mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
1220 lines
40 KiB
C++
1220 lines
40 KiB
C++
#if defined(_WIN32)
|
|
# include <windows.h>
|
|
# include <io.h>
|
|
#else
|
|
# include <sys/file.h>
|
|
# include <sys/ioctl.h>
|
|
# include <unistd.h>
|
|
#endif
|
|
|
|
#if defined(LLAMA_USE_CURL)
|
|
# include <curl/curl.h>
|
|
#endif
|
|
|
|
#include <signal.h>
|
|
|
|
#include <climits>
|
|
#include <cstdarg>
|
|
#include <cstdio>
|
|
#include <cstring>
|
|
#include <filesystem>
|
|
#include <iostream>
|
|
#include <list>
|
|
#include <sstream>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
#include "chat.h"
|
|
#include "common.h"
|
|
#include "json.hpp"
|
|
#include "linenoise.cpp/linenoise.h"
|
|
#include "llama-cpp.h"
|
|
#include "log.h"
|
|
|
|
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || defined(_WIN32)
|
|
[[noreturn]] static void sigint_handler(int) {
|
|
printf("\n" LOG_COL_DEFAULT);
|
|
exit(0); // not ideal, but it's the only way to guarantee exit in all cases
|
|
}
|
|
#endif
|
|
|
|
GGML_ATTRIBUTE_FORMAT(1, 2)
|
|
static std::string fmt(const char * fmt, ...) {
|
|
va_list ap;
|
|
va_list ap2;
|
|
va_start(ap, fmt);
|
|
va_copy(ap2, ap);
|
|
const int size = vsnprintf(NULL, 0, fmt, ap);
|
|
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
|
|
std::string buf;
|
|
buf.resize(size);
|
|
const int size2 = vsnprintf(const_cast<char *>(buf.data()), buf.size() + 1, fmt, ap2);
|
|
GGML_ASSERT(size2 == size);
|
|
va_end(ap2);
|
|
va_end(ap);
|
|
|
|
return buf;
|
|
}
|
|
|
|
GGML_ATTRIBUTE_FORMAT(1, 2)
|
|
static int printe(const char * fmt, ...) {
|
|
va_list args;
|
|
va_start(args, fmt);
|
|
const int ret = vfprintf(stderr, fmt, args);
|
|
va_end(args);
|
|
|
|
return ret;
|
|
}
|
|
|
|
static std::string strftime_fmt(const char * fmt, const std::tm & tm) {
|
|
std::ostringstream oss;
|
|
oss << std::put_time(&tm, fmt);
|
|
|
|
return oss.str();
|
|
}
|
|
|
|
class Opt {
|
|
public:
|
|
int init(int argc, const char ** argv) {
|
|
ctx_params = llama_context_default_params();
|
|
model_params = llama_model_default_params();
|
|
context_size_default = ctx_params.n_batch;
|
|
ngl_default = model_params.n_gpu_layers;
|
|
common_params_sampling sampling;
|
|
temperature_default = sampling.temp;
|
|
|
|
if (argc < 2) {
|
|
printe("Error: No arguments provided.\n");
|
|
print_help();
|
|
return 1;
|
|
}
|
|
|
|
// Parse arguments
|
|
if (parse(argc, argv)) {
|
|
printe("Error: Failed to parse arguments.\n");
|
|
print_help();
|
|
return 1;
|
|
}
|
|
|
|
// If help is requested, show help and exit
|
|
if (help) {
|
|
print_help();
|
|
return 2;
|
|
}
|
|
|
|
ctx_params.n_batch = context_size >= 0 ? context_size : context_size_default;
|
|
ctx_params.n_ctx = ctx_params.n_batch;
|
|
model_params.n_gpu_layers = ngl >= 0 ? ngl : ngl_default;
|
|
temperature = temperature >= 0 ? temperature : temperature_default;
|
|
|
|
return 0; // Success
|
|
}
|
|
|
|
llama_context_params ctx_params;
|
|
llama_model_params model_params;
|
|
std::string model_;
|
|
std::string chat_template_file;
|
|
std::string user;
|
|
bool use_jinja = false;
|
|
int context_size = -1, ngl = -1;
|
|
float temperature = -1;
|
|
bool verbose = false;
|
|
|
|
private:
|
|
int context_size_default = -1, ngl_default = -1;
|
|
float temperature_default = -1;
|
|
bool help = false;
|
|
|
|
bool parse_flag(const char ** argv, int i, const char * short_opt, const char * long_opt) {
|
|
return strcmp(argv[i], short_opt) == 0 || strcmp(argv[i], long_opt) == 0;
|
|
}
|
|
|
|
int handle_option_with_value(int argc, const char ** argv, int & i, int & option_value) {
|
|
if (i + 1 >= argc) {
|
|
return 1;
|
|
}
|
|
|
|
option_value = std::atoi(argv[++i]);
|
|
|
|
return 0;
|
|
}
|
|
|
|
int handle_option_with_value(int argc, const char ** argv, int & i, float & option_value) {
|
|
if (i + 1 >= argc) {
|
|
return 1;
|
|
}
|
|
|
|
option_value = std::atof(argv[++i]);
|
|
|
|
return 0;
|
|
}
|
|
|
|
int handle_option_with_value(int argc, const char ** argv, int & i, std::string & option_value) {
|
|
if (i + 1 >= argc) {
|
|
return 1;
|
|
}
|
|
|
|
option_value = argv[++i];
|
|
|
|
return 0;
|
|
}
|
|
|
|
int parse(int argc, const char ** argv) {
|
|
bool options_parsing = true;
|
|
for (int i = 1, positional_args_i = 0; i < argc; ++i) {
|
|
if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) {
|
|
if (handle_option_with_value(argc, argv, i, context_size) == 1) {
|
|
return 1;
|
|
}
|
|
} else if (options_parsing &&
|
|
(strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "-ngl") == 0 || strcmp(argv[i], "--ngl") == 0)) {
