ikawrakow/ik_llama.cpp
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ik_llama.cpp/common/common.cpp
Samuel Oliveira Alves ea94afe777 Speculative checkpoints for recurrent models (#1669) 
* server: spec checkpoints for recurrent models

* fix: save/restore sampler state during speculative checkpoint

When speculative decoding rejects draft tokens and restores the
recurrent state checkpoint, the sampler (RNG, grammar, prev tokens)
must also be restored to maintain consistency. Without this, the
sampler state reflects the rejected draft tokens, leading to
potential divergence.

Uses common_sampler_clone() to snapshot the sampler before the
speculative batch decode, and restores it on rejection.

* server: snapshot recurrent state in tensor

* reset ngram mod state for rejected tokens

* server: refactor checkpoint state logic

* speculative: fix sampler for checkpoints

* recurrent model: implement recurrent kernel checkpoint

* recurrent model: refactor api

* spec: free rbudget before overwriting
2026-04-24 09:59:30 +02:00

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//
// Copyright (C) 2023-2025 The llama.cpp authors
// Copyright (C) 2024-2025 Iwan Kawrakow
// MIT license
// SPDX-License-Identifier: MIT
//
#if defined(_MSC_VER)
#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
#endif
#include "common.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "llama-vocab.h"
#include "llama.h"
#include "chat.h"
#include "json-schema-to-grammar.h"
#include <algorithm>
#include <cinttypes>
#include <climits>
#include <cmath>
#include <codecvt>
#include <cstdlib>
#include <cstdarg>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <iterator>
#include <regex>
#include <sstream>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
#include <sys/sysctl.h>
#endif
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
# define NOMINMAX
#endif
#include <locale>
#include <windows.h>
#include <fcntl.h>
#include <io.h>
#else
#include <sys/ioctl.h>
#include <sys/stat.h>
#include <unistd.h>
#endif
#if defined(LLAMA_USE_CURL)
#include <curl/curl.h>
#include <curl/easy.h>
#include <thread>
#include <future>
#endif
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
#define GGML_USE_CUDA_SYCL
#endif
#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
#define GGML_USE_CUDA_SYCL_VULKAN
#endif
#if defined(LLAMA_USE_CURL)
#ifdef __linux__
#include <linux/limits.h>
#elif defined(_WIN32)
#define PATH_MAX MAX_PATH
#else
#include <sys/syslimits.h>
#endif
#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
#endif // LLAMA_USE_CURL
#ifdef GGML_USE_RPC
# include "ggml-rpc.h"
#endif
using json = nlohmann::ordered_json;
common_time_meas::common_time_meas(int64_t & t_acc, bool disable) : t_start_us(disable ? -1 : ggml_time_us()), t_acc(t_acc) {}
common_time_meas::~common_time_meas() {
if (t_start_us >= 0) {
t_acc += ggml_time_us() - t_start_us;
}
}
//
// Environment variable utils
//
template<typename T>
static typename std::enable_if<std::is_same<T, std::string>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
target = value ? std::string(value) : target;
}
template<typename T>
static typename std::enable_if<!std::is_same<T, bool>::value && std::is_integral<T>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
target = value ? std::stoi(value) : target;
}
template<typename T>
static typename std::enable_if<std::is_floating_point<T>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
target = value ? std::stof(value) : target;
}
template<typename T>
static typename std::enable_if<std::is_same<T, bool>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
if (value) {
std::string val(value);
target = val == "1" || val == "true";
}
}
//
// CPU utils
//
int32_t cpu_get_num_physical_cores() {
#ifdef __linux__
// enumerate the set of thread siblings, num entries is num cores
std::unordered_set<std::string> siblings;
for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
+ std::to_string(cpu) + "/topology/thread_siblings");
if (!thread_siblings.is_open()) {
break; // no more cpus
}
std::string line;
if (std::getline(thread_siblings, line)) {
siblings.insert(line);
}
}
if (!siblings.empty()) {
return static_cast<int32_t>(siblings.size());
}
#elif defined(__APPLE__) && defined(__MACH__)
int32_t num_physical_cores;
size_t len = sizeof(num_physical_cores);
int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
if (result == 0) {
return num_physical_cores;
}
result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
if (result == 0) {
return num_physical_cores;
}
#elif defined(_WIN32)
//TODO: Implement
#endif
unsigned int n_threads = std::thread::hardware_concurrency();
return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
}
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
#include <pthread.h>
static void cpuid(unsigned leaf, unsigned subleaf,
unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) {
__asm__("movq\t%%rbx,%%rsi\n\t"
"cpuid\n\t"
"xchgq\t%%rbx,%%rsi"
: "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx)
: "0"(leaf), "2"(subleaf));
}
static int pin_cpu(int cpu) {
cpu_set_t mask;
CPU_ZERO(&mask);
CPU_SET(cpu, &mask);
return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask);
}
static bool is_hybrid_cpu(void) {
unsigned eax, ebx, ecx, edx;
cpuid(7, 0, &eax, &ebx, &ecx, &edx);
return !!(edx & (1u << 15));
}
static bool is_running_on_efficiency_core(void) {
unsigned eax, ebx, ecx, edx;
cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx);
int intel_atom = 0x20;
int core_type = (eax & 0xff000000u) >> 24;
return core_type == intel_atom;
}
static int cpu_count_math_cpus(int n_cpu) {
int result = 0;
for (int cpu = 0; cpu < n_cpu; ++cpu) {
if (pin_cpu(cpu)) {
return -1;
}
if (is_running_on_efficiency_core()) {
continue; // efficiency cores harm lockstep threading
}
++cpu; // hyperthreading isn't useful for linear algebra
++result;
}
return result;
}
#endif // __x86_64__ && __linux__
/**
* Returns number of CPUs on system that are useful for math.
*/
int32_t cpu_get_num_math() {
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
if (n_cpu < 1) {
return cpu_get_num_physical_cores();
}
if (is_hybrid_cpu()) {
cpu_set_t affinity;
if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
int result = cpu_count_math_cpus(n_cpu);
pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
if (result > 0) {
return result;
}
}
}
#endif
return cpu_get_num_physical_cores();
}
//
// Arg utils
//
common_webui common_webui_from_name(const std::string& format) {
if (format == "none") {
return COMMON_WEBUI_NONE;
}
else if (format == "auto") {
return COMMON_WEBUI_AUTO;
}
else if (format == "llamacpp") {
return COMMON_WEBUI_LLAMACPP;
}
else {
return COMMON_WEBUI_AUTO;
}
}
thinking_tokens thinking_tokens_from_string(const std::string& format) {
thinking_tokens think_token;
std::string token_string = string_strip(format);
if (token_string == "none" || token_string == "None") {
think_token.exclude = false;
return think_token;
}
else if (token_string == "auto" || token_string == "Auto") {
think_token.exclude = true;
think_token.begin = "<think>";
think_token.end = "</think>";
return think_token;
}
// Use user provided think tokens
auto start_end = string_split(format, ",");
if (start_end.size() == 2) {
think_token.exclude = true;
think_token.begin = start_end[0];
think_token.end = start_end[1];
}
return think_token;
}
static std::string read_file(const std::string& fname) {
std::ifstream file(fname);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
}
std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
file.close();
return content;
}
static std::string parse_device_list(const std::string& value) {
if (value==" " || value.find("-")!= std::string::npos) {
throw std::invalid_argument("no devices specified");
}
return value;
}
static std::string add_rpc_devices(std::string& servers) {
std::string rpc_devices;
#ifdef GGML_USE_RPC
std::vector<std::string> rpc_servers = string_split(servers, ",");
if (rpc_servers.empty()) {
throw std::invalid_argument("no RPC servers specified");
}
for (auto& server : rpc_servers) {
uint32_t dev_count = ggml_backend_rpc_get_device_count(server.c_str());
uint32_t device = 0;
for (uint32_t i = 0; i < dev_count; ++i) {
const auto buft = ggml_backend_rpc_buffer_type(server.c_str(), device);
if (buft != nullptr) {
rpc_devices = rpc_devices + server + "|" + std::to_string(device) + ",";
++device;
}
}
}
if (!rpc_devices.empty()) {
rpc_devices = rpc_devices.substr(0, rpc_devices.size() - 1); // remove trailing comma
}
#endif
return rpc_devices;
}
std::pair<long, std::vector<char>> common_remote_get_content(const std::string& url, const common_remote_params&) {
if (!url.empty()) {
throw std::runtime_error("error: built without CURL, cannot download file from the internet");
}
return {};
}
//
// CLI argument parsing
//
std::pair<int, char**> parse_command_line(const std::string& commandLine) {
std::vector<std::string> tokens;
std::string current;
bool inQuotes = false;
for (size_t i = 0; i < commandLine.length(); i++) {
char c = commandLine[i];
if (c == '\"') {
inQuotes = !inQuotes;
}
else if (c == ' ' && !inQuotes) {
if (!current.empty()) {
tokens.push_back(current);
current.clear();
}
}
else {
current += c;
}
}
if (!current.empty()) {
tokens.push_back(current);
}
int argc = static_cast<int>(tokens.size());
char** argv = new char* [static_cast<size_t>(argc) + 1];
for (int i = 0; i < argc; i++) {
argv[i] = new char[tokens[i].length() + 1];
std::strcpy(argv[i], tokens[i].c_str());
}
argv[argc] = nullptr;
return { argc, argv };
}
void free_command_line(int argc, char** argv) {
if (argv == nullptr) return;
for (int i = 0; i < argc; i++) {
delete[] argv[i];
}
delete[] argv;
}
void gpt_params_handle_model_default(gpt_params & params) {
if (!params.hf_repo.empty()) {
// short-hand to avoid specifying --hf-file -> default it to --model
if (params.hf_file.empty()) {
if (params.model.empty()) {
throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
}
params.hf_file = params.model;
} else if (params.model.empty()) {
params.model = fs_get_cache_file(string_split(params.hf_file, "/").back());
}
} else if (!params.model_url.empty()) {
if (params.model.empty()) {
auto f = string_split(params.model_url, "#").front();
f = string_split(f, "?").front();
params.model = fs_get_cache_file(string_split(f, "/").back());
}
} else if (params.model.empty()) {
params.model = DEFAULT_MODEL_PATH;
}
}
static bool is_truthy(const std::string & value) {
return value == "on" || value == "enabled" || value == "true" || value == "1";
}
static bool is_falsey(const std::string & value) {
return value == "off" || value == "disabled" || value == "false" || value == "0";
}
static bool is_autoy(const std::string & value) {
return value == "auto" || value == "-1";
}
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
bool invalid_param = false;
std::string arg;
const std::string arg_prefix = "--";
common_params_sampling & sparams = params.sparams;
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
throw std::invalid_argument("error: unknown argument: " + arg);
}
if (invalid_param) {
throw std::invalid_argument("error: invalid parameter for argument: " + arg);
}
}
if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
}
gpt_params_handle_model_default(params);
if (params.hf_token.empty()) {
get_env("HF_TOKEN", params.hf_token);
}
if (params.escape) {
if (!params.prompt_is_binary) {
string_process_escapes(params.prompt);
}
string_process_escapes(params.input_prefix);
string_process_escapes(params.input_suffix);
string_process_escapes(sparams.cfg_negative_prompt);
for (auto & antiprompt : params.antiprompt) {
string_process_escapes(antiprompt);
}
}
for (auto & rep : params.speculative.replacements) {
string_process_escapes(rep.first);
string_process_escapes(rep.second);
}
if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back();
params.kv_overrides.back().key[0] = 0;
}
if (!params.tensor_buft_overrides.empty()) {
params.tensor_buft_overrides.push_back({nullptr, nullptr});
}
if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
throw std::runtime_error(string_format(
"error: the supplied chat template is not supported: %s%s\n",
params.chat_template.c_str(),
params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates"
));
}
return true;
}
void gpt_params_parse_from_env(gpt_params & params) {
// we only care about server-related params for now
get_env("LLAMA_ARG_MODEL", params.model);
get_env("LLAMA_ARG_MODEL_URL", params.model_url);
get_env("LLAMA_ARG_MODEL_ALIAS", params.model_alias);
get_env("LLAMA_ARG_HF_REPO", params.hf_repo);
get_env("LLAMA_ARG_HF_FILE", params.hf_file);
get_env("LLAMA_ARG_THREADS", params.n_threads);
get_env("LLAMA_ARG_CTX_SIZE", params.n_ctx);
get_env("LLAMA_ARG_N_PARALLEL", params.n_parallel);
get_env("LLAMA_ARG_BATCH", params.n_batch);
get_env("LLAMA_ARG_UBATCH", params.n_ubatch);
get_env("LLAMA_ARG_N_GPU_LAYERS", params.n_gpu_layers);
get_env("LLAMA_ARG_THREADS_HTTP", params.n_threads_http);
get_env("LLAMA_ARG_CHAT_TEMPLATE", params.chat_template);
get_env("LLAMA_ARG_N_PREDICT", params.n_predict);
get_env("LLAMA_ARG_ENDPOINT_METRICS", params.endpoint_metrics);
get_env("LLAMA_ARG_ENDPOINT_SLOTS", params.endpoint_slots);
get_env("LLAMA_ARG_EMBEDDINGS", params.embedding);
get_env("LLAMA_ARG_FLASH_ATTN", params.flash_attn);
get_env("LLAMA_ARG_DEFRAG_THOLD", params.defrag_thold);
get_env("LLAMA_ARG_CONT_BATCHING", params.cont_batching);
get_env("LLAMA_ARG_HOST", params.hostname);
get_env("LLAMA_ARG_PORT", params.port);
get_env("LLAMA_ARG_CACHE_TYPE_K", params.cache_type_k);
get_env("LLAMA_ARG_CACHE_TYPE_V", params.cache_type_v);
get_env("LLAMA_ARG_MLOCK", params.use_mlock);
get_env("LLAMA_ARG_K_CACHE_HADAMARD", params.k_cache_hadamard);
get_env("LLAMA_ARG_V_CACHE_HADAMARD", params.v_cache_hadamard);
}
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
gpt_params_parse_from_env(params);
const auto params_org = params; // the example can modify the default params
try {
if (!gpt_params_parse_ex(argc, argv, params) || params.usage) {
params = params_org;
params.usage = true;
return false;
}
} catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
params = params_org;
return false;
}
return true;
}
namespace {
bool parse_buft_overrides(const std::string& value, std::vector<llama_model_tensor_buft_override>& overrides) {
/* static */ std::map<std::string, ggml_backend_buffer_type_t> buft_list;
if (buft_list.empty()) {
// enumerate all the devices and add their buffer types to the list
for (size_t i = 0; i < ggml_backend_reg_get_count(); ++i) {
//auto * dev = ggml_backend_reg_get_name(i);
auto * buft = ggml_backend_reg_get_default_buffer_type(i);
if (buft) {
buft_list[ggml_backend_buft_name(buft)] = buft;
}
}
}
for (const auto & override : string_split<std::string>(value, ',')) {
std::string::size_type pos = override.find('=');
if (pos == std::string::npos) {
fprintf(stderr, "Invalid buft override argument %s\n", value.c_str());
return false;
}
std::string tensor_name = override.substr(0, pos);
std::string buffer_type = override.substr(pos + 1);
if (buft_list.find(buffer_type) == buft_list.end()) {
fprintf(stderr, "Available buffer types:\n");
for (const auto & it : buft_list) {
fprintf(stderr, " %s\n", ggml_backend_buft_name(it.second));
}
return false;
}
overrides.push_back({strdup(tensor_name.c_str()), buft_list.at(buffer_type)});
}
return true;
}
template<class T1, class T2>
std::vector<std::pair<T1,T2>> string_split_pairs(const std::string & str, char delim) {
std::vector<std::pair<T1,T2>> values;
std::istringstream str_stream(str);
std::string token;
T1 first_value;
int i = 0;
while (std::getline(str_stream, token, delim)) {
std::istringstream token_stream(token);
if (i%2 == 0) {
token_stream >> first_value;
} else {
T2 value;
token_stream >> value;
values.emplace_back(first_value, value);
}
i++;
}
return values;
}
}
#define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; }
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
const char split_delim = ',';
common_params_sampling & sparams = params.sparams;
if (arg == "-s" || arg == "--seed") {
CHECK_ARG
// TODO: this is temporary, in the future the sampling state will be moved fully to llama_sampling_context.
