mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-30 08:42:00 +00:00
353ec251a4
* Improve performance by changing std::map to std::unordered_map and std::map<id, token> id_to_token; to std::vector<token> id_to_token; * fix last commit on gpt_vocab_init add vocab.id_to_token.resize(vocab.token_to_id.size()); * Removed include <map> * Nest struct token score inside gpt_vocab * renamed token to tok
656 lines
22 KiB
C++
656 lines
22 KiB
C++
#include "utils.h"
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#include <cassert>
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#include <cstring>
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#include <fstream>
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#include <regex>
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#include <iostream>
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#include <iterator>
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#include <queue>
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#include <string>
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#include <math.h>
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#if defined(_MSC_VER) || defined(__MINGW32__)
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#include <malloc.h> // using malloc.h with MSC/MINGW
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#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
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#include <alloca.h>
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#endif
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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// determine sensible default number of threads.
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// std::thread::hardware_concurrency may not be equal to the number of cores, or may return 0.
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#ifdef __linux__
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std::ifstream cpuinfo("/proc/cpuinfo");
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params.n_threads = std::count(std::istream_iterator<std::string>(cpuinfo),
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std::istream_iterator<std::string>(),
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std::string("processor"));
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#endif
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if (params.n_threads == 0) {
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params.n_threads = std::max(1, (int32_t) std::thread::hardware_concurrency());
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}
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for (int i = 1; i < argc; i++) {
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std::string arg = argv[i];
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if (arg == "-s" || arg == "--seed") {
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params.seed = std::stoi(argv[++i]);
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} else if (arg == "-t" || arg == "--threads") {
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params.n_threads = std::stoi(argv[++i]);
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} else if (arg == "-p" || arg == "--prompt") {
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params.prompt = argv[++i];
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} else if (arg == "-f" || arg == "--file") {
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std::ifstream file(argv[++i]);
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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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params.n_predict = std::stoi(argv[++i]);
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} else if (arg == "--top_k") {
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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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params.n_ctx = std::stoi(argv[++i]);
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} else if (arg == "--memory_f16") {
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params.memory_f16 = true;
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} else if (arg == "--top_p") {
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params.top_p = std::stof(argv[++i]);
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} else if (arg == "--temp") {
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params.temp = std::stof(argv[++i]);
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} else if (arg == "--repeat_last_n") {
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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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params.repeat_penalty = std::stof(argv[++i]);
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} else if (arg == "-b" || arg == "--batch_size") {
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params.n_batch = std::stoi(argv[++i]);
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} else if (arg == "-m" || arg == "--model") {
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params.model = 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 == "-ins" || arg == "--instruct") {
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params.instruct = true;
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} else if (arg == "--color") {
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params.use_color = true;
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} else if (arg == "-r" || arg == "--reverse-prompt") {
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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 == "--ignore-eos") {
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params.ignore_eos = true;
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} else if (arg == "--n_parts") {
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params.n_parts = std::stoi(argv[++i]);
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} else if (arg == "-h" || arg == "--help") {
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gpt_print_usage(argc, argv, 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 {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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gpt_print_usage(argc, argv, params);
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exit(0);
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}
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}
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return true;
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}
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void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stderr, "usage: %s [options]\n", argv[0]);
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fprintf(stderr, "\n");
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fprintf(stderr, "options:\n");
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fprintf(stderr, " -h, --help show this help message and exit\n");
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fprintf(stderr, " -i, --interactive run in interactive mode\n");
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fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");
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fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
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fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT (can be\n");
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fprintf(stderr, " specified more than once for multiple prompts).\n");
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fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n");
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fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
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fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
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fprintf(stderr, " prompt to start generation with (default: empty)\n");
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fprintf(stderr, " --random-prompt start with a randomized prompt.\n");
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fprintf(stderr, " -f FNAME, --file FNAME\n");
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fprintf(stderr, " prompt file to start generation.\n");
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fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
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fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
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fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
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fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
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fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
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fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
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fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");
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fprintf(stderr, " --memory_f16 use f16 instead of f32 for memory key+value\n");
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fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
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fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
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fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
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fprintf(stderr, " --perplexity compute perplexity over the prompt\n");
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fprintf(stderr, " -m FNAME, --model FNAME\n");
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fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
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fprintf(stderr, "\n");
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}
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std::string gpt_random_prompt(std::mt19937 & rng) {
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const int r = rng() % 10;
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switch (r) {
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case 0: return "So";
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case 1: return "Once upon a time";
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case 2: return "When";
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case 3: return "The";
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case 4: return "After";
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case 5: return "If";
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case 6: return "import";
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case 7: return "He";
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case 8: return "She";
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case 9: return "They";
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default: return "To";
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}
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return "The";
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}
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void replace(std::string & str, const std::string & needle, const std::string & replacement) {
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size_t pos = 0;
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while ((pos = str.find(needle, pos)) != std::string::npos) {
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str.replace(pos, needle.length(), replacement);
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pos += replacement.length();
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}
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}
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std::unordered_map<std::string, int32_t> json_parse(const std::string & fname) {
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std::unordered_map<std::string, int32_t> result;
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// read file into string
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std::string json;
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{
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std::ifstream ifs(fname);
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if (!ifs) {
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fprintf(stderr, "Failed to open %s\n", fname.c_str());
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exit(1);
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}
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json = std::string((std::istreambuf_iterator<char>(ifs)),
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(std::istreambuf_iterator<char>()));