|
|
if (handle_option_with_value(argc, argv, i, ngl) == 1) {
|
|
return 1;
|
|
}
|
|
} else if (options_parsing && strcmp(argv[i], "--temp") == 0) {
|
|
if (handle_option_with_value(argc, argv, i, temperature) == 1) {
|
|
return 1;
|
|
}
|
|
} else if (options_parsing &&
|
|
(parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) {
|
|
verbose = true;
|
|
} else if (options_parsing && strcmp(argv[i], "--jinja") == 0) {
|
|
use_jinja = true;
|
|
} else if (options_parsing && strcmp(argv[i], "--chat-template-file") == 0){
|
|
if (handle_option_with_value(argc, argv, i, chat_template_file) == 1) {
|
|
return 1;
|
|
}
|
|
use_jinja = true;
|
|
} else if (options_parsing && parse_flag(argv, i, "-h", "--help")) {
|
|
help = true;
|
|
return 0;
|
|
} else if (options_parsing && strcmp(argv[i], "--") == 0) {
|
|
options_parsing = false;
|
|
} else if (positional_args_i == 0) {
|
|
if (!argv[i][0] || argv[i][0] == '-') {
|
|
return 1;
|
|
}
|
|
|
|
++positional_args_i;
|
|
model_ = argv[i];
|
|
} else if (positional_args_i == 1) {
|
|
++positional_args_i;
|
|
user = argv[i];
|
|
} else {
|
|
user += " " + std::string(argv[i]);
|
|
}
|
|
}
|
|
|
|
if (model_.empty()){
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
void print_help() const {
|
|
printf(
|
|
"Description:\n"
|
|
" Runs a llm\n"
|
|
"\n"
|
|
"Usage:\n"
|
|
" llama-run [options] model [prompt]\n"
|
|
"\n"
|
|
"Options:\n"
|
|
" -c, --context-size <value>\n"
|
|
" Context size (default: %d)\n"
|
|
" --chat-template-file <path>\n"
|
|
" Path to the file containing the chat template to use with the model.\n"
|
|
" Only supports jinja templates and implicitly sets the --jinja flag.\n"
|
|
" --jinja\n"
|
|
" Use jinja templating for the chat template of the model\n"
|
|
" -n, -ngl, --ngl <value>\n"
|
|
" Number of GPU layers (default: %d)\n"
|
|
" --temp <value>\n"
|
|
" Temperature (default: %.1f)\n"
|
|
" -v, --verbose, --log-verbose\n"
|
|
" Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n"
|
|
" -h, --help\n"
|
|
" Show help message\n"
|
|
"\n"
|
|
"Commands:\n"
|
|
" model\n"
|
|
" Model is a string with an optional prefix of \n"
|
|
" huggingface:// (hf://), ollama://, https:// or file://.\n"
|
|
" If no protocol is specified and a file exists in the specified\n"
|
|
" path, file:// is assumed, otherwise if a file does not exist in\n"
|
|
" the specified path, ollama:// is assumed. Models that are being\n"
|
|
" pulled are downloaded with .partial extension while being\n"
|
|
" downloaded and then renamed as the file without the .partial\n"
|
|
" extension when complete.\n"
|
|
"\n"
|
|
"Examples:\n"
|
|
" llama-run llama3\n"
|
|
" llama-run ollama://granite-code\n"
|
|
" llama-run ollama://smollm:135m\n"
|
|
" llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf\n"
|
|
" llama-run "
|
|
"huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n"
|
|
" llama-run https://example.com/some-file1.gguf\n"
|
|
" llama-run some-file2.gguf\n"
|
|
" llama-run file://some-file3.gguf\n"
|
|
" llama-run --ngl 999 some-file4.gguf\n"
|
|
" llama-run --ngl 999 some-file5.gguf Hello World\n",
|
|
context_size_default, ngl_default, temperature_default);
|
|
}
|
|
};
|
|
|
|
struct progress_data {
|
|
size_t file_size = 0;
|
|
std::chrono::steady_clock::time_point start_time = std::chrono::steady_clock::now();
|
|
bool printed = false;
|
|
};
|
|
|
|
static int get_terminal_width() {
|
|
#if defined(_WIN32)
|
|
CONSOLE_SCREEN_BUFFER_INFO csbi;
|
|
GetConsoleScreenBufferInfo(GetStdHandle(STD_OUTPUT_HANDLE), &csbi);
|
|
return csbi.srWindow.Right - csbi.srWindow.Left + 1;
|
|
#else
|
|
struct winsize w;
|
|
ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
|
|
return w.ws_col;
|
|
#endif
|
|
}
|
|
|
|
class File {
|
|
public:
|
|
FILE * file = nullptr;
|
|
|
|
FILE * open(const std::string & filename, const char * mode) {
|
|
file = ggml_fopen(filename.c_str(), mode);
|
|
|
|
return file;
|
|
}
|
|
|
|
int lock() {
|
|
if (file) {
|
|
# ifdef _WIN32
|
|
fd = _fileno(file);
|
|
hFile = (HANDLE) _get_osfhandle(fd);
|
|
if (hFile == INVALID_HANDLE_VALUE) {
|
|
fd = -1;
|
|
|
|
return 1;
|
|
}
|
|
|
|
OVERLAPPED overlapped = {};
|
|
if (!LockFileEx(hFile, LOCKFILE_EXCLUSIVE_LOCK | LOCKFILE_FAIL_IMMEDIATELY, 0, MAXDWORD, MAXDWORD,
|
|
&overlapped)) {
|
|
fd = -1;
|
|
|
|
return 1;
|
|
}
|
|
# else
|
|
fd = fileno(file);
|
|
if (flock(fd, LOCK_EX | LOCK_NB) != 0) {
|
|
fd = -1;
|
|
|
|
return 1;
|
|
}
|
|
# endif
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
std::string to_string() {
|
|
fseek(file, 0, SEEK_END);
|
|
const size_t size = ftell(file);
