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faf69d4237
llama.cpp/common/common.cpp
Georgi Gerganov faf69d4237
llama : sanitize invalid tokens (#9357) 
* common : do not add null tokens during warmup

ggml-ci

* llama : check that the input tokens are valid

ggml-ci

* tests : fix batch size of bert model

ggml-ci
2024-09-08 00:33:13 +03:00

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#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 "json.hpp"
#include "json-schema-to-grammar.h"
#include "llama.h"
#include <algorithm>
#include <cinttypes>
#include <cmath>
#include <codecvt>
#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>
#include <climits>
#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
using json = nlohmann::ordered_json;
//
// 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) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
// TODO: windows + arm64 + mingw64
unsigned int n_threads_win = std::thread::hardware_concurrency();
unsigned int default_threads = n_threads_win > 0 ? (n_threads_win <= 4 ? n_threads_win : n_threads_win / 2) : 4;
DWORD buffer_size = 0;
if (!GetLogicalProcessorInformationEx(RelationProcessorCore, nullptr, &buffer_size)) {
if (GetLastError() != ERROR_INSUFFICIENT_BUFFER) {
return default_threads;
}
}
std::vector<char> buffer(buffer_size);
if (!GetLogicalProcessorInformationEx(RelationProcessorCore, reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data()), &buffer_size)) {
return default_threads;
}
int32_t num_physical_cores = 0;
PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data());
while (buffer_size > 0) {
if (info->Relationship == RelationProcessorCore) {
num_physical_cores += info->Processor.GroupCount;
}
buffer_size -= info->Size;
info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(reinterpret_cast<char*>(info) + info->Size);
}
return num_physical_cores > 0 ? num_physical_cores : default_threads;
#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();
}
// Helper for setting process priority
#if defined(_WIN32)
bool set_process_priority(enum ggml_sched_priority prio) {
if (prio == GGML_SCHED_PRIO_NORMAL) {
return true;
}
DWORD p = NORMAL_PRIORITY_CLASS;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: p = NORMAL_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_MEDIUM: p = ABOVE_NORMAL_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_HIGH: p = HIGH_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS; break;
}
if (!SetPriorityClass(GetCurrentProcess(), p)) {
fprintf(stderr, "warn: failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
return false;
}
return true;
}
#else // MacOS and POSIX
#include <sys/types.h>
#include <sys/resource.h>
bool set_process_priority(enum ggml_sched_priority prio) {
if (prio == GGML_SCHED_PRIO_NORMAL) {
return true;
}
int p = 0;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: p = 0; break;
case GGML_SCHED_PRIO_MEDIUM: p = -5; break;
case GGML_SCHED_PRIO_HIGH: p = -10; break;
case GGML_SCHED_PRIO_REALTIME: p = -20; break;
}
if (!setpriority(PRIO_PROCESS, 0, p)) {
fprintf(stderr, "warn: failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
return false;
}
return true;
}
#endif
//
// CLI argument parsing
//
#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
LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
static std::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);
}
static 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;
}
}
void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
int32_t n_set = 0;
if (cpuparams.n_threads < 0) {
// Assuming everything about cpuparams is invalid
if (role_model != nullptr) {
cpuparams = *role_model;
} else {
cpuparams.n_threads = cpu_get_num_math();
}
}
for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
if (cpuparams.cpumask[i]) {
n_set++;
}
}
if (n_set && n_set < cpuparams.n_threads) {
// Not enough set bits, may experience performance issues.
fprintf(stderr, "warn: Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
}
}
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options) {
std::string arg;
const std::string arg_prefix = "--";
gpt_sampler_params & sparams = params.sparams;
std::unordered_map<std::string, llama_arg *> arg_to_options;
for (auto & opt : options) {
for (const auto & arg : opt.args) {
arg_to_options[arg] = &opt;
}
}
// handle environment variables
for (auto & opt : options) {
std::string value;
if (opt.get_value_from_env(value)) {
try {
if (opt.handler_void && (value == "1" || value == "true")) {
opt.handler_void(params);
}
if (opt.handler_int) {
opt.handler_int(params, std::stoi(value));
}
if (opt.handler_string) {
opt.handler_string(params, value);
continue;
}
} catch (std::exception & e) {
throw std::invalid_argument(format(
"error while handling environment variable \"%s\": %s\n\n", opt.env, e.what()));
}
}
}
// handle command line arguments
auto check_arg = [&](int i) {
if (i+1 >= argc) {
throw std::invalid_argument("expected value for argument");
}
};
for (int i = 1; i < argc; i++) {
const std::string arg_prefix = "--";
std::string arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (arg_to_options.find(arg) == arg_to_options.end()) {
throw std::invalid_argument(format("error: invalid argument: %s", arg.c_str()));
}
auto opt = *arg_to_options[arg];
if (opt.has_value_from_env()) {
fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
}
try {
if (opt.handler_void) {
opt.handler_void(params);
continue;
}
// arg with single value
check_arg(i);
std::string val = argv[++i];
if (opt.handler_int) {
opt.handler_int(params, std::stoi(val));
continue;
}
if (opt.handler_string) {
opt.handler_string(params, val);
continue;
}
// arg with 2 values
check_arg(i);
std::string val2 = argv[++i];
if (opt.handler_str_str) {
opt.handler_str_str(params, val, val2);
continue;
}
} catch (std::exception & e) {
throw std::invalid_argument(format(
"error while handling argument \"%s\": %s\n\n"
"usage:\n%s\n\nto show complete usage, run with -h",
arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str()));
}
}
postprocess_cpu_params(params.cpuparams, nullptr);
postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams_batch, &params.cpuparams_batch);
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.escape) {
string_process_escapes(params.prompt);
string_process_escapes(params.input_prefix);
string_process_escapes(params.input_suffix);
for (auto & antiprompt : params.antiprompt) {
string_process_escapes(antiprompt);
}
}
if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back();
params.kv_overrides.back().key[0] = 0;
}
if (sparams.seed == LLAMA_DEFAULT_SEED) {
sparams.seed = time(NULL);
}
return true;
}
bool gpt_params_parse(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options) {
const auto params_org = params; // the example can modify the default params
try {
if (!gpt_params_parse_ex(argc, argv, params, options)) {
params = params_org;
return false;
}
if (params.usage) {
gpt_params_print_usage(params, options);
if (params.print_usage) {
params.print_usage(argc, argv);
}
exit(0);
}
} catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
params = params_org;
return false;
}
return true;
}
bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
size_t dash_loc = range.find('-');
if (dash_loc == std::string::npos) {
fprintf(stderr, "Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
return false;
}
size_t start_i;
size_t end_i;
if (dash_loc == 0) {
start_i = 0;
} else {
start_i = std::stoull(range.substr(0, dash_loc));
if (start_i >= GGML_MAX_N_THREADS) {
fprintf(stderr, "Start index out of bounds!\n");
return false;
}
}
if (dash_loc == range.length() - 1) {
end_i = GGML_MAX_N_THREADS - 1;
} else {
end_i = std::stoull(range.substr(dash_loc + 1));
if (end_i >= GGML_MAX_N_THREADS) {
fprintf(stderr, "End index out of bounds!\n");
return false;
}
}
for (size_t i = start_i; i <= end_i; i++) {
boolmask[i] = true;
}
return true;
}
bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) {
// Discard potential 0x prefix
size_t start_i = 0;
if (mask.length() >= 2 && mask.substr(0, 2) == "0x") {
start_i = 2;
}
size_t num_digits = mask.length() - start_i;
if (num_digits > 128) num_digits = 128;
size_t end_i = num_digits + start_i;
for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) {
char c = mask.at(i);
int8_t id = c;
if ((c >= '0' && c <= '9')) {
