702 lines
31 KiB
C++
702 lines
31 KiB
C++
#include "common.h"
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#include <cassert>
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#include <iostream>
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#include <cstring>
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#include <fstream>
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#include <string>
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#include <iterator>
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#include <algorithm>
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#include <sstream>
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#include <unordered_set>
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#include <regex>
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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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#define NOMINMAX
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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 <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.size() > 0) {
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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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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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bool escape_prompt = 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") {
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escape_prompt = 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 == "-gqa" || arg == "--gqa") {
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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_gqa = std::stoi(argv[i]);
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} else if (arg == "-eps" || arg == "--rms-norm-eps") {
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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.rms_norm_eps = std::stof(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-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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params.n_batch = std::min(512, params.n_batch);
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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 == "--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 == "-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 == "-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 == "--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 == "--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 == "--mul-mat-q" || arg == "-mmq") {
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#ifdef GGML_USE_CUBLAS
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params.mul_mat_q = true;
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#else
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fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\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 == "--mtest") {
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params.mem_test = true;
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} else if (arg == "--numa") {
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params.numa = true;
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} else if (arg == "--export") {
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params.export_cgraph = 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 == "--perplexity") {
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params.perplexity = true;
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} else if (arg == "--hellaswag") {
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params.hellaswag = true;
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} else if (arg == "--hellaswag-tasks") {
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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.hellaswag_tasks = std::stoi(argv[i]);
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} else if (arg == "--ignore-eos") {
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params.logit_bias[llama_token_eos()] = -INFINITY;
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} else if (arg == "--no-penalize-nl") {
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params.penalize_nl = false;
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} else if (arg == "-l" || arg == "--logit-bias") {
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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::stringstream ss(argv[i]);
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llama_token key;
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char sign;
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std::string value_str;
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try {
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if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
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params.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
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} else {
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throw std::exception();
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}
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} catch (const std::exception&) {
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invalid_param = true;
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break;
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}
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} else if (arg == "-h" || arg == "--help") {
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gpt_print_usage(argc, argv, default_params);
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exit(0);
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} else if (arg == "--random-prompt") {
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params.random_prompt = true;
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} else if (arg == "--in-prefix-bos") {
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params.input_prefix_bos = true;
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} else if (arg == "--in-prefix") {
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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.input_prefix = argv[i];
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} else if (arg == "--in-suffix") {
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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.input_suffix = argv[i];
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} else if (arg == "--grammar") {
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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.grammar = argv[i];
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} else if (arg == "--grammar-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(
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std::istreambuf_iterator<char>(file),
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std::istreambuf_iterator<char>(),
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std::back_inserter(params.grammar)
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);