|
|
fseek(file, 0, SEEK_SET);
|
|
std::string out;
|
|
out.resize(size);
|
|
const size_t read_size = fread(&out[0], 1, size, file);
|
|
if (read_size != size) {
|
|
printe("Error reading file: %s", strerror(errno));
|
|
}
|
|
|
|
return out;
|
|
}
|
|
|
|
~File() {
|
|
if (fd >= 0) {
|
|
# ifdef _WIN32
|
|
if (hFile != INVALID_HANDLE_VALUE) {
|
|
OVERLAPPED overlapped = {};
|
|
UnlockFileEx(hFile, 0, MAXDWORD, MAXDWORD, &overlapped);
|
|
}
|
|
# else
|
|
flock(fd, LOCK_UN);
|
|
# endif
|
|
}
|
|
|
|
if (file) {
|
|
fclose(file);
|
|
}
|
|
}
|
|
|
|
private:
|
|
int fd = -1;
|
|
# ifdef _WIN32
|
|
HANDLE hFile = nullptr;
|
|
# endif
|
|
};
|
|
|
|
#ifdef LLAMA_USE_CURL
|
|
class HttpClient {
|
|
public:
|
|
int init(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
|
|
const bool progress, std::string * response_str = nullptr) {
|
|
if (std::filesystem::exists(output_file)) {
|
|
return 0;
|
|
}
|
|
|
|
std::string output_file_partial;
|
|
curl = curl_easy_init();
|
|
if (!curl) {
|
|
return 1;
|
|
}
|
|
|
|
progress_data data;
|
|
File out;
|
|
if (!output_file.empty()) {
|
|
output_file_partial = output_file + ".partial";
|
|
if (!out.open(output_file_partial, "ab")) {
|
|
printe("Failed to open file for writing\n");
|
|
|
|
return 1;
|
|
}
|
|
|
|
if (out.lock()) {
|
|
printe("Failed to exclusively lock file\n");
|
|
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
set_write_options(response_str, out);
|
|
data.file_size = set_resume_point(output_file_partial);
|
|
set_progress_options(progress, data);
|
|
set_headers(headers);
|
|
CURLcode res = perform(url);
|
|
if (res != CURLE_OK){
|
|
printe("Fetching resource '%s' failed: %s\n", url.c_str(), curl_easy_strerror(res));
|
|
return 1;
|
|
}
|
|
if (!output_file.empty()) {
|
|
std::filesystem::rename(output_file_partial, output_file);
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
~HttpClient() {
|
|
if (chunk) {
|
|
curl_slist_free_all(chunk);
|
|
}
|
|
|
|
if (curl) {
|
|
curl_easy_cleanup(curl);
|
|
}
|
|
}
|
|
|
|
private:
|
|
CURL * curl = nullptr;
|
|
struct curl_slist * chunk = nullptr;
|
|
|
|
void set_write_options(std::string * response_str, const File & out) {
|
|
if (response_str) {
|
|
curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, capture_data);
|
|
curl_easy_setopt(curl, CURLOPT_WRITEDATA, response_str);
|
|
} else {
|
|
curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_data);
|
|
curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.file);
|
|
}
|
|
}
|
|
|
|
size_t set_resume_point(const std::string & output_file) {
|
|
size_t file_size = 0;
|
|
if (std::filesystem::exists(output_file)) {
|
|
file_size = std::filesystem::file_size(output_file);
|
|
curl_easy_setopt(curl, CURLOPT_RESUME_FROM_LARGE, static_cast<curl_off_t>(file_size));
|
|
}
|
|
|
|
return file_size;
|
|
}
|
|
|
|
void set_progress_options(bool progress, progress_data & data) {
|
|
if (progress) {
|
|
curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
|
|
curl_easy_setopt(curl, CURLOPT_XFERINFODATA, &data);
|
|
curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, update_progress);
|
|
}
|
|
}
|
|
|
|
void set_headers(const std::vector<std::string> & headers) {
|
|
if (!headers.empty()) {
|
|
if (chunk) {
|
|
curl_slist_free_all(chunk);
|
|
chunk = 0;
|
|
}
|
|
|
|
for (const auto & header : headers) {
|
|
chunk = curl_slist_append(chunk, header.c_str());
|
|
}
|
|
|
|
curl_easy_setopt(curl, CURLOPT_HTTPHEADER, chunk);
|
|
}
|
|
}
|
|
|
|
CURLcode perform(const std::string & url) {
|
|
curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
|
|
curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
|
|
curl_easy_setopt(curl, CURLOPT_DEFAULT_PROTOCOL, "https");
|
|
curl_easy_setopt(curl, CURLOPT_FAILONERROR, 1L);
|
|
return curl_easy_perform(curl);
|
|
}
|
|
|
|
static std::string human_readable_time(double seconds) {
|
|
int hrs = static_cast<int>(seconds) / 3600;
|
|
int mins = (static_cast<int>(seconds) % 3600) / 60;
|
|
int secs = static_cast<int>(seconds) % 60;
|
|
|
|
if (hrs > 0) {
|
|
return fmt("%dh %02dm %02ds", hrs, mins, secs);
|
|
} else if (mins > 0) {
|
|
return fmt("%dm %02ds", mins, secs);
|
|
} else {
|
|
return fmt("%ds", secs);
|
|
}
|
|
}
|
|
|
|
static std::string human_readable_size(curl_off_t size) {
|
|
static const char * suffix[] = { "B", "KB", "MB", "GB", "TB" };
|
|
char length = sizeof(suffix) / sizeof(suffix[0]);
|
|
int i = 0;
|
|
double dbl_size = size;
|
|
if (size > 1024) {
|
|
for (i = 0; (size / 1024) > 0 && i < length - 1; i++, size /= 1024) {
|
|
dbl_size = size / 1024.0;
|
|
}
|
