mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
1286 lines
54 KiB
C++
1286 lines
54 KiB
C++
#include "common.h"
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#include "build-info.h"
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#include "llama.h"
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#include <algorithm>
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#include <cassert>
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#include <cmath>
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#include <cstring>
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#include <ctime>
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#include <fstream>
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#include <iterator>
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#include <iostream>
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#include <regex>
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#include <sstream>
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#include <string>
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#include <unordered_set>
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#include <vector>
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#include <cinttypes>
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#if defined(__APPLE__) && defined(__MACH__)
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#include <sys/types.h>
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#include <sys/sysctl.h>
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#endif
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#if defined(_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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# define NOMINMAX
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#endif
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#include <codecvt>
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#include <locale>
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#include <windows.h>
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#include <fcntl.h>
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#include <io.h>
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#else
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#include <sys/ioctl.h>
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#include <sys/stat.h>
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#include <unistd.h>
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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int32_t get_num_physical_cores() {
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#ifdef __linux__
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// enumerate the set of thread siblings, num entries is num cores
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std::unordered_set<std::string> siblings;
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for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
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std::ifstream thread_siblings("/sys/devices/system/cpu"
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+ std::to_string(cpu) + "/topology/thread_siblings");
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if (!thread_siblings.is_open()) {
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break; // no more cpus
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}
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std::string line;
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if (std::getline(thread_siblings, line)) {
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siblings.insert(line);
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}
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}
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if (!siblings.empty()) {
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return static_cast<int32_t>(siblings.size());
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}
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#elif defined(__APPLE__) && defined(__MACH__)
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int32_t num_physical_cores;
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size_t len = sizeof(num_physical_cores);
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int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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#elif defined(_WIN32)
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//TODO: Implement
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#endif
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unsigned int n_threads = std::thread::hardware_concurrency();
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return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
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}
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static void process_escapes(std::string& input) {
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std::size_t input_len = input.length();
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std::size_t output_idx = 0;
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for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
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if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
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switch (input[++input_idx]) {
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case 'n': input[output_idx++] = '\n'; break;
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case 'r': input[output_idx++] = '\r'; break;
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case 't': input[output_idx++] = '\t'; break;
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case '\'': input[output_idx++] = '\''; break;
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case '\"': input[output_idx++] = '\"'; break;
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case '\\': input[output_idx++] = '\\'; break;
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default: input[output_idx++] = '\\';
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input[output_idx++] = input[input_idx]; break;
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}
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} else {
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input[output_idx++] = input[input_idx];
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}
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}
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input.resize(output_idx);
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}
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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bool invalid_param = false;
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std::string arg;
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gpt_params default_params;
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const std::string arg_prefix = "--";
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for (int i = 1; i < argc; i++) {
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arg = argv[i];
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if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
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std::replace(arg.begin(), arg.end(), '_', '-');
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}
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if (arg == "-s" || arg == "--seed") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.seed = std::stoul(argv[i]);
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} else if (arg == "-t" || arg == "--threads") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_threads = std::stoi(argv[i]);
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if (params.n_threads <= 0) {
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params.n_threads = std::thread::hardware_concurrency();
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}
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} else if (arg == "-p" || arg == "--prompt") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.prompt = argv[i];
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} else if (arg == "-e" || arg == "--escape") {
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params.escape = true;
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} else if (arg == "--prompt-cache") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.path_prompt_cache = argv[i];