id -= '0';
} else if (c >= 'a' && c <= 'f') {
id -= 'a' - 10;
} else if (c >= 'A' && c <= 'F') {
id -= 'A' - 10;
} else {
fprintf(stderr, "Invalid hex character '%c' at position %d\n", c, int32_t(i));
return false;
}
boolmask[ n ] = boolmask[ n ] || ((id & 8) != 0);
boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0);
boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0);
boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0);
}
return true;
}
static std::vector<std::string> break_str_into_lines(std::string input, size_t max_char_per_line) {
std::vector<std::string> result;
std::istringstream iss(input);
std::string line;
auto add_line = [&](const std::string& l) {
if (l.length() <= max_char_per_line) {
result.push_back(l);
} else {
std::istringstream line_stream(l);
std::string word, current_line;
while (line_stream >> word) {
if (current_line.length() + !current_line.empty() + word.length() > max_char_per_line) {
if (!current_line.empty()) result.push_back(current_line);
current_line = word;
} else {
current_line += (!current_line.empty() ? " " : "") + word;
}
}
if (!current_line.empty()) result.push_back(current_line);
}
};
while (std::getline(iss, line)) {
add_line(line);
}
return result;
}
std::string llama_arg::to_string() {
// params for printing to console
const static int n_leading_spaces = 40;
const static int n_char_per_line_help = 70; // TODO: detect this based on current console
std::string leading_spaces(n_leading_spaces, ' ');
std::ostringstream ss;
for (const auto arg : args) {
if (arg == args.front()) {
if (args.size() == 1) {
ss << arg;
} else {
ss << format("%-7s", arg) << ", ";
}
} else {
ss << arg << (arg != args.back() ? ", " : "");
}
}
if (value_hint) ss << " " << value_hint;
if (value_hint_2) ss << " " << value_hint_2;
if (ss.tellp() > n_leading_spaces - 3) {
// current line is too long, add new line
ss << "\n" << leading_spaces;
} else {
// padding between arg and help, same line
ss << std::string(leading_spaces.size() - ss.tellp(), ' ');
}
const auto help_lines = break_str_into_lines(help, n_char_per_line_help);
for (const auto & line : help_lines) {
ss << (&line == &help_lines.front() ? "" : leading_spaces) << line << "\n";
}
return ss.str();
}
void gpt_params_print_usage(gpt_params & params, std::vector<llama_arg> & options) {
auto print_options = [](std::vector<llama_arg *> & options) {
for (llama_arg * opt : options) {
printf("%s", opt->to_string().c_str());
}
};
std::vector<llama_arg *> common_options;
std::vector<llama_arg *> specific_options;
for (auto & opt : options) {
// in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
if (opt.in_example(params.curr_ex)) {
specific_options.push_back(&opt);
} else {
common_options.push_back(&opt);
}
}
printf("----- common options -----\n\n");
print_options(common_options);
// TODO: maybe convert enum llama_example to string
printf("\n\n----- example-specific options -----\n\n");
print_options(specific_options);
}
std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex) {
return gpt_params_parser_init(params, ex, nullptr);
}
std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex, std::function<void(int, char **)> print_usage) {
std::vector<llama_arg> options;
params.print_usage = print_usage;
params.curr_ex = ex;
std::string sampler_type_chars;
std::string sampler_type_names;
for (const auto & sampler : params.sparams.samplers) {
sampler_type_chars += gpt_sampler_type_to_chr(sampler);
sampler_type_names += gpt_sampler_type_to_str(sampler) + ";";
}
sampler_type_names.pop_back();
/**
* filter options by example
* rules:
* - all examples inherit options from LLAMA_EXAMPLE_COMMON
* - if LLAMA_EXAMPLE_* is set (other than COMMON), we only show the option in the corresponding example
* - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
*/
std::unordered_set<std::string> seen_args;
auto add_opt = [&](llama_arg arg) {
if (arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) {
// make sure there is no argument duplications
for (const auto & a : arg.args) {
if (seen_args.find(a) == seen_args.end()) {
seen_args.insert(a);
} else {
throw std::runtime_error(format("found duplicated argument in source code: %s", a));
}
}
options.push_back(std::move(arg));
}
};
add_opt(llama_arg(
{"-h", "--help", "--usage"},
"print usage and exit",
[](gpt_params & params) {
params.usage = true;
}
));
add_opt(llama_arg(
{"--version"},
"show version and build info",
[](gpt_params &) {
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);
}
));
add_opt(llama_arg(
{"-v", "--verbose"},
"print verbose information",
[](gpt_params & params) {
params.verbosity = 1;
}
));
add_opt(llama_arg(
{"--verbosity"}, "N",
format("set specific verbosity level (default: %d)", params.verbosity),
[](gpt_params & params, int value) {
params.verbosity = value;
}
));
add_opt(llama_arg(
{"--verbose-prompt"},
format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
[](gpt_params & params) {
params.verbose_prompt = true;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"--no-display-prompt"},
format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
[](gpt_params & params) {
params.display_prompt = false;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"-co", "--color"},
format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
[](gpt_params & params) {
params.use_color = true;
}
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
add_opt(llama_arg(
{"-s", "--seed"}, "SEED",
format("RNG seed (default: %d, use random seed for < 0)", params.sparams.seed),
[](gpt_params & params, const std::string & value) {
params.sparams.seed = std::stoul(value);
}
));
add_opt(llama_arg(
{"-t", "--threads"}, "N",
format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
[](gpt_params & params, int value) {
params.cpuparams.n_threads = value;
if (params.cpuparams.n_threads <= 0) {
params.cpuparams.n_threads = std::thread::hardware_concurrency();
}
}
).set_env("LLAMA_ARG_THREADS"));
add_opt(llama_arg(
{"-tb", "--threads-batch"}, "N",
"number of threads to use during batch and prompt processing (default: same as --threads)",
[](gpt_params & params, int value) {
params.cpuparams_batch.n_threads = value;
if (params.cpuparams_batch.n_threads <= 0) {
params.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
}
));
add_opt(llama_arg(
{"-td", "--threads-draft"}, "N",
"number of threads to use during generation (default: same as --threads)",
[](gpt_params & params, int value) {
params.draft_cpuparams.n_threads = value;
if (params.draft_cpuparams.n_threads <= 0) {
params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"-tbd", "--threads-batch-draft"}, "N",
"number of threads to use during batch and prompt processing (default: same as --threads-draft)",
[](gpt_params & params, int value) {
params.draft_cpuparams_batch.n_threads = value;
if (params.draft_cpuparams_batch.n_threads <= 0) {
params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"-C", "--cpu-mask"}, "M",
"CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")",
[](gpt_params & params, const std::string & value) {
std::string mask = value;
params.cpuparams.mask_valid = true;
if (!parse_cpu_mask(mask, params.cpuparams.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
));
add_opt(llama_arg(
{"-Cr", "--cpu-range"}, "lo-hi",
"range of CPUs for affinity. Complements --cpu-mask",
[](gpt_params & params, const std::string & value) {
std::string range = value;
params.cpuparams.mask_valid = true;
if (!parse_cpu_range(range, params.cpuparams.cpumask)) {
throw std::invalid_argument("invalid range");
}
}
));
add_opt(llama_arg(
{"--cpu-strict"}, "<0|1>",
format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu),
[](gpt_params & params, const std::string & value) {
params.cpuparams.strict_cpu = std::stoul(value);
}
));
add_opt(llama_arg(
{"--poll"}, "<0...100>",
format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll),
[](gpt_params & params, const std::string & value) {
params.cpuparams.poll = std::stoul(value);
}
));
add_opt(llama_arg(
{"-Cb", "--cpu-mask-batch"}, "M",
"CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)",
[](gpt_params & params, const std::string & value) {
std::string mask = value;
params.cpuparams_batch.mask_valid = true;
if (!parse_cpu_mask(mask, params.cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
));
add_opt(llama_arg(
