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llama.cpp/examples/server/server.cpp
Randall Fitzgerald 794db3e7b9 Server Example Refactor and Improvements (#1570) 
A major rewrite for the server example.

Note that if you have built something on the previous server API, it will probably be incompatible.
Check out the examples for how a typical chat app could work.

This took a lot of effort, there are 24 PR's closed in the submitter's repo alone, over 160 commits and a lot of comments and testing.

Summary of the changes:

- adds missing generation parameters: tfs_z, typical_p, repeat_last_n, repeat_penalty, presence_penalty, frequency_penalty, mirostat, penalize_nl, seed, ignore_eos
- applies missing top k sampler
- removes interactive mode/terminal-like behavior, removes exclude parameter
- moves threads and batch size to server command-line parameters
- adds LoRA loading and matches command line parameters with main example
- fixes stopping on EOS token and with the specified token amount with n_predict 
- adds server timeouts, host, and port settings
- adds expanded generation complete response; adds generation settings, stop reason, prompt truncated, model used, and final text
- sets defaults for unspecified parameters between requests
- removes /next-token endpoint and as_loop parameter, adds stream parameter and server-sent events for streaming
- adds CORS headers to responses
- adds request logging, exception printing and optional verbose logging
- adds better stopping words handling when matching multiple tokens and while streaming, or when it finishes on a partial stop string
- adds printing an error when it can't bind to the host/port specified
- fixes multi-byte character handling and replaces invalid UTF-8 characters on responses
- prints timing and build info on startup
- adds logit bias to request parameters
- removes embedding mode
- updates documentation; adds streaming Node.js and Bash examples
- fixes code formatting
- sets server threads to 1 since the current global state doesn't work well with simultaneous requests
- adds truncation of the input prompt and better context reset
- removes token limit from the input prompt
- significantly simplified the logic and removed a lot of variables

---------

Co-authored-by: anon998 <131767832+anon998@users.noreply.github.com>
Co-authored-by: Henri Vasserman <henv@hot.ee>
Co-authored-by: Felix Hellmann <privat@cirk2.de>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: Lesaun Harvey <Lesaun@gmail.com>
2023-06-17 14:53:04 +03:00

