body, "top_p", default_params.top_p);
llama.params.tfs_z = json_value(body, "tfs_z", default_params.tfs_z);
llama.params.typical_p = json_value(body, "typical_p", default_params.typical_p);
llama.params.repeat_last_n = json_value(body, "repeat_last_n", default_params.repeat_last_n);
llama.params.temp = json_value(body, "temperature", default_params.temp);
llama.params.repeat_penalty = json_value(body, "repeat_penalty", default_params.repeat_penalty);
llama.params.presence_penalty = json_value(body, "presence_penalty", default_params.presence_penalty);
llama.params.frequency_penalty = json_value(body, "frequency_penalty", default_params.frequency_penalty);
llama.params.mirostat = json_value(body, "mirostat", default_params.mirostat);
llama.params.mirostat_tau = json_value(body, "mirostat_tau", default_params.mirostat_tau);
llama.params.mirostat_eta = json_value(body, "mirostat_eta", default_params.mirostat_eta);
llama.params.penalize_nl = json_value(body, "penalize_nl", default_params.penalize_nl);
llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep);
llama.params.seed = json_value(body, "seed", default_params.seed);
llama.params.grammar = json_value(body, "grammar", default_params.grammar);
llama.params.n_probs = json_value(body, "n_probs", default_params.n_probs);
if (body.count("prompt") != 0)
{
slot->prompt = body["prompt"];
}
else
{
slot->prompt = "";
}
llama.params.logit_bias.clear();
if (json_value(body, "ignore_eos", false))
{
llama.params.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY;
}
const auto &logit_bias = body.find("logit_bias");
if (logit_bias != body.end() && logit_bias->is_array())
{
const int n_vocab = llama_n_vocab(llama.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())
{
llama.params.logit_bias[tok] = el[1].get<float>();
}
else if (el[1].is_boolean() && !el[1].get<bool>())
{
llama.params.logit_bias[tok] = -INFINITY;
}
}
}
}
}
slot->params.antiprompt.clear();
const auto &stop = body.find("stop");
if (stop != body.end() && stop->is_array())
{
for (const auto &word : *stop)
{
if (!word.empty())
{
slot->params.antiprompt.push_back(word);
}
}
}
llama.ctx_sampling = llama_sampling_context_init(llama.params, llama.grammar);
LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama, slot));
}
// static void parse_options_infill(const json &body, llama_server_context &llama)
// {
// if (body.count("input_prefix") != 0)
// {
// llama.params.input_prefix = body["input_prefix"];
// }
// else
// {
// llama.params.input_prefix = "";
// }
// if (body.count("input_suffix") != 0)
// {
// llama.params.input_suffix = body["input_suffix"];
// }
// else
// {
// llama.params.input_suffix = "";
// }
// parse_options_completion(body, slot, llama);
// }
static void log_server_request(const Request &req, const Response &res)
{
LOG_INFO("request", {
{"remote_addr", req.remote_addr},
{"remote_port", req.remote_port},
{"status", res.status},
{"method", req.method},
{"path", req.path},
{"params", req.params},
});
LOG_VERBOSE("request", {
{"request", req.body},
{"response", res.body},
});
}
static bool is_at_eob(llama_server_context &server_context, const llama_token *tokens, const size_t n_tokens) {
return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx);
}
// Function matching type llama_beam_search_callback_fn_t.
// Custom callback example is called each time the beams lengths increase:
// * Show progress by printing ',' following by number of convergent beam tokens if any.
// * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
// This is also called when the stop condition is met.
// Collect tokens into std::vector<llama_token> response which is pointed to by callback_data.
// AVOID HEADACHES unnecessaries
// static void beam_search_callback(void *callback_data, llama_beams_state beams_state) {
// auto & llama = *static_cast<llama_server_context*>(callback_data);
// // Mark beams as EOS as needed.