params.seed = std::stoul(argv[i]);
sparams.seed = std::stoul(argv[i]);
return true;
}
if (arg == "-t" || arg == "--threads") {
CHECK_ARG
params.n_threads = std::stoi(argv[i]);
if (params.n_threads <= 0) {
params.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-tb" || arg == "--threads-batch") {
CHECK_ARG
params.n_threads_batch = std::stoi(argv[i]);
if (params.n_threads_batch <= 0) {
params.n_threads_batch = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-td" || arg == "--threads-draft") {
CHECK_ARG
params.speculative.n_threads = std::stoi(argv[i]);
if (params.speculative.n_threads <= 0) {
params.speculative.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-tbd" || arg == "--threads-batch-draft") {
CHECK_ARG
params.speculative.n_threads_batch = std::stoi(argv[i]);
if (params.speculative.n_threads_batch <= 0) {
params.speculative.n_threads_batch = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-p" || arg == "--prompt") {
CHECK_ARG
params.prompt = argv[i];
params.prompt_is_binary = false;
return true;
}
if (arg == "-e" || arg == "--escape") {
params.escape = true;
return true;
}
if (arg == "--no-escape") {
params.escape = false;
return true;
}
if (arg == "--prompt-cache") {
CHECK_ARG
params.path_prompt_cache = argv[i];
return true;
}
if (arg == "--prompt-cache-all") {
params.prompt_cache_all = true;
return true;
}
if (arg == "--prompt-cache-ro") {
params.prompt_cache_ro = true;
return true;
}
if (arg == "-bf" || arg == "--binary-file") {
CHECK_ARG
std::ifstream file(argv[i], std::ios::binary);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
// store the external file name in params
params.prompt_file = argv[i];
std::ostringstream ss;
ss << file.rdbuf();
params.prompt = ss.str();
fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
params.prompt_is_binary = true;
return true;
}
if (arg == "-f" || arg == "--file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
// store the external file name in params
params.prompt_file = argv[i];
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
if (!params.prompt.empty() && params.prompt.back() == '\n') {
params.prompt.pop_back();
}
params.prompt_is_binary = false;
return true;
}
if (arg == "--in-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
params.in_files.push_back(argv[i]);
return true;
}
if (arg == "-n" || arg == "--predict" || arg == "--n-predict") {
CHECK_ARG
params.n_predict = std::stoi(argv[i]);
return true;
}
if (arg == "--top-k") {
CHECK_ARG
sparams.top_k = std::stoi(argv[i]);
return true;
}
if (arg == "-c" || arg == "--ctx-size") {
CHECK_ARG
params.n_ctx = std::stoi(argv[i]);
return true;
}
if (arg == "-cd" || arg == "--ctx-size-draft") {
CHECK_ARG
params.speculative.n_ctx = std::stoi(argv[i]);
return true;
}
if (arg == "--grp-attn-n" || arg == "-gan") {
CHECK_ARG
params.grp_attn_n = std::stoi(argv[i]);
return true;
}
if (arg == "--grp-attn-w" || arg == "-gaw") {
CHECK_ARG
params.grp_attn_w = std::stoi(argv[i]);
return true;
}
if (arg == "--rope-freq-base") {
CHECK_ARG
params.rope_freq_base = std::stof(argv[i]);
return true;
}
if (arg == "--rope-freq-scale") {
CHECK_ARG
params.rope_freq_scale = std::stof(argv[i]);
return true;
}
if (arg == "--rope-scaling") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
else { invalid_param = true; }
return true;
}
if (arg == "--rope-scale") {
CHECK_ARG
params.rope_freq_scale = 1.0f / std::stof(argv[i]);
return true;
}
if (arg == "--yarn-orig-ctx") {
CHECK_ARG
params.yarn_orig_ctx = std::stoi(argv[i]);
return true;
}
if (arg == "--yarn-ext-factor") {
CHECK_ARG
params.yarn_ext_factor = std::stof(argv[i]);
return true;
}
if (arg == "--yarn-attn-factor") {
CHECK_ARG
params.yarn_attn_factor = std::stof(argv[i]);
return true;
}
if (arg == "--yarn-beta-fast") {
CHECK_ARG
params.yarn_beta_fast = std::stof(argv[i]);
return true;
}
if (arg == "--yarn-beta-slow") {
CHECK_ARG
params.yarn_beta_slow = std::stof(argv[i]);
return true;
}
if (arg == "--pooling") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
else { invalid_param = true; }
return true;
}
if (arg == "--attention") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
else { invalid_param = true; }
return true;
}
if (arg == "--defrag-thold" || arg == "-dt") {
CHECK_ARG
params.defrag_thold = std::stof(argv[i]);
return true;
}
if (arg == "--max-extra-alloc" || arg == "-mea") {
CHECK_ARG
params.max_extra_alloc_MiB = std::stoi(argv[i]);
return true;
}
if (arg == "-nrep" || arg == "--n-repetitions") {
CHECK_ARG
params.nrep = std::stoi(argv[i]);
return true;
}
if (arg == "--samplers") {
CHECK_ARG
const auto sampler_names = string_split(argv[i], ";");
sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true);
return true;
}
if (arg == "--sampling-seq") {
CHECK_ARG
sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]);
return true;
}
if (arg == "--top-p") {
CHECK_ARG
sparams.top_p = std::stof(argv[i]);
return true;
}
if (arg == "--min-p") {
CHECK_ARG
sparams.min_p = std::stof(argv[i]);
return true;
}
if (arg == "--temp") {
CHECK_ARG
sparams.temp = std::stof(argv[i]);
sparams.temp = std::max(sparams.temp, 0.0f);
return true;
}
if (arg == "--tfs") {
CHECK_ARG
sparams.tfs_z = std::stof(argv[i]);
return true;
}
if (arg == "--typical") {
CHECK_ARG
sparams.typical_p = std::stof(argv[i]);
return true;
}
if (arg == "--repeat-last-n") {
CHECK_ARG
sparams.penalty_last_n = std::stoi(argv[i]);
sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
return true;
}
if (arg == "--repeat-penalty") {
CHECK_ARG
sparams.penalty_repeat = std::stof(argv[i]);
return true;
}
if (arg == "--frequency-penalty") {
CHECK_ARG
sparams.penalty_freq = std::stof(argv[i]);
return true;
}
if (arg == "--presence-penalty") {
CHECK_ARG
sparams.penalty_present = std::stof(argv[i]);
return true;
}
if (arg == "--dynatemp-range") {
CHECK_ARG
sparams.dynatemp_range = std::stof(argv[i]);
return true;
}
if (arg == "--dynatemp-exp") {
CHECK_ARG
sparams.dynatemp_exponent = std::stof(argv[i]);
return true;
}
if (arg == "--mirostat") {
CHECK_ARG
sparams.mirostat = std::stoi(argv[i]);
return true;
}
if (arg == "--mirostat-lr") {
CHECK_ARG
sparams.mirostat_eta = std::stof(argv[i]);
return true;
}
if (arg == "--mirostat-ent") {
CHECK_ARG
sparams.mirostat_tau = std::stof(argv[i]);
return true;
}
if (arg == "--xtc-probability") {
CHECK_ARG
sparams.xtc_probability = std::stof(argv[i]);
return true;
}
if (arg == "--xtc-threshold") {
CHECK_ARG
sparams.xtc_threshold = std::stof(argv[i]);
return true;
}
if (arg == "--top-n-sigma") {
CHECK_ARG
sparams.top_n_sigma = std::stof(argv[i]);
return true;
}
if (arg == "--dry-multiplier") {
CHECK_ARG
sparams.dry_multiplier = std::stof(argv[i]);
return true;
}
if (arg == "--dry-base") {
CHECK_ARG
sparams.dry_base = std::stof(argv[i]);
return true;
}
if (arg == "--dry-allowed-length") {
CHECK_ARG
sparams.dry_allowed_length = std::stof(argv[i]);
return true;
}
if (arg == "--dry-penalty-last-n") {
CHECK_ARG
sparams.dry_penalty_last_n = std::stof(argv[i]);
return true;
}
if (arg == "--dry-sequence-breaker") {
CHECK_ARG
static bool defaults_cleared = false;
if (!defaults_cleared) {
params.sparams.dry_sequence_breakers.clear();
defaults_cleared = true;
}
std::string value= std::string(argv[i]);
if (value == "none") {
params.sparams.dry_sequence_breakers.clear();
}
else {
for (size_t i = 0; i < value.size(); i++)
{
params.sparams.dry_sequence_breakers.emplace_back(std::string{}+value[i]);
}
}
return true;
}
if (arg == "--adaptive-target") {
CHECK_ARG
sparams.adaptive_target = std::stof(argv[i]);
return true;
}
if (arg == "--adaptive-decay") {
CHECK_ARG
sparams.adaptive_decay = std::stof(argv[i]);
return true;
}
if (arg == "--adaptive-updt-w-cur") {
sparams.adaptive_updt_w_cur = true;
return true;
}
if (arg == "--spec-replace") {
CHECK_ARG
std::string target = argv[i];
CHECK_ARG
std::string draft = argv[i];
params.speculative.replacements.emplace_back(std::move(target), std::move(draft));
return true;
}
if (arg == "--cfg-negative-prompt") {
CHECK_ARG
sparams.cfg_negative_prompt = argv[i];
return true;
}
if (arg == "--cfg-negative-prompt-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
sparams.cfg_negative_prompt.pop_back();
}
return true;
}
if (arg == "--cfg-scale") {
CHECK_ARG
sparams.cfg_scale = std::stof(argv[i]);
return true;
}
if (arg == "-b" || arg == "--batch-size") {
CHECK_ARG
params.n_batch = std::stoi(argv[i]);
return true;
}
if (arg == "-ub" || arg == "--ubatch-size") {
CHECK_ARG
params.n_ubatch = std::stoi(argv[i]);
return true;
}
if (arg == "--keep") {
CHECK_ARG
params.n_keep = std::stoi(argv[i]);
return true;
}
if (arg == "--draft" || arg == "--draft-max" || arg == "--draft-n") {
CHECK_ARG
params.speculative.n_max = std::stoi(argv[i]);
return true;
}
if (arg == "--draft-min" || arg == "--draft-n-min") {
CHECK_ARG
params.speculative.n_min = std::stoi(argv[i]);
return true;
}
if (arg == "--draft-p-min") {
CHECK_ARG
params.speculative.p_min = std::stof(argv[i]);
return true;
}
if (arg == "--recurrent-ckpt-mode") {
CHECK_ARG
const std::string val = argv[i];
if (val == "auto" || val == "AUTO") {
params.speculative.recurrent_ckpt_mode = LLAMA_SPEC_CKPT_AUTO;
} else if (val == "per-step" || val == "PER_STEP") {
params.speculative.recurrent_ckpt_mode = LLAMA_SPEC_CKPT_PER_STEP;
} else if (val == "gpu-fallback" || val == "GPU_FALLBACK") {
params.speculative.recurrent_ckpt_mode = LLAMA_SPEC_CKPT_GPU_FALLBACK;
} else if (val == "cpu" || val == "CPU") {
params.speculative.recurrent_ckpt_mode = LLAMA_SPEC_CKPT_CPU;
} else {
throw std::invalid_argument("unknown --recurrent-ckpt-mode value: " + val +
"; expected auto, per-step, gpu-fallback, or cpu");
}
return true;
}
if (arg == "--spec-autotune") {
params.speculative.autotune = true;
return true;
}
if (arg == "--chunks") {
CHECK_ARG
params.n_chunks = std::stoi(argv[i]);
return true;
}
if (arg == "-np" || arg == "--parallel") {
CHECK_ARG
params.n_parallel = std::stoi(argv[i]);
return true;
}
if (arg == "-ns" || arg == "--sequences") {
CHECK_ARG
params.n_sequences = std::stoi(argv[i]);
return true;
}
if (arg == "--p-split" || arg == "-ps") {
CHECK_ARG
params.p_split = std::stof(argv[i]);
return true;
}
if (arg == "-m" || arg == "--model") {
CHECK_ARG
params.model = argv[i];
return true;
}
if (arg == "-md" || arg == "--model-draft") {
CHECK_ARG
params.speculative.model = argv[i];
return true;
}
if (arg == "--spec-type") {
CHECK_ARG
std::string value = argv[i];
if (value == "none") {
params.speculative.type = COMMON_SPECULATIVE_TYPE_NONE;
} else if (value == "ngram-cache") {
params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_CACHE;
} else if (value == "ngram-simple") {
params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE;
} else if (value == "ngram-map-k") {
params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K;
} else if (value == "ngram-map-k4v") {
params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V;
} else if (value == "ngram-mod") {
params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MOD;
} else if (value == "suffix") {
params.speculative.type = COMMON_SPECULATIVE_TYPE_SUFFIX;
} else {
throw std::invalid_argument("unknown speculative decoding type without draft model");
}
return true;
}
if (arg == "--spec-ngram-size-n") {
CHECK_ARG
int value = std::stoi(argv[i]);
if (value < 1 || value > 1024) {
throw std::invalid_argument("ngram size N must be between 1 and 1024 inclusive");
}
params.speculative.ngram_size_n = value;
return true;
}
if (arg == "--spec-ngram-size-m") {
CHECK_ARG
int value = std::stoi(argv[i]);
if (value < 1 || value > 1024) {
throw std::invalid_argument("ngram size M must be between 1 and 1024 inclusive");
}
params.speculative.ngram_size_m = value;
return true;
}
if (arg == "--spec-ngram-min-hits") {
CHECK_ARG
int value = std::stoi(argv[i]);
if (value < 1) {
throw std::invalid_argument("ngram min hits must be at least 1");
}
params.speculative.ngram_min_hits = value;
return true;
}
if (arg == "--suffix-pattern-len") {
CHECK_ARG
int value = std::stoi(argv[i]);
if (value < 1) {
throw std::invalid_argument("suffix pattern length must be at least 1");
}
params.speculative.suffix_min_match_len = value;
return true;
}
if (arg == "--suffix-max-depth") {
CHECK_ARG
int value = std::stoi(argv[i]);
if (value < 1) {
throw std::invalid_argument("suffix max depth must be at least 1");
}
params.speculative.suffix_max_depth = value;
return true;
}
if (arg == "--suffix-corpus") {
CHECK_ARG
params.speculative.suffix_corpus = argv[i];
return true;
}
if (arg == "-a" || arg == "--alias") {
CHECK_ARG
params.model_alias = argv[i];
return true;
}
if (arg == "-mu" || arg == "--model-url") {
CHECK_ARG
params.model_url = argv[i];
return true;
}
if (arg == "-hft" || arg == "--hf-token") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.hf_token = argv[i];
return true;
}
if (arg == "-hfr" || arg == "--hf-repo") {
CHECK_ARG
params.hf_repo = argv[i];
return true;
}
if (arg == "-hff" || arg == "--hf-file") {
CHECK_ARG
params.hf_file = argv[i];
return true;
}
if (arg == "--lora") {
CHECK_ARG
params.lora_adapters.push_back({
std::string(argv[i]),
1.0,
});
return true;
}
if (arg == "--lora-scaled") {
CHECK_ARG
std::string lora_adapter = argv[i];
CHECK_ARG
params.lora_adapters.push_back({
lora_adapter,
std::stof(argv[i]),
});
return true;
}
if (arg == "--lora-init-without-apply") {
params.lora_init_without_apply = true;
return true;
}
if (arg == "--control-vector") {
CHECK_ARG
params.control_vectors.push_back({ 1.0f, argv[i], });
return true;
}
if (arg == "--control-vector-scaled") {
CHECK_ARG
const char* fname = argv[i];
CHECK_ARG
params.control_vectors.push_back({ std::stof(argv[i]), fname, });
return true;
}
if (arg == "--control-vector-layer-range") {
CHECK_ARG
params.control_vector_layer_start = std::stoi(argv[i]);
CHECK_ARG
params.control_vector_layer_end = std::stoi(argv[i]);
return true;
}
if (arg == "--mmproj") {
CHECK_ARG
params.mmproj.path = argv[i];
return true;
}
if (arg == "--mmproj-url") {
CHECK_ARG
params.mmproj.url = argv[i];
return true;
}
if (arg == "--no-mmproj-offload") {
params.mmproj_use_gpu = false;
return true;
}
if (arg == "--mtmd-kq-type") {
CHECK_ARG
params.mtmd_kq_type = argv[i];
return true;
}
if (arg == "--image" || arg == "--audio") {
CHECK_ARG
params.image.emplace_back(argv[i]);
return true;
}
if (arg == "--image-min-tokens") {
CHECK_ARG
params.image_min_tokens = std::stoi(argv[i]);
return true;
}
if (arg == "--image-max-tokens") {
CHECK_ARG
params.image_max_tokens = std::stoi(argv[i]);
return true;
}
if (arg == "-i" || arg == "--interactive") {
params.interactive = true;
return true;
}
if (arg == "-sp" || arg == "--special") {
params.special = true;
return true;
}
if (arg == "--embedding" || arg == "--embeddings") {
params.embedding = true;
return true;
}
if (arg == "--embd-normalize") {
CHECK_ARG
params.embd_normalize = std::stoi(argv[i]);
return true;
}
if (arg == "--embd-output-format") {
CHECK_ARG
params.embd_out = argv[i];
return true;
}
if (arg == "--embd-separator") {
CHECK_ARG
params.embd_sep = argv[i];
return true;
}
if (arg == "-if" || arg == "--interactive-first") {
params.interactive_first = true;
return true;
}
if (arg == "-cnv" || arg == "--conversation") {
params.conversation = true;
return true;
}
if (arg == "--infill") {
params.infill = true;
return true;
}
if (arg == "-dkvc" || arg == "--dump-kv-cache") {
params.dump_kv_cache = true;
return true;
}
if (arg == "-nkvo" || arg == "--no-kv-offload") {
params.no_kv_offload = true;
return true;
}
if (arg == "-ctk" || arg == "--cache-type-k") {
params.cache_type_k = argv[++i];
return true;
}
if (arg == "-ctv" || arg == "--cache-type-v") {
params.cache_type_v = argv[++i];
return true;
}
if (arg == "-ctk-first" || arg == "--cache-type-k-first") {
CHECK_ARG
auto p = string_split(argv[i], ",");
if (p.size() != 2) {
invalid_param = true;
} else {
params.type_k_first = p[0];
params.n_k_first = std::stoi(p[1].c_str());
}
return true;
}
if (arg == "-ctk-last" || arg == "--cache-type-k-last") {
CHECK_ARG
auto p = string_split(argv[i], ",");
if (p.size() != 2) {
invalid_param = true;
} else {
params.type_k_last = p[0];
params.n_k_last = std::stoi(p[1].c_str());
}
return true;
}
if (arg == "-ctv-first" || arg == "--cache-type-v-first") {
CHECK_ARG
auto p = string_split(argv[i], ",");
if (p.size() != 2) {
invalid_param = true;
} else {
params.type_v_first = p[0];
params.n_v_first = std::stoi(p[1].c_str());
}
return true;
}
if (arg == "-ctv-last" || arg == "--cache-type-v-last") {
CHECK_ARG
auto p = string_split(argv[i], ",");
if (p.size() != 2) {
invalid_param = true;
} else {
params.type_v_last = p[0];
params.n_v_last = std::stoi(p[1].c_str());
}
return true;
}
if (arg == "-ctkd" || arg == "--cache-type-k-draft") {
params.speculative.cache_type_k = argv[++i];
return true;
}
if (arg == "-ctvd" || arg == "--cache-type-v-draft") {
params.speculative.cache_type_v = argv[++i];
return true;
}
if (arg == "-mli" || arg == "--multiline-input") {
params.multiline_input = true;
return true;
}
if (arg == "--simple-io") {
params.simple_io = true;
return true;
}
if (arg == "-cb" || arg == "--cont-batching") {
params.cont_batching = true;
return true;
}
if (arg == "-nocb" || arg == "--no-cont-batching") {
params.cont_batching = false;
return true;
}
if (arg == "-no-fa" || arg == "--no-flash-attn") {
params.flash_attn = false;