{"-Crb", "--cpu-range-batch"}, "lo-hi",
"ranges of CPUs for affinity. Complements --cpu-mask-batch",
[](gpt_params & params, const std::string & value) {
std::string range = value;
params.cpuparams_batch.mask_valid = true;
if (!parse_cpu_range(range, params.cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid range");
}
}
));
add_opt(llama_arg(
{"--cpu-strict-batch"}, "<0|1>",
"use strict CPU placement (default: same as --cpu-strict)",
[](gpt_params & params, int value) {
params.cpuparams_batch.strict_cpu = value;
}
));
add_opt(llama_arg(
{"--poll-batch"}, "<0|1>",
"use polling to wait for work (default: same as --poll)",
[](gpt_params & params, int value) {
params.cpuparams_batch.poll = value;
}
));
add_opt(llama_arg(
{"-Cd", "--cpu-mask-draft"}, "M",
"Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
[](gpt_params & params, const std::string & value) {
std::string mask = value;
params.draft_cpuparams.mask_valid = true;
if (!parse_cpu_mask(mask, params.draft_cpuparams.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"-Crd", "--cpu-range-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft",
[](gpt_params & params, const std::string & value) {
std::string range = value;
params.draft_cpuparams.mask_valid = true;
if (!parse_cpu_range(range, params.draft_cpuparams.cpumask)) {
throw std::invalid_argument("invalid range");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"--cpu-strict-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: same as --cpu-strict)",
[](gpt_params & params, int value) {
params.draft_cpuparams.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"--poll-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: same as --poll])",
[](gpt_params & params, int value) {
params.draft_cpuparams.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
[](gpt_params & params, const std::string & value) {
std::string range = value;
params.draft_cpuparams_batch.mask_valid = true;
if (!parse_cpu_range(range, params.draft_cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"--cpu-strict-batch-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: --cpu-strict-draft)",
[](gpt_params & params, int value) {
params.draft_cpuparams_batch.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"--poll-batch-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: --poll-draft)",
[](gpt_params & params, int value) {
params.draft_cpuparams_batch.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"--draft"}, "N",
format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
[](gpt_params & params, int value) {
params.n_draft = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"-ps", "--p-split"}, "N",
format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
[](gpt_params & params, const std::string & value) {
params.p_split = std::stof(value);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"-lcs", "--lookup-cache-static"}, "FNAME",
"path to static lookup cache to use for lookup decoding (not updated by generation)",
[](gpt_params & params, const std::string & value) {
params.lookup_cache_static = value;
}
));
add_opt(llama_arg(
{"-lcd", "--lookup-cache-dynamic"}, "FNAME",
"path to dynamic lookup cache to use for lookup decoding (updated by generation)",
[](gpt_params & params, const std::string & value) {
params.lookup_cache_dynamic = value;
}
));
add_opt(llama_arg(
{"-c", "--ctx-size"}, "N",
format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
[](gpt_params & params, int value) {
params.n_ctx = value;
}
).set_env("LLAMA_ARG_CTX_SIZE"));
add_opt(llama_arg(
{"-n", "--predict", "--n-predict"}, "N",
format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict),
[](gpt_params & params, int value) {
params.n_predict = value;
}
).set_env("LLAMA_ARG_N_PREDICT"));
add_opt(llama_arg(
{"-b", "--batch-size"}, "N",
format("logical maximum batch size (default: %d)", params.n_batch),
[](gpt_params & params, int value) {
params.n_batch = value;
}
).set_env("LLAMA_ARG_BATCH"));
add_opt(llama_arg(
{"-ub", "--ubatch-size"}, "N",
format("physical maximum batch size (default: %d)", params.n_ubatch),
[](gpt_params & params, int value) {
params.n_ubatch = value;
}
).set_env("LLAMA_ARG_UBATCH"));
add_opt(llama_arg(
{"--keep"}, "N",
format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep),
[](gpt_params & params, int value) {
params.n_keep = value;
}
));
add_opt(llama_arg(
{"--chunks"}, "N",
format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
[](gpt_params & params, int value) {
params.n_chunks = value;
}
));
add_opt(llama_arg(
{"-fa", "--flash-attn"},
format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
[](gpt_params & params) {
params.flash_attn = true;
}
).set_env("LLAMA_ARG_FLASH_ATTN"));
add_opt(llama_arg(
{"-p", "--prompt"}, "PROMPT",
ex == LLAMA_EXAMPLE_MAIN
? "prompt to start generation with\nif -cnv is set, this will be used as system prompt"
: "prompt to start generation with",
[](gpt_params & params, const std::string & value) {
params.prompt = value;
}
));
add_opt(llama_arg(
{"-f", "--file"}, "FNAME",
"a file containing the prompt (default: none)",
[](gpt_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
}
// store the external file name in params
params.prompt_file = value;
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();
}
}
));
add_opt(llama_arg(
{"--in-file"}, "FNAME",
"an input file (repeat to specify multiple files)",
[](gpt_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
}
params.in_files.push_back(value);
}
));
add_opt(llama_arg(
{"-bf", "--binary-file"}, "FNAME",
"binary file containing the prompt (default: none)",
[](gpt_params & params, const std::string & value) {
std::ifstream file(value, std::ios::binary);
if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
}
// store the external file name in params
params.prompt_file = value;
std::ostringstream ss;
ss << file.rdbuf();
params.prompt = ss.str();
fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str());
}
));
add_opt(llama_arg(
{"-e", "--escape"},
format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
[](gpt_params & params) {
params.escape = true;
}
));
add_opt(llama_arg(
{"--no-escape"},
"do not process escape sequences",
[](gpt_params & params) {
params.escape = false;
}
));
add_opt(llama_arg(
{"-ptc", "--print-token-count"}, "N",
format("print token count every N tokens (default: %d)", params.n_print),
[](gpt_params & params, int value) {
params.n_print = value;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"--prompt-cache"}, "FNAME",
"file to cache prompt state for faster startup (default: none)",
[](gpt_params & params, const std::string & value) {
params.path_prompt_cache = value;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"--prompt-cache-all"},
"if specified, saves user input and generations to cache as well\n",
[](gpt_params & params) {
params.prompt_cache_all = true;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"--prompt-cache-ro"},
"if specified, uses the prompt cache but does not update it",
[](gpt_params & params) {
params.prompt_cache_ro = true;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"-r", "--reverse-prompt"}, "PROMPT",
"halt generation at PROMPT, return control in interactive mode\n",
[](gpt_params & params, const std::string & value) {
params.antiprompt.emplace_back(value);
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"-sp", "--special"},
format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
[](gpt_params & params) {
params.special = true;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"-cnv", "--conversation"},
format(
"run in conversation mode:\n"
"- does not print special tokens and suffix/prefix\n"
"- interactive mode is also enabled\n"
"(default: %s)",
params.conversation ? "true" : "false"
),
[](gpt_params & params) {
params.conversation = true;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"-i", "--interactive"},
format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"),
[](gpt_params & params) {
params.interactive = true;
}
).set_examples({LLAMA_EXAMPLE_INFILL}));
add_opt(llama_arg(
{"-if", "--interactive-first"},
format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"),
[](gpt_params & params) {
params.interactive_first = true;
}
).set_examples({LLAMA_EXAMPLE_INFILL}));
add_opt(llama_arg(
{"-mli", "--multiline-input"},