// for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
// llama_beam_view& beam_view = beams_state.beam_views[i];
// if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) {
// beam_view.eob = true;
// }
// }
// printf(","); // Show progress
// if (const size_t n = beams_state.common_prefix_length) {
// llama.generated_token_probs.resize(llama.generated_token_probs.size() + n);
// assert(0u < beams_state.n_beams);
// const llama_token * tokens = beams_state.beam_views[0].tokens;
// const auto map = [](llama_token tok) { return completion_token_output{{},tok}; };
// std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map);
// printf("%zu", n);
// }
// fflush(stdout);
// #if 0 // DEBUG: print current beams for this iteration
// std::cout << "\n\nCurrent beams:\n";
// for (size_t i=0 ; i < beams_state.n_beams ; ++i) {
// std::cout << "beams["<<i<<"]: " << ostream_beam_view{state.ctx,beams_state.beam_views[i]} << std::endl;
// }
// #endif
// }
struct token_translator {
llama_context * ctx;
std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
std::string operator()(const completion_token_output & cto) const { return (*this)(cto.tok); }
};
static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot & slot)
{
auto & gtps = slot.generated_token_probs;
auto translator = token_translator{llama.ctx};
auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); };
const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen);
if (slot.generated_text.capacity() < slot.generated_text.size() + len) {
slot.generated_text.reserve(slot.generated_text.size() + len);
}
for (const completion_token_output & cto : gtps) {
slot.generated_text += translator(cto);
}
}
int main(int argc, char **argv)
{
// own arguments required by this example
gpt_params params;
server_params sparams;
// struct that contains llama context and inference
llama_server_context llama;
server_params_parse(argc, argv, sparams, params);
if (params.model_alias == "unknown")
{
params.model_alias = params.model;
}
llama_backend_init(params.numa);
LOG_INFO("build info", {{"build", BUILD_NUMBER},
{"commit", BUILD_COMMIT}});
LOG_INFO("system info", {
{"n_threads", params.n_threads},
{"n_threads_batch", params.n_threads_batch},
{"total_threads", std::thread::hardware_concurrency()},
{"system_info", llama_print_system_info()},
});
// load the model
if (!llama.loadModel(params))
{
return 1;
}
llama.initialize();
Server svr;
svr.set_default_headers({{"Server", "llama.cpp"},
{"Access-Control-Allow-Origin", "*"},
{"Access-Control-Allow-Headers", "content-type"}});
// this is only called if no index.html is found in the public --path
svr.Get("/", [](const Request &, Response &res)
{
res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html");
return false; });
// this is only called if no index.js is found in the public --path
svr.Get("/index.js", [](const Request &, Response &res)
{
res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
return false; });
// this is only called if no index.html is found in the public --path
svr.Get("/completion.js", [](const Request &, Response &res)
{
res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
return false; });
// this is only called if no index.html is found in the public --path
svr.Get("/json-schema-to-grammar.mjs", [](const Request &, Response &res)
{
res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript");
return false; });
svr.Get("/props", [&llama](const Request & /*req*/, Response &res)
{
res.set_header("Access-Control-Allow-Origin", "*");
json data = {
{ "user_name", llama.user_name.c_str() },
{ "assistant_name", llama.assistant_name.c_str() }
};
res.set_content(data.dump(), "application/json"); });
svr.Post("/completion", [&llama](const Request &req, Response &res)
{
//auto lock = llama.lock();
json data = json::parse(req.body);
llama_client_slot* slot = llama.getSlot(json_value(data, "slot_id", -1));
if(slot == nullptr) {
LOG_TEE("slot unavailable\n");
res.status = 404;
res.set_content("slot_error", "text/plain");
return;
}
if(data.contains("system_prompt")) {
llama.processSystemPromptData(data["system_prompt"]);
}
// llama_reset_timings(llama.ctx);
slot->reset();
parse_options_completion(json::parse(req.body), slot, llama);
if (!llama.launchSlot(slot))
{
res.status = 400;
return;
}
if (!slot->params.stream) {
// if (llama.params.n_beams) {
// // Fill llama.generated_token_probs vector with final beam.
// llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams,
// llama.n_past, llama.n_remain);
// // Translate llama.generated_token_probs to llama.generated_text.