return true;
}
if (arg == "-fa" || arg == "--flash-attn") {
CHECK_ARG
std::string next_arg{argv[i]};
for (auto& c : next_arg) c = std::tolower(c);
if (next_arg == "auto" || next_arg == "1" || next_arg == "on") {
params.flash_attn = true;
}
else if (next_arg == "off" || next_arg == "0") {
params.flash_attn = false;
}
else {
invalid_param = true;
}
return true;
}
if (arg == "-mla" || arg == "--mla-use") {
CHECK_ARG
params.mla_attn = std::stoi(argv[i]);
return true;
}
if (arg == "-amb" || arg == "--attention-max-batch") {
CHECK_ARG
params.attn_max_batch = std::stoi(argv[i]);
return true;
}
if (arg == "-no-fmoe" || arg == "--no-fused-moe") {
params.fused_moe_up_gate = false;
return true;
}
if (arg == "-ger" || arg == "--grouped-expert-routing") {
params.grouped_expert_routing = true;
return true;
}
if (arg == "-no-fug" || arg == "--no-fused-up-gate") {
params.fused_up_gate = false;
return true;
}
if (arg == "-no-mmad" || arg == "--no-fused-mul-multiadd") {
params.fused_mmad = false;
return true;
}
if (arg == "-rcache" || arg == "--rope-cache") {
fprintf(stderr, "=================================================================================\n");
fprintf(stderr, " -rcache, --rope-cache is no longer supported\n");
fprintf(stderr, "=================================================================================\n");
//params.rope_cache = true;
return true;
}
if (arg == "-gr" || arg == "--graph-reuse") {
params.graph_reuse = true;
return true;
}
if (arg == "-no-gr" || arg == "--no-graph-reuse") {
params.graph_reuse = false;
return true;
}
if (arg == "-ser" || arg == "--smart-expert-reduction") {
CHECK_ARG
auto values = string_split_pairs<int,float>(argv[i], ',');
if (values.size() == 1) {
params.min_experts = values.front().first;
params.thresh_experts = values.front().second;
} else {
invalid_param = true;
}
return true;
}
if (arg == "-co" || arg == "--color") {
params.use_color = true;
return true;
}
if (arg == "--mlock") {
params.use_mlock = true;
return true;
}
if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
CHECK_ARG
params.n_gpu_layers = std::stoi(argv[i]);
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
return true;
}
if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--n-gpu-layers-draft") {
CHECK_ARG
params.speculative.n_gpu_layers = std::stoi(argv[i]);
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
return true;
}
if (arg == "--main-gpu" || arg == "-mg") {
CHECK_ARG
params.main_gpu = std::stoi(argv[i]);
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
else if (arg == "--max-gpu") {
CHECK_ARG
params.max_gpu = std::stoi(argv[i]);
return true;
}
if (arg == "--split-mode" || arg == "-sm") {
CHECK_ARG
std::string arg_next = argv[i];
if (arg_next == "none") {
params.split_mode = LLAMA_SPLIT_MODE_NONE;
}
else if (arg_next == "layer") {
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
}
else if (arg_next == "attn") {
params.split_mode = LLAMA_SPLIT_MODE_ATTN;
}
else if (arg_next == "graph") {
params.split_mode = LLAMA_SPLIT_MODE_GRAPH;
}
else {
invalid_param = true;
return true;
}
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--tensor-split" || arg == "-ts") {
CHECK_ARG
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, {} };
if (split_arg.size() >= llama_max_devices()) {
invalid_param = true;
return true;
}
for (size_t i = 0; i < llama_max_devices(); ++i) {
if (i < split_arg.size()) {
params.tensor_split[i] = std::stof(split_arg[i]);
}
else {
params.tensor_split[i] = 0.0f;
}
}
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--rpc") {
CHECK_ARG
#ifdef GGML_USE_RPC
std::string servers(argv[i]);
servers = add_rpc_devices(servers);
if (servers.empty()) {
return false;
}
params.rpc_servers = servers;
#endif
return true;
}
if (arg == "--override-kv") {
CHECK_ARG
if (!string_parse_kv_override(argv[i], params.kv_overrides)) {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
return true;
}
return true;
}
if (arg == "--override-tensor" || arg == "-ot") {
CHECK_ARG
if (!parse_buft_overrides(std::string{ argv[i] }, params.tensor_buft_overrides)) {
fprintf(stderr, "error: Invalid tensor buffer type override: %s\n", argv[i]);
invalid_param = true;
}
return true;
}
if (arg == "-cuda" || arg == "--cuda-params") {
CHECK_ARG
params.cuda_params = argv[i];
return true;
}
if (arg == "-mtp" || arg == "--multi-token-prediction") {
params.has_mtp = true;
return true;
}
if (arg == "-no-mtp" || arg == "--no-multi-token-prediction") {
params.has_mtp = false;
return true;
}
if (arg == "-draft" || arg == "--draft-params") {
CHECK_ARG
params.speculative.params = argv[i];
return true;
}
if (arg == "--cpu-moe" || arg == "-cmoe") {
params.ncmoe = 999;
//params.tensor_buft_overrides.push_back({strdup("\\.ffn_(up|down|gate|gate_up)_exps\\.weight"), ggml_backend_cpu_buffer_type()});
return true;
}
if (arg == "--n-cpu-moe" || arg == "-ncmoe") {
CHECK_ARG
int32_t n_layers = std::stoi(argv[i]);
if (n_layers < 0) {
fprintf(stderr, "error: Invalid value for --n-cpu-moe: %d (must be >= 0)\n", n_layers);
invalid_param = true;
return true;
}
params.ncmoe = n_layers;
//for (int32_t l = 0; l < n_layers; ++l) {
// std::string pattern = "blk\\." + std::to_string(l) + "\\.(ffn_(up|down|gate|gate_up)_exps\\.weight)";
// params.tensor_buft_overrides.push_back({strdup(pattern.c_str()), ggml_backend_cpu_buffer_type()});
//}
return true;
}
if (arg == "--fit") {
params.fit = true;
return true;
}
if (arg == "--defer-experts") {
params.defer_experts = true;
params.warmup = false;
return true;
}
if (arg == "--fit-margin") {
CHECK_ARG;
int32_t margin = std::stoi(argv[i]);
if (margin < 0) {
fprintf(stderr, "error: Invalid value for --fit-margin: %d (must be >= 0)\n", margin);
invalid_param = true;
} else {
params.fit_margin = margin;
}
return true;
}
if (arg == "-wgt" || arg == "--worst-graph-tokens") {
CHECK_ARG;
params.worst_graph_tokens = std::stoi(argv[i]);
return true;
}
if (arg == "--no-mmap") {
params.use_mmap = false;
return true;
}
if (arg == "-rtr" || arg == "--run-time-repack") {
params.repack_tensors = true;
params.use_mmap = false;
return true;
}
if (arg == "-thp" || arg == "--transparent-huge-pages") {
params.use_thp = true;
return true;
}
if (arg == "-vq" || arg == "--validate-quants") {
params.validate_quants = true;
return true;
}
if (arg == "-mqkv" || arg == "--merge-qkv") {
params.merge_qkv = true;
return true;
}
if (arg == "-muge" || arg == "--merge-up-gate-experts") {
params.merge_up_gate_exps = true;
return true;
}
if (arg == "-khad" || arg == "--k-cache-hadamard") {
params.k_cache_hadamard = true;
return true;
}
if (arg == "-vhad" || arg == "--v-cache-hadamard") {
params.v_cache_hadamard = true;
return true;
}
if (arg == "-smgs" || arg == "--split-mode-graph-scheduling") {
params.split_mode_graph_scheduling = true;
return true;
}
if (arg == "-sas" || arg == "--scheduler-async") {
params.scheduler_async = true;
return true;
}
if (arg == "-fdn" || arg == "--fused-delta-net") {
CHECK_ARG
fprintf(stderr, "=================== %s has been deprecated\n", arg.c_str());
return true;
}
if (arg == "-smf16" || arg == "--split-mode-f16") {
params.reduce_type = "f16";
//params.split_mode_f16 = true;
return true;
}
if (arg == "-smf32" || arg == "--split-mode-f32") {
params.reduce_type = "f32";
//params.split_mode_f16 = false;
return true;
}
if (arg == "-grt" || arg == "--graph-reduce-type") {
CHECK_ARG
params.reduce_type = argv[i];
return true;
}
if (arg == "--numa") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
else { invalid_param = true; }
return true;
}
if (arg == "-dev" || arg == "--device") {
CHECK_ARG
std::string value(argv[i]);
params.devices = parse_device_list(value);
return true;
}
if (arg == "-devd" || arg == "--device-draft") {
CHECK_ARG
std::string value(argv[i]);
params.speculative.devices = parse_device_list(value);
return true;
}
if (arg == "-v" || arg == "--verbose") {
params.verbosity = 1;
return true;
}
if (arg == "--verbosity") {
CHECK_ARG
params.verbosity = std::stoi(argv[i]);
return true;
}
if (arg == "--verbose-prompt") {
params.verbose_prompt = true;
return true;
}
if (arg == "--no-display-prompt") {
params.display_prompt = false;
return true;
}
if (arg == "-r" || arg == "--reverse-prompt") {
CHECK_ARG
params.antiprompt.emplace_back(argv[i]);
return true;
}
if (arg == "--banned-string-file") {
CHECK_ARG
std::string files = read_file(std::string(argv[i]));
std::vector<std::string> ban_strings=string_split(files, "\n");
std::vector<std::string> ban_phrases;
for (auto& str : ban_strings) {
std::erase(str, '"');
if (!str.empty()) {
ban_phrases.push_back(str);
}
}
params.ban_phrases = ban_phrases;
return true;
}
if (arg == "--banned-n") {
CHECK_ARG
params.banned_n = std::stoi(argv[i]);
return true;
}
if (arg == "--allowlist-unicode-rule") {
CHECK_ARG
if (params.allow_ruless.size() == 0) {
params.allow_ruless.push_back({});
}
params.allow_ruless.back().push_back(argparse_allowlist_unicode_rule(argv[i]));
return true;
}
if (arg == "--allowlist-pieces") {
CHECK_ARG
params.allow_pieces.push_back(argv[i]);
return true;
}
if (arg == "--allowlist-keyword") {
CHECK_ARG
params.allow_kws.push_back(argv[i]);
params.allow_ruless.push_back({});
return true;
}
if (arg == "--allowlist-keyword-delay") {
CHECK_ARG
params.allow_kw_delay = std::stoul(argv[i]);
return true;
}
if (arg == "-ld" || arg == "--logdir") {
CHECK_ARG
params.logdir = argv[i];
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
params.logdir += DIRECTORY_SEPARATOR;
}
return true;
}
if (arg == "-lcs" || arg == "--lookup-cache-static") {
CHECK_ARG
params.lookup_cache_static = argv[i];
return true;
}
if (arg == "-lcd" || arg == "--lookup-cache-dynamic") {
CHECK_ARG
params.lookup_cache_dynamic = argv[i];
return true;
}
if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
CHECK_ARG
params.logits_file = argv[i];
return true;
}
if (arg == "--perplexity" || arg == "--all-logits") {
params.logits_all = true;
return true;
}
if (arg == "--ppl-stride") {
CHECK_ARG
params.ppl_stride = std::stoi(argv[i]);
return true;
}
if (arg == "--ppl-output-type") {
CHECK_ARG
params.ppl_output_type = std::stoi(argv[i]);
return true;
}
if (arg == "-ptc" || arg == "--print-token-count") {
CHECK_ARG
params.n_print = std::stoi(argv[i]);
return true;
}
if (arg == "--check-tensors") {
params.check_tensors = true;
return true;
}
if (arg == "--hellaswag") {
params.hellaswag = true;
return true;
}
if (arg == "--hellaswag-tasks") {
CHECK_ARG
params.hellaswag_tasks = std::stoi(argv[i]);
return true;
}
if (arg == "--winogrande") {
params.winogrande = true;
return true;
}
if (arg == "--winogrande-tasks") {
CHECK_ARG
params.winogrande_tasks = std::stoi(argv[i]);
return true;
}
if (arg == "--multiple-choice") {
params.multiple_choice = true;
return true;
}
if (arg == "--multiple-choice-tasks") {
CHECK_ARG
params.multiple_choice_tasks = std::stoi(argv[i]);
return true;
}
if (arg == "--kl-divergence") {
params.kl_divergence = true;
return true;
}
if (arg == "--ignore-eos") {
params.ignore_eos = true;
return true;
}
if (arg == "--penalize-nl") {
sparams.penalize_nl = true;
return true;
}
if (arg == "-l" || arg == "--logit-bias") {
CHECK_ARG
std::stringstream ss(argv[i]);
llama_token key;
char sign;
std::string value_str;
try {
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
}
else {
throw std::exception();
}
}
catch (const std::exception&) {
invalid_param = true;
return true;
}
return true;
}
if (arg == "-h" || arg == "--help" || arg == "--usage" ) {
params.usage = true;
return true;
}
if (arg == "--version") {
fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
exit(0);
}
if (arg == "--dry-run" || arg == "-dr") {
params.dry_run = true;
return true;
}
if (arg == "--in-prefix-bos") {
params.input_prefix_bos = true;
params.enable_chat_template = false;
return true;
}
if (arg == "--in-prefix") {
CHECK_ARG
params.input_prefix = argv[i];
params.enable_chat_template = false;
return true;
}
if (arg == "--in-suffix") {
CHECK_ARG
params.input_suffix = argv[i];
params.enable_chat_template = false;
return true;
}
if (arg == "--spm-infill") {
params.spm_infill = true;
return true;
}
if (arg == "--grammar") {
CHECK_ARG
sparams.grammar = { COMMON_GRAMMAR_TYPE_USER, argv[i] };
return true;
}
if (arg == "--grammar-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
sparams.grammar = {COMMON_GRAMMAR_TYPE_USER, read_file(argv[i])};
return true;
}
if (arg == "-j" || arg == "--json-schema") {
CHECK_ARG
sparams.grammar = { COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, json_schema_to_grammar(json::parse(argv[i]))};
return true;
}
if (arg == "--offload-policy" || arg == "-op") {
CHECK_ARG
auto p = string_split_pairs<int,int>(argv[i], ',');
if (p.empty()) {
fprintf(stderr, "error: Invalid offload policy argument: %s\n", argv[i]);
invalid_param = true;
} else {
params.offload_policy.insert(params.offload_policy.end(), p.begin(), p.end());
}
return true;
}
if (arg == "--no-offload-only-active-experts" || arg == "-no-ooae") {
params.only_active_exps = false;
return true;
}
if (arg == "--host") {
CHECK_ARG
params.hostname = argv[i];
return true;
}
if (arg == "--port") {
CHECK_ARG
params.port = std::stoi(argv[i]);
return true;
}
if (arg == "--send-done") {
params.send_done = true;
return true;
}
if (arg == "--path") {
CHECK_ARG
params.public_path = argv[i];
return true;
}
if (arg == "--webui") {
CHECK_ARG
params.webui = common_webui_from_name(std::string(argv[i]));
return true;
}
if (arg == "--api-key") {
CHECK_ARG
params.api_keys.push_back(argv[i]);
return true;
}
if (arg == "--api-key-file") {
CHECK_ARG
std::ifstream key_file(argv[i]);
if (!key_file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::string key;
while (std::getline(key_file, key)) {
if (!key.empty()) {
params.api_keys.push_back(key);
}
}
key_file.close();
return true;
}
if (arg == "--ssl-key-file") {
CHECK_ARG
params.ssl_file_key = argv[i];
return true;
}
if (arg == "--ssl-cert-file") {
CHECK_ARG
params.ssl_file_cert = argv[i];
return true;
}
if (arg == "--timeout" || arg == "-to") {
CHECK_ARG
params.timeout_read = std::stoi(argv[i]);
params.timeout_write = std::stoi(argv[i]);
return true;
}
if (arg == "--threads-http") {
CHECK_ARG
params.n_threads_http = std::stoi(argv[i]);
return true;
}
if (arg == "-spf" || arg == "--system-prompt-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::string system_prompt;
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(system_prompt)
);
params.system_prompt = system_prompt;
return true;
}
if (arg == "--log-format") {
CHECK_ARG
if (std::strcmp(argv[i], "json") == 0) {
params.log_json = true;
} else if (std::strcmp(argv[i], "text") == 0) {
params.log_json = false;
} else {
invalid_param = true;
return true;
}
return true;
}
if (arg == "--no-slots") {
params.endpoint_slots = false;
return true;
}
if (arg == "--metrics") {
params.endpoint_metrics = true;
return true;
}
if (arg == "--slot-save-path") {
CHECK_ARG
params.slot_save_path = argv[i];
// if doesn't end with DIRECTORY_SEPARATOR, add it
if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
params.slot_save_path += DIRECTORY_SEPARATOR;
}
return true;
}
if (arg == "--reasoning-tokens") {
CHECK_ARG
params.think_tokens = thinking_tokens_from_string(std::string(argv[i]));
return true;
}
if (arg == "--reasoning-budget") {
CHECK_ARG
params.reasoning_budget = std::stoi(argv[i]);
return true;
}
if (arg == "--sql-save-file") {
CHECK_ARG
params.sql_save_file = argv[i];
return true;
}
if (arg == "--sqlite-zstd-ext-file") {
CHECK_ARG
params.sqlite_zstd_ext_file = argv[i];
return true;
}
if (arg == "--chat-template") {
CHECK_ARG
if (!common_chat_verify_template(argv[i], true)) {
fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
invalid_param = true;
return true;
}
params.chat_template = argv[i];
return true;
}
if (arg == "--chat-template-file") {
CHECK_ARG
std::string chat_template = read_file(std::string(argv[i]));
if (!common_chat_verify_template(chat_template, true)) {
fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
invalid_param = true;
return true;
}
params.chat_template = chat_template;
return true;
}
if (arg == "--jinja") {
params.use_jinja = true;
return true;
}
if (arg == "--peg") {
return true;
}
if (arg == "--chat-template-kwargs") {
CHECK_ARG
std::string value = argv[i];
auto parsed = json::parse(value);
for (const auto& item : parsed.items()) {
if (item.key() == "enable_thinking") {
LOG_WRN("Setting 'enable_thinking' via --chat-template-kwargs is deprecated. "
"Use --reasoning on / --reasoning off instead.\n");
}
params.default_template_kwargs[item.key()] = item.value().dump();
}
return true;
}
if (arg == "--reasoning-format") {
CHECK_ARG
std::string value = argv[i];
params.reasoning_format = common_reasoning_format_from_name(value);
return true;
}
if (arg == "-rea" || arg == "--reasoning") {
CHECK_ARG
std::string value = argv[i];
if (is_truthy(value)) {
params.enable_reasoning = 1;
params.default_template_kwargs["enable_thinking"] = "true";
} else if (is_falsey(value)) {
params.enable_reasoning = 0;
params.default_template_kwargs["enable_thinking"] = "false";
} else if (is_autoy(value)) {
params.enable_reasoning = -1;
} else {
throw std::invalid_argument(
string_format("error: unknown value for --reasoning: '%s'\n", value.c_str()));
}
return true;
}
if (arg == "--reasoning-budget-message") {
CHECK_ARG