"allows you to write or paste multiple lines without ending each in '\\'",
[](gpt_params & params) {
params.multiline_input = true;
}
).set_examples({LLAMA_EXAMPLE_INFILL}));
add_opt(llama_arg(
{"--in-prefix-bos"},
"prefix BOS to user inputs, preceding the `--in-prefix` string",
[](gpt_params & params) {
params.input_prefix_bos = true;
params.enable_chat_template = false;
}
).set_examples({LLAMA_EXAMPLE_INFILL}));
add_opt(llama_arg(
{"--in-prefix"}, "STRING",
"string to prefix user inputs with (default: empty)",
[](gpt_params & params, const std::string & value) {
params.input_prefix = value;
params.enable_chat_template = false;
}
).set_examples({LLAMA_EXAMPLE_INFILL}));
add_opt(llama_arg(
{"--in-suffix"}, "STRING",
"string to suffix after user inputs with (default: empty)",
[](gpt_params & params, const std::string & value) {
params.input_suffix = value;
params.enable_chat_template = false;
}
).set_examples({LLAMA_EXAMPLE_INFILL}));
add_opt(llama_arg(
{"--no-warmup"},
"skip warming up the model with an empty run",
[](gpt_params & params) {
params.warmup = false;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(llama_arg(
{"--spm-infill"},
format(
"use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)",
params.spm_infill ? "enabled" : "disabled"
),
[](gpt_params & params) {
params.spm_infill = true;
}
).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL}));
add_opt(llama_arg(
{"--samplers"}, "SAMPLERS",
format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
[](gpt_params & params, const std::string & value) {
const auto sampler_names = string_split(value, ';');
params.sparams.samplers = gpt_sampler_types_from_names(sampler_names, true);
}
));
add_opt(llama_arg(
{"--sampling-seq"}, "SEQUENCE",
format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
[](gpt_params & params, const std::string & value) {
params.sparams.samplers = gpt_sampler_types_from_chars(value);
}
));
add_opt(llama_arg(
{"--ignore-eos"},
"ignore end of stream token and continue generating (implies --logit-bias EOS-inf)",
[](gpt_params & params) {
params.sparams.ignore_eos = true;
}
));
add_opt(llama_arg(
{"--penalize-nl"},
format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
[](gpt_params & params) {
params.sparams.penalize_nl = true;
}
));
add_opt(llama_arg(
{"--temp"}, "N",
format("temperature (default: %.1f)", (double)params.sparams.temp),
[](gpt_params & params, const std::string & value) {
params.sparams.temp = std::stof(value);
params.sparams.temp = std::max(params.sparams.temp, 0.0f);
}
));
add_opt(llama_arg(
{"--top-k"}, "N",
format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
[](gpt_params & params, int value) {
params.sparams.top_k = value;
}
));
add_opt(llama_arg(
{"--top-p"}, "N",
format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
[](gpt_params & params, const std::string & value) {
params.sparams.top_p = std::stof(value);
}
));
add_opt(llama_arg(
{"--min-p"}, "N",
format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
[](gpt_params & params, const std::string & value) {
params.sparams.min_p = std::stof(value);
}
));
add_opt(llama_arg(
{"--tfs"}, "N",
format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
[](gpt_params & params, const std::string & value) {
params.sparams.tfs_z = std::stof(value);
}
));
add_opt(llama_arg(
{"--typical"}, "N",
format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
[](gpt_params & params, const std::string & value) {
params.sparams.typ_p = std::stof(value);
}
));
add_opt(llama_arg(
{"--repeat-last-n"}, "N",
format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n),
[](gpt_params & params, int value) {
params.sparams.penalty_last_n = value;
params.sparams.n_prev = std::max(params.sparams.n_prev, params.sparams.penalty_last_n);
}
));
add_opt(llama_arg(
{"--repeat-penalty"}, "N",
format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
[](gpt_params & params, const std::string & value) {
params.sparams.penalty_repeat = std::stof(value);
}
));
add_opt(llama_arg(
{"--presence-penalty"}, "N",
format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
[](gpt_params & params, const std::string & value) {
params.sparams.penalty_present = std::stof(value);
}
));
add_opt(llama_arg(
{"--frequency-penalty"}, "N",
format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
[](gpt_params & params, const std::string & value) {
params.sparams.penalty_freq = std::stof(value);
}
));
add_opt(llama_arg(
{"--dynatemp-range"}, "N",
format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
[](gpt_params & params, const std::string & value) {
params.sparams.dynatemp_range = std::stof(value);
}
));
add_opt(llama_arg(
{"--dynatemp-exp"}, "N",
format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
[](gpt_params & params, const std::string & value) {
params.sparams.dynatemp_exponent = std::stof(value);
}
));
add_opt(llama_arg(
{"--mirostat"}, "N",
format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat),
[](gpt_params & params, int value) {
params.sparams.mirostat = value;
}
));
add_opt(llama_arg(
{"--mirostat-lr"}, "N",
format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
[](gpt_params & params, const std::string & value) {
params.sparams.mirostat_eta = std::stof(value);
}
));
add_opt(llama_arg(
{"--mirostat-ent"}, "N",
format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
[](gpt_params & params, const std::string & value) {
params.sparams.mirostat_tau = std::stof(value);
}
));
add_opt(llama_arg(
{"-l", "--logit-bias"}, "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'",
[](gpt_params & params, const std::string & value) {
std::stringstream ss(value);
llama_token key;
char sign;
std::string value_str;
try {
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
params.sparams.logit_bias.push_back({key, bias});
} else {
throw std::invalid_argument("invalid input format");
}
} catch (const std::exception&) {
throw std::invalid_argument("invalid input format");
}
}
));
add_opt(llama_arg(
{"--grammar"}, "GRAMMAR",
format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
[](gpt_params & params, const std::string & value) {
params.sparams.grammar = value;
}
));
add_opt(llama_arg(
{"--grammar-file"}, "FNAME",
"file to read grammar from",
[](gpt_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
}
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(params.sparams.grammar)
);
}
));
add_opt(llama_arg(
{"-j", "--json-schema"}, "SCHEMA",
"JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
[](gpt_params & params, const std::string & value) {
params.sparams.grammar = json_schema_to_grammar(json::parse(value));
}
));
add_opt(llama_arg(
{"--pooling"}, "{none,mean,cls,last}",
"pooling type for embeddings, use model default if unspecified",
[](gpt_params & params, const std::string & value) {
/**/ 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 { throw std::invalid_argument("invalid value"); }
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
add_opt(llama_arg(
{"--attention"}, "{causal,non,causal}",
"attention type for embeddings, use model default if unspecified",
[](gpt_params & params, const std::string & value) {
/**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
else { throw std::invalid_argument("invalid value"); }
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
add_opt(llama_arg(
{"--rope-scaling"}, "{none,linear,yarn}",
"RoPE frequency scaling method, defaults to linear unless specified by the model",
[](gpt_params & params, const std::string & value) {
/**/ 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 { throw std::invalid_argument("invalid value"); }
}
));
add_opt(llama_arg(
{"--rope-scale"}, "N",
"RoPE context scaling factor, expands context by a factor of N",
[](gpt_params & params, const std::string & value) {
params.rope_freq_scale = 1.0f / std::stof(value);
}
));
add_opt(llama_arg(
{"--rope-freq-base"}, "N",
"RoPE base frequency, used by NTK-aware scaling (default: loaded from model)",
[](gpt_params & params, const std::string & value) {
params.rope_freq_base = std::stof(value);
}
));
add_opt(llama_arg(
{"--rope-freq-scale"}, "N",
"RoPE frequency scaling factor, expands context by a factor of 1/N",
[](gpt_params & params, const std::string & value) {
params.rope_freq_scale = std::stof(value);
}
));
add_opt(llama_arg(
{"--yarn-orig-ctx"}, "N",
format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx),