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} else if (arg == "--prompt-cache-all") {
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params.prompt_cache_all = true;
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} else if (arg == "--prompt-cache-ro") {
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params.prompt_cache_ro = true;
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} else if (arg == "-f" || arg == "--file") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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std::ifstream file(argv[i]);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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invalid_param = true;
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break;
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}
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std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
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if (params.prompt.back() == '\n') {
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params.prompt.pop_back();
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}
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} else if (arg == "-n" || arg == "--n-predict") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_predict = std::stoi(argv[i]);
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} else if (arg == "--top-k") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.top_k = std::stoi(argv[i]);
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} else if (arg == "-c" || arg == "--ctx-size") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_ctx = std::stoi(argv[i]);
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} else if (arg == "--rope-freq-base") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.rope_freq_base = std::stof(argv[i]);
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} else if (arg == "--rope-freq-scale") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.rope_freq_scale = std::stof(argv[i]);
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} else if (arg == "--rope-scale") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.rope_freq_scale = 1.0f/std::stof(argv[i]);
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} else if (arg == "--memory-f32") {
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params.memory_f16 = false;
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} else if (arg == "--top-p") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.top_p = std::stof(argv[i]);
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} else if (arg == "--temp") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.temp = std::stof(argv[i]);
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} else if (arg == "--tfs") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.tfs_z = std::stof(argv[i]);
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} else if (arg == "--typical") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.typical_p = std::stof(argv[i]);
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} else if (arg == "--repeat-last-n") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.repeat_last_n = std::stoi(argv[i]);
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} else if (arg == "--repeat-penalty") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.repeat_penalty = std::stof(argv[i]);
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} else if (arg == "--frequency-penalty") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.frequency_penalty = std::stof(argv[i]);
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} else if (arg == "--presence-penalty") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.presence_penalty = std::stof(argv[i]);
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} else if (arg == "--mirostat") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.mirostat = std::stoi(argv[i]);
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} else if (arg == "--mirostat-lr") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.mirostat_eta = std::stof(argv[i]);
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} else if (arg == "--mirostat-ent") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.mirostat_tau = std::stof(argv[i]);
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} else if (arg == "--cfg-negative-prompt") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.cfg_negative_prompt = argv[i];
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} else if (arg == "--cfg-negative-prompt-file") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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std::ifstream file(argv[i]);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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invalid_param = true;
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break;
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}
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std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.cfg_negative_prompt));
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if (params.cfg_negative_prompt.back() == '\n') {
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params.cfg_negative_prompt.pop_back();
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}
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} else if (arg == "--cfg-scale") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.cfg_scale = std::stof(argv[i]);
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} else if (arg == "-b" || arg == "--batch-size") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_batch = std::stoi(argv[i]);
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} else if (arg == "--keep") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_keep = std::stoi(argv[i]);
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} else if (arg == "--draft") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_draft = std::stoi(argv[i]);
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} else if (arg == "--chunks") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_chunks = std::stoi(argv[i]);
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} else if (arg == "-np" || arg == "--parallel") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_parallel = std::stoi(argv[i]);
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} else if (arg == "-ns" || arg == "--sequences") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_sequences = std::stoi(argv[i]);
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} else if (arg == "-m" || arg == "--model") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.model = argv[i];
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} else if (arg == "-md" || arg == "--model-draft") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.model_draft = argv[i];
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} else if (arg == "-a" || arg == "--alias") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.model_alias = argv[i];
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} else if (arg == "--lora") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.lora_adapter = argv[i];
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params.use_mmap = false;
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} else if (arg == "--lora-base") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.lora_base = argv[i];
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} else if (arg == "-i" || arg == "--interactive") {
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params.interactive = true;
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} else if (arg == "--embedding") {
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params.embedding = true;
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} else if (arg == "--interactive-first") {
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params.interactive_first = true;
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} else if (arg == "-ins" || arg == "--instruct") {
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params.instruct = true;
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} else if (arg == "--multiline-input") {
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params.multiline_input = true;
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} else if (arg == "--simple-io") {
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params.simple_io = true;
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} else if (arg == "--hot-plug") {