std::string value = argv[i];
params.reasoning_budget_message = value;
return true;
}
if (arg == "--skip-chat-parsing") {
CHECK_ARG
params.force_pure_content_parser = true;
return true;
}
if (arg == "--no-prefill-assistant") {
CHECK_ARG
params.prefill_assistant = false;
return true;
}
if (arg == "--parallel-tool-calls") {
params.parallel_tool_calls = true;
return true;
}
if (arg == "--slot-prompt-similarity" || arg == "-sps") {
CHECK_ARG
params.slot_prompt_similarity = std::stof(argv[i]);
return true;
}
if (arg == "-pps") {
params.is_pp_shared = true;
return true;
}
if (arg == "-npp") {
CHECK_ARG
auto p = string_split<int>(argv[i], split_delim);
params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
return true;
}
if (arg == "-ntg") {
CHECK_ARG
auto p = string_split<int>(argv[i], split_delim);
params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
return true;
}
if (arg == "-npl") {
CHECK_ARG
auto p = string_split<int>(argv[i], split_delim);
params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
return true;
}
if (arg == "--context-file") {
CHECK_ARG
std::ifstream file(argv[i], std::ios::binary);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
params.context_files.push_back(argv[i]);
return true;
}
if (arg == "--chunk-size") {
CHECK_ARG
params.chunk_size = std::stoi(argv[i]);
return true;
}
if (arg == "--chunk-separator") {
CHECK_ARG
params.chunk_separator = argv[i];
return true;
}
if (arg == "--junk") {
CHECK_ARG
params.n_junk = std::stoi(argv[i]);
return true;
}
if (arg == "--no-context-shift") {
params.ctx_shift = false;
return true;
}
if (arg == "--context-shift") {
CHECK_ARG
std::string next_arg{ argv[i] };
for (auto& c : next_arg) c = std::tolower(c);
if (next_arg == "auto" || next_arg == "1" || next_arg == "on") {
params.ctx_shift = true;
}
else if (next_arg == "off" || next_arg == "0") {
params.ctx_shift = false;
}
else {
invalid_param = true;
}
return true;
}
if (arg == "--ctx-checkpoints") {
CHECK_ARG
params.ctx_checkpoints_n = std::stoi(argv[i]);
return true;
}
if (arg == "--ctx-checkpoints-interval") {
CHECK_ARG
params.ctx_checkpoints_interval = std::stoi(argv[i]);
return true;
}
if (arg == "--ctx-checkpoints-tolerance") {
CHECK_ARG
params.ctx_checkpoints_tolerance = std::stoi(argv[i]);
return true;
}
if (arg == "-cram" || arg == "--cache-ram") {
CHECK_ARG
params.cache_ram_mib = std::stoi(argv[i]);
return true;
}
if (arg == "-crs" || arg == "--cache-ram-similarity") {
CHECK_ARG
params.cache_ram_similarity = std::stof(argv[i]);
return true;
}
if (arg == "-cram-n-min" || arg == "--cache-ram-n-min") {
CHECK_ARG
params.cache_ram_n_min = std::stoi(argv[i]);
return true;
}
if (arg == "--pos") {
CHECK_ARG
params.i_pos = std::stoi(argv[i]);
return true;
}
if (arg == "-o" || arg == "--output" || arg == "--output-file") {
CHECK_ARG
params.out_file = argv[i];
params.cvector_outfile = argv[i];
params.lora_outfile = argv[i];
return true;
}
if (arg == "-ofreq" || arg == "--output-frequency") {
CHECK_ARG
params.n_out_freq = std::stoi(argv[i]);
return true;
}
if (arg == "--save-frequency") {
CHECK_ARG
params.n_save_freq = std::stoi(argv[i]);
return true;
}
if (arg == "--process-output") {
params.process_output = true;
return true;
}
if (arg == "--output-tensor-name") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.output_tensor_name = argv[i];
return true;
}
if (arg == "--no-ppl") {
params.compute_ppl = false;
return true;
}
if (arg == "--chunk" || arg == "--from-chunk") {
CHECK_ARG
params.i_chunk = std::stoi(argv[i]);
return true;
}
// cvector params
if (arg == "--positive-file") {
CHECK_ARG
params.cvector_positive_file = argv[i];
return true;
}
if (arg == "--negative-file") {
CHECK_ARG
params.cvector_negative_file = argv[i];
return true;
}
if (arg == "--pca-batch") {
CHECK_ARG
params.n_pca_batch = std::stoi(argv[i]);
return true;
}
if (arg == "--pca-iter") {
CHECK_ARG
params.n_pca_iterations = std::stoi(argv[i]);
return true;
}
if (arg == "--method") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
else { invalid_param = true; }
return true;
}
if (arg == "--no-warmup") {
params.warmup = false;
return true;
}
if (arg == "--warmup-batch" || arg == "-wb") {
params.batch_warmup = true;
return true;
}
if (arg == "--output-format") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "jsonl") { params.sweep_bench_output_jsonl = true; }
else if (value == "md") { params.sweep_bench_output_jsonl = false; }
else { invalid_param = true; }
return true;
}
if (arg == "--minilog") {
params.minilog = true;
return true;
}
#ifndef LOG_DISABLE_LOGS
// Parse args for logging parameters
if (log_param_single_parse(argv[i])) {
// Do nothing, log_param_single_parse automatically does it's thing
// and returns if a match was found and parsed.
return true;
}
if (log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i])) {
// We have a matching known parameter requiring an argument,
// now we need to check if there is anything after this argv
// and flag invalid_param or parse it.
CHECK_ARG
if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) {
invalid_param = true;
return true;
}
return true;
}
// End of Parse args for logging parameters
#endif // LOG_DISABLE_LOGS
return false;
}
#ifdef __GNUC__
#ifdef __MINGW32__
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
#endif
void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
const common_params_sampling & sparams = params.sparams;
std::string sampler_type_chars;
std::string sampler_type_names;
for (const auto sampler_type : sparams.samplers_sequence) {
sampler_type_chars += static_cast<char>(sampler_type);
sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";";
}
sampler_type_names.pop_back();
struct option_info {
LLAMA_COMMON_ATTRIBUTE_FORMAT(4, 5)
option_info(const std::string & tags, const char * args, const char * desc, ...) : tags(tags), args(args), desc(desc) {
va_list args_list;
va_start(args_list, desc);
char buffer[1024];
vsnprintf(buffer, sizeof(buffer), desc, args_list);
va_end(args_list);
this->desc = buffer;
}
option_info(const std::string & grp) : grp(grp) {}
std::string tags;
std::string args;
std::string desc;
std::string grp;
};
std::vector<option_info> options;
// TODO: filter by tags
options.push_back({ "general" });
options.push_back({ "*", "-h, --help, --usage", "print usage and exit" });
options.push_back({ "*", " --version", "show version and build info" });
options.push_back({ "*", "-v, --verbose", "print verbose information" });
options.push_back({ "*", " --minilog", "print important information" });
options.push_back({ "*", " --verbosity N", "set specific verbosity level (default: %d)", params.verbosity });
options.push_back({ "*", " --verbose-prompt", "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" });
options.push_back({ "*", "-dr, --dry-run", "skip loading tensors in the files"});
options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
options.push_back({ "*", "-s, --seed SEED", "RNG seed (default: %d, use random seed for < 0)", params.seed });
options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.n_threads });
options.push_back({ "*", "-tb, --threads-batch N", "number of threads to use during batch and prompt processing (default: same as --threads)" });
options.push_back({ "speculative", "-td, --threads-draft N", "number of threads to use during generation (default: same as --threads)" });
options.push_back({ "speculative", "-tbd, --threads-batch-draft N",
"number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
options.push_back({ "speculative", "-ps, --p-split N", "speculative decoding split probability (default: %.1f)", (double)params.p_split });
options.push_back({ "*", "-lcs, --lookup-cache-static FNAME",
"path to static lookup cache to use for lookup decoding (not updated by generation)" });
options.push_back({ "*", "-lcd, --lookup-cache-dynamic FNAME",
"path to dynamic lookup cache to use for lookup decoding (updated by generation)" });
options.push_back({ "*", "-c, --ctx-size N", "size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx });
options.push_back({ "*", "-cd, --ctx-size-draft N", "size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx });
options.push_back({ "*", "--ctx-checkpoints N", "max number of context checkpoints to create per slot (default: %d)",params.ctx_checkpoints_n});
options.push_back({ "*", "--ctx-checkpoints-interval N", "minimum number of tokens between each context checkpoint. (default: %d, <=0 disable)",params.ctx_checkpoints_interval});
options.push_back({ "*", "--ctx-checkpoints-tolerance N", "the number of tokens before the full prompt to create the checkpoint. (default: %d, <=0 disable)",params.ctx_checkpoints_tolerance});
options.push_back({ "*", "-cram, --cache-ram N", "set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)",params.cache_ram_mib });
options.push_back({ "*", "-crs, --cache-ram-similarity N", "max of similarity of prompt tokens to cache tokens that triggers prompt cache (default: %.2f).",params.cache_ram_similarity });
options.push_back({ "*", "-cram-n-min --cache-ram-n-min N", "minimum number of the cached tokens that triggers prompt cache (default: %d).", params.cache_ram_n_min });
options.push_back({ "*", "-n, --predict N", "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict });
options.push_back({ "*", "-b, --batch-size N", "logical maximum batch size (default: %d)", params.n_batch });
options.push_back({ "*", "-ub, --ubatch-size N", "physical maximum batch size (default: %d)", params.n_ubatch });
options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
options.push_back({ "*", "-no-fa, --no-flash-attn", "disable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
options.push_back({ "*", "-fa, --flash-attn (auto|on|off|0|1)", "set Flash Attention (default: %s)", params.flash_attn ? "on" : "off" });
options.push_back({ "*", "-mla, --mla-use", "enable MLA (default: %d)", params.mla_attn });
options.push_back({ "*", "-amb, --attention-max-batch", "max batch size for attention computations (default: %d)", params.attn_max_batch});
options.push_back({ "*", "-no-fmoe, --no-fused-moe", "disable fused MoE (default: %s)", params.fused_moe_up_gate ? "enabled" : "disabled" });
options.push_back({ "*", "-ger, --grouped-expert-routing", "enable grouped expert routing (default: %s)", params.grouped_expert_routing ? "enabled" : "disabled" });
options.push_back({ "*", "-no-fug, --no-fused-up-gate", "disable fused up-gate (default: %s)", params.fused_up_gate ? "enabled" : "disabled" });
options.push_back({ "*", "-no-mmad, --no-fused-mul-multiadd", "disable fused mul-multi_add (default: %s)", params.fused_mmad? "enabled" : "disabled" });
//options.push_back({ "*", "-rcache, --rope-cache", "enable RoPE cache (default: %s)", params.rope_cache ? "enabled" : "disabled" });
options.push_back({ "*", "-gr, --graph-reuse", "enable graph reuse (default: %s)", params.graph_reuse ? "enabled" : "disabled" });
options.push_back({ "*", "-no-gr, --no-graph-reuse", "disable graph reuse (default: %s)", !params.graph_reuse ? "enabled" : "disabled" });
options.push_back({ "*", "-ser, --smart-expert-reduction", "experts reduction (default: %d,%g)", params.min_experts, params.thresh_experts});
options.push_back({ "*", "-mqkv, --merge-qkv,", "merge Q,K,V (default: %d)", params.merge_qkv});
options.push_back({ "*", "-muge, --merge-up-gate-experts,","merge ffn_up/gate_exps (default: %d)", params.merge_up_gate_exps});
options.push_back({ "*", "-khad, --k-cache-hadamard,", "Use Hadamard transform for K-cache (default: %d)", params.k_cache_hadamard});
options.push_back({ "*", "-vhad, --v-cache-hadamard,", "Use Hadamard transform for V-cache (default: %d)", params.v_cache_hadamard});
options.push_back({ "*", "-smf16, --split-mode-f16,", "Use f16 for data exchange between GPUs (default: %d)", true});
options.push_back({ "*", "-smf32, --split-mode-f32,", "Use f32 for data exchange between GPUs (default: %d)", false});
options.push_back({ "*", "-grt, --graph-reduce-type", "Type for data exchange between GPUs (default: %s)", "f32"});
options.push_back({ "*", "-smgs, --split-mode-graph-scheduling,", "Force Split Mode Graph Scheduling (default: %d)", params.split_mode_graph_scheduling});
options.push_back({ "*", "-sas, --scheduler_async,", "Async evaluation of compute graphs: %d)", params.scheduler_async});
options.push_back({ "*", "-vq, --validate-quants", "validate quantized data while loading the model (default: %d)", params.validate_quants});
options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with\n"
"in conversation mode, this will be used as system prompt\n"
"(default: '%s')", params.prompt.c_str() });
options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" });
options.push_back({ "*", " --in-file FNAME", "an input file (repeat to specify multiple files)" });
options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" });
options.push_back({ "*", "-e, --escape", "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" });
options.push_back({ "*", " --no-escape", "do not process escape sequences" });
options.push_back({ "main", "-ptc, --print-token-count N", "print token count every N tokens (default: %d)", params.n_print });
options.push_back({ "main", " --prompt-cache FNAME", "file to cache prompt state for faster startup (default: none)" });
options.push_back({ "main", " --prompt-cache-all", "if specified, saves user input and generations to cache as well\n"
"not supported with --interactive or other interactive options" });
options.push_back({ "main", " --prompt-cache-ro", "if specified, uses the prompt cache but does not update it" });
options.push_back({ "main", "-r, --reverse-prompt PROMPT",
"halt generation at PROMPT, return control in interactive mode\n"
"can be specified more than once for multiple prompts" });
options.push_back({ "main", "-sp, --special", "special tokens output enabled (default: %s)", params.special ? "true" : "false" });
options.push_back({ "main", "-cnv, --conversation", "run in conversation mode, does not print special tokens and suffix/prefix\n"
"if suffix/prefix are not specified, default chat template will be used\n"
"(default: %s)", params.conversation ? "true" : "false" });
options.push_back({ "main infill", "-i, --interactive", "run in interactive mode (default: %s)", params.interactive ? "true" : "false" });
options.push_back({ "main infill", "-if, --interactive-first", "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" });
options.push_back({ "main infill", "-mli, --multiline-input", "allows you to write or paste multiple lines without ending each in '\\'" });
options.push_back({ "main infill", " --in-prefix-bos", "prefix BOS to user inputs, preceding the `--in-prefix` string" });
options.push_back({ "main infill", " --in-prefix STRING", "string to prefix user inputs with (default: empty)" });
options.push_back({ "main infill", " --in-suffix STRING", "string to suffix after user inputs with (default: empty)" });
options.push_back({ "main", " --no-warmup", "skip warming up the model with an empty run" });
options.push_back({ "server infill",
" --spm-infill", "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", params.spm_infill ? "enabled" : "disabled" });
options.push_back({ "sampling" });
options.push_back({ "*", " --samplers SAMPLERS", "samplers that will be used for generation in the order, separated by \';\'\n"
"(default: %s)", sampler_type_names.c_str() });
options.push_back({ "*", " --sampling-seq SEQUENCE",
"simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str() });
options.push_back({ "*", " --ignore-eos", "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)" });
options.push_back({ "*", " --penalize-nl", "penalize newline tokens (default: %s)", sparams.penalize_nl ? "true" : "false" });
options.push_back({ "*", " --temp N", "temperature (default: %.1f)", (double)sparams.temp });
options.push_back({ "*", " --top-k N", "top-k sampling (default: %d, 0 = disabled)", sparams.top_k });
options.push_back({ "*", " --top-p N", "top-p sampling (default: %.1f, 1.0 = disabled)", (double)sparams.top_p });
options.push_back({ "*", " --min-p N", "min-p sampling (default: %.1f, 0.0 = disabled)", (double)sparams.min_p });
options.push_back({ "*", " --tfs N", "tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)sparams.tfs_z });
options.push_back({ "*", " --typical N", "locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)sparams.typical_p });
options.push_back({ "*", " --repeat-last-n N", "last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", sparams.penalty_last_n });
options.push_back({ "*", " --repeat-penalty N", "penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)sparams.penalty_repeat });
options.push_back({ "*", " --presence-penalty N", "repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_present });
options.push_back({ "*", " --frequency-penalty N", "repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_freq });
options.push_back({ "*", " --dynatemp-range N", "dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)sparams.dynatemp_range });
options.push_back({ "*", " --dynatemp-exp N", "dynamic temperature exponent (default: %.1f)", (double)sparams.dynatemp_exponent });
options.push_back({ "*", " --mirostat N", "use Mirostat sampling.\n"
"Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", sparams.mirostat });
options.push_back({ "*", " --mirostat-lr N", "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta });
options.push_back({ "*", " --mirostat-ent N", "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau });
options.push_back({ "*", " --xtc-probability p", "xtc probability (default: %.1f, 0.0 = disabled)", (double)sparams.xtc_probability });
options.push_back({ "*", " --xtc-threshold t", "xtc threshold (default: %.1f, >0.5 = disabled)", (double)sparams.xtc_threshold});
options.push_back({ "*", " --top-n-sigma t", "top-n-sigma parmeter (default: %.1f, 0.0 = disabled)", (double)sparams.top_n_sigma});
options.push_back({ "*", " --adaptive-target", "adaptive-p sampling: (default: %.2f, <0.0 = disabled)", (double)sparams.adaptive_target});
options.push_back({ "*", " --adaptive-decay", "adaptive-p sampling: (default: %.2f)", (double)sparams.adaptive_decay});
options.push_back({ "*", " --adaptive-updt-w-cur", "adaptive-p sampling: (default: %s)", sparams.adaptive_updt_w_cur ? "true" : "false"});
options.push_back({ "*", " --banned-string-file", "file path of the list of banned strings on each line" });
options.push_back({ "*", " --banned-n", "number of tokens banned in the phrase during rewind. -1 means all tokens: (default: %d)",params.banned_n });
options.push_back({ "*", " --allowlist-unicode-rule",
"rule for allowlisting unicode script and/or codepoints. disabled without any rule. format: `LOWER..UPPER,SCRIPT:BIAS`\n"
"if unspecified: LOWER = 0, UPPER = -1(=max), SCRIPT=\"\", BIAS = 0. at least one of LOWER, UPPER, or SCRIPT is required\n" });
options.push_back({ "*", " --allowlist-pieces", "allowlist each token in argument. inherits max BIAS in --allowlist-unicode-rule. overrides --allowlist-unicode-rule" });
options.push_back({ "*", " --allowlist-keyword", "keyword to expire earlier allowlist rules if matched during generation. does not affect later rules" });
options.push_back({ "*", " --allowlist-keyword-delay",
"# tokens to delay matching for the first keyword (default: %zu)", params.allow_kw_delay });
options.push_back({ "*", " -l TOKEN_ID(+/-)BIAS", "modifies the likelihood of token appearing in the completion,\n"
"i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
"or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" });
options.push_back({ "main", " --cfg-negative-prompt PROMPT",
"negative prompt to use for guidance (default: '%s')", sparams.cfg_negative_prompt.c_str() });
options.push_back({ "main", " --cfg-negative-prompt-file FNAME",
"negative prompt file to use for guidance" });
options.push_back({ "main", " --cfg-scale N", "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale });
options.push_back({ "template" });
options.push_back({ "main", " --jinja",
"set custom jinja chat template (default: template taken from model's metadata)\n"
"if suffix/prefix are specified, template will be disabled\n"
"only commonly used templates are accepted:\n"
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
options.push_back({ "main", " --parallel-tool-calls", "enable parallel tool calls\n" });
options.push_back({ "main", " --chat-template JINJA_TEMPLATE",
"use jinja template for chat (default: disabled)\n" });
options.push_back({ "main", " --chat-template-file file_with_JINJA_TEMPLATE",
"load jinja template for chat from the file\n" });
options.push_back({ "main", " --reasoning-format FORMAT",
"controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
"- none: leaves thoughts unparsed in `message.content`\n"
"- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
"- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`\n"
"(default: none)", });
options.push_back({ "main", "-rea, --reasoning", "[on|off|auto]"
"Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))" });
options.push_back({ "main", " --chat-template-kwargs JSON", "sets additional params for the json template parser"});
options.push_back({ "main", " --reasoning-budget N", "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)" });
options.push_back({ "main", " --reasoning-tokens FORMAT", "exclude reasoning tokens to select the slot more accurately.\n"
"none: include all tokens\n"
"auto: exclude all tokens between <think> and </think>\n"
"Or comma separated start and end tokens such as [THINK],[/THINK]\n"
"(default: auto)" });
options.push_back({ "main", " --reasoning-budget-message", "message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)" });
options.push_back({ "main", " --skip-chat-parsing", "force a pure content parser, even if a Jinja template is specified; model will output everything "
"in the content section, including any reasoning and/or tool calls (default: disabled)" });
options.push_back({ "main", " --reasoning-budget N", "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)" });
options.push_back({ "main", " --no-prefill-assistant", "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n"
"when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n" });
options.push_back({ "main", " -ptc, --parallel-tool-calls", "enable parallel tool calls\n" });
options.push_back({ "grammar" });
options.push_back({ "*", " --grammar GRAMMAR", "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.grammar.c_str() });
options.push_back({ "*", " --grammar-file FNAME", "file to read grammar from" });
options.push_back({ "*", "-j, --json-schema SCHEMA",
"JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n"
"For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" });
options.push_back({ "embedding" });
options.push_back({ "embedding", " --pooling {none,mean,cls,last}",
"pooling type for embeddings, use model default if unspecified" });
options.push_back({ "embedding", " --attention {causal,non-causal}",
"attention type for embeddings, use model default if unspecified" });
options.push_back({ "context hacking" });
options.push_back({ "*", " --rope-scaling {none,linear,yarn}",
"RoPE frequency scaling method, defaults to linear unless specified by the model" });
options.push_back({ "*", " --rope-scale N", "RoPE context scaling factor, expands context by a factor of N" });
options.push_back({ "*", " --rope-freq-base N", "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)" });
options.push_back({ "*", " --rope-freq-scale N", "RoPE frequency scaling factor, expands context by a factor of 1/N" });
options.push_back({ "*", " --yarn-orig-ctx N", "YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx });
options.push_back({ "*", " --yarn-ext-factor N", "YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor });
options.push_back({ "*", " --yarn-attn-factor N", "YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor });
options.push_back({ "*", " --yarn-beta-slow N", "YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow });
options.push_back({ "*", " --yarn-beta-fast N", "YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast });
options.push_back({ "*", "-gan, --grp-attn-n N", "group-attention factor (default: %d)", params.grp_attn_n });
options.push_back({ "*", "-gaw, --grp-attn-w N", "group-attention width (default: %.1f)", (double)params.grp_attn_w });
options.push_back({ "*", "-dkvc, --dump-kv-cache", "verbose print of the KV cache" });
options.push_back({ "*", "-nkvo, --no-kv-offload", "disable KV offload" });
options.push_back({ "*", "-ctk, --cache-type-k TYPE", "KV cache data type for K (default: %s)", params.cache_type_k.c_str() });
options.push_back({ "*", "-ctv, --cache-type-v TYPE", "KV cache data type for V (default: %s)", params.cache_type_v.c_str() });
options.push_back({ "*", "-ctk-first, --cache-type-k-first TYPE,N", "KV cache data type for the first N layers of K (default: %s,-1)", params.type_k_first.c_str() });
options.push_back({ "*", "-ctv-last, --cache-type-k-last TYPE,N", "KV cache data type for the last N layers of K (default: %s,-1)", params.type_k_last.c_str() });
options.push_back({ "*", "-ctv-first, --cache-type-v-first TYPE,N", "KV cache data type for the first N layers of V (default: %s,-1)", params.type_v_first.c_str() });
options.push_back({ "*", "-ctk-last, --cache-type-v-last TYPE,N", "KV cache data type for the last N layers of V (default: %s,-1)", params.type_v_last.c_str() });
options.push_back({ "*", "-ctkd, --cache-type-k-draft TYPE", "KV cache data type for K for the draft model" });
options.push_back({ "*", "-ctvd, --cache-type-v-draft TYPE", "KV cache data type for V for the draft model" });
options.push_back({ "perplexity" });
options.push_back({ "perplexity", " --all-logits", "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" });
options.push_back({ "perplexity", " --hellaswag", "compute HellaSwag score over random tasks from datafile supplied with -f" });
options.push_back({ "perplexity", " --hellaswag-tasks N", "number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks });
options.push_back({ "perplexity", " --winogrande", "compute Winogrande score over random tasks from datafile supplied with -f" });
options.push_back({ "perplexity", " --winogrande-tasks N", "number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks });
options.push_back({ "perplexity", " --multiple-choice", "compute multiple choice score over random tasks from datafile supplied with -f" });
options.push_back({ "perplexity", " --multiple-choice-tasks N",
"number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks });
options.push_back({ "perplexity", " --kl-divergence", "computes KL-divergence to logits provided via --kl-divergence-base" });
options.push_back({ "perplexity", " --ppl-stride N", "stride for perplexity calculation (default: %d)", params.ppl_stride });
options.push_back({ "perplexity", " --ppl-output-type {0,1}",
"output type for perplexity calculation (default: %d)", params.ppl_output_type });
options.push_back({ "parallel" });
options.push_back({ "*", "-dt, --defrag-thold N", "KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold });
options.push_back({ "*", "-mea, --max-extra-alloc", "Max extra VRAM allocation per GPU (default: %d)", params.max_extra_alloc_MiB});
options.push_back({ "*", "-np, --parallel N", "number of parallel sequences to decode (default: %d)", params.n_parallel });
options.push_back({ "*", "-ns, --sequences N", "number of sequences to decode (default: %d)", params.n_sequences });
options.push_back({ "*", "-cb, --cont-batching", "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" });
options.push_back({ "*", "-nocb, --no-cont-batching", "disable continuous batching" });
options.push_back({ "multi-modality" });
options.push_back({ "*", " --mmproj FILE", "path to a multimodal projector file for LLaVA. see examples/llava/README.md" });
options.push_back({ "*", " --image FILE", "path to an image file. use with multimodal models. Specify multiple times for batching" });
options.push_back({ "*", " --image-min-tokens N", "minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)"});
options.push_back({ "*", " --image-max-tokens N", "maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)" });
options.push_back({ "*", " --mtmd-kq-type TYPE", "data type for multimodality K*Q (default: %s)", params.mtmd_kq_type.c_str() });
options.push_back({ "*", " --no-context-shift", "disable context-shift." });
options.push_back({ "*", "--context-shift (auto|on|off|0|1)", "set context-shift (default: %s)", params.ctx_shift ? "on" : "off" });
options.push_back({ "backend" });
options.push_back({ "*", " --rpc SERVERS", "comma separated list of RPC servers" });
options.push_back({ "*", "-cuda, --cuda-params", "comma separate list of cuda parameters" });
options.push_back({ "*", "-draft, --draft-params", "comma separate list of draft model parameters" });
if (llama_supports_mlock()) {
options.push_back({ "*", " --mlock", "force system to keep model in RAM rather than swapping or compressing" });
}
if (llama_supports_mmap()) {
options.push_back({ "*", " --no-mmap", "do not memory-map model (slower load but may reduce pageouts if not using mlock)" });
}
options.push_back({ "*", " --run-time-repack", "repack tensors if interleaved variant is available"});
options.push_back({ "*", " --cpu-moe", "keep all MoE weights in CPU memory"});
options.push_back({ "*", " --n-cpu-moe N", "keep MoE weights of the first N layers in CPU memory"});
options.push_back({ "*", " --defer-experts", "defer expert mmap residency on Linux to reduce model load time"});
options.push_back({ "*", " --fit-margin N", "safety margin in MiB when auto-fitting model offloading"});
options.push_back({ "*", "-wgt, --worst-graph-tokens N", "number of tokens to use for worst-case graph"});
options.push_back({ "*", " --fit", "automatically determine which tensors to offload to the GPU(s)"});
options.push_back({ "*", " --numa TYPE", "attempt optimizations that help on some NUMA systems\n"
" - distribute: spread execution evenly over all nodes\n"
" - isolate: only spawn threads on CPUs on the node that execution started on\n"
" - numactl: use the CPU map provided by numactl\n"
"if run without this previously, it is recommended to drop the system page cache before using this\n"
"see https://github.com/ggerganov/llama.cpp/issues/1437" });
if (llama_supports_gpu_offload()) {
options.push_back({ "*", "-ngl, --gpu-layers N",
"number of layers to store in VRAM" });
options.push_back({ "*", "-ngld, --gpu-layers-draft N",
"number of layers to store in VRAM for the draft model" });
options.push_back({ "*", "-sm, --split-mode SPLIT_MODE",
"how to split the model across multiple GPUs, one of:\n"
" - none: use one GPU only\n"
" - graph: split model tensors and computation graph across GPUs\n"
" - layer (default): split layers and KV across GPUs\n" });
options.push_back({ "*", "-ts, --tensor-split SPLIT",
"fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1" });
options.push_back({ "*", "-dev, --device dev1,dev2",
"comma-separated list of devices to use for offloading (none = don't offload)\n"
"Example: CUDA0,CUDA1,RPC[192.168.0.1:8080]\n" });
options.push_back({ "*", "-devd, --device-draft dev1,dev2",
"comma-separated list of devices to use for offloading for the draft model (none = don't offload)\n"
"Example: CUDA0,CUDA1,RPC[192.168.0.1:8080]\n" });
options.push_back({ "*", "-mg, --main-gpu i", "the GPU to use for the model (with split-mode = none),\n"
"or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu });
options.push_back({ "*", "--max-gpu i", "max. number of GPUs to use at a time with split mode 'graph', (default: %d)", params.max_gpu });
}
options.push_back({ "model" });
options.push_back({ "*", " --check-tensors", "check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false" });
options.push_back({ "*", " --override-kv KEY=TYPE:VALUE",
"advanced option to override model metadata by key. may be specified multiple times.\n"
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false" });
options.push_back({ "*", " --lora FNAME", "apply LoRA adapter (can be repeated to use multiple adapters)" });
options.push_back({ "*", " --lora-scaled FNAME S", "apply LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
options.push_back({ "*", " --control-vector FNAME", "add a control vector\n"
"note: this argument can be repeated to add multiple control vectors" });
options.push_back({ "*", " --control-vector-scaled FNAME SCALE",
"add a control vector with user defined scaling SCALE\n"
"note: this argument can be repeated to add multiple scaled control vectors" });
options.push_back({ "*", " --control-vector-layer-range START END",
"layer range to apply the control vector(s) to, start and end inclusive" });
options.push_back({ "*", "-m, --model FNAME", "model path (default: models/$filename with filename from --hf-file\n"
"or --model-url if set, otherwise %s)", DEFAULT_MODEL_PATH });
options.push_back({ "*", "-md, --model-draft FNAME", "draft model for speculative decoding (default: unused)" });
options.push_back({ "*", "-mu, --model-url MODEL_URL", "model download url (default: unused)" });
options.push_back({ "*", "-hfr, --hf-repo REPO", "Hugging Face model repository (default: unused)" });
options.push_back({ "*", "-hff, --hf-file FILE", "Hugging Face model file (default: unused)" });
options.push_back({ "*", "-hft, --hf-token TOKEN", "Hugging Face access token (default: value from HF_TOKEN environment variable)" });
options.push_back({ "*", "-mtp, --multi-token-prediction", "whether to use multi-token-prediction (if supported) (default: %s)", params.has_mtp ? "true" : "false" });
options.push_back({ "*", "-no-mtp, --no-multi-token-prediction", "whether to use multi-token-prediction (if supported) (default: %s)", !params.has_mtp ? "true" : "false" });
options.push_back({ "*", "--draft-max, --draft, --draft-n N",
"number of tokens to draft for speculative decoding (default: %d)", params.speculative.n_max });
options.push_back({ "*", "--draft-min, --draft-n-min N", "minimum number of draft tokens to use for speculative decoding" });
options.push_back({ "*", "--draft-p-min P", "minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min });
options.push_back({ "*", "--recurrent-ckpt-mode MODE", "checkpoint strategy for recurrent/hybrid speculative decoding\n"
" auto auto-select: per-step if CUDA full-GPU, gpu-fallback otherwise (default)\n"