[](gpt_params & params, int value) {
params.yarn_orig_ctx = value;
}
));
add_opt(llama_arg(
{"--yarn-ext-factor"}, "N",
format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
[](gpt_params & params, const std::string & value) {
params.yarn_ext_factor = std::stof(value);
}
));
add_opt(llama_arg(
{"--yarn-attn-factor"}, "N",
format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
[](gpt_params & params, const std::string & value) {
params.yarn_attn_factor = std::stof(value);
}
));
add_opt(llama_arg(
{"--yarn-beta-slow"}, "N",
format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
[](gpt_params & params, const std::string & value) {
params.yarn_beta_slow = std::stof(value);
}
));
add_opt(llama_arg(
{"--yarn-beta-fast"}, "N",
format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
[](gpt_params & params, const std::string & value) {
params.yarn_beta_fast = std::stof(value);
}
));
add_opt(llama_arg(
{"-gan", "--grp-attn-n"}, "N",
format("group-attention factor (default: %d)", params.grp_attn_n),
[](gpt_params & params, int value) {
params.grp_attn_n = value;
}
));
add_opt(llama_arg(
{"-gaw", "--grp-attn-w"}, "N",
format("group-attention width (default: %.1f)", (double)params.grp_attn_w),
[](gpt_params & params, int value) {
params.grp_attn_w = value;
}
));
add_opt(llama_arg(
{"-dkvc", "--dump-kv-cache"},
"verbose print of the KV cache",
[](gpt_params & params) {
params.dump_kv_cache = true;
}
));
add_opt(llama_arg(
{"-nkvo", "--no-kv-offload"},
"disable KV offload",
[](gpt_params & params) {
params.no_kv_offload = true;
}
));
add_opt(llama_arg(
{"-ctk", "--cache-type-k"}, "TYPE",
format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
[](gpt_params & params, const std::string & value) {
// TODO: get the type right here
params.cache_type_k = value;
}
));
add_opt(llama_arg(
{"-ctv", "--cache-type-v"}, "TYPE",
format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
[](gpt_params & params, const std::string & value) {
// TODO: get the type right here
params.cache_type_v = value;
}
));
add_opt(llama_arg(
{"--all-logits"},
format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
[](gpt_params & params) {
params.logits_all = true;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"--hellaswag"},
"compute HellaSwag score over random tasks from datafile supplied with -f",
[](gpt_params & params) {
params.hellaswag = true;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"--hellaswag-tasks"}, "N",
format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks),
[](gpt_params & params, int value) {
params.hellaswag_tasks = value;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"--winogrande"},
"compute Winogrande score over random tasks from datafile supplied with -f",
[](gpt_params & params) {
params.winogrande = true;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"--winogrande-tasks"}, "N",
format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks),
[](gpt_params & params, int value) {
params.winogrande_tasks = value;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"--multiple-choice"},
"compute multiple choice score over random tasks from datafile supplied with -f",
[](gpt_params & params) {
params.multiple_choice = true;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"--multiple-choice-tasks"}, "N",
format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks),
[](gpt_params & params, int value) {
params.multiple_choice_tasks = value;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"--kl-divergence"},
"computes KL-divergence to logits provided via --kl-divergence-base",
[](gpt_params & params) {
params.kl_divergence = true;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"--ppl-stride"}, "N",
format("stride for perplexity calculation (default: %d)", params.ppl_stride),
[](gpt_params & params, int value) {
params.ppl_stride = value;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"--ppl-output-type"}, "<0|1>",
format("output type for perplexity calculation (default: %d)", params.ppl_output_type),
[](gpt_params & params, int value) {
params.ppl_output_type = value;
}
).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(llama_arg(
{"-dt", "--defrag-thold"}, "N",
format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
[](gpt_params & params, const std::string & value) {
params.defrag_thold = std::stof(value);
}
).set_env("LLAMA_ARG_DEFRAG_THOLD"));
add_opt(llama_arg(
{"-np", "--parallel"}, "N",
format("number of parallel sequences to decode (default: %d)", params.n_parallel),
[](gpt_params & params, int value) {
params.n_parallel = value;
}
));
add_opt(llama_arg(
{"-ns", "--sequences"}, "N",
format("number of sequences to decode (default: %d)", params.n_sequences),
[](gpt_params & params, int value) {
params.n_sequences = value;
}
));
add_opt(llama_arg(
{"-cb", "--cont-batching"},
format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
[](gpt_params & params) {
params.cont_batching = true;
}
).set_env("LLAMA_ARG_CONT_BATCHING"));
add_opt(llama_arg(
{"-nocb", "--no-cont-batching"},
"disable continuous batching",
[](gpt_params & params) {
params.cont_batching = false;
}
).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
add_opt(llama_arg(
{"--mmproj"}, "FILE",
"path to a multimodal projector file for LLaVA. see examples/llava/README.md",
[](gpt_params & params, const std::string & value) {
params.mmproj = value;
}
).set_examples({LLAMA_EXAMPLE_LLAVA}));
add_opt(llama_arg(
{"--image"}, "FILE",
"path to an image file. use with multimodal models. Specify multiple times for batching",
[](gpt_params & params, const std::string & value) {
params.image.emplace_back(value);
}
).set_examples({LLAMA_EXAMPLE_LLAVA}));
#ifdef GGML_USE_RPC
add_opt(llama_arg(
{"--rpc"}, "SERVERS",
"comma separated list of RPC servers",
[](gpt_params & params, const std::string & value) {
params.rpc_servers = value;
}
));
#endif
add_opt(llama_arg(
{"--mlock"},
"force system to keep model in RAM rather than swapping or compressing",
[](gpt_params & params) {
params.use_mlock = true;
}
));
add_opt(llama_arg(
{"--no-mmap"},
"do not memory-map model (slower load but may reduce pageouts if not using mlock)",
[](gpt_params & params) {
params.use_mmap = false;
}
));
add_opt(llama_arg(
{"--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",
[](gpt_params & params, const std::string & value) {
/**/ 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 { throw std::invalid_argument("invalid value"); }
}
));
add_opt(llama_arg(
{"-ngl", "--gpu-layers"}, "N",
"number of layers to store in VRAM",
[](gpt_params & params, int value) {
params.n_gpu_layers = value;
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");
}
}
).set_env("LLAMA_ARG_N_GPU_LAYERS"));
add_opt(llama_arg(
{"-ngld", "--gpu-layers-draft"}, "N",
"number of layers to store in VRAM for the draft model",
[](gpt_params & params, int value) {
params.n_gpu_layers_draft = value;
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");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"-sm", "--split-mode"}, "{none,layer,row}",
"how to split the model across multiple GPUs, one of:\n"
"- none: use one GPU only\n"
"- layer (default): split layers and KV across GPUs\n"
"- row: split rows across GPUs",
[](gpt_params & params, const std::string & value) {
std::string arg_next = value;
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 == "row") {
#ifdef GGML_USE_SYCL
fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
exit(1);
#endif // GGML_USE_SYCL
params.split_mode = LLAMA_SPLIT_MODE_ROW;
}
else {
throw std::invalid_argument("invalid value");
}
#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
}
));
add_opt(llama_arg(
{"-ts", "--tensor-split"}, "N0,N1,N2,...",
"fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1",
[](gpt_params & params, const std::string & value) {
std::string arg_next = value;
// 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()) {
throw std::invalid_argument(
format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices())
);
}
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
}
));
add_opt(llama_arg(
{"-mg", "--main-gpu"}, "INDEX",
format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu),
[](gpt_params & params, int value) {
params.main_gpu = value;
#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
}
));