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params.hot_plug = true;
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} else if (arg == "--color") {
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params.use_color = true;
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} else if (arg == "--mlock") {
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params.use_mlock = true;
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} else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
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params.n_gpu_layers = std::stoi(argv[i]);
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#else
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fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
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fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
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#endif
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} else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
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params.n_gpu_layers_draft = std::stoi(argv[i]);
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#else
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fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n");
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fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
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#endif
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} else if (arg == "--main-gpu" || arg == "-mg") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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#ifdef GGML_USE_CUBLAS
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params.main_gpu = std::stoi(argv[i]);
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#else
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fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
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#endif
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} else if (arg == "--tensor-split" || arg == "-ts") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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#ifdef GGML_USE_CUBLAS
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std::string arg_next = argv[i];
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// split string by , and /
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const std::regex regex{R"([,/]+)"};
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std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
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std::vector<std::string> split_arg{it, {}};
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GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
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for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
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if (i < split_arg.size()) {
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params.tensor_split[i] = std::stof(split_arg[i]);
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} else {
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params.tensor_split[i] = 0.0f;
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}
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}
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#else
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fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
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#endif // GGML_USE_CUBLAS
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} else if (arg == "--no-mul-mat-q" || arg == "-nommq") {
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#ifdef GGML_USE_CUBLAS
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params.mul_mat_q = false;
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#else
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fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n");
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#endif // GGML_USE_CUBLAS
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} else if (arg == "--low-vram" || arg == "-lv") {
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#ifdef GGML_USE_CUBLAS
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params.low_vram = true;
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#else
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fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
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#endif // GGML_USE_CUBLAS
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} else if (arg == "--no-mmap") {
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params.use_mmap = false;
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} else if (arg == "--numa") {
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params.numa = true;
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} else if (arg == "--verbose-prompt") {
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params.verbose_prompt = true;
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} else if (arg == "-r" || arg == "--reverse-prompt") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.antiprompt.push_back(argv[i]);
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} else if (arg == "-ld" || arg == "--logdir") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.logdir = argv[i];
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if (params.logdir.back() != DIRECTORY_SEPARATOR) {
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params.logdir += DIRECTORY_SEPARATOR;
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}
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} else if (arg == "--perplexity" || arg == "--all-logits") {
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params.logits_all = true;
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} else if (arg == "--ppl-stride") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.ppl_stride = std::stoi(argv[i]);
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} else if (arg == "--ppl-output-type") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.ppl_output_type = std::stoi(argv[i]);
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} else if (arg == "--hellaswag") {
|
|
params.hellaswag = true;
|
|
} else if (arg == "--hellaswag-tasks") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.hellaswag_tasks = std::stoi(argv[i]);
|
|
} else if (arg == "--ignore-eos") {
|
|
params.ignore_eos = true;
|
|
} else if (arg == "--no-penalize-nl") {
|
|
params.penalize_nl = false;
|
|
} else if (arg == "-l" || arg == "--logit-bias") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::stringstream ss(argv[i]);
|
|
llama_token key;
|
|
char sign;
|
|
std::string value_str;
|
|
try {
|
|
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
|
|
params.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
|
|
} else {
|
|
throw std::exception();
|
|
}
|
|
} catch (const std::exception&) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
} else if (arg == "-h" || arg == "--help") {
|
|
gpt_print_usage(argc, argv, default_params);
|
|
#ifndef LOG_DISABLE_LOGS
|
|
log_print_usage();
|
|
#endif // LOG_DISABLE_LOGS
|
|
exit(0);
|
|
} else if (arg == "--random-prompt") {
|
|
params.random_prompt = true;
|
|
} else if (arg == "--in-prefix-bos") {
|
|
params.input_prefix_bos = true;
|
|
} else if (arg == "--in-prefix") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.input_prefix = argv[i];
|
|
} else if (arg == "--in-suffix") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.input_suffix = argv[i];
|
|
} else if (arg == "--grammar") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.grammar = argv[i];
|
|
} else if (arg == "--grammar-file") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::copy(
|
|
std::istreambuf_iterator<char>(file),
|
|
std::istreambuf_iterator<char>(),
|
|
std::back_inserter(params.grammar)
|
|
);
|
|
#ifndef LOG_DISABLE_LOGS
|
|
// Parse args for logging parameters
|
|
} else if ( log_param_single_parse( argv[i] ) ) {
|
|
// Do nothing, log_param_single_parse automatically does it's thing
|
|
// and returns if a match was found and parsed.
|
|
} else if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) {
|
|
// We have a matching known parameter requiring an argument,
|
|
// now we need to check if there is anything after this argv
|
|
// and flag invalid_param or parse it.