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#include "common.h"
#include "llama.h"
#include "build-info.h"
// single thread
#define CPPHTTPLIB_THREAD_POOL_COUNT 1
#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"
#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";
int32_t port = 8080;
int32_t read_timeout = 600;
int32_t write_timeout = 600;
};
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;
}
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_str(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);
fprintf(stdout, "%.*s\n", (int)str.size(), str.data());
fflush(stdout);
}
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__)
struct llama_server_context {
bool stream = false;
bool has_next_token = false;
std::string generated_text;
size_t num_tokens_predicted = 0;
size_t n_past = 0;
size_t n_remain = 0;
std::vector<llama_token> embd;
std::vector<llama_token> last_n_tokens;
llama_context * ctx = nullptr;
gpt_params params;
bool truncated = false;
bool stopped_eos = false;
bool stopped_word = false;
bool stopped_limit = false;
std::string stopping_word;
int32_t multibyte_pending = 0;
~llama_server_context() {
if (ctx) {
llama_free(ctx);
ctx = nullptr;
}
}
void rewind() {
params.antiprompt.clear();
num_tokens_predicted = 0;
generated_text = "";
generated_text.reserve(params.n_ctx);
truncated = false;
stopped_eos = false;
stopped_word = false;
stopped_limit = false;
stopping_word = "";
multibyte_pending = 0;
n_remain = 0;
n_past = 0;
}
bool loadModel(const gpt_params & params_) {
params = params_;
ctx = llama_init_from_gpt_params(params);
if (ctx == nullptr) {
LOG_ERROR("unable to load model", { { "model", params_.model } });
return false;
}
last_n_tokens.resize(params.n_ctx);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
return true;
}
void loadPrompt() {
params.prompt.insert(0, 1, ' '); // always add a first space
std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
if (params.n_keep < 0) {
params.n_keep = (int)prompt_tokens.size();
}
params.n_keep = std::min(params.n_ctx - 4, params.n_keep);
// if input prompt is too big, truncate like normal
if (prompt_tokens.size() >= (size_t)params.n_ctx) {
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 = (prompt_tokens.size() - 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 = prompt_tokens.size();
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 == prompt_tokens.size()) {
// 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);
}
llama_token nextToken() {
llama_token result = -1;
if (embd.size() >= (size_t)params.n_ctx) {
// Reset context
const int n_left = (params.n_ctx - params.n_keep) / 2;
std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + params.n_keep);
new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end());
embd = new_tokens;
n_past = params.n_keep;
truncated = true;
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()) },
});
}
while (n_past < embd.size()) {
int n_eval = (int)embd.size() - n_past;
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads)) {
LOG_ERROR("failed to eval", {
{ "n_eval", n_eval },
{ "n_past", n_past },
{ "n_threads", params.n_threads },
{ "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
});
has_next_token = false;
return result;
}
n_past += n_eval;
}
// out of user input, sample next token
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
const float top_p = params.top_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const int32_t repeat_last_n = params.repeat_last_n < 0 ? params.n_ctx : params.repeat_last_n;
const float repeat_penalty = params.repeat_penalty;
const float alpha_presence = params.presence_penalty;
const float alpha_frequency = params.frequency_penalty;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
const bool penalize_nl = params.penalize_nl;
llama_token id = 0;
{
auto * logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
// Apply params.logit_bias map
for (const auto & it : params.logit_bias) {
logits[it.first] += it.second;
}
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// Apply penalties
float nl_logit = logits[llama_token_nl()];
auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx);
llama_sample_repetition_penalty(ctx, &candidates_p,
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
last_n_repeat, repeat_penalty);
llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
last_n_repeat, alpha_frequency, alpha_presence);
if (!penalize_nl) {
logits[llama_token_nl()] = nl_logit;
}
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p);
}
}
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
num_tokens_predicted++;
}
// add it to the context
embd.push_back(id);
result = id;
// decrement remaining sampling budget
--n_remain;
if (!embd.empty() && embd.back() == llama_token_eos()) {
//stopping_word = llama_token_to_str(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 std::string & text, const size_t last_token_size,
const stop_type type) {
size_t stop_pos = std::string::npos;
for (const std::string & word : 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) {
stopping_word = word;
stopped_word = true;
has_next_token = false;
}
stop_pos = pos;
}
}
return stop_pos;
}
std::string doCompletion() {
const llama_token token = nextToken();
const std::string token_text = token == -1 ? "" : llama_token_to_str(ctx, token);
generated_text += token_text;
if (multibyte_pending > 0) {
multibyte_pending -= token_text.size();
} else if (token_text.size() == 1) {
const char c = token_text[0];
// 2-byte characters: 110xxxxx 10xxxxxx
if ((c & 0xE0) == 0xC0) {
multibyte_pending = 1;
// 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
} else if ((c & 0xF0) == 0xE0) {
multibyte_pending = 2;
// 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
} else if ((c & 0xF8) == 0xF0) {
multibyte_pending = 3;
} else {
multibyte_pending = 0;
}
}
if (multibyte_pending > 0 && !has_next_token) {
has_next_token = true;
n_remain++;
}
if (!has_next_token && n_remain == 0) {
stopped_limit = true;
}
LOG_VERBOSE("next token", {
{ "token", token },
{ "token_text", llama_token_to_str(ctx, token) },
{ "has_next_token", has_next_token },
{ "n_remain", n_remain },
{ "num_tokens_predicted", num_tokens_predicted },
{ "stopped_eos", stopped_eos },
{ "stopped_word", stopped_word },
{ "stopped_limit", stopped_limit },
{ "stopping_word", stopping_word },
});
return token_text;
}
};
static void server_print_usage(const char * argv0, const gpt_params & params,
const server_params & sparams) {
fprintf(stderr, "usage: %s [options]\n", argv0);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n");
if (llama_mlock_supported()) {
fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
}
if (llama_mmap_supported()) {
fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
fprintf(stderr, " number of layers to store in VRAM\n");
fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n");
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
fprintf(stderr, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
#endif
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, " -a ALIAS, --alias ALIAS\n");