add_opt(llama_arg(
{"--check-tensors"},
format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
[](gpt_params & params) {
params.check_tensors = true;
}
));
add_opt(llama_arg(
{"--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",
[](gpt_params & params, const std::string & value) {
if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) {
throw std::runtime_error(format("error: Invalid type for KV override: %s\n", value.c_str()));
}
}
));
add_opt(llama_arg(
{"--lora"}, "FNAME",
"path to LoRA adapter (can be repeated to use multiple adapters)",
[](gpt_params & params, const std::string & value) {
params.lora_adapters.push_back({ std::string(value), 1.0 });
}
).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
add_opt(llama_arg(
{"--lora-scaled"}, "FNAME", "SCALE",
"path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
[](gpt_params & params, const std::string & fname, const std::string & scale) {
params.lora_adapters.push_back({ fname, std::stof(scale) });
}
).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
add_opt(llama_arg(
{"--control-vector"}, "FNAME",
"add a control vector\nnote: this argument can be repeated to add multiple control vectors",
[](gpt_params & params, const std::string & value) {
params.control_vectors.push_back({ 1.0f, value, });
}
));
add_opt(llama_arg(
{"--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",
[](gpt_params & params, const std::string & fname, const std::string & scale) {
params.control_vectors.push_back({ std::stof(scale), fname });
}
));
add_opt(llama_arg(
{"--control-vector-layer-range"}, "START", "END",
"layer range to apply the control vector(s) to, start and end inclusive",
[](gpt_params & params, const std::string & start, const std::string & end) {
params.control_vector_layer_start = std::stoi(start);
params.control_vector_layer_end = std::stoi(end);
}
));
add_opt(llama_arg(
{"-a", "--alias"}, "STRING",
"set alias for model name (to be used by REST API)",
[](gpt_params & params, const std::string & value) {
params.model_alias = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL"));
add_opt(llama_arg(
{"-m", "--model"}, "FNAME",
ex == LLAMA_EXAMPLE_EXPORT_LORA
? std::string("model path from which to load base model")
: format(
"model path (default: `models/$filename` with filename from `--hf-file` "
"or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH
),
[](gpt_params & params, const std::string & value) {
params.model = value;
}
).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL"));
add_opt(llama_arg(
{"-md", "--model-draft"}, "FNAME",
"draft model for speculative decoding (default: unused)",
[](gpt_params & params, const std::string & value) {
params.model_draft = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(llama_arg(
{"-mu", "--model-url"}, "MODEL_URL",
"model download url (default: unused)",
[](gpt_params & params, const std::string & value) {
params.model_url = value;
}
).set_env("LLAMA_ARG_MODEL_URL"));
add_opt(llama_arg(
{"-hfr", "--hf-repo"}, "REPO",
"Hugging Face model repository (default: unused)",
[](gpt_params & params, const std::string & value) {
params.hf_repo = value;
}
).set_env("LLAMA_ARG_HF_REPO"));
add_opt(llama_arg(
{"-hff", "--hf-file"}, "FILE",
"Hugging Face model file (default: unused)",
[](gpt_params & params, const std::string & value) {
params.hf_file = value;
}
).set_env("LLAMA_ARG_HF_FILE"));
add_opt(llama_arg(
{"-hft", "--hf-token"}, "TOKEN",
"Hugging Face access token (default: value from HF_TOKEN environment variable)",
[](gpt_params & params, const std::string & value) {
params.hf_token = value;
}
).set_env("HF_TOKEN"));
add_opt(llama_arg(
{"--context-file"}, "FNAME",
"file to load context from (repeat to specify multiple files)",
[](gpt_params & params, const std::string & value) {
std::ifstream file(value, std::ios::binary);
if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
}
params.context_files.push_back(value);
}
).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
add_opt(llama_arg(
{"--chunk-size"}, "N",
format("minimum length of embedded text chunks (default: %d)", params.chunk_size),
[](gpt_params & params, int value) {
params.chunk_size = value;
}
).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
add_opt(llama_arg(
{"--chunk-separator"}, "STRING",
format("separator between chunks (default: '%s')", params.chunk_separator.c_str()),
[](gpt_params & params, const std::string & value) {
params.chunk_separator = value;
}
).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
add_opt(llama_arg(
{"--junk"}, "N",
format("number of times to repeat the junk text (default: %d)", params.n_junk),
[](gpt_params & params, int value) {
params.n_junk = value;
}
).set_examples({LLAMA_EXAMPLE_PASSKEY}));
add_opt(llama_arg(
{"--pos"}, "N",
format("position of the passkey in the junk text (default: %d)", params.i_pos),
[](gpt_params & params, int value) {
params.i_pos = value;
}
).set_examples({LLAMA_EXAMPLE_PASSKEY}));
add_opt(llama_arg(
{"-o", "--output"}, "FNAME",
format("output file (default: '%s')",
ex == LLAMA_EXAMPLE_EXPORT_LORA
? params.lora_outfile.c_str()
: ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR
? params.cvector_outfile.c_str()
: params.out_file.c_str()),
[](gpt_params & params, const std::string & value) {
params.out_file = value;
params.cvector_outfile = value;
params.lora_outfile = value;
}
).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA}));
add_opt(llama_arg(
{"-ofreq", "--output-frequency"}, "N",
format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
[](gpt_params & params, int value) {
params.n_out_freq = value;
}
).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(llama_arg(
{"--save-frequency"}, "N",
format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
[](gpt_params & params, int value) {
params.n_save_freq = value;
}
).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(llama_arg(
{"--process-output"},
format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"),
[](gpt_params & params) {
params.process_output = true;
}
).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(llama_arg(
{"--no-ppl"},
format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
[](gpt_params & params) {
params.compute_ppl = false;
}
).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(llama_arg(
{"--chunk"}, "N",
format("start processing the input from chunk N (default: %d)", params.i_chunk),
[](gpt_params & params, int value) {
params.i_chunk = value;
}
).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(llama_arg(
{"-pps"},
format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"),
[](gpt_params & params) {
params.is_pp_shared = true;
}
).set_examples({LLAMA_EXAMPLE_BENCH}));
add_opt(llama_arg(
{"-npp"}, "n0,n1,...",
"number of prompt tokens",
[](gpt_params & params, const std::string & value) {
auto p = string_split<int>(value, ',');
params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
}
).set_examples({LLAMA_EXAMPLE_BENCH}));
add_opt(llama_arg(
{"-ntg"}, "n0,n1,...",
"number of text generation tokens",
[](gpt_params & params, const std::string & value) {
auto p = string_split<int>(value, ',');
params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
}
).set_examples({LLAMA_EXAMPLE_BENCH}));
add_opt(llama_arg(
{"-npl"}, "n0,n1,...",
"number of parallel prompts",
[](gpt_params & params, const std::string & value) {
auto p = string_split<int>(value, ',');
params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
}
).set_examples({LLAMA_EXAMPLE_BENCH}));
add_opt(llama_arg(
{"--embd-normalize"}, "N",
format("normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize),
[](gpt_params & params, int value) {
params.embd_normalize = value;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
add_opt(llama_arg(
{"--embd-output-format"}, "FORMAT",
"empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix",
[](gpt_params & params, const std::string & value) {
params.embd_out = value;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
add_opt(llama_arg(
{"--embd-separator"}, "STRING",
"separator of embendings (default \\n) for example \"<#sep#>\"",
[](gpt_params & params, const std::string & value) {
params.embd_sep = value;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
add_opt(llama_arg(
{"--host"}, "HOST",
format("ip address to listen (default: %s)", params.hostname.c_str()),