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
// End of Parse args for logging parameters
|
|
#endif // LOG_DISABLE_LOGS
|
|
} else {
|
|
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
|
gpt_print_usage(argc, argv, default_params);
|
|
exit(1);
|
|
}
|
|
}
|
|
if (invalid_param) {
|
|
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
|
|
gpt_print_usage(argc, argv, default_params);
|
|
exit(1);
|
|
}
|
|
if (params.prompt_cache_all &&
|
|
(params.interactive || params.interactive_first ||
|
|
params.instruct)) {
|
|
fprintf(stderr, "error: --prompt-cache-all not supported in interactive mode yet\n");
|
|
gpt_print_usage(argc, argv, default_params);
|
|
exit(1);
|
|
}
|
|
|
|
if (params.escape) {
|
|
process_escapes(params.prompt);
|
|
process_escapes(params.input_prefix);
|
|
process_escapes(params.input_suffix);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|
printf("usage: %s [options]\n", argv[0]);
|
|
printf("\n");
|
|
printf("options:\n");
|
|
printf(" -h, --help show this help message and exit\n");
|
|
printf(" -i, --interactive run in interactive mode\n");
|
|
printf(" --interactive-first run in interactive mode and wait for input right away\n");
|
|
printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n");
|
|
printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n");
|
|
printf(" -r PROMPT, --reverse-prompt PROMPT\n");
|
|
printf(" halt generation at PROMPT, return control in interactive mode\n");
|
|
printf(" (can be specified more than once for multiple prompts).\n");
|
|
printf(" --color colorise output to distinguish prompt and user input from generations\n");
|
|
printf(" -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
|
|
printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
|
printf(" -p PROMPT, --prompt PROMPT\n");
|
|
printf(" prompt to start generation with (default: empty)\n");
|
|
printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
|
|
printf(" --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n");
|
|
printf(" --prompt-cache-all if specified, saves user input and generations to cache as well.\n");
|
|
printf(" not supported with --interactive or other interactive options\n");
|
|
printf(" --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n");
|
|
printf(" --random-prompt start with a randomized prompt.\n");
|
|
printf(" --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n");
|
|
printf(" --in-prefix STRING string to prefix user inputs with (default: empty)\n");
|
|
printf(" --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
|
|
printf(" -f FNAME, --file FNAME\n");
|
|
printf(" prompt file to start generation.\n");
|
|
printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
|
|
printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
|
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
|
printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
|
|
printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
|
|
printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
|
|
printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p);
|
|
printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n);
|
|
printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty);
|
|
printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty);
|
|
printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty);
|
|
printf(" --mirostat N use Mirostat sampling.\n");
|
|
printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
|
|
printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat);
|
|
printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta);
|
|
printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau);
|
|
printf(" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
|
|
printf(" modifies the likelihood of token appearing in the completion,\n");
|
|
printf(" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
|
|
printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
|
|
printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
|
|
printf(" --grammar-file FNAME file to read grammar from\n");
|
|
printf(" --cfg-negative-prompt PROMPT\n");
|
|
printf(" negative prompt to use for guidance. (default: empty)\n");
|
|
printf(" --cfg-negative-prompt-file FNAME\n");
|
|
printf(" negative prompt file to use for guidance. (default: empty)\n");
|
|
printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
|
|
printf(" --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale);
|
|
printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base);
|
|
printf(" --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale);
|
|
printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
|
|
printf(" --no-penalize-nl do not penalize newline token\n");
|
|
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
|
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
|
|
printf(" --temp N temperature (default: %.1f)\n", (double)params.temp);
|
|
printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n");
|
|
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
|
|
printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
|
|
printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
|
|
printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
|
|
printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
|
|
printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel);
|
|
printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences);
|
|
printf(" --hot-plug enable hot-plugging of new sequences for decoding (default: disabled)\n");
|
|
if (llama_mlock_supported()) {
|
|
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
|
}
|
|
if (llama_mmap_supported()) {
|
|
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
|
}
|
|
printf(" --numa attempt optimizations that help on some NUMA systems\n");
|
|
printf(" if run without this previously, it is recommended to drop the system page cache before using this\n");
|
|
printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n");
|
|
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
printf(" -ngl N, --n-gpu-layers N\n");
|
|
printf(" number of layers to store in VRAM\n");
|
|
printf(" -ngld N, --n-gpu-layers-draft N\n");
|
|
printf(" number of layers to store in VRAM for the draft model\n");
|
|
printf(" -ts SPLIT --tensor-split SPLIT\n");
|
|
printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
|
printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
|
printf(" -lv, --low-vram don't allocate VRAM scratch buffer\n");
|
|
#ifdef GGML_USE_CUBLAS
|
|
printf(" -nommq, --no-mul-mat-q\n");
|
|
printf(" use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n");
|
|
printf(" Not recommended since this is both slower and uses more VRAM.\n");
|
|
#endif // GGML_USE_CUBLAS
|
|
#endif
|
|
printf(" --verbose-prompt print prompt before generation\n");
|
|
fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n");
|
|
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
|
printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
|
|
printf(" -m FNAME, --model FNAME\n");
|
|
printf(" model path (default: %s)\n", params.model.c_str());
|
|
printf(" -md FNAME, --model-draft FNAME\n");
|
|
printf(" draft model for speculative decoding (default: %s)\n", params.model.c_str());
|
|
printf(" -ld LOGDIR, --logdir LOGDIR\n");
|
|
printf(" path under which to save YAML logs (no logging if unset)\n");
|
|
printf("\n");
|
|
}
|
|
|
|
std::string gpt_random_prompt(std::mt19937 & rng) {
|
|
const int r = rng() % 10;
|
|
switch (r) {
|
|
case 0: return "So";
|
|
case 1: return "Once upon a time";
|
|
case 2: return "When";
|
|
case 3: return "The";
|
|
case 4: return "After";
|
|
case 5: return "If";
|
|
case 6: return "import";
|
|
case 7: return "He";
|
|
case 8: return "She";
|
|
case 9: return "They";
|
|
default: return "To";
|
|
}
|
|
|
|
return "The";
|
|
}
|
|
|
|
//
|
|
// Model utils
|
|
//
|
|
|
|
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
|
|
auto lparams = llama_context_default_params();
|
|
|
|
lparams.n_ctx = params.n_ctx;
|
|
lparams.n_batch = params.n_batch;
|
|
if (params.n_gpu_layers != -1) {
|
|
lparams.n_gpu_layers = params.n_gpu_layers;
|
|
}
|
|
lparams.main_gpu = params.main_gpu;
|
|
lparams.tensor_split = params.tensor_split;
|
|
lparams.low_vram = params.low_vram;
|
|
lparams.mul_mat_q = params.mul_mat_q;
|
|
lparams.seed = params.seed;
|
|
lparams.f16_kv = params.memory_f16;
|
|
lparams.use_mmap = params.use_mmap;
|
|
lparams.use_mlock = params.use_mlock;
|
|
lparams.logits_all = params.logits_all;
|
|
lparams.embedding = params.embedding;
|
|
lparams.rope_freq_base = params.rope_freq_base;
|
|
lparams.rope_freq_scale = params.rope_freq_scale;
|
|
|
|
return lparams;
|
|
}
|
|
|
|
std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
|
|
auto lparams = llama_context_params_from_gpt_params(params);
|
|
|
|
llama_model * model = llama_load_model_from_file(params.model.c_str(), lparams);
|
|
if (model == NULL) {
|
|
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
|
|
return std::make_tuple(nullptr, nullptr);
|
|
}
|
|
|
|
llama_context * lctx = llama_new_context_with_model(model, lparams);
|
|
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 std::make_tuple(nullptr, nullptr);
|
|
}
|
|
|
|
if (!params.lora_adapter.empty()) {
|
|
int err = llama_model_apply_lora_from_file(model,
|
|
params.lora_adapter.c_str(),
|
|
params.lora_base.empty() ? NULL : params.lora_base.c_str(),
|
|
params.n_threads);
|
|
if (err != 0) {
|
|
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
|
|
llama_free(lctx);
|
|
llama_free_model(model);
|
|
return std::make_tuple(nullptr, nullptr);
|
|
}
|
|
}
|
|
|
|
if (params.ignore_eos) {
|
|
params.logit_bias[llama_token_eos(lctx)] = -INFINITY;
|
|
}
|
|
|
|
{
|
|
LOG("warming up the model with an empty run\n");
|
|
|
|
std::vector<llama_token> tmp = { llama_token_bos(lctx), llama_token_eos(lctx), };
|
|
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0), params.n_threads);
|
|
llama_kv_cache_tokens_rm(lctx, -1, -1);
|
|
llama_reset_timings(lctx);
|
|
}
|
|
|
|
return std::make_tuple(model, lctx);
|
|
}
|
|
|
|
//
|
|
// Vocab utils
|
|
//
|
|
|
|
std::vector<llama_token> llama_tokenize(
|
|
struct llama_context * ctx,
|
|
const std::string & text,
|
|
bool add_bos) {
|
|
// upper limit for the number of tokens
|
|
int n_tokens = text.length() + add_bos;
|
|
std::vector<llama_token> result(n_tokens);
|
|
n_tokens = llama_tokenize(ctx, text.data(), text.length(), result.data(), result.size(), add_bos);