|
}
|
|
|
|
return fmt("%.2f %s", dbl_size, suffix[i]);
|
|
}
|
|
|
|
static int update_progress(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t,
|
|
curl_off_t) {
|
|
progress_data * data = static_cast<progress_data *>(ptr);
|
|
if (total_to_download <= 0) {
|
|
return 0;
|
|
}
|
|
|
|
total_to_download += data->file_size;
|
|
const curl_off_t now_downloaded_plus_file_size = now_downloaded + data->file_size;
|
|
const curl_off_t percentage = calculate_percentage(now_downloaded_plus_file_size, total_to_download);
|
|
std::string progress_prefix = generate_progress_prefix(percentage);
|
|
|
|
const double speed = calculate_speed(now_downloaded, data->start_time);
|
|
const double tim = (total_to_download - now_downloaded) / speed;
|
|
std::string progress_suffix =
|
|
generate_progress_suffix(now_downloaded_plus_file_size, total_to_download, speed, tim);
|
|
|
|
int progress_bar_width = calculate_progress_bar_width(progress_prefix, progress_suffix);
|
|
std::string progress_bar;
|
|
generate_progress_bar(progress_bar_width, percentage, progress_bar);
|
|
|
|
print_progress(progress_prefix, progress_bar, progress_suffix);
|
|
data->printed = true;
|
|
|
|
return 0;
|
|
}
|
|
|
|
static curl_off_t calculate_percentage(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download) {
|
|
return (now_downloaded_plus_file_size * 100) / total_to_download;
|
|
}
|
|
|
|
static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", static_cast<long int>(percentage)); }
|
|
|
|
static double calculate_speed(curl_off_t now_downloaded, const std::chrono::steady_clock::time_point & start_time) {
|
|
const auto now = std::chrono::steady_clock::now();
|
|
const std::chrono::duration<double> elapsed_seconds = now - start_time;
|
|
return now_downloaded / elapsed_seconds.count();
|
|
}
|
|
|
|
static std::string generate_progress_suffix(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download,
|
|
double speed, double estimated_time) {
|
|
const int width = 10;
|
|
return fmt("%*s/%*s%*s/s%*s", width, human_readable_size(now_downloaded_plus_file_size).c_str(), width,
|
|
human_readable_size(total_to_download).c_str(), width, human_readable_size(speed).c_str(), width,
|
|
human_readable_time(estimated_time).c_str());
|
|
}
|
|
|
|
static int calculate_progress_bar_width(const std::string & progress_prefix, const std::string & progress_suffix) {
|
|
int progress_bar_width = get_terminal_width() - progress_prefix.size() - progress_suffix.size() - 3;
|
|
if (progress_bar_width < 1) {
|
|
progress_bar_width = 1;
|
|
}
|
|
|
|
return progress_bar_width;
|
|
}
|
|
|
|
static std::string generate_progress_bar(int progress_bar_width, curl_off_t percentage,
|
|
std::string & progress_bar) {
|
|
const curl_off_t pos = (percentage * progress_bar_width) / 100;
|
|
for (int i = 0; i < progress_bar_width; ++i) {
|
|
progress_bar.append((i < pos) ? "█" : " ");
|
|
}
|
|
|
|
return progress_bar;
|
|
}
|
|
|
|
static void print_progress(const std::string & progress_prefix, const std::string & progress_bar,
|
|
const std::string & progress_suffix) {
|
|
printe("\r" LOG_CLR_TO_EOL "%s%s| %s", progress_prefix.c_str(), progress_bar.c_str(), progress_suffix.c_str());
|
|
}
|
|
// Function to write data to a file
|
|
static size_t write_data(void * ptr, size_t size, size_t nmemb, void * stream) {
|
|
FILE * out = static_cast<FILE *>(stream);
|
|
return fwrite(ptr, size, nmemb, out);
|
|
}
|
|
|
|
// Function to capture data into a string
|
|
static size_t capture_data(void * ptr, size_t size, size_t nmemb, void * stream) {
|
|
std::string * str = static_cast<std::string *>(stream);
|
|
str->append(static_cast<char *>(ptr), size * nmemb);
|
|
return size * nmemb;
|
|
}
|
|
};
|
|
#endif
|
|
|
|
class LlamaData {
|
|
public:
|
|
llama_model_ptr model;
|
|
llama_sampler_ptr sampler;
|
|
llama_context_ptr context;
|
|
std::vector<llama_chat_message> messages; // TODO: switch to common_chat_msg
|
|
std::list<std::string> msg_strs;
|
|
std::vector<char> fmtted;
|
|
llama_pos n_past = 0;
|
|
|
|
int init(Opt & opt) {
|
|
model = initialize_model(opt);
|
|
if (!model) {
|
|
return 1;
|
|
}
|
|
|
|
context = initialize_context(model, opt);
|
|
if (!context) {
|
|
return 1;
|
|
}
|
|
|
|
sampler = initialize_sampler(opt);
|
|
|
|
return 0;
|
|
}
|
|
|
|
private:
|
|
#ifdef LLAMA_USE_CURL
|
|
int download(const std::string & url, const std::string & output_file, const bool progress,
|
|
const std::vector<std::string> & headers = {}, std::string * response_str = nullptr) {
|
|
HttpClient http;
|
|
if (http.init(url, headers, output_file, progress, response_str)) {