fprintf(stderr, " set an alias for the model, will be added as `model` field in completion response\n");
fprintf(stderr, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port);
fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
fprintf(stderr, "\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 == "--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 == "--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 == "-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.", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--low-vram" || arg == "-lv")
{
#ifdef GGML_USE_CUBLAS
params.low_vram = true;
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\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 = 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 {
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) {
const auto eos_bias = llama.params.logit_bias.find(llama_token_eos());
const bool ignore_eos = eos_bias != llama.params.logit_bias.end() &&
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
return json {
{ "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 },
};
}
static json format_final_response(llama_server_context & llama, const std::string & content) {
return json {
{ "content", content },
{ "stop", true },
{ "model", llama.params.model_alias },
{ "tokens_predicted", llama.num_tokens_predicted },
{ "generation_settings", format_generation_settings(llama) },
{ "prompt", llama.params.prompt },
{ "truncated", llama.truncated },
{ "stopped_eos", llama.stopped_eos },
{ "stopped_word", llama.stopped_word },
{ "stopped_limit", llama.stopped_limit },
{ "stopping_word", llama.stopping_word },
};
}
static json format_partial_response(const std::string & content) {
return json {
{ "content", content },
{ "stop", false },
};
}
static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
return json {
{ "tokens", tokens }
};
}
static void parse_options_completion(const json & body, llama_server_context & llama) {
gpt_params default_params;
llama.stream = body.value("stream", false);
llama.params.n_predict = body.value("n_predict", default_params.n_predict);
llama.params.top_k = body.value("top_k", default_params.top_k);
llama.params.top_p = body.value("top_p", default_params.top_p);
llama.params.tfs_z = body.value("tfs_z", default_params.tfs_z);
llama.params.typical_p = body.value("typical_p", default_params.typical_p);
llama.params.repeat_last_n = body.value("repeat_last_n", default_params.repeat_last_n);
llama.params.temp = body.value("temperature", default_params.temp);
llama.params.repeat_penalty = body.value("repeat_penalty", default_params.repeat_penalty);
llama.params.presence_penalty = body.value("presence_penalty", default_params.presence_penalty);
llama.params.frequency_penalty = body.value("frequency_penalty", default_params.frequency_penalty);
llama.params.mirostat = body.value("mirostat", default_params.mirostat);
llama.params.mirostat_tau = body.value("mirostat_tau", default_params.mirostat_tau);
llama.params.mirostat_eta = body.value("mirostat_eta", default_params.mirostat_eta);
llama.params.penalize_nl = body.value("penalize_nl", default_params.penalize_nl);
llama.params.n_keep = body.value("n_keep", default_params.n_keep);
llama.params.seed = body.value("seed", default_params.seed);
llama.params.prompt = body.value("prompt", default_params.prompt);
llama.params.logit_bias.clear();
if (body.value("ignore_eos", false)) {
llama.params.logit_bias[llama_token_eos()] = -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.ctx);
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;
}
}
}
}
}
llama.params.antiprompt.clear();
const auto & stop = body.find("stop");
if (stop != body.end() && stop->is_array()) {
for (const auto & word : *stop) {
if (!word.empty()) {
llama.params.antiprompt.push_back(word);
}
}
}
LOG_VERBOSE("completion parameters parsed", format_generation_settings(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 },
{ "path", req.path },
{ "request", req.body },
{ "response", res.body },
});
}
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_init_backend();
LOG_INFO("build info", {
{ "build", BUILD_NUMBER },
{ "commit", BUILD_COMMIT }
});
LOG_INFO("system info", {
{ "n_threads", params.n_threads },
{ "total_threads", std::thread::hardware_concurrency() },
{ "system_info", llama_print_system_info() },
});
// load the model
if (!llama.loadModel(params)) {
return 1;
}
Server svr;
svr.set_default_headers({
{ "Access-Control-Allow-Origin", "*" },
{ "Access-Control-Allow-Headers", "content-type" }
});
svr.Get("/", [](const Request &, Response & res) {
res.set_content("<h1>llama.cpp server works</h1>", "text/html");
});
svr.Post("/completion", [&llama](const Request & req, Response & res) {
llama.rewind();
llama_reset_timings(llama.ctx);
parse_options_completion(json::parse(req.body), llama);
llama.loadPrompt();
llama.beginCompletion();
if (!llama.stream) {
size_t stop_pos = std::string::npos;
while (llama.has_next_token) {
const std::string token_text = llama.doCompletion();
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());
}
const json data = format_final_response(llama, llama.generated_text);
llama_print_timings(llama.ctx);
res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace),
"application/json");
} else {
const auto chunked_content_provider = [&](size_t, DataSink & sink) {
size_t sent_count = 0;
while (llama.has_next_token) {
const std::string token_text = llama.doCompletion();
if (llama.multibyte_pending > 0) {
continue;
}
size_t pos = std::min(sent_count, llama.generated_text.size());
const std::string str_test = llama.generated_text.substr(pos);
size_t stop_pos =
llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL);
if (stop_pos != std::string::npos) {
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 {
stop_pos = llama.findStoppingStrings(str_test, token_text.size(),
STOP_PARTIAL);
}
const std::string to_send = llama.generated_text.substr(pos, stop_pos);
sent_count += to_send.size();
const json data = llama.has_next_token
? format_partial_response(to_send)
// Generation is done, send extra information.
: format_final_response(llama, to_send);
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;
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider);
}
});
svr.Options(R"(/.*)", [](const Request &, Response & res) {
return res.set_content("", "application/json");
});
svr.Post("/tokenize", [&llama](const Request & req, Response & res) {
const json body = json::parse(req.body);
const std::string content = body["content"].get<std::string>();
const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
const json data = format_tokenizer_response(tokens);
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 auto * 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;
});
// 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)) {
LOG_ERROR("couldn't bind to server socket", {
{ "hostname", sparams.hostname },
{ "port", sparams.port },
});
return 1;
}
LOG_INFO("HTTP server listening", {
{ "hostname", sparams.hostname },
{ "port", sparams.port },
});
if (!svr.listen_after_bind()) {
return 1;
}
return 0;
}
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