" per-step save SSM state per draft step in VRAM; no re-decode on rejection\n"
" gpu-fallback copy state to GPU buffer; re-decode on rejection\n"
" cpu serialise state via llama_state_seq; re-decode on rejection" });
options.push_back({ "*", "--spec-type Name [none | ngram - cache | ngram - simple | ngram - map - k | ngram - map - k4v | ngram - mod | suffix]", "type of speculative decoding to use when no draft model is provided (default: %d)\n", (int)params.speculative.type});
options.push_back({ "*", "--spec-ngram-size-n N", "ngram size N for ngram-simple/ngram-map speculative decoding, length of lookup n-gram (default: %d)\n",params.speculative.ngram_size_n });
options.push_back({ "*", "--spec-ngram-size-m N", "ngram size M for ngram-simple/ngram-map speculative decoding, length of draft m-gram (default: %d)\n", params.speculative.ngram_size_m });
options.push_back({ "*", "--spec-ngram-min-hits N", "minimum hits for ngram-map speculative decoding (default: %d)\n", params.speculative.ngram_min_hits });
options.push_back({ "*", "--suffix-pattern-len N", "minimum context match length for suffix decoding (default: %d)", params.speculative.suffix_min_match_len });
options.push_back({ "*", "--suffix-max-depth N", "suffix tree maximum depth for suffix decoding (default: %d)", params.speculative.suffix_max_depth });
options.push_back({ "*", "--suffix-corpus PATH", "corpus file to pre-warm the suffix tree: .json (array of strings or conversation messages) or .bin (raw int32 token IDs)" });
options.push_back({ "*", "--spec-autotune", "automatically tune speculative params to maximize tokens/sec" });
options.push_back({ "retrieval" });
options.push_back({ "retrieval", " --context-file FNAME", "file to load context from (repeat to specify multiple files)" });
options.push_back({ "retrieval", " --chunk-size N", "minimum length of embedded text chunks (default: %d)", params.chunk_size });
options.push_back({ "retrieval", " --chunk-separator STRING",
"separator between chunks (default: '%s')", params.chunk_separator.c_str() });
options.push_back({ "passkey" });
options.push_back({ "passkey", " --junk N", "number of times to repeat the junk text (default: %d)", params.n_junk });
options.push_back({ "passkey", " --pos N", "position of the passkey in the junk text (default: %d)", params.i_pos });
options.push_back({ "imatrix" });
options.push_back({ "imatrix", "-o, --output FNAME", "output file (default: '%s')", params.out_file.c_str() });
options.push_back({ "imatrix", " --output-frequency N", "output the imatrix every N iterations (default: %d)", params.n_out_freq });
options.push_back({ "imatrix", " --save-frequency N", "save an imatrix copy every N iterations (default: %d)", params.n_save_freq });
options.push_back({ "imatrix", " --process-output", "collect data for the output tensor (default: %s)", params.process_output ? "true" : "false" });
options.push_back({ "imatrix", " --no-ppl", "do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false" });
options.push_back({ "imatrix", " --chunk N", "start processing the input from chunk N (default: %d)", params.i_chunk });
options.push_back({ "bench" });
options.push_back({ "bench", "-pps", "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" });
options.push_back({ "bench", "-npp n0,n1,...", "number of prompt tokens" });
options.push_back({ "bench", "-ntg n0,n1,...", "number of text generation tokens" });
options.push_back({ "bench", "-npl n0,n1,...", "number of parallel prompts" });
options.push_back({ "embedding" });
options.push_back({ "embedding", " --embd-normalize", "normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize });
options.push_back({ "embedding", " --embd-output-format", "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix" });
options.push_back({ "embedding", " --embd-separator", "separator of embendings (default \\n) for example \"<#sep#>\"" });
options.push_back({ "server" });
options.push_back({ "server", " --host HOST", "ip address to listen (default: %s)", params.hostname.c_str() });
options.push_back({ "server", " --port PORT", "port to listen (default: %d)", params.port });
options.push_back({ "server", " --path PATH", "path to serve static files from (default: %s)", params.public_path.c_str() });
options.push_back({ "server", " --embedding(s)", "restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled" });
options.push_back({ "server", " --webui NAME",
"controls which webui to server:\n"
"- none: disable webui\n"
"- auto: default webui \n"
"- llamacpp: llamacpp webui \n"
"(default: auto)", });
options.push_back({ "server", " --api-key KEY", "API key to use for authentication (default: none)" });
options.push_back({ "server", " --api-key-file FNAME", "path to file containing API keys (default: none)" });
options.push_back({ "server", " --ssl-key-file FNAME", "path to file a PEM-encoded SSL private key" });
options.push_back({ "server", " --ssl-cert-file FNAME", "path to file a PEM-encoded SSL certificate" });
options.push_back({ "server", " --timeout N", "server read/write timeout in seconds (default: %d)", params.timeout_read });
options.push_back({ "server", " --threads-http N", "number of threads used to process HTTP requests (default: %d)", params.n_threads_http });
options.push_back({ "server", " --system-prompt-file FNAME",
"set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" });
options.push_back({ "server", " --log-format {text,json}",
"log output format: json or text (default: json)" });
options.push_back({ "server", " --metrics", "enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled" });
options.push_back({ "server", " --no-slots", "disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled" });
options.push_back({ "server", " --slot-save-path PATH", "path to save slot kv cache (default: disabled)" });
options.push_back({ "server", " --chat-template JINJA_TEMPLATE",
"set custom jinja chat template (default: template taken from model's metadata)\n"
"only commonly used templates are accepted:\n"
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
options.push_back({ "server", "-sps, --slot-prompt-similarity SIMILARITY",
"how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity });
options.push_back({ "server", " --lora-init-without-apply", "load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"});
#ifndef LOG_DISABLE_LOGS
options.push_back({ "logging" });
options.push_back({ "*", " --simple-io", "use basic IO for better compatibility in subprocesses and limited consoles" });
options.push_back({ "*", "-ld, --logdir LOGDIR", "path under which to save YAML logs (no logging if unset)" });
options.push_back({ "logging", " --log-test", "Run simple logging test" });
options.push_back({ "logging", " --log-disable", "Disable trace logs" });
options.push_back({ "logging", " --log-enable", "Enable trace logs" });
options.push_back({ "logging", " --log-file FNAME", "Specify a log filename (without extension)" });
options.push_back({ "logging", " --log-new", "Create a separate new log file on start. "
"Each log file will have unique name: \"<name>.<ID>.log\"" });
options.push_back({ "logging", " --log-append", "Don't truncate the old log file." });
#endif // LOG_DISABLE_LOGS
options.push_back({ "cvector" });
options.push_back({ "cvector", "-o, --output FNAME", "output file (default: '%s')", params.cvector_outfile.c_str() });
options.push_back({ "cvector", " --positive-file FNAME", "positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str() });
options.push_back({ "cvector", " --negative-file FNAME", "negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str() });
options.push_back({ "cvector", " --pca-batch N", "batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch });
options.push_back({ "cvector", " --pca-iter N", "number of iterations used for PCA (default: %d)", params.n_pca_iterations });
options.push_back({ "cvector", " --method {pca,mean}", "dimensionality reduction method to be used (default: pca)" });
options.push_back({ "export-lora" });
options.push_back({ "export-lora", "-m, --model", "model path from which to load base model (default '%s')", params.model.c_str() });
options.push_back({ "export-lora", " --lora FNAME", "path to LoRA adapter (can be repeated to use multiple adapters)" });
options.push_back({ "export-lora", " --lora-scaled FNAME S", "path to LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
options.push_back({ "*", "-t, --threads N", "number of threads to use during computation (default: %d)", params.n_threads });
options.push_back({ "export-lora", "-o, --output FNAME", "output file (default: '%s')", params.lora_outfile.c_str() });
printf("usage: %s [options]\n", argv[0]);
for (const auto & o : options) {
if (!o.grp.empty()) {
printf("\n%s:\n\n", o.grp.c_str());
continue;
}
printf(" %-32s", o.args.c_str());
if (o.args.length() > 30) {
printf("\n%34s", "");
}
const auto desc = o.desc;
size_t start = 0;
size_t end = desc.find('\n');
while (end != std::string::npos) {
printf("%s\n%34s", desc.substr(start, end - start).c_str(), "");
start = end + 1;
end = desc.find('\n', start);
}
printf("%s\n", desc.substr(start).c_str());
}
printf("\n");
}
std::string gpt_params_get_system_info(const gpt_params & params) {
std::ostringstream os;
os << "system_info: n_threads = " << params.n_threads;
if (params.n_threads_batch != -1) {
os << " (n_threads_batch = " << params.n_threads_batch << ")";
}
os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();
return os.str();
}
//
// String utils
//
std::string string_format(const char* fmt, ...) {
va_list ap;
va_list ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
std::string regex_escape(const std::string& s) {
static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]");
return std::regex_replace(s, special_chars, "\\$0");
}
std::string string_join(const std::vector<std::string>& values, const std::string& separator) {
std::ostringstream result;
for (size_t i = 0; i < values.size(); ++i) {
if (i > 0) {
result << separator;
}
result << values[i];
}
return result.str();
}
std::vector<std::string> string_split(const std::string& str, const std::string& delimiter) {
std::vector<std::string> parts;
size_t start = 0;
size_t end = str.find(delimiter);
while (end != std::string::npos) {
parts.push_back(str.substr(start, end - start));
start = end + delimiter.length();
end = str.find(delimiter, start);
}
parts.push_back(str.substr(start));
return parts;
}
std::vector<std::string> string_split(const std::string& str, char delim) {
std::vector<std::string> values;
std::istringstream str_stream(str);
std::string token;
while (std::getline(str_stream, token, delim)) {
std::string value;
std::istringstream token_stream(token);
token_stream >> value;
values.push_back(value);
}
return values;
}
std::string string_repeat(const std::string & str, size_t n) {
if (n == 0) {
return "";
}
std::string result;
result.reserve(str.length() * n);
for (size_t i = 0; i < n; ++i) {
result += str;
}
return result;
}
static bool is_utf8_whitespace(uint8_t c) {
// Basic ASCII whitespace
if (c <= 0x7F) return isspace(c);
// Else: Not whitespace (or you'd need a full Unicode table)
return false;
}
std::string string_strip(const std::string & str) {
size_t start = 0;
size_t end = str.size();
while (start < end && is_utf8_whitespace(str[start])) {
start++;
}
while (end > start && is_utf8_whitespace(str[end - 1])) {
end--;
}
return str.substr(start, end - start);
}
std::string string_get_sortable_timestamp() {
using clock = std::chrono::system_clock;
const clock::time_point current_time = clock::now();
const time_t as_time_t = clock::to_time_t(current_time);
char timestamp_no_ns[100];
std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
current_time.time_since_epoch() % 1000000000).count();
char timestamp_ns[11];
snprintf(timestamp_ns, 11, "%09" PRId64, ns);
return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
}
// could be improved to support more languages
std::string string_lower(const std::string& str) {
std::string result = str;
for (char& c : result) {
if (c >= 'A' && c <= 'Z') {
c = static_cast<char>(c + ('a' - 'A'));
}
}
return result;
}
void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return; // Avoid infinite loop if 'search' is an empty string
}
size_t pos = 0;
while ((pos = s.find(search, pos)) != std::string::npos) {
s.replace(pos, search.length(), replace);
pos += replace.length();
}
}
bool string_ends_with(const std::string_view& str, const std::string_view& suffix) {
return str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), suffix.size(), suffix) == 0;
}
size_t string_find_partial_stop(const std::string_view& str, const std::string_view& stop) {
if (!str.empty() && !stop.empty()) {
const char text_last_char = str.back();
for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
if (stop[char_index] == text_last_char) {
const auto current_partial = stop.substr(0, char_index + 1);
if (string_ends_with(str, current_partial)) {
return str.size() - char_index - 1;
}
}
}
}
return std::string::npos;
}
void string_process_escapes(std::string & input) {
std::size_t input_len = input.length();
std::size_t output_idx = 0;
for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
switch (input[++input_idx]) {
case 'n': input[output_idx++] = '\n'; break;
case 'r': input[output_idx++] = '\r'; break;
case 't': input[output_idx++] = '\t'; break;
case '\'': input[output_idx++] = '\''; break;
case '\"': input[output_idx++] = '\"'; break;
case '\\': input[output_idx++] = '\\'; break;
case 'x':
// Handle \x12, etc
if (input_idx + 2 < input_len) {
const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
char *err_p = nullptr;
const long val = std::strtol(x, &err_p, 16);
if (err_p == x + 2) {
input_idx += 2;
input[output_idx++] = char(val);
break;
}
}
// fall through
default: input[output_idx++] = '\\';
input[output_idx++] = input[input_idx]; break;
}
} else {
input[output_idx++] = input[input_idx];
}
}
input.resize(output_idx);
}
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
const char * sep = strchr(data, '=');
if (sep == nullptr || sep - data >= 128) {
fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
return false;
}
llama_model_kv_override kvo;
std::strncpy(kvo.key, data, sep - data);
kvo.key[sep - data] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
kvo.val_f64 = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.val_bool = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.val_bool = false;
} else {
fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
return false;
}
} else if (strncmp(sep, "str:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
if (strlen(sep) > 127) {
fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
return false;
}
strncpy(kvo.val_str, sep, 127);
kvo.val_str[127] = '\0';
} else {
fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
return false;
}
overrides.emplace_back(std::move(kvo));
return true;
}
//
// Filesystem utils
//
// Validate if a filename is safe to use
// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
bool fs_validate_filename(const std::string & filename) {
if (!filename.length()) {
// Empty filename invalid
return false;
}
if (filename.length() > 255) {
// Limit at common largest possible filename on Linux filesystems
// to avoid unnecessary further validation
// (On systems with smaller limits it will be caught by the OS)
return false;
}
std::u32string filename_utf32;
try {
#if defined(__clang__)
# pragma clang diagnostic push
# pragma clang diagnostic ignored "-Wdeprecated-declarations"
#elif defined(__GNUC__)
# pragma GCC diagnostic push
# pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
#if defined(__clang__)
# pragma clang diagnostic pop
#elif defined(__GNUC__)
# pragma GCC diagnostic pop
#endif
filename_utf32 = converter.from_bytes(filename);
// If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
// or invalid encodings were encountered. Reject such attempts
std::string filename_reencoded = converter.to_bytes(filename_utf32);
if (filename_reencoded != filename) {
return false;
}
} catch (const std::exception &) {
return false;
}
// Check for forbidden codepoints:
// - Control characters
// - Unicode equivalents of illegal characters
// - UTF-16 surrogate pairs
// - UTF-8 replacement character
// - Byte order mark (BOM)
// - Illegal characters: / \ : * ? " < > |
for (char32_t c : filename_utf32) {
if (c <= 0x1F // Control characters (C0)
|| c == 0x7F // Control characters (DEL)
|| (c >= 0x80 && c <= 0x9F) // Control characters (C1)
|| c == 0xFF0E // Fullwidth Full Stop (period equivalent)
|| c == 0x2215 // Division Slash (forward slash equivalent)
|| c == 0x2216 // Set Minus (backslash equivalent)
|| (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
|| c == 0xFFFD // Replacement Character (UTF-8)
|| c == 0xFEFF // Byte Order Mark (BOM)
|| c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
|| c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
return false;
}
}
// Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
// Unicode and other whitespace is not affected, only 0x20 space
if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
return false;
}
// Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
if (filename.find("..") != std::string::npos) {
return false;
}
// Reject "."