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
#else
|
|
int download(const std::string &, const std::string &, const bool, const std::vector<std::string> & = {},
|
|
std::string * = nullptr) {
|
|
printe("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
|
|
|
|
return 1;
|
|
}
|
|
#endif
|
|
|
|
// Helper function to handle model tag extraction and URL construction
|
|
std::pair<std::string, std::string> extract_model_and_tag(std::string & model, const std::string & base_url) {
|
|
std::string model_tag = "latest";
|
|
const size_t colon_pos = model.find(':');
|
|
if (colon_pos != std::string::npos) {
|
|
model_tag = model.substr(colon_pos + 1);
|
|
model = model.substr(0, colon_pos);
|
|
}
|
|
|
|
std::string url = base_url + model + "/manifests/" + model_tag;
|
|
|
|
return { model, url };
|
|
}
|
|
|
|
// Helper function to download and parse the manifest
|
|
int download_and_parse_manifest(const std::string & url, const std::vector<std::string> & headers,
|
|
nlohmann::json & manifest) {
|
|
std::string manifest_str;
|
|
int ret = download(url, "", false, headers, &manifest_str);
|
|
if (ret) {
|
|
return ret;
|
|
}
|
|
|
|
manifest = nlohmann::json::parse(manifest_str);
|
|
|
|
return 0;
|
|
}
|
|
|
|
int huggingface_dl(std::string & model, const std::string & bn) {
|
|
// Find the second occurrence of '/' after protocol string
|
|
size_t pos = model.find('/');
|
|
pos = model.find('/', pos + 1);
|
|
std::string hfr, hff;
|
|
std::vector<std::string> headers = { "User-Agent: llama-cpp", "Accept: application/json" };
|
|
std::string url;
|
|
|
|
if (pos == std::string::npos) {
|
|
auto [model_name, manifest_url] = extract_model_and_tag(model, "https://huggingface.co/v2/");
|
|
hfr = model_name;
|
|
|
|
nlohmann::json manifest;
|
|
int ret = download_and_parse_manifest(manifest_url, headers, manifest);
|
|
if (ret) {
|
|
return ret;
|
|
}
|
|
|
|
hff = manifest["ggufFile"]["rfilename"];
|
|
} else {
|
|
hfr = model.substr(0, pos);
|
|
hff = model.substr(pos + 1);
|
|
}
|
|
|
|
url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff;
|
|
|
|
return download(url, bn, true, headers);
|
|
}
|
|
|
|
int ollama_dl(std::string & model, const std::string & bn) {
|
|
const std::vector<std::string> headers = { "Accept: application/vnd.docker.distribution.manifest.v2+json" };
|
|
if (model.find('/') == std::string::npos) {
|
|
model = "library/" + model;
|
|
}
|
|
|
|
auto [model_name, manifest_url] = extract_model_and_tag(model, "https://registry.ollama.ai/v2/");
|
|
nlohmann::json manifest;
|
|
int ret = download_and_parse_manifest(manifest_url, {}, manifest);
|
|
if (ret) {
|
|
return ret;
|
|
}
|
|
|
|
std::string layer;
|
|
for (const auto & l : manifest["layers"]) {
|
|
if (l["mediaType"] == "application/vnd.ollama.image.model") {
|
|
layer = l["digest"];
|
|
break;
|
|
}
|
|
}
|
|
|
|
std::string blob_url = "https://registry.ollama.ai/v2/" + model_name + "/blobs/" + layer;
|
|
|
|
return download(blob_url, bn, true, headers);
|
|
}
|
|
|
|
int github_dl(const std::string & model, const std::string & bn) {
|
|
std::string repository = model;
|
|
std::string branch = "main";
|
|
const size_t at_pos = model.find('@');
|
|
if (at_pos != std::string::npos) {
|
|
repository = model.substr(0, at_pos);
|
|
branch = model.substr(at_pos + 1);
|
|
}
|
|
|
|
const std::vector<std::string> repo_parts = string_split(repository, "/");
|
|
if (repo_parts.size() < 3) {
|
|
printe("Invalid GitHub repository format\n");
|
|
return 1;
|
|
}
|
|
|
|
const std::string & org = repo_parts[0];
|
|
const std::string & project = repo_parts[1];
|
|
std::string url = "https://raw.githubusercontent.com/" + org + "/" + project + "/" + branch;
|
|
for (size_t i = 2; i < repo_parts.size(); ++i) {
|
|
url += "/" + repo_parts[i];
|
|
}
|
|
|
|
return download(url, bn, true);
|
|
}
|
|
|
|
int s3_dl(const std::string & model, const std::string & bn) {
|
|
const size_t slash_pos = model.find('/');
|
|
if (slash_pos == std::string::npos) {
|
|
return 1;
|
|
}
|
|
|
|
const std::string bucket = model.substr(0, slash_pos);
|
|
const std::string key = model.substr(slash_pos + 1);
|
|
const char * access_key = std::getenv("AWS_ACCESS_KEY_ID");
|
|
const char * secret_key = std::getenv("AWS_SECRET_ACCESS_KEY");
|
|
if (!access_key || !secret_key) {
|
|
printe("AWS credentials not found in environment\n");
|
|
return 1;
|
|
}
|
|
|
|
// Generate AWS Signature Version 4 headers
|
|
// (Implementation requires HMAC-SHA256 and date handling)
|
|
// Get current timestamp
|
|
const time_t now = time(nullptr);
|
|