[](gpt_params & params, const std::string & value) {
params.hostname = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST"));
add_opt(llama_arg(
{"--port"}, "PORT",
format("port to listen (default: %d)", params.port),
[](gpt_params & params, int value) {
params.port = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT"));
add_opt(llama_arg(
{"--path"}, "PATH",
format("path to serve static files from (default: %s)", params.public_path.c_str()),
[](gpt_params & params, const std::string & value) {
params.public_path = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--embedding", "--embeddings"},
format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
[](gpt_params & params) {
params.embedding = true;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
add_opt(llama_arg(
{"--api-key"}, "KEY",
"API key to use for authentication (default: none)",
[](gpt_params & params, const std::string & value) {
params.api_keys.push_back(value);
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
add_opt(llama_arg(
{"--api-key-file"}, "FNAME",
"path to file containing API keys (default: none)",
[](gpt_params & params, const std::string & value) {
std::ifstream key_file(value);
if (!key_file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
}
std::string key;
while (std::getline(key_file, key)) {
if (!key.empty()) {
params.api_keys.push_back(key);
}
}
key_file.close();
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--ssl-key-file"}, "FNAME",
"path to file a PEM-encoded SSL private key",
[](gpt_params & params, const std::string & value) {
params.ssl_file_key = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--ssl-cert-file"}, "FNAME",
"path to file a PEM-encoded SSL certificate",
[](gpt_params & params, const std::string & value) {
params.ssl_file_cert = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--timeout"}, "N",
format("server read/write timeout in seconds (default: %d)", params.timeout_read),
[](gpt_params & params, int value) {
params.timeout_read = value;
params.timeout_write = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--threads-http"}, "N",
format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http),
[](gpt_params & params, int value) {
params.n_threads_http = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
add_opt(llama_arg(
{"-spf", "--system-prompt-file"}, "FNAME",
"set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications",
[](gpt_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
}
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;
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--log-format"}, "{text, json}",
"log output format: json or text (default: json)",
[](gpt_params & params, const std::string & value) {
if (value == "json") {
params.log_json = true;
} else if (value == "text") {
params.log_json = false;
} else {
throw std::invalid_argument("invalid value");
}
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--metrics"},
format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"),
[](gpt_params & params) {
params.endpoint_metrics = true;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS"));
add_opt(llama_arg(
{"--no-slots"},
format("disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
[](gpt_params & params) {
params.endpoint_slots = false;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS"));
add_opt(llama_arg(
{"--slot-save-path"}, "PATH",
"path to save slot kv cache (default: disabled)",
[](gpt_params & params, const std::string & value) {
params.slot_save_path = value;
// 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;
}
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--chat-template"}, "JINJA_TEMPLATE",
"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:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
[](gpt_params & params, const std::string & value) {
if (!llama_chat_verify_template(value)) {
throw std::runtime_error(format(
"error: the supplied chat template is not supported: %s\n"
"note: llama.cpp does not use jinja parser, we only support commonly used templates\n",
value.c_str()
));
}
params.chat_template = value;
}
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
add_opt(llama_arg(
{"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
format("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),
[](gpt_params & params, const std::string & value) {
params.slot_prompt_similarity = std::stof(value);
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--lora-init-without-apply"},
format("load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"),
[](gpt_params & params) {
params.lora_init_without_apply = true;
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--simple-io"},
"use basic IO for better compatibility in subprocesses and limited consoles",
[](gpt_params & params) {
params.simple_io = true;
}
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
add_opt(llama_arg(
{"-ld", "--logdir"}, "LOGDIR",
"path under which to save YAML logs (no logging if unset)",
[](gpt_params & params, const std::string & value) {
params.logdir = value;
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
params.logdir += DIRECTORY_SEPARATOR;
}
}
));
add_opt(llama_arg(
{"--positive-file"}, "FNAME",
format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
[](gpt_params & params, const std::string & value) {
params.cvector_positive_file = value;
}
).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
add_opt(llama_arg(
{"--negative-file"}, "FNAME",
format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()),
[](gpt_params & params, const std::string & value) {
params.cvector_negative_file = value;
}
).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
add_opt(llama_arg(
{"--pca-batch"}, "N",
format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch),
[](gpt_params & params, int value) {
params.n_pca_batch = value;
}
).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
add_opt(llama_arg(
{"--pca-iter"}, "N",
format("number of iterations used for PCA (default: %d)", params.n_pca_iterations),
[](gpt_params & params, int value) {
params.n_pca_iterations = value;
}
).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
add_opt(llama_arg(
{"--method"}, "{pca, mean}",
"dimensionality reduction method to be used (default: pca)",
[](gpt_params & params, const std::string & value) {
/**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
else { throw std::invalid_argument("invalid value"); }
}
).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
add_opt(llama_arg(
{"--output-format"}, "{md,jsonl}",
"output format for batched-bench results (default: md)",
[](gpt_params & params, const std::string & value) {
/**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; }
else if (value == "md") { params.batched_bench_output_jsonl = false; }
else { std::invalid_argument("invalid value"); }
}
).set_examples({LLAMA_EXAMPLE_BENCH}));
#ifndef LOG_DISABLE_LOGS
// TODO: make this looks less weird
add_opt(llama_arg(
{"--log-test"},
"Log test",
[](gpt_params &) { log_param_single_parse("--log-test"); }
));
add_opt(llama_arg(
{"--log-disable"},
"Log disable",
[](gpt_params &) { log_param_single_parse("--log-disable"); }
));
add_opt(llama_arg(
{"--log-enable"},
"Log enable",
[](gpt_params &) { log_param_single_parse("--log-enable"); }
));
add_opt(llama_arg(
{"--log-new"},
"Log new",
[](gpt_params &) { log_param_single_parse("--log-new"); }
));
add_opt(llama_arg(
{"--log-append"},
"Log append",
[](gpt_params &) { log_param_single_parse("--log-append"); }
));
add_opt(llama_arg(
{"--log-file"}, "FNAME",
"Log file",
[](gpt_params &, const std::string & value) { log_param_pair_parse(false, "--log-file", value); }
));
#endif // LOG_DISABLE_LOGS
return options;
}
std::string gpt_params_get_system_info(const gpt_params & params) {
std::ostringstream os;
os << "system_info: n_threads = " << params.cpuparams.n_threads;
if (params.cpuparams_batch.n_threads != -1) {
os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")";
}
#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
// TODO: windows + arm64 + mingw64
DWORD logicalProcessorCount = GetActiveProcessorCount(ALL_PROCESSOR_GROUPS);
os << " / " << logicalProcessorCount << " | " << llama_print_system_info();
#else
os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();
#endif
return os.str();
}
//
// String utils