if (filename == ".") {
return false;
}
return true;
}
#ifdef _WIN32
static std::wstring utf8_to_wstring(const std::string& str) {
if (str.empty()) {
return std::wstring();
}
int size = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0);
if (size <= 0) {
return std::wstring();
}
std::wstring wstr(size, 0);
MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), &wstr[0], size);
return wstr;
}
#endif
// returns true if successful, false otherwise
bool fs_create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
std::wstring wpath = utf8_to_wstring(path);
// if the path already exists, check whether it's a directory
const DWORD attributes = GetFileAttributesW(wpath.c_str());
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return true;
}
size_t pos_slash = 0;
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
const std::wstring subpath = wpath.substr(0, pos_slash);
const wchar_t * test = subpath.c_str();
const bool success = CreateDirectoryW(test, NULL);
if (!success) {
const DWORD error = GetLastError();
// if the path already exists, ensure that it's a directory
if (error == ERROR_ALREADY_EXISTS) {
const DWORD attributes = GetFileAttributesW(subpath.c_str());
if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return false;
}
} else {
return false;
}
}
pos_slash += 1;
}
return true;
#else
// if the path already exists, check whether it's a directory
struct stat info;
if (stat(path.c_str(), &info) == 0) {
return S_ISDIR(info.st_mode);
}
size_t pos_slash = 1; // skip leading slashes for directory creation
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
const std::string subpath = path.substr(0, pos_slash);
struct stat info;
// if the path already exists, ensure that it's a directory
if (stat(subpath.c_str(), &info) == 0) {
if (!S_ISDIR(info.st_mode)) {
return false;
}
} else {
// create parent directories
const int ret = mkdir(subpath.c_str(), 0755);
if (ret != 0) {
return false;
}
}
pos_slash += 1;
}
return true;
#endif // _WIN32
}
std::string fs_get_cache_directory() {
std::string cache_directory = "";
auto ensure_trailing_slash = [](std::string p) {
// Make sure to add trailing slash
if (p.back() != DIRECTORY_SEPARATOR) {
p += DIRECTORY_SEPARATOR;
}
return p;
};
if (getenv("LLAMA_CACHE")) {
cache_directory = std::getenv("LLAMA_CACHE");
} else {
#ifdef __linux__
if (std::getenv("XDG_CACHE_HOME")) {
cache_directory = std::getenv("XDG_CACHE_HOME");
} else {
cache_directory = std::getenv("HOME") + std::string("/.cache/");
}
#elif defined(__APPLE__)
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
#elif defined(_WIN32)
cache_directory = std::getenv("LOCALAPPDATA");
#endif // __linux__
cache_directory = ensure_trailing_slash(cache_directory);
cache_directory += "llama.cpp";
}
return ensure_trailing_slash(cache_directory);
}
std::string fs_get_cache_file(const std::string & filename) {
GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
std::string cache_directory = fs_get_cache_directory();
const bool success = fs_create_directory_with_parents(cache_directory);
if (!success) {
throw std::runtime_error("failed to create cache directory: " + cache_directory);
}
return cache_directory + filename;
}
//
// Model utils
//
struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
llama_init_result iparams;
auto mparams = common_model_params_to_llama(params);
llama_model * model = nullptr;
if (!params.hf_repo.empty() && !params.hf_file.empty()) {
model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else if (!params.model_url.empty()) {
model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else {
model = llama_model_load_from_file(params.model.c_str(), mparams);
}
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return iparams;
}
auto cparams = common_context_params_to_llama(params);
llama_context * lctx = llama_init_from_model(model, cparams);
if (lctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
return iparams;
}
for (auto [op, on_off] : params.offload_policy) {
llama_set_offload_policy(lctx, op, on_off);
}
if (!params.control_vectors.empty()) {
if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
const auto cvec = llama_control_vector_load(params.control_vectors);
if (cvec.n_embd == -1) {
llama_free(lctx);
llama_free_model(model);
return iparams;
}
int err = llama_control_vector_apply(lctx,
cvec.data.data(),
cvec.data.size(),
cvec.n_embd,
params.control_vector_layer_start,
params.control_vector_layer_end);
if (err) {
llama_free(lctx);
llama_free_model(model);
return iparams;
}
}
// load and optionally apply lora adapters
for (auto & la : params.lora_adapters) {
llama_lora_adapter_container loaded_la;
loaded_la.path = la.path;
loaded_la.scale = la.scale;
loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
if (loaded_la.adapter == nullptr) {
fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
llama_free(lctx);
llama_free_model(model);
return iparams;
}
iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
}
if (!params.lora_init_without_apply) {
llama_lora_adapters_apply(lctx, iparams.lora_adapters);
}
if (params.ignore_eos) {
params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
}
if (params.sparams.dry_penalty_last_n == -1) {
LOG("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
params.sparams.dry_penalty_last_n = llama_n_ctx(lctx);
}
if (params.warmup) {
LOG("warming up the model with an empty run\n");
std::vector<llama_token> tmp;
llama_token bos = llama_token_bos(model);
llama_token eos = llama_token_eos(model);
// some models (e.g. T5) don't have a BOS token
if (bos != -1) {
tmp.push_back(bos);
}
else
{
tmp.push_back(eos);
}
if (llama_model_has_encoder(model)) {
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
decoder_start_token_id = bos;
}
tmp.clear();
tmp.push_back(decoder_start_token_id);
}
if (llama_model_has_decoder(model)) {
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
}
llama_kv_cache_clear(lctx);
llama_synchronize(lctx);
llama_reset_timings(lctx);
}
iparams.model = model;
iparams.context = lctx;
return iparams;
}
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
llama_lora_adapter_clear(ctx);
for (auto & la : lora_adapters) {
if (la.scale != 0.0f) {
llama_lora_adapter_set(ctx, la.adapter, la.scale);
}
}
}
static ggml_type kv_cache_type_from_str(const std::string & s) {
if (s == "f32") {
return GGML_TYPE_F32;
}
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "bf16") {
return GGML_TYPE_BF16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
if (s == "q4_0") {
return GGML_TYPE_Q4_0;
}
if (s == "q4_1") {
return GGML_TYPE_Q4_1;
}
if (s == "iq4_nl") {
return GGML_TYPE_IQ4_NL;
}
if (s == "q5_0") {
return GGML_TYPE_Q5_0;
}
if (s == "q5_1") {
return GGML_TYPE_Q5_1;
}
if (s == "q6_0") {
return GGML_TYPE_Q6_0;
}
if (s == "q8_KV") {
return GGML_TYPE_Q8_KV;
}
throw std::runtime_error("Invalid cache type: " + s);
}
static std::pair<int, int> get_batch_ubatch(const gpt_params & params) {
int n_batch = params.n_batch;
int n_ubatch = params.n_ubatch;
if (params.n_ctx > 0) {
n_batch = std::min(n_batch, params.n_ctx);
}
if (!params.mmproj.path.empty()) {
// temporary fix for qwen mtmd
n_batch = std::max(n_batch, n_ubatch);
n_ubatch = n_batch;
fprintf(stdout, "Adjust batch size for mtmd: u_batch = %d, batch = %d\n", n_ubatch, n_batch);
} else {
n_ubatch = std::min(n_batch, n_ubatch);
}
return {n_batch, n_ubatch};
}
struct llama_model_params common_model_params_to_llama(const gpt_params & params) {
auto mparams = llama_model_default_params();
mparams.devices = params.devices.c_str();
if (params.n_gpu_layers != -1) {
mparams.n_gpu_layers = params.n_gpu_layers;
}
mparams.mla = params.mla_attn;
mparams.dry_run = params.dry_run;
mparams.rpc_servers = params.rpc_servers.c_str();
mparams.main_gpu = params.main_gpu;
mparams.max_gpu = params.max_gpu;
mparams.ncmoe = params.ncmoe;
mparams.fit = params.fit;
mparams.fit_margin = params.fit_margin;
mparams.worst_graph_tokens = params.worst_graph_tokens;
mparams.type_k = kv_cache_type_from_str(params.cache_type_k);
mparams.type_v = kv_cache_type_from_str(params.cache_type_v);
mparams.type_k_first = kv_cache_type_from_str(params.type_k_first);
mparams.type_k_last = kv_cache_type_from_str(params.type_k_last );
mparams.type_v_first = kv_cache_type_from_str(params.type_v_first);
mparams.type_v_last = kv_cache_type_from_str(params.type_v_last );
mparams.n_k_first = params.n_k_first;
mparams.n_k_last = params.n_k_last;
mparams.n_v_first = params.n_v_first;
mparams.n_v_last = params.n_v_last;
mparams.max_ctx_size = params.n_ctx;
mparams.n_seq_max = params.n_parallel;
mparams.n_ubatch = get_batch_ubatch(params).second;
mparams.amb = params.attn_max_batch;
mparams.split_mode = params.split_mode;
mparams.tensor_split = params.tensor_split;
mparams.use_mmap = params.use_mmap;
mparams.use_mlock = params.use_mlock;
mparams.check_tensors = params.check_tensors;
mparams.repack_tensors = params.repack_tensors;
mparams.use_thp = params.use_thp;
mparams.validate_quants = params.validate_quants;
mparams.merge_qkv = params.merge_qkv;
mparams.merge_up_gate_exps = params.merge_up_gate_exps;
mparams.mtp = params.has_mtp;
mparams.flash_attn = params.flash_attn;
mparams.defer_experts = params.defer_experts;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;
} else {
GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
mparams.kv_overrides = params.kv_overrides.data();
}
if (params.tensor_buft_overrides.empty()) {
mparams.tensor_buft_overrides = NULL;
} else {
GGML_ASSERT(params.tensor_buft_overrides.back().pattern == nullptr && "Tensor buffer overrides not terminated with empty pattern");
mparams.tensor_buft_overrides = params.tensor_buft_overrides.data();
}
if (!mparams.flash_attn && ggml_is_quantized(mparams.type_v)) {
throw std::runtime_error("Quantized V cache cannot be used without flash attention");
}
return mparams;
}
static ggml_type ggml_type_from_str(const std::string & s) {
if (s == "f32") {
return GGML_TYPE_F32;
}
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "bf16") {
return GGML_TYPE_BF16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
throw std::runtime_error("Invalid graph reduce type: " + s);
}
struct llama_context_params common_context_params_to_llama(const gpt_params & params) {
auto cparams = llama_context_default_params();
auto [n_batch, n_ubatch] = get_batch_ubatch(params);
cparams.n_ctx = params.n_ctx;
cparams.n_seq_max = params.n_parallel;
cparams.n_batch = n_batch;
cparams.n_ubatch = n_ubatch;
cparams.n_threads = params.n_threads;
cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
cparams.seed = params.seed;
cparams.logits_all = params.logits_all;
cparams.embeddings = params.embedding;
cparams.worst_case_tokens = params.worst_graph_tokens;
cparams.rope_scaling_type = params.rope_scaling_type;
cparams.rope_freq_base = params.rope_freq_base;
cparams.rope_freq_scale = params.rope_freq_scale;
cparams.yarn_ext_factor = params.yarn_ext_factor;
cparams.yarn_attn_factor = params.yarn_attn_factor;
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
cparams.pooling_type = params.pooling_type;
cparams.attention_type = params.attention_type;
cparams.defrag_thold = params.defrag_thold;
cparams.cb_eval = params.cb_eval;
cparams.cb_eval_user_data = params.cb_eval_user_data;
cparams.offload_kqv = !params.no_kv_offload;
cparams.flash_attn = params.flash_attn;
cparams.mla_attn = params.mla_attn;
cparams.attn_max_batch = params.attn_max_batch;
cparams.fused_moe_up_gate = params.fused_moe_up_gate;
cparams.grouped_expert_routing = params.grouped_expert_routing;
cparams.fused_up_gate = params.fused_up_gate;
cparams.fused_mmad = params.fused_mmad;
cparams.rope_cache = params.rope_cache;
cparams.graph_reuse = params.graph_reuse;
cparams.k_cache_hadamard = params.k_cache_hadamard;
cparams.v_cache_hadamard = params.v_cache_hadamard;
cparams.split_mode_graph_scheduling = params.split_mode_graph_scheduling;
//cparams.split_mode_f16 = params.split_mode_f16;
cparams.scheduler_async = params.scheduler_async;
cparams.min_experts = params.min_experts;
cparams.thresh_experts = params.thresh_experts;
cparams.only_active_experts = params.only_active_exps;
cparams.max_extra_alloc = params.max_extra_alloc_MiB;
cparams.mtp = params.has_mtp;
cparams.mtp_op_type = MTP_OP_NONE;
cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
cparams.type_reduce = ggml_type_from_str(params.reduce_type);
if (!cparams.flash_attn && ggml_is_quantized(cparams.type_v)) {
throw std::runtime_error("Quantized V cache cannot be used without flash attention");
}
cparams.type_k_first = kv_cache_type_from_str(params.type_k_first);
cparams.type_k_last = kv_cache_type_from_str(params.type_k_last );
cparams.type_v_first = kv_cache_type_from_str(params.type_v_first);
cparams.type_v_last = kv_cache_type_from_str(params.type_v_last );
cparams.n_k_first = params.n_k_first;
cparams.n_k_last = params.n_k_last;
cparams.n_v_first = params.n_v_first;
cparams.n_v_last = params.n_v_last;
if (!cparams.flash_attn && ggml_is_quantized(cparams.type_v_first) && cparams.n_v_first > 0) {
throw std::runtime_error("Quantized V cache cannot be used without flash attention");
}
if (!cparams.flash_attn && ggml_is_quantized(cparams.type_v_last) && cparams.n_v_last > 0) {
throw std::runtime_error("Quantized V cache cannot be used without flash attention");
}
if (!params.offload_policy.empty()) cparams.offload_policy = (void *)&params.offload_policy;
if (!params.cuda_params.empty()) cparams.cuda_params = (void *)params.cuda_params.data();
return cparams;
}
#ifdef LLAMA_USE_CURL
static bool starts_with(const std::string & str, const std::string & prefix) {
// While we wait for C++20's std::string::starts_with...
return str.rfind(prefix, 0) == 0;
}
static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
// Initialize libcurl
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
if (!curl) {
fprintf(stderr, "%s: error initializing libcurl\n", __func__);
return false;
}
bool force_download = false;
// Set the URL, allow to follow http redirection
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
// Check if hf-token or bearer-token was specified
if (!hf_token.empty()) {
std::string auth_header = "Authorization: Bearer ";
auth_header += hf_token.c_str();
struct curl_slist *http_headers = NULL;
http_headers = curl_slist_append(http_headers, auth_header.c_str());
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
}
#if defined(_WIN32)
// CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
// operating system. Currently implemented under MS-Windows.
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
#endif
// Check if the file already exists locally
struct stat model_file_info;
auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
// If the file exists, check its JSON metadata companion file.
std::string metadata_path = path + ".json";
nlohmann::json metadata;
std::string etag;
std::string last_modified;
if (file_exists) {
// Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
std::ifstream metadata_in(metadata_path);
if (metadata_in.good()) {
try {
metadata_in >> metadata;
fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
if (metadata.contains("url") && metadata.at("url").is_string()) {
auto previous_url = metadata.at("url").get<std::string>();
if (previous_url != url) {
fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
return false;
}
}
if (metadata.contains("etag") && metadata.at("etag").is_string()) {
etag = metadata.at("etag");
}
if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
last_modified = metadata.at("lastModified");
}
} catch (const nlohmann::json::exception & e) {
fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
return false;
}
}
} else {
fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
}
// Send a HEAD request to retrieve the etag and last-modified headers
struct llama_load_model_from_url_headers {
std::string etag;
std::string last_modified;
};
llama_load_model_from_url_headers headers;
{
typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
static std::regex header_regex("([^:]+): (.*)\r\n");
static std::regex etag_regex("ETag", std::regex_constants::icase);
static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
std::string header(buffer, n_items);
std::smatch match;
if (std::regex_match(header, match, header_regex)) {
const std::string & key = match[1];
const std::string & value = match[2];
if (std::regex_match(key, match, etag_regex)) {
headers->etag = value;
} else if (std::regex_match(key, match, last_modified_regex)) {
headers->last_modified = value;
}
}
return n_items;
};
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
CURLcode res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
return false;
}
long http_code = 0;
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
if (http_code != 200) {
// HEAD not supported, we don't know if the file has changed
// force trigger downloading
force_download = true;
fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
}
}
bool should_download = !file_exists || force_download;
if (!should_download) {
if (!etag.empty() && etag != headers.etag) {
fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
should_download = true;
} else if (!last_modified.empty() && last_modified != headers.last_modified) {
fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
should_download = true;
}
}
if (should_download) {
std::string path_temporary = path + ".downloadInProgress";
if (file_exists) {
fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
if (remove(path.c_str()) != 0) {
fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
return false;
}
}
// Set the output file
struct FILE_deleter {
void operator()(FILE * f) const {
fclose(f);
}
};
std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
if (!outfile) {
fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
return false;
}
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
return fwrite(data, size, nmemb, (FILE *)fd);
};
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
// display download progress
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
// helper function to hide password in URL
auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
std::size_t protocol_pos = url.find("://");
if (protocol_pos == std::string::npos) {
return url; // Malformed URL
}
std::size_t at_pos = url.find('@', protocol_pos + 3);
if (at_pos == std::string::npos) {
return url; // No password in URL
}
return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
};
// start the download
fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
auto res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
return false;
}
long http_code = 0;
curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
if (http_code < 200 || http_code >= 400) {
fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
return false;
}
// Causes file to be closed explicitly here before we rename it.
outfile.reset();
// Write the updated JSON metadata file.