const tm tm = *gmtime(&now);
|
|
const std::string date = strftime_fmt("%Y%m%d", tm);
|
|
const std::string datetime = strftime_fmt("%Y%m%dT%H%M%SZ", tm);
|
|
const std::vector<std::string> headers = {
|
|
"Authorization: AWS4-HMAC-SHA256 Credential=" + std::string(access_key) + "/" + date +
|
|
"/us-east-1/s3/aws4_request",
|
|
"x-amz-content-sha256: UNSIGNED-PAYLOAD", "x-amz-date: " + datetime
|
|
};
|
|
|
|
const std::string url = "https://" + bucket + ".s3.amazonaws.com/" + key;
|
|
|
|
return download(url, bn, true, headers);
|
|
}
|
|
|
|
std::string basename(const std::string & path) {
|
|
const size_t pos = path.find_last_of("/\\");
|
|
if (pos == std::string::npos) {
|
|
return path;
|
|
}
|
|
|
|
return path.substr(pos + 1);
|
|
}
|
|
|
|
int rm_until_substring(std::string & model_, const std::string & substring) {
|
|
const std::string::size_type pos = model_.find(substring);
|
|
if (pos == std::string::npos) {
|
|
return 1;
|
|
}
|
|
|
|
model_ = model_.substr(pos + substring.size()); // Skip past the substring
|
|
return 0;
|
|
}
|
|
|
|
int resolve_model(std::string & model_) {
|
|
int ret = 0;
|
|
if (string_starts_with(model_, "file://") || std::filesystem::exists(model_)) {
|
|
rm_until_substring(model_, "://");
|
|
|
|
return ret;
|
|
}
|
|
|
|
const std::string bn = basename(model_);
|
|
if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://") ||
|
|
string_starts_with(model_, "hf.co/")) {
|
|
rm_until_substring(model_, "hf.co/");
|
|
rm_until_substring(model_, "://");
|
|
ret = huggingface_dl(model_, bn);
|
|
} else if ((string_starts_with(model_, "https://") || string_starts_with(model_, "http://")) &&
|
|
!string_starts_with(model_, "https://ollama.com/library/")) {
|
|
ret = download(model_, bn, true);
|
|
} else if (string_starts_with(model_, "github:") || string_starts_with(model_, "github://")) {
|
|
rm_until_substring(model_, "github:");
|
|
rm_until_substring(model_, "://");
|
|
ret = github_dl(model_, bn);
|
|
} else if (string_starts_with(model_, "s3://")) {
|
|
rm_until_substring(model_, "://");
|
|
ret = s3_dl(model_, bn);
|
|
} else { // ollama:// or nothing
|
|
rm_until_substring(model_, "ollama.com/library/");
|
|
rm_until_substring(model_, "://");
|
|
ret = ollama_dl(model_, bn);
|
|
}
|
|
|
|
model_ = bn;
|
|
|
|
return ret;
|
|
}
|
|
|
|
// Initializes the model and returns a unique pointer to it
|
|
llama_model_ptr initialize_model(Opt & opt) {
|
|
ggml_backend_load_all();
|
|
resolve_model(opt.model_);
|
|
printe("\r" LOG_CLR_TO_EOL "Loading model");
|
|
llama_model_ptr model(llama_model_load_from_file(opt.model_.c_str(), opt.model_params));
|
|
if (!model) {
|
|
printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str());
|
|
}
|
|
|
|
printe("\r" LOG_CLR_TO_EOL);
|
|
return model;
|
|
}
|
|
|
|
// Initializes the context with the specified parameters
|
|
llama_context_ptr initialize_context(const llama_model_ptr & model, const Opt & opt) {
|
|
llama_context_ptr context(llama_init_from_model(model.get(), opt.ctx_params));
|
|
if (!context) {
|
|
printe("%s: error: failed to create the llama_context\n", __func__);
|
|
}
|
|
|
|
return context;
|
|
}
|
|
|
|
// Initializes and configures the sampler
|
|
llama_sampler_ptr initialize_sampler(const Opt & opt) {
|
|
llama_sampler_ptr sampler(llama_sampler_chain_init(llama_sampler_chain_default_params()));
|
|
llama_sampler_chain_add(sampler.get(), llama_sampler_init_min_p(0.05f, 1));
|
|
llama_sampler_chain_add(sampler.get(), llama_sampler_init_temp(opt.temperature));
|
|
llama_sampler_chain_add(sampler.get(), llama_sampler_init_dist(LLAMA_DEFAULT_SEED));
|
|
|
|
return sampler;
|
|
}
|
|
};
|
|
|
|
// Add a message to `messages` and store its content in `msg_strs`
|
|
static void add_message(const char * role, const std::string & text, LlamaData & llama_data) {
|
|
llama_data.msg_strs.push_back(std::move(text));
|
|
llama_data.messages.push_back({ role, llama_data.msg_strs.back().c_str() });
|
|
}
|
|
|
|
// Function to apply the chat template and resize `formatted` if needed
|
|
static int apply_chat_template(const struct common_chat_templates * tmpls, LlamaData & llama_data, const bool append, bool use_jinja) {
|
|
common_chat_templates_inputs inputs;
|
|
for (const auto & msg : llama_data.messages) {
|
|
common_chat_msg cmsg;
|
|
cmsg.role = msg.role;
|
|
cmsg.content = msg.content;
|
|
inputs.messages.push_back(cmsg);
|
|
}
|
|
inputs.add_generation_prompt = append;
|
|
inputs.use_jinja = use_jinja;
|
|
|
|
auto chat_params = common_chat_templates_apply(tmpls, inputs);
|
|
// TODO: use other params for tool calls.