// append_to_generated_text_from_generated_token_probs(llama);
// } else {
// size_t stop_pos = std::string::npos;
// while (llama.has_next_token) {
// const completion_token_output token_with_probs = llama.doCompletion();
// const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok);
// stop_pos = llama.findStoppingStrings(llama.generated_text,
// token_text.size(), STOP_FULL);
// }
// if (stop_pos == std::string::npos) {
// stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL);
// }
// if (stop_pos != std::string::npos) {
// llama.generated_text.erase(llama.generated_text.begin() + stop_pos,
// llama.generated_text.end());
// }
// }
auto probs = llama.generated_token_probs;
if (llama.params.n_probs > 0 && llama.stopped_word) {
const std::vector<llama_token> stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false);
probs = std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size());
}
// const json data = format_final_response(llama, llama.generated_text, probs);
// llama_print_timings(llama.ctx);
// res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace),
// "application/json");
} else {
printf("processing -> %s\n", slot->isProcessing() ? "true" : "false");
const auto chunked_content_provider = [slot](size_t, DataSink & sink) {
size_t sent_count = 0;
size_t sent_token_probs_index = 0;
while(slot->isProcessing()) {
if(slot->hasNewToken()) { // new token notification
// const completion_token_output token = slot->next();
// std::string token_str = llama_token_to_piece(llama.ctx, token.tok);
size_t pos = std::min(sent_count, llama.generated_text.size());
const std::string str_test = llama.generated_text.substr(pos);
bool is_stop_full = false;
size_t stop_pos =
llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL);
if (stop_pos != std::string::npos) {
is_stop_full = true;
llama.generated_text.erase(
llama.generated_text.begin() + pos + stop_pos,
llama.generated_text.end());
pos = std::min(sent_count, llama.generated_text.size());
} else {
is_stop_full = false;
stop_pos = llama.findStoppingStrings(str_test, token_text.size(),
STOP_PARTIAL);
}
if (
stop_pos == std::string::npos ||
// Send rest of the text if we are at the end of the generation
(!llama.has_next_token && !is_stop_full && stop_pos > 0)
) {
const std::string to_send = llama.generated_text.substr(pos, std::string::npos);
sent_count += to_send.size();
std::vector<completion_token_output> probs_output = {};
if (llama.params.n_probs > 0) {
const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false);
size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size());
size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size());
if (probs_pos < probs_stop_pos) {
probs_output = std::vector<completion_token_output>(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos);
}
sent_token_probs_index = probs_stop_pos;
}
const json data = format_partial_response(llama, to_send, probs_output);
const std::string str =
"data: " +
data.dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {
{ "to_send", str }
});
if (!sink.write(str.data(), str.size())) {
LOG_VERBOSE("stream closed", {});
llama_print_timings(llama.ctx);
return false;
}
}
if (!llama.has_next_token) {
// Generation is done, send extra information.
const json data = format_final_response(
llama,
"",
std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index)
);
// const std::string str =
// "data: " +
// data.dump(-1, ' ', false, json::error_handler_t::replace) +
// "\n\n";
// LOG_VERBOSE("data stream", {
// { "to_send", str }
// });
// if (!sink.write(str.data(), str.size())) {
// LOG_VERBOSE("stream closed", {});
// llama_print_timings(llama.ctx);
// return false;
// }
sink.done();
return true;
};
auto on_complete = [&] (bool) {
//llama.mutex.unlock();
slot->sent_tokens = 0;
slot->generated_token_probs.clear();
slot->release();
};
//lock.release();
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
} });
// svr.Post("/infill", [&llama](const Request &req, Response &res)
// {
// auto lock = llama.lock();
// llama.rewind();
// llama_reset_timings(llama.ctx);
// parse_options_infill(json::parse(req.body), llama);
// if (!llama.loadGrammar())
// {
// res.status = 400;
// return;
// }
// llama.loadInfill();
// llama.beginCompletion();
// const auto chunked_content_provider = [&](size_t, DataSink & sink) {
// size_t sent_count = 0;
// size_t sent_token_probs_index = 0;
// while (llama.has_next_token) {
// const completion_token_output token_with_probs = llama.doCompletion();
// if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) {
// continue;
// }
// const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok);
// size_t pos = std::min(sent_count, llama.generated_text.size());
// const std::string str_test = llama.generated_text.substr(pos);
// bool is_stop_full = false;
// size_t stop_pos =
// llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL);
// if (stop_pos != std::string::npos) {
// is_stop_full = true;
// llama.generated_text.erase(
// llama.generated_text.begin() + pos + stop_pos,
// llama.generated_text.end());
// pos = std::min(sent_count, llama.generated_text.size());
// } else {
// is_stop_full = false;
// stop_pos = llama.findStoppingStrings(str_test, token_text.size(),
// STOP_PARTIAL);
// }
// if (
// stop_pos == std::string::npos ||
// // Send rest of the text if we are at the end of the generation
// (!llama.has_next_token && !is_stop_full && stop_pos > 0)
// ) {
// const std::string to_send = llama.generated_text.substr(pos, std::string::npos);
// sent_count += to_send.size();
// std::vector<completion_token_output> probs_output = {};
if (llama.params.n_probs > 0) {
const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false);
size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size());
size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size());
if (probs_pos < probs_stop_pos) {
probs_output = std::vector<completion_token_output>(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos);
}
sent_token_probs_index = probs_stop_pos;
}
// const json data = format_partial_response(llama, to_send, probs_output);
// const std::string str =
// "data: " +
// data.dump(-1, ' ', false, json::error_handler_t::replace) +
// "\n\n";
// LOG_VERBOSE("data stream", {
// { "to_send", str }
// });
// if (!sink.write(str.data(), str.size())) {
// LOG_VERBOSE("stream closed", {});
// llama_print_timings(llama.ctx);
// return false;
// }
// }
// if (!llama.has_next_token) {
// // Generation is done, send extra information.