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_tokenize(ctx, text.data(), text.length(), result.data(), result.size(), add_bos);
|
|
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) {
|
|
std::vector<char> result(8, 0);
|
|
const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_token_to_piece(ctx, token, result.data(), result.size());
|
|
GGML_ASSERT(check == -n_tokens);
|
|
} else {
|
|
result.resize(n_tokens);
|
|
}
|
|
|
|
return std::string(result.data(), result.size());
|
|
}
|
|
|
|
std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
|
|
const llama_token bos_id = llama_token_bos(ctx);
|
|
|
|
std::string piece;
|
|
std::string result;
|
|
|
|
for (size_t i = 0; i < tokens.size(); ++i) {
|
|
piece = llama_token_to_piece(ctx, tokens[i]);
|
|
|
|
// remove the leading space of the first non-BOS token
|
|
if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {
|
|
piece = piece.substr(1);
|
|
}
|
|
|
|
result += piece;
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
|
|
std::string piece;
|
|
std::string result;
|
|
|
|
for (size_t i = 0; i < tokens.size(); ++i) {
|
|
piece = llama_token_to_piece(ctx, tokens[i]);
|
|
|
|
result += piece;
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
//
|
|
// Sampling utils
|
|
//
|
|
|
|
llama_token llama_sample_token(
|
|
struct llama_context * ctx,
|
|
struct llama_context * ctx_guidance,
|
|
struct llama_grammar * grammar,
|
|
const struct gpt_params & params,
|
|
const std::vector<llama_token> & last_tokens,
|
|
std::vector<llama_token_data> & candidates,
|
|
int idx) {
|
|
const int n_ctx = llama_n_ctx(ctx);
|
|
const int n_vocab = llama_n_vocab(ctx);
|
|
|
|
const float temp = params.temp;
|
|
const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
|
|
const float top_p = params.top_p;
|
|
const float tfs_z = params.tfs_z;
|
|
const float typical_p = params.typical_p;
|
|
const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
|
|
const float repeat_penalty = params.repeat_penalty;
|
|
const float alpha_presence = params.presence_penalty;
|
|
const float alpha_frequency = params.frequency_penalty;
|
|
const int mirostat = params.mirostat;
|
|
const float mirostat_tau = params.mirostat_tau;
|
|
const float mirostat_eta = params.mirostat_eta;
|
|
const bool penalize_nl = params.penalize_nl;
|
|
|
|
llama_token id = 0;
|
|
|
|
float * logits = llama_get_logits(ctx) + idx * n_vocab;
|
|
|
|
// Apply params.logit_bias map
|
|
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
|
|
logits[it->first] += it->second;
|
|
}
|
|
|
|
candidates.clear();
|
|
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
|
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
|
|
}
|
|
|
|
llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
|
|
|
|
if (ctx_guidance) {
|
|
llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
|
|
}
|
|
|
|
// apply penalties
|
|
if (!last_tokens.empty()) {
|
|
const float nl_logit = logits[llama_token_nl(ctx)];
|
|
const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx);
|
|
|
|
llama_sample_repetition_penalty(ctx, &cur_p,
|
|
last_tokens.data() + last_tokens.size() - last_n_repeat,
|
|
last_n_repeat, repeat_penalty);
|
|
llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
|
|
last_tokens.data() + last_tokens.size() - last_n_repeat,
|
|
last_n_repeat, alpha_frequency, alpha_presence);
|
|
|
|
if (!penalize_nl) {
|
|
for (size_t idx = 0; idx < cur_p.size; idx++) {
|
|
if (cur_p.data[idx].id == llama_token_nl(ctx)) {
|
|
cur_p.data[idx].logit = nl_logit;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (grammar != NULL) {
|
|
llama_sample_grammar(ctx, &cur_p, grammar);
|
|
}
|
|
|
|
if (temp <= 0) {
|
|
// Greedy sampling
|
|
id = llama_sample_token_greedy(ctx, &cur_p);
|
|
} else {
|
|
if (mirostat == 1) {
|
|
static float mirostat_mu = 2.0f * mirostat_tau;
|
|
const int mirostat_m = 100;
|
|
llama_sample_temperature(ctx, &cur_p, temp);
|
|
id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
|
|
} else if (mirostat == 2) {
|
|
static float mirostat_mu = 2.0f * mirostat_tau;
|
|
llama_sample_temperature(ctx, &cur_p, temp);
|
|
id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
|
|
} else {
|
|
// Temperature sampling
|
|
llama_sample_top_k (ctx, &cur_p, top_k, 1);
|
|
llama_sample_tail_free (ctx, &cur_p, tfs_z, 1);
|
|
llama_sample_typical (ctx, &cur_p, typical_p, 1);
|
|
llama_sample_top_p (ctx, &cur_p, top_p, 1);
|
|
llama_sample_temperature(ctx, &cur_p, temp);
|
|
|
|
{
|
|
const int n_top = 10;
|
|
LOG("top %d candidates:\n", n_top);
|
|
|
|
for (int i = 0; i < n_top; i++) {
|
|
const llama_token id = cur_p.data[i].id;
|
|
LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
|
|
}
|
|
}
|
|
|
|
id = llama_sample_token(ctx, &cur_p);
|
|
|
|
LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
|
|
}
|
|
}
|
|
// printf("`%d`", candidates_p.size);
|
|
|
|
if (grammar != NULL) {
|
|
llama_grammar_accept_token(ctx, grammar, id);
|
|
}
|
|
|
|
return id;
|
|
}
|
|
|
|
//
|
|
// YAML utils
|
|
//
|
|
|
|
// returns true if successful, false otherwise
|
|
bool 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
|
|
}
|
|
|
|