|
|
auto result = chat_params.prompt;
|
|
llama_data.fmtted.resize(result.size() + 1);
|
|
memcpy(llama_data.fmtted.data(), result.c_str(), result.size() + 1);
|
|
return result.size();
|
|
}
|
|
|
|
// Function to tokenize the prompt
|
|
static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt,
|
|
std::vector<llama_token> & prompt_tokens, const LlamaData & llama_data) {
|
|
const bool is_first = llama_kv_self_used_cells(llama_data.context.get()) == 0;
|
|
|
|
const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
|
|
prompt_tokens.resize(n_prompt_tokens);
|
|
if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), is_first,
|
|
true) < 0) {
|
|
printe("failed to tokenize the prompt\n");
|
|
return -1;
|
|
}
|
|
|
|
return n_prompt_tokens;
|
|
}
|
|
|
|
// Check if we have enough space in the context to evaluate this batch
|
|
static int check_context_size(const llama_context_ptr & ctx, const llama_batch_ext_ptr & batch) {
|
|
const int n_ctx = llama_n_ctx(ctx.get());
|
|
const int n_ctx_used = llama_kv_self_used_cells(ctx.get());
|
|
if (n_ctx_used + llama_batch_ext_get_n_tokens(batch.get()) > n_ctx) {
|
|
printf(LOG_COL_DEFAULT "\n");
|
|
printe("context size exceeded\n");
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
// convert the token to a string
|
|
static int convert_token_to_string(const llama_vocab * vocab, const llama_token token_id, std::string & piece) {
|
|
char buf[256];
|
|
int n = llama_token_to_piece(vocab, token_id, buf, sizeof(buf), 0, true);
|
|
if (n < 0) {
|
|
printe("failed to convert token to piece\n");
|
|
return 1;
|
|
}
|
|
|
|
piece = std::string(buf, n);
|
|
return 0;
|
|
}
|
|
|
|
static void print_word_and_concatenate_to_response(const std::string & piece, std::string & response) {
|
|
printf("%s", piece.c_str());
|
|
fflush(stdout);
|
|
response += piece;
|
|
}
|
|
|
|
// helper function to evaluate a prompt and generate a response
|
|
static int generate(LlamaData & llama_data, const std::string & prompt, std::string & response) {
|
|
const llama_vocab * vocab = llama_model_get_vocab(llama_data.model.get());
|
|
|
|
std::vector<llama_token> tokens;
|
|
if (tokenize_prompt(vocab, prompt, tokens, llama_data) < 0) {
|
|
return 1;
|
|
}
|
|
|
|
// prepare a batch for the prompt
|
|
auto batch = llama_batch_ext_ptr::init_from_text(tokens.data(), tokens.size(), llama_data.n_past, 0, true);
|
|
llama_token new_token_id;
|
|
while (true) {
|
|
check_context_size(llama_data.context, batch);
|
|
if (llama_decode_ext(llama_data.context.get(), batch.get())) {
|
|
printe("failed to decode\n");
|
|
return 1;
|
|
}
|
|
|
|
llama_data.n_past += llama_batch_ext_get_n_tokens(batch.get());
|
|
|
|
// sample the next token, check is it an end of generation?
|
|
new_token_id = llama_sampler_sample(llama_data.sampler.get(), llama_data.context.get(), -1);
|
|
if (llama_vocab_is_eog(vocab, new_token_id)) {
|
|
break;
|
|
}
|
|
|
|
std::string piece;
|
|
if (convert_token_to_string(vocab, new_token_id, piece)) {
|
|
return 1;
|
|
}
|
|
|
|
print_word_and_concatenate_to_response(piece, response);
|
|
|
|
// prepare the next batch with the sampled token
|
|
batch.reset(llama_batch_ext_init_from_text(&new_token_id, 1, llama_data.n_past, 0, true));
|
|
}
|
|
|
|
printf(LOG_COL_DEFAULT);
|
|
return 0;
|
|
}
|
|
|
|
static int read_user_input(std::string & user_input) {
|
|
static const char * prompt_prefix_env = std::getenv("LLAMA_PROMPT_PREFIX");
|
|
static const char * prompt_prefix = prompt_prefix_env ? prompt_prefix_env : "> ";
|
|
#ifdef WIN32
|
|
printf("\r" LOG_CLR_TO_EOL LOG_COL_DEFAULT "%s", prompt_prefix);
|
|
|
|
std::getline(std::cin, user_input);
|
|
if (std::cin.eof()) {
|
|
printf("\n");
|
|
return 1;
|
|
}
|
|
#else
|
|
std::unique_ptr<char, decltype(&std::free)> line(const_cast<char *>(linenoise(prompt_prefix)), free);
|
|
if (!line) {
|
|
return 1;
|
|
}
|
|
|
|
user_input = line.get();
|
|
#endif
|
|
|
|
if (user_input == "/bye") {
|
|
return 1;
|
|
}
|
|
|
|
if (user_input.empty()) {
|
|
return 2;
|
|
}
|
|
|
|
#ifndef WIN32
|
|
linenoiseHistoryAdd(line.get());
|
|
#endif
|
|
|
|
return 0; // Should have data in happy path
|
|
}
|
|
|
|
// Function to generate a response based on the prompt
|
|
static int generate_response(LlamaData & llama_data, const std::string & prompt, std::string & response,
|
|
const bool stdout_a_terminal) {
|
|
// Set response color
|
|
if (stdout_a_terminal) {
|
|
printf(LOG_COL_YELLOW);
|
|
}
|
|
|
|
if (generate(llama_data, prompt, response)) {
|
|
printe("failed to generate response\n");
|
|
return 1;
|
|
}
|
|
|
|
// End response with color reset and newline
|
|
printf("\n%s", stdout_a_terminal ? LOG_COL_DEFAULT : "");
|
|
return 0;
|
|
}
|
|
|
|
// Helper function to apply the chat template and handle errors
|
|
static int apply_chat_template_with_error_handling(const common_chat_templates * tmpls, LlamaData & llama_data, const bool append, int & output_length, bool use_jinja) {
|
|
const int new_len = apply_chat_template(tmpls, llama_data, append, use_jinja);