//
std::vector<std::string> string_split(std::string input, char separator) {
std::vector<std::string> parts;
size_t separator_pos = input.find(separator);
while (separator_pos != std::string::npos) {
std::string part = input.substr(0, separator_pos);
parts.emplace_back(part);
input = input.substr(separator_pos + 1);
separator_pos = input.find(separator);
}
parts.emplace_back(input);
return parts;
}
std::string string_strip(const std::string & str) {
size_t start = 0;
size_t end = str.size();
while (start < end && std::isspace(str[start])) {
start++;
}
while (end > start && std::isspace(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);
}
void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}
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 {
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
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;
}
// returns true if successful, false otherwise
bool fs_create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
std::wstring wpath = converter.from_bytes(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 = llama_model_params_from_gpt_params(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_load_model_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 = llama_context_params_from_gpt_params(params);
llama_context * lctx = llama_new_context_with_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;
}
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.sparams.ignore_eos && llama_token_eos(model) == -1) {
fprintf(stderr, "%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sparams.ignore_eos = false;
}
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 != LLAMA_TOKEN_NULL) {
tmp.push_back(bos);
}
if (eos != LLAMA_TOKEN_NULL) {
tmp.push_back(eos);
}
if (tmp.empty()) {
tmp.push_back(0);
}
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 == -1) {
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_perf_reset(lctx, LLAMA_PERF_TYPE_CONTEXT);
}
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);
}
}
}
struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
auto mparams = llama_model_default_params();
if (params.n_gpu_layers != -1) {
mparams.n_gpu_layers = params.n_gpu_layers;
}
mparams.rpc_servers = params.rpc_servers.c_str();
mparams.main_gpu = params.main_gpu;
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;
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();
}
return mparams;
}
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 == "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;
}
throw std::runtime_error("Invalid cache type: " + s);
}
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
auto cparams = llama_context_default_params();
cparams.n_ctx = params.n_ctx;
cparams.n_seq_max = params.n_parallel;
cparams.n_batch = params.n_batch;
cparams.n_ubatch = params.n_ubatch;
cparams.n_threads = params.cpuparams.n_threads;
cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ?
params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
cparams.logits_all = params.logits_all;
cparams.embeddings = params.embedding;
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.type_k = kv_cache_type_from_str(params.cache_type_k);
cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
return cparams;
}
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) {
struct ggml_threadpool_params tpp;
ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults
if (params.mask_valid) {
std::memcpy(&tpp.cpumask, &params.cpumask, GGML_MAX_N_THREADS);
}
tpp.prio = params.priority;
tpp.poll = params.poll;
tpp.strict_cpu = params.strict_cpu;
return tpp;
}
#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_load_model_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 llama_batch_clear(struct llama_batch & batch) {
batch.n_tokens = 0;
}
void llama_batch_add(
struct llama_batch & batch,
llama_token id,
llama_pos pos,
const std::vector<llama_seq_id> & seq_ids,
bool logits) {
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> llama_tokenize(
const struct llama_context * ctx,
const std::string & text,
bool add_special,
bool parse_special) {
return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
}
std::vector<llama_token> llama_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::string llama_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_detokenize(llama_context * ctx, 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(llama_get_model(ctx), 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(llama_get_model(ctx), 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;
}
//
// Chat template utils
//
bool llama_chat_verify_template(const std::string & tmpl) {
llama_chat_message chat[] = {{"user", "test"}};
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
return res >= 0;
}
std::string llama_chat_apply_template(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & msgs,
bool add_ass) {
int alloc_size = 0;
bool fallback = false; // indicate if we must fallback to default chatml
std::vector<llama_chat_message> chat;
for (auto & msg : msgs) {
chat.push_back({msg.role.c_str(), msg.content.c_str()});
alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
}
const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
std::vector<char> buf(alloc_size);
// run the first time to get the total output length
int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
// error: chat template is not supported
if (res < 0) {
if (ptr_tmpl != nullptr) {
// if the custom "tmpl" is not supported, we throw an error
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
throw std::runtime_error("this custom template is not supported");
} else {
// If the built-in template is not supported, we default to chatml
res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
fallback = true;
}
}
// if it turns out that our buffer is too small, we resize it
if ((size_t) res > buf.size()) {
buf.resize(res);
res = llama_chat_apply_template(
fallback ? nullptr : model,
fallback ? "chatml" : ptr_tmpl,
chat.data(), chat.size(), add_ass, buf.data(), buf.size());
}
std::string formatted_chat(buf.data(), res);
return formatted_chat;
}
std::string llama_chat_format_single(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & past_msg,
const llama_chat_msg & new_msg,
bool add_ass) {
std::ostringstream ss;
auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
std::vector<llama_chat_msg> chat_new(past_msg);
// if the past_msg ends with a newline, we must preserve it in the formatted version
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
ss << "\n";
};
// format chat with new_msg
chat_new.push_back(new_msg);
auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
// get the diff part
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
return ss.str();
}
std::string llama_chat_format_example(const struct llama_model * model,
const std::string & tmpl) {
std::vector<llama_chat_msg> msgs = {
{"system", "You are a helpful assistant"},
{"user", "Hello"},
{"assistant", "Hi there"},
{"user", "How are you?"},
};
return llama_chat_apply_template(model, tmpl, msgs, true);
}
//
// 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 llama_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 llama_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 auto & 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);
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, "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.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);
fprintf(stream, "ignore_eos: %s # default: false\n", sparams.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 (const auto & logit_bias : sparams.logit_bias) {
fprintf(stream, " %d: %f", logit_bias.token, logit_bias.bias);
}
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, "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, "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.model_draft.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, "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, "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, "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.cpuparams.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, "typ_p: %f # default: 1.0\n", sparams.typ_p);
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");
}
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