void dump_vector_float_yaml(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 dump_vector_int_yaml(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 dump_string_yaml_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 (!data_str.empty() && (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 = "\"" + 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;
|
|
}
|
|
}
|
|
|
|
std::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 dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,
|
|
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
|
|
fprintf(stream, "build_commit: %s\n", BUILD_COMMIT);
|
|
fprintf(stream, "build_number: %d\n", 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_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_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "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_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");
|
|
|
|
#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(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);
|
|
dump_string_yaml_multiline(stream, "cfg_negative_prompt", params.cfg_negative_prompt.c_str());
|
|
fprintf(stream, "cfg_scale: %f # default: 1.0\n", params.cfg_scale);
|
|
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
|
|
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
|
|
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
|
|
fprintf(stream, "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", params.frequency_penalty);
|
|
dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str());
|
|
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
|
|
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
|
|
fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
|
|
|
|
const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx));
|
|
const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY;
|
|
fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
|
|
|
|
dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str());
|
|
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
|
|
dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str());
|
|
fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false");
|
|
fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
|
|
fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
|
|
fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
|
|
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
|
|
|
|
fprintf(stream, "logit_bias:\n");
|
|
for (std::pair<llama_token, float> lb : params.logit_bias) {
|
|
if (ignore_eos && lb.first == logit_bias_eos->first) {
|
|
continue;
|
|
}
|
|
fprintf(stream, " %d: %f", lb.first, lb.second);
|
|
}
|
|
|
|
fprintf(stream, "lora: %s\n", params.lora_adapter.c_str());
|
|
fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
|
|
fprintf(stream, "low_vram: %s # default: false\n", params.low_vram ? "true" : "false");
|
|
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
|
|
fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false");
|
|
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", params.mirostat);
|
|
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", params.mirostat_tau);
|
|
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta);
|
|
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
|
|
fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
|
|
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", params.n_probs);
|
|
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
|
|
fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
|
|
fprintf(stream, "no_penalize_nl: %s # default: false\n", !params.penalize_nl ? "true" : "false");
|
|
fprintf(stream, "numa: %s # default: false\n", params.numa ? "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", params.presence_penalty);
|
|
dump_string_yaml_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");
|
|
dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens);
|
|
fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false");
|
|
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", params.repeat_penalty);
|
|
|
|
fprintf(stream, "reverse_prompt:\n");
|
|
for (std::string ap : params.antiprompt) {
|
|
size_t pos = 0;
|
|
while ((pos = ap.find('\n', pos)) != std::string::npos) {
|
|
ap.replace(pos, 1, "\\n");
|
|
pos += 1;
|
|
}
|
|
|
|
fprintf(stream, " - %s\n", ap.c_str());
|
|
}
|
|
|
|
fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
|
|
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
|
|
fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed);
|
|
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
|
|
fprintf(stream, "hot_plug: %s # default: false\n", params.hot_plug ? "true" : "false");
|
|
fprintf(stream, "temp: %f # default: 0.8\n", params.temp);
|
|
|
|
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
|
|
dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
|
|
|
|
fprintf(stream, "tfs: %f # default: 1.0\n", params.tfs_z);
|
|
fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency());
|
|
fprintf(stream, "top_k: %d # default: 40\n", params.top_k);
|
|
fprintf(stream, "top_p: %f # default: 0.95\n", params.top_p);
|
|
fprintf(stream, "typical_p: %f # default: 1.0\n", params.typical_p);
|
|
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
|
|
}
|