|
|
if (new_len < 0) {
|
|
printe("failed to apply the chat template\n");
|
|
return -1;
|
|
}
|
|
|
|
output_length = new_len;
|
|
return 0;
|
|
}
|
|
|
|
// Helper function to handle user input
|
|
static int handle_user_input(std::string & user_input, const std::string & user) {
|
|
if (!user.empty()) {
|
|
user_input = user;
|
|
return 0; // No need for interactive input
|
|
}
|
|
|
|
return read_user_input(user_input); // Returns true if input ends the loop
|
|
}
|
|
|
|
static bool is_stdin_a_terminal() {
|
|
#if defined(_WIN32)
|
|
HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE);
|
|
DWORD mode;
|
|
return GetConsoleMode(hStdin, &mode);
|
|
#else
|
|
return isatty(STDIN_FILENO);
|
|
#endif
|
|
}
|
|
|
|
static bool is_stdout_a_terminal() {
|
|
#if defined(_WIN32)
|
|
HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE);
|
|
DWORD mode;
|
|
return GetConsoleMode(hStdout, &mode);
|
|
#else
|
|
return isatty(STDOUT_FILENO);
|
|
#endif
|
|
}
|
|
|
|
// Function to handle user input
|
|
static int get_user_input(std::string & user_input, const std::string & user) {
|
|
while (true) {
|
|
const int ret = handle_user_input(user_input, user);
|
|
if (ret == 1) {
|
|
return 1;
|
|
}
|
|
|
|
if (ret == 2) {
|
|
continue;
|
|
}
|
|
|
|
break;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
// Reads a chat template file to be used
|
|
static std::string read_chat_template_file(const std::string & chat_template_file) {
|
|
File file;
|
|
if (!file.open(chat_template_file, "r")) {
|
|
printe("Error opening chat template file '%s': %s", chat_template_file.c_str(), strerror(errno));
|
|
return "";
|
|
}
|
|
|
|
return file.to_string();
|
|
}
|
|
|
|
static int process_user_message(const Opt & opt, const std::string & user_input, LlamaData & llama_data,
|
|
const common_chat_templates_ptr & chat_templates, int & prev_len,
|
|
const bool stdout_a_terminal) {
|
|
add_message("user", opt.user.empty() ? user_input : opt.user, llama_data);
|
|
int new_len;
|
|
if (apply_chat_template_with_error_handling(chat_templates.get(), llama_data, true, new_len, opt.use_jinja) < 0) {
|
|
return 1;
|
|
}
|
|
|
|
std::string prompt(llama_data.fmtted.begin() + prev_len, llama_data.fmtted.begin() + new_len);
|
|
std::string response;
|
|
if (generate_response(llama_data, prompt, response, stdout_a_terminal)) {
|
|
return 1;
|
|
}
|
|
|
|
if (!opt.user.empty()) {
|
|
return 2;
|
|
}
|
|
|
|
add_message("assistant", response, llama_data);
|
|
if (apply_chat_template_with_error_handling(chat_templates.get(), llama_data, false, prev_len, opt.use_jinja) < 0) {
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
// Main chat loop function
|
|
static int chat_loop(LlamaData & llama_data, const Opt & opt) {
|
|
int prev_len = 0;
|
|
llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
|
|
std::string chat_template;
|
|
if (!opt.chat_template_file.empty()) {
|
|
chat_template = read_chat_template_file(opt.chat_template_file);
|
|
}
|
|
|
|
common_chat_templates_ptr chat_templates = common_chat_templates_init(llama_data.model.get(), chat_template);
|
|
static const bool stdout_a_terminal = is_stdout_a_terminal();
|
|
while (true) {
|
|
// Get user input
|
|
std::string user_input;
|
|
if (get_user_input(user_input, opt.user) == 1) {
|
|
return 0;
|
|
}
|
|
|
|
const int ret = process_user_message(opt, user_input, llama_data, chat_templates, prev_len, stdout_a_terminal);
|
|
if (ret == 1) {
|
|
return 1;
|
|
} else if (ret == 2) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static void log_callback(const enum ggml_log_level level, const char * text, void * p) {
|
|
const Opt * opt = static_cast<Opt *>(p);
|
|
if (opt->verbose || level == GGML_LOG_LEVEL_ERROR) {
|
|
printe("%s", text);
|
|
}
|
|
}
|
|
|
|
static std::string read_pipe_data() {
|
|
std::ostringstream result;
|
|
result << std::cin.rdbuf(); // Read all data from std::cin
|
|
return result.str();
|
|
}
|
|
|
|
static void ctrl_c_handling() {
|
|
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
|
|
struct sigaction sigint_action;
|
|
sigint_action.sa_handler = sigint_handler;
|
|
sigemptyset(&sigint_action.sa_mask);
|
|
sigint_action.sa_flags = 0;
|
|
sigaction(SIGINT, &sigint_action, NULL);
|
|
#elif defined(_WIN32)
|
|
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
|
|
return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
|
|
};
|
|
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
|
|
#endif
|
|
}
|
|
|
|
int main(int argc, const char ** argv) {
|
|
ctrl_c_handling();
|
|
Opt opt;
|
|
const int ret = opt.init(argc, argv);
|
|
if (ret == 2) {
|
|
return 0;
|
|
} else if (ret) {
|
|
return 1;
|
|
}
|
|
|
|
if (!is_stdin_a_terminal()) {
|
|
if (!opt.user.empty()) {
|
|
opt.user += "\n\n";
|
|
}
|
|
|
|
opt.user += read_pipe_data();
|
|
}
|
|
|
|
llama_log_set(log_callback, &opt);
|
|
LlamaData llama_data;
|
|
if (llama_data.init(opt)) {
|
|
return 1;
|
|
}
|
|
|
|
if (chat_loop(llama_data, opt)) {
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|