metadata.update({
{"url", url},
{"etag", headers.etag},
{"lastModified", headers.last_modified}
});
std::ofstream(metadata_path) << metadata.dump(4);
fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
return false;
}
}
return true;
}
struct llama_model * llama_load_model_from_url(
const char * model_url,
const char * path_model,
const char * hf_token,
const struct llama_model_params & params) {
// Basic validation of the model_url
if (!model_url || strlen(model_url) == 0) {
fprintf(stderr, "%s: invalid model_url\n", __func__);
return NULL;
}
if (!llama_download_file(model_url, path_model, hf_token)) {
return NULL;
}
// check for additional GGUFs split to download
int n_split = 0;
{
struct gguf_init_params gguf_params = {
/*.no_alloc = */ true,
/*.ctx = */ NULL,
};
auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
if (!ctx_gguf) {
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, path_model);
return NULL;
}
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
if (key_n_split >= 0) {
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
}
gguf_free(ctx_gguf);
}
if (n_split > 1) {
char split_prefix[PATH_MAX] = {0};
char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
// Verify the first split file format
// and extract split URL and PATH prefixes
{
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
fprintf(stderr, "\n%s: unexpected model file name: %s"
" n_split=%d\n", __func__, path_model, n_split);
return NULL;
}
if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
fprintf(stderr, "\n%s: unexpected model url: %s"
" n_split=%d\n", __func__, model_url, n_split);
return NULL;
}
}
// Prepare download in parallel
std::vector<std::future<bool>> futures_download;
for (int idx = 1; idx < n_split; idx++) {
futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split, hf_token](int download_idx) -> bool {
char split_path[PATH_MAX] = {0};
llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
return llama_download_file(split_url, split_path, hf_token);
}, idx));
}
// Wait for all downloads to complete
for (auto & f : futures_download) {
if (!f.get()) {
return NULL;
}
}
}
return llama_model_load_from_file(path_model, params);
}
struct llama_model * llama_load_model_from_hf(
const char * repo,
const char * model,
const char * path_model,
const char * hf_token,
const struct llama_model_params & params) {
// construct hugging face model url:
//
// --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf
// https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf
//
// --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf
// https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf
//
std::string model_url = "https://huggingface.co/";
model_url += repo;
model_url += "/resolve/main/";
model_url += model;
return llama_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
}
#else
struct llama_model * llama_load_model_from_url(
const char * /*model_url*/,
const char * /*path_model*/,
const char * /*hf_token*/,
const struct llama_model_params & /*params*/) {
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
return nullptr;
}
struct llama_model * llama_load_model_from_hf(
const char * /*repo*/,
const char * /*model*/,
const char * /*path_model*/,
const char * /*hf_token*/,
const struct llama_model_params & /*params*/) {
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
return nullptr;
}
#endif // LLAMA_USE_CURL
//
// Batch utils
//
void common_batch_clear(struct llama_batch & batch) {
batch.n_tokens = 0;
}
void common_batch_add(
struct llama_batch & batch,
llama_token id,
llama_pos pos,
const std::vector<llama_seq_id> & seq_ids,
bool logits) {
GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded");
batch.token [batch.n_tokens] = id;
batch.pos [batch.n_tokens] = pos;
batch.n_seq_id[batch.n_tokens] = seq_ids.size();
for (size_t i = 0; i < seq_ids.size(); ++i) {
batch.seq_id[batch.n_tokens][i] = seq_ids[i];
}
batch.logits [batch.n_tokens] = logits;
batch.n_tokens++;
}
//
// Vocab utils
//
std::vector<llama_token> common_tokenize(
const struct llama_context * ctx,
const std::string & text,
bool add_special,
bool parse_special) {
return common_tokenize(llama_get_model(ctx), text, add_special, parse_special);
}
std::vector<llama_token> common_tokenize(
const struct llama_model * model,
const std::string & text,
bool add_special,
bool parse_special) {
// upper limit for the number of tokens
int n_tokens = text.length() + 2 * add_special;
std::vector<llama_token> result(n_tokens);
n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
}
return result;
}
std::vector<llama_token> llama_tokenize(
const struct llama_vocab* vocab,
const std::string& text,
bool add_special,
bool parse_special) {
// upper limit for the number of tokens
int n_tokens = text.length() + 2 * add_special;
std::vector<llama_token> result(n_tokens);
n_tokens = llama_vocab_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
if (n_tokens == std::numeric_limits<int32_t>::min()) {
throw std::runtime_error("Tokenization failed: input text too large, tokenization result exceeds int32_t limit");
}
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_vocab_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
GGML_ASSERT(check == -n_tokens);
}
else {
result.resize(n_tokens);
}
return result;
}
std::vector<llama_token> common_tokenize(
const struct llama_vocab * vocab,
const std::string & text,
bool add_special,
bool parse_special){
return llama_tokenize(vocab, text, add_special, parse_special);
}
std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
if (n_chars < 0) {
piece.resize(-n_chars);
int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
GGML_ASSERT(check == -n_chars);
}
else {
piece.resize(n_chars);
}
return piece;
}
std::string llama_token_to_piece(const struct llama_model* model, llama_token token, bool special) {
std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
const int n_chars = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
if (n_chars < 0) {
piece.resize(-n_chars);
int check = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
GGML_ASSERT(check == -n_chars);
}
else {
piece.resize(n_chars);
}
return piece;
}
std::string common_detokenize(const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
return common_detokenize(vocab, tokens, special);
}
std::string common_detokenize(const struct llama_vocab * vocab, const std::vector<llama_token> & tokens, bool special) {
std::string text;
text.resize(std::max(text.capacity(), tokens.size()));
int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
if (n_chars < 0) {
text.resize(-n_chars);
n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization
}
text.resize(n_chars);
// NOTE: the original tokenizer decodes bytes after collecting the pieces.
return text;
}
std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) {
std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
const int n_chars = llama_token_to_piece_vocab(vocab, token, &piece[0], piece.size(), 0, special);
if (n_chars < 0) {
piece.resize(-n_chars);
int check = llama_token_to_piece_vocab(vocab, token, &piece[0], piece.size(), 0, special);
GGML_ASSERT(check == -n_chars);
} else {
piece.resize(n_chars);
}
return piece;
}
bool llama_should_add_bos_token(const llama_model * model) {
const int add_bos = llama_add_bos_token(model);
const llama_vocab * vocab = llama_get_model_vocab(model);
return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_SPM);
}
//
// KV cache utils
//
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
llama_kv_cache_view_cell * c_curr = view.cells;
llama_seq_id * cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
if (i % row_size == 0) {
printf("\n%5d: ", i);
}
int seq_count = 0;
for (int j = 0; j < view.n_seq_max; j++) {
if (cs_curr[j] >= 0) { seq_count++; }
}
putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
}
printf("\n=== Done dumping\n");
}
void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
std::unordered_map<llama_seq_id, size_t> seqs;
llama_kv_cache_view_cell * c_curr = view.cells;
llama_seq_id * cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
for (int j = 0; j < view.n_seq_max; j++) {
if (cs_curr[j] < 0) { continue; }
if (seqs.find(cs_curr[j]) == seqs.end()) {
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
const size_t sz = seqs.size();
seqs[cs_curr[j]] = sz;
}
}
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
}
printf("=== Sequence legend: ");
for (const auto & it : seqs) {
printf("%zu=%d, ", it.second, it.first);
}
printf("'+'=other sequence ids");
c_curr = view.cells;
cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
if (i % row_size == 0) {
printf("\n%5d: ", i);
}
for (int j = 0; j < view.n_seq_max; j++) {
if (cs_curr[j] >= 0) {
const auto & it = seqs.find(cs_curr[j]);
putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
} else {
putchar('.');
}
}
putchar(' ');
}
printf("\n=== Done dumping\n");
}
//
// Embedding utils
//
void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
double sum = 0.0;
switch (embd_norm) {
case -1: // no normalisation
sum = 1.0;
break;
case 0: // max absolute
for (int i = 0; i < n; i++) {
if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
}
sum /= 32760.0; // make an int16 range
break;
case 2: // euclidean
for (int i = 0; i < n; i++) {
sum += inp[i] * inp[i];
}
sum = std::sqrt(sum);
break;
default: // p-norm (euclidean is p-norm p=2)
for (int i = 0; i < n; i++) {
sum += std::pow(std::abs(inp[i]), embd_norm);
}
sum = std::pow(sum, 1.0 / embd_norm);
break;
}
const float norm = sum > 0.0 ? 1.0 / sum : 0.0f;
for (int i = 0; i < n; i++) {
out[i] = inp[i] * norm;
}
}
float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){
double sum = 0.0;
double sum1 = 0.0;
double sum2 = 0.0;
for (int i = 0; i < n; i++) {
sum += embd1[i] * embd2[i];
sum1 += embd1[i] * embd1[i];
sum2 += embd2[i] * embd2[i];
}
// Handle the case where one or both vectors are zero vectors
if (sum1 == 0.0 || sum2 == 0.0) {
if (sum1 == 0.0 && sum2 == 0.0) {
return 1.0f; // two zero vectors are similar
}
return 0.0f;
}
return sum / (sqrt(sum1) * sqrt(sum2));
}
//
// Control vector utils
//
static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
llama_control_vector_data result = { -1, {} };
ggml_context * ctx = nullptr;
struct gguf_init_params meta_gguf_params = {
/* .no_alloc = */ false,
/* .ctx = */ &ctx,
};
struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
if (!ctx_gguf) {
fprintf(stderr, "%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
return result;
}
int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
if (n_tensors == 0) {
fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
}
for (int i = 0; i < n_tensors; i++) {
std::string name = gguf_get_tensor_name(ctx_gguf, i);
int layer_idx = -1;
// split on '.'
size_t dotpos = name.find('.');
if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
try {
layer_idx = std::stoi(name.substr(dotpos + 1));
} catch (...) {
layer_idx = -1;
}
}
if (layer_idx < 0) {
fprintf(stderr, "%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
} else if (layer_idx == 0) {
fprintf(stderr, "%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
if (tensor->type != GGML_TYPE_F32) {
fprintf(stderr, "%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
if (ggml_n_dims(tensor) != 1) {
fprintf(stderr, "%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
if (result.n_embd == -1) {
result.n_embd = ggml_nelements(tensor);
} else if (ggml_nelements(tensor) != result.n_embd) {
fprintf(stderr, "%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
// extend if necessary - do not store data for layer 0 (it's not used)
result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f);
const float * src = (const float *) tensor->data;
float * dst = result.data.data() + result.n_embd * (layer_idx - 1); // layer 1 at [0]
for (int j = 0; j < result.n_embd; j++) {
dst[j] += src[j] * load_info.strength; // allows multiple directions for same layer in same file
}
}
if (result.n_embd == -1) {
fprintf(stderr, "%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
result.data.clear();
}
gguf_free(ctx_gguf);
ggml_free(ctx);
return result;
}
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
llama_control_vector_data result = { -1, {} };
for (const auto & info : load_infos) {
auto cur = llama_control_vector_load_one(info);
if (cur.n_embd == -1) {
result.n_embd = -1;
break;
}
if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
fprintf(stderr, "%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
result.n_embd = -1;
break;
}
if (result.n_embd == -1) {
result = std::move(cur);
} else {
result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f); // extend if necessary
for (size_t i = 0; i < cur.data.size(); i++) {
result.data[i] += cur.data[i];
}
}
}
if (result.n_embd == -1) {
fprintf(stderr, "%s: no valid control vector files passed\n", __func__);
result.data.clear();
}
return result;
}
//
// YAML utils
//
void yaml_dump_vector_float(FILE * stream, const char * prop_name, const std::vector<float> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%e, ", data[i]);
}
fprintf(stream, "%e]\n", data.back());
}
void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector<int> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%d, ", data[i]);
}
fprintf(stream, "%d]\n", data.back());
}
void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) {
std::string data_str(data == NULL ? "" : data);
if (data_str.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
size_t pos_start = 0;
size_t pos_found = 0;
if (std::isspace(data_str[0]) || std::isspace(data_str.back())) {
data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)");
data_str = "\"" + data_str + "\"";
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
if (data_str.find('\n') == std::string::npos) {
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
fprintf(stream, "%s: |\n", prop_name);
while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
pos_start = pos_found + 1;
}
}
void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
const common_params_sampling & sparams = params.sparams;
fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT);
fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER);
fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false");
fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false");
fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false");
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false");
fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");
#ifdef NDEBUG
fprintf(stream, "debug: false\n");
#else
fprintf(stream, "debug: true\n");
#endif // NDEBUG
fprintf(stream, "model_desc: %s\n", model_desc);
fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx)));
#ifdef __OPTIMIZE__
fprintf(stream, "optimize: true\n");
#else
fprintf(stream, "optimize: false\n");
#endif // __OPTIMIZE__
fprintf(stream, "time: %s\n", timestamp.c_str());
fprintf(stream, "\n");
fprintf(stream, "###############\n");
fprintf(stream, "# User Inputs #\n");
fprintf(stream, "###############\n");
fprintf(stream, "\n");
fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "dry_allowed_length: %d # default: 2\n", sparams.dry_allowed_length);
fprintf(stream, "dry_base: %.2f # default: 1.75\n", sparams.dry_base);
fprintf(stream, "dry_multiplier: %.1f # default: 0.0\n", sparams.dry_multiplier);
fprintf(stream, "dry_penalty_last_n: %d # default: -1 (0 = disable, -1 = context size)\n", sparams.dry_penalty_last_n);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
yaml_dump_string_multiline(stream, "grammar", sparams.grammar.grammar.c_str());
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));
const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str());
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str());
fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
fprintf(stream, "logit_bias:\n");
for (std::pair<llama_token, float> lb : sparams.logit_bias) {
if (ignore_eos && lb.first == logit_bias_eos->first) {
continue;
}
fprintf(stream, " %d: %f", lb.first, lb.second);
}
fprintf(stream, "lora:\n");
for (auto & la : params.lora_adapters) {
if (la.scale == 1.0f) {
fprintf(stream, " - %s\n", la.path.c_str());
}
}
fprintf(stream, "lora_scaled:\n");
for (auto & la : params.lora_adapters) {
if (la.scale != 1.0f) {
fprintf(stream, " - %s: %f\n", la.path.c_str(), la.scale);
}
}
fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false");
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "max_gpu: %d # default: 0\n", params.max_gpu);
fprintf(stream, "ncmoe: %d # default: 0\n", params.ncmoe);
fprintf(stream, "fit: %d # default: false\n", params.fit);
fprintf(stream, "fit_margin: %d # default: 0\n", params.fit_margin);
fprintf(stream, "worst_graph_tokens: %d # default: 0\n", params.worst_graph_tokens);
fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
fprintf(stream, "xtc_probability: %f # default: 0.0\n", sparams.xtc_probability);
fprintf(stream, "xtc_threshold: %f # default: 0.0\n", sparams.xtc_threshold);
fprintf(stream, "top_n_sigma: %f # default: 0.0\n", sparams.top_n_sigma);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
fprintf(stream, "model_draft: %s # default:\n", params.speculative.model.c_str());
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
fprintf(stream, "repack: %s # default: false\n", params.repack_tensors ? "true" : "false");
fprintf(stream, "use_thp: %s # default: false\n", params.use_thp ? "true" : "false");
fprintf(stream, "validate_quants: %s # default: false\n", params.validate_quants ? "true" : "false");
fprintf(stream, "merge_qkv: %s # default: false\n", params.merge_qkv ? "true" : "false");
fprintf(stream, "merge_up_gate_exps: %s # default: false\n", params.merge_up_gate_exps ? "true" : "false");
fprintf(stream, "defer_experts: %s # default: false\n", params.defer_experts ? "true" : "false");
fprintf(stream, "max_extra_alloc: %d # default: 256\n", params.max_extra_alloc_MiB);
fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str());
fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens);
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat);
fprintf(stream, "reverse_prompt:\n");
for (std::string ap : params.antiprompt) {
size_t pos = 0;
while ((pos = ap.find('\n', pos)) != std::string::npos) {
ap.replace(pos, 1, "\\n");
pos += 1;
}
fprintf(stream, " - %s\n", ap.c_str());
}
fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
fprintf(stream, "mla_attn: %d # default: 0\n", params.mla_attn);
fprintf(stream, "attn_max_batch: %d # default: 0\n", params.attn_max_batch);
fprintf(stream, "fused_moe: %s # default: false\n", params.fused_moe_up_gate ? "true" : "false");
fprintf(stream, "grouped_expert_routing: %s # default: false\n", params.grouped_expert_routing ? "true" : "false");
fprintf(stream, "fused_up_gate: %s # default: true\n", params.fused_up_gate ? "true" : "false");
fprintf(stream, "fused_mmad: %s # default: true\n", params.fused_mmad ? "true" : "false");
fprintf(stream, "rope_cache: %s # default: false\n", params.rope_cache ? "true" : "false");
fprintf(stream, "graph_reuse: %s # default: false\n", params.graph_reuse ? "true" : "false");
fprintf(stream, "k_cache_hadamard: %s # default: false\n", params.k_cache_hadamard ? "true" : "false");
fprintf(stream, "v_cache_hadamard: %s # default: false\n", params.v_cache_hadamard ? "true" : "false");
fprintf(stream, "split_mode_graph_scheduling: %s # default: false\n", params.split_mode_graph_scheduling ? "true" : "false");
//fprintf(stream, "split_mode_f16: %s # default: true\n", params.split_mode_f16 ? "true" : "false");
fprintf(stream, "reduce_type: %s # default f16\n", params.reduce_type.c_str());
fprintf(stream, "scheduler_async: %s # default: false\n", params.scheduler_async ? "true" : "false");
fprintf(stream, "ser: %d,%g # defaulr: -1,0\n", params.min_experts, params.thresh_experts);
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
fprintf(stream, "adaptive_target: %f # default: -1.0\n", sparams.adaptive_target);
fprintf(stream, "adaptive_decay: %f # default: 0.9\n", sparams.adaptive_decay);
fprintf(stream, "adaptive_updt_w_cur: %s # default: false\n", sparams.adaptive_updt_w_cur ? "true" : "false");
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
}
//
// Argparse utils
//
std::tuple<uint32_t, uint32_t, std::string, float> argparse_allowlist_unicode_rule(std::string argstr) {
// format:
// LOWER..UPPER,SCRIPT:BIAS
auto subs = string_split(argstr, ":");
float bias = subs.size() == 1 ? 0 : std::stof(subs[1]);
subs = string_split(subs[0], ",");
std::string script = std::all_of(subs.back().begin(), subs.back().end(), [](char c) {
return std::isalpha(c);
}) ? string_lower(subs.back()) : "*";
if (script == "ascii") {
return { 0x000000, 0x00007F, "*", bias };
}
uint32_t first = 0;
uint32_t last = -1;
if ((script == "*") || (subs.size() > 1)) {
subs = string_split(subs.front(), ".");
if (!subs.front().empty()) {
first = std::stoul(subs.front());
}
if (!subs.back().empty()) {
last = std::stoul(subs.back());
}
}
return { std::min(first, last), std::max(first, last), script, bias };
}
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