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|
} 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 (escape_prompt) {
|
|
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) {
|
|
fprintf(stdout, "usage: %s [options]\n", argv[0]);
|
|
fprintf(stdout, "\n");
|
|
fprintf(stdout, "options:\n");
|
|
fprintf(stdout, " -h, --help show this help message and exit\n");
|
|
fprintf(stdout, " -i, --interactive run in interactive mode\n");
|
|
fprintf(stdout, " --interactive-first run in interactive mode and wait for input right away\n");
|
|
fprintf(stdout, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");
|
|
fprintf(stdout, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n");
|
|
fprintf(stdout, " -r PROMPT, --reverse-prompt PROMPT\n");
|
|
fprintf(stdout, " halt generation at PROMPT, return control in interactive mode\n");
|
|
fprintf(stdout, " (can be specified more than once for multiple prompts).\n");
|
|
fprintf(stdout, " --color colorise output to distinguish prompt and user input from generations\n");
|
|
fprintf(stdout, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
|
|
fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
|
fprintf(stdout, " -p PROMPT, --prompt PROMPT\n");
|
|
fprintf(stdout, " prompt to start generation with (default: empty)\n");
|
|
fprintf(stdout, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
|
|
fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n");
|
|
fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n");
|
|
fprintf(stdout, " not supported with --interactive or other interactive options\n");
|
|
fprintf(stdout, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n");
|
|
fprintf(stdout, " --random-prompt start with a randomized prompt.\n");
|
|
fprintf(stdout, " --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n");
|
|
fprintf(stdout, " --in-prefix STRING string to prefix user inputs with (default: empty)\n");
|
|
fprintf(stdout, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
|
|
fprintf(stdout, " -f FNAME, --file FNAME\n");
|
|
fprintf(stdout, " prompt file to start generation.\n");
|
|
fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
|
|
fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
|
fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
|
fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa);
|
|
fprintf(stdout, " -eps N, --rms-norm-eps N rms norm eps (TEMP!!! use 1e-5 for LLaMAv2) (default: %.1e)\n", params.rms_norm_eps);
|
|
fprintf(stdout, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
|
|
fprintf(stdout, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
|
|
fprintf(stdout, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
|
|
fprintf(stdout, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p);
|
|
fprintf(stdout, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n);
|
|
fprintf(stdout, " --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty);
|
|
fprintf(stdout, " --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty);
|
|
fprintf(stdout, " --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty);
|
|
fprintf(stdout, " --mirostat N use Mirostat sampling.\n");
|
|
fprintf(stdout, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
|
|
fprintf(stdout, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat);
|
|
fprintf(stdout, " --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta);
|
|
fprintf(stdout, " --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau);
|
|
fprintf(stdout, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
|
|
fprintf(stdout, " modifies the likelihood of token appearing in the completion,\n");
|
|
fprintf(stdout, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
|
|
fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
|
|
fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
|
|
fprintf(stdout, " --grammar-file FNAME file to read grammar from\n");
|
|
fprintf(stdout, " --cfg-negative-prompt PROMPT \n");
|
|
fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n");
|
|
fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
|
|
fprintf(stdout, " --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale);
|
|
fprintf(stdout, " --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base);
|
|
fprintf(stdout, " --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale);
|
|
fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
|
|
fprintf(stdout, " --no-penalize-nl do not penalize newline token\n");
|
|
fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
|
fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
|
|
fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp);
|
|
fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n");
|
|
fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
|
|
fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
|
|
fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
|
|
fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
|
|
if (llama_mlock_supported()) {
|
|
fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
|
}
|
|
if (llama_mmap_supported()) {
|
|
fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
|
}
|
|
fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n");
|
|
fprintf(stdout, " if run without this previously, it is recommended to drop the system page cache before using this\n");
|
|
fprintf(stdout, " see https://github.com/ggerganov/llama.cpp/issues/1437\n");
|
|
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
fprintf(stdout, " -ngl N, --n-gpu-layers N\n");
|
|
fprintf(stdout, " number of layers to store in VRAM\n");
|
|
fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
|
|
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
|
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
|
|
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n" );
|
|
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
|
|
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
|
|
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
|
|
#endif
|
|
fprintf(stdout, " --mtest compute maximum memory usage\n");
|
|
fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n");
|
|
fprintf(stdout, " --verbose-prompt print prompt before generation\n");
|
|
fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n");
|
|
fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
|
fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
|
|
fprintf(stdout, " -m FNAME, --model FNAME\n");
|
|
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
|
|
fprintf(stdout, "\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";
|
|
}
|
|
|
|
// TODO: not great allocating this every time
|
|
std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
|
|
// initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
|
|
std::vector<llama_token> res(text.size() + (int) add_bos);
|
|
const int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
|
|
assert(n >= 0);
|
|
res.resize(n);
|
|
|
|
return res;
|
|
}
|
|
|
|
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;
|
|
lparams.n_gqa = params.n_gqa;
|
|
lparams.rms_norm_eps = params.rms_norm_eps;
|
|
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.perplexity;
|
|
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(const 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);
|
|
}
|
|
}
|
|
|
|
return std::make_tuple(model, lctx);
|
|
}
|