// const json data = format_final_response(
// llama,
// "",
// std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index)
// );
// const std::string str =
// "data: " +
// data.dump(-1, ' ', false, json::error_handler_t::replace) +
// "\n\n";
// LOG_VERBOSE("data stream", {
// { "to_send", str }
// });
// if (!sink.write(str.data(), str.size())) {
// LOG_VERBOSE("stream closed", {});
// llama_print_timings(llama.ctx);
// return false;
// }
// }
// }
// llama_print_timings(llama.ctx);
// sink.done();
// return true;
// };
// const auto on_complete = [&](bool) {
// llama.mutex.unlock();
// };
// lock.release();
// res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
// });
// svr.Get("/model.json", [&llama](const Request &, Response &res)
// {
// const json data = format_generation_settings(llama);
// return res.set_content(data.dump(), "application/json"); });
svr.Options(R"(/.*)", [](const Request &, Response &res)
{ return res.set_content("", "application/json"); });
svr.Post("/tokenize", [&llama](const Request &req, Response &res)
{
auto lock = llama.lock();
const json body = json::parse(req.body);
std::vector<llama_token> tokens;
if (body.count("content") != 0)
{
tokens = llama.tokenize(body["content"], false);
}
const json data = format_tokenizer_response(tokens);
return res.set_content(data.dump(), "application/json"); });
svr.Post("/detokenize", [&llama](const Request &req, Response &res)
{
auto lock = llama.lock();
const json body = json::parse(req.body);
std::string content;
if (body.count("tokens") != 0)
{
const std::vector<llama_token> tokens = body["tokens"];
content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
}
const json data = format_detokenized_response(content);
return res.set_content(data.dump(), "application/json"); });
// svr.Post("/embedding", [&llama](const Request &req, Response &res)
// {
// auto lock = llama.lock();
// const json body = json::parse(req.body);
// llama.rewind();
// llama_reset_timings(llama.ctx);
// if (body.count("content") != 0)
// {
// llama.prompt = body["content"];
// }
// else
// {
// llama.prompt = "";
// }
// llama.params.n_predict = 0;
// llama.loadPrompt();
// llama.beginCompletion();
// llama.doCompletion();
// const json data = format_embedding_response(llama);
// return res.set_content(data.dump(), "application/json"); });
svr.set_logger(log_server_request);
svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep)
{
const char fmt[] = "500 Internal Server Error\n%s";
char buf[BUFSIZ];
try {
std::rethrow_exception(std::move(ep));
} catch (std::exception & e) {
snprintf(buf, sizeof(buf), fmt, e.what());
} catch (...) {
snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
}
res.set_content(buf, "text/plain");
res.status = 500; });
svr.set_error_handler([](const Request &, Response &res)
{
if (res.status == 400) {
res.set_content("Invalid request", "text/plain");
} else if (res.status != 500) {
res.set_content("File Not Found", "text/plain");
res.status = 404;
} });
// set timeouts and change hostname and port
svr.set_read_timeout(sparams.read_timeout);
svr.set_write_timeout(sparams.write_timeout);
if (!svr.bind_to_port(sparams.hostname, sparams.port))
{
fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
return 1;
}
// Set the base directory for serving static files
svr.set_base_dir(sparams.public_path);
// to make it ctrl+clickable:
printf("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
LOG_INFO("HTTP server listening", {
{"hostname", sparams.hostname},
{"port", sparams.port},
});
std::thread t([&llama]()
{
bool running = true;
while (running)
{
running = llama.updateSlots();
} });
if (!svr.listen_after_bind())
{
return 1;
}
llama_backend_free();
return 0;
}
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