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}
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if (json[0] != '{') {
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return result;
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}
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// parse json
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{
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bool has_key = false;
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bool in_token = false;
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std::string str_key = "";
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std::string str_val = "";
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int n = json.size();
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for (int i = 1; i < n; ++i) {
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if (!in_token) {
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if (json[i] == ' ') continue;
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if (json[i] == '"') {
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in_token = true;
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continue;
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}
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} else {
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if (json[i] == '\\' && i+1 < n) {
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if (has_key == false) {
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str_key += json[i];
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} else {
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str_val += json[i];
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}
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++i;
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} else if (json[i] == '"') {
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if (has_key == false) {
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has_key = true;
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++i;
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while (json[i] == ' ') ++i;
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++i; // :
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while (json[i] == ' ') ++i;
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if (json[i] != '\"') {
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while (json[i] != ',' && json[i] != '}') {
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str_val += json[i++];
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}
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has_key = false;
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} else {
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in_token = true;
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continue;
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}
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} else {
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has_key = false;
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}
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::replace(str_key, "\\u0120", " " ); // \u0120 -> space
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::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
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::replace(str_key, "\\\"", "\""); // \\\" -> "
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try {
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result[str_key] = std::stoi(str_val);
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} catch (...) {
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//fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
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}
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str_key = "";
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str_val = "";
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in_token = false;
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continue;
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}
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if (has_key == false) {
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str_key += json[i];
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} else {
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str_val += json[i];
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}
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}
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}
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}
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return result;
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}
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static size_t utf8_len(char src) {
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const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
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uint8_t highbits = static_cast<uint8_t>(src) >> 4;
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return lookup[highbits];
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}
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struct llama_sp_symbol {
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using index = int;
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index prev;
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index next;
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const char * text;
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size_t n;
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};
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struct llama_sp_bigram {
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struct comparator {
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bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
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return (l.score < r.score) || (l.score == r.score && l.left > r.left);
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}
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};
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using queue_storage = std::vector<llama_sp_bigram>;
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using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
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llama_sp_symbol::index left;
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llama_sp_symbol::index right;
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float score;
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size_t size;
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};
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// original implementation:
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// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
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struct llama_tokenizer {
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llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {}
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void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
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// split string into utf8 chars
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int index = 0;
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size_t offs = 0;
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while (offs < text.size()) {
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llama_sp_symbol sym;
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size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
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sym.text = text.c_str() + offs;
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sym.n = char_len;
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offs += char_len;
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sym.prev = index - 1;
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sym.next = offs == text.size() ? -1 : index + 1;
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index++;
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symbols_.emplace_back(std::move(sym));
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}
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// seed the work queue with all possible 2-character tokens.
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for (size_t i = 1; i < symbols_.size(); ++i) {
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try_add_bigram(i - 1, i);
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}
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// keep substituting the highest frequency pairs for as long as we can.
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while (!work_queue_.empty()) {
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auto bigram = work_queue_.top();
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work_queue_.pop();
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auto & left_sym = symbols_[bigram.left];
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auto & right_sym = symbols_[bigram.right];
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// if one of the symbols already got merged, skip it.
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if (left_sym.n == 0 || right_sym.n == 0 ||
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left_sym.n + right_sym.n != bigram.size) {
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continue;
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}
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// merge the right sym into the left one
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left_sym.n += right_sym.n;
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right_sym.n = 0;
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//printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
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// remove the right sym from the chain
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left_sym.next = right_sym.next;
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if (right_sym.next >= 0) {
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symbols_[right_sym.next].prev = bigram.left;
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}
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// find more substitutions
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try_add_bigram(left_sym.prev, bigram.left);
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try_add_bigram(bigram.left, left_sym.next);
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}
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for (int i = 0; i != -1; i = symbols_[i].next) {
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auto & symbol = symbols_[i];
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auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n));
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if (token == vocab_.token_to_id.end()) {
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// output any symbols that did not form tokens as bytes.
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for (int j = 0; j < (int) symbol.n; ++j) {
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llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
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output.push_back(token_id);
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}
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} else {
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output.push_back((*token).second);
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}
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}
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}
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private:
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void try_add_bigram(int left, int right) {
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if (left == -1 || right == -1) {
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return;
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}
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const std::string text = std::string(symbols_[left].text, symbols_[left].n + symbols_[right].n);
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auto token = vocab_.token_to_id.find(text);
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if (token == vocab_.token_to_id.end()) {
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return;
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}
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if (static_cast<size_t>((*token).second) >= vocab_.id_to_token.size()) {
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return;
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}
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const auto &tok_score = vocab_.id_to_token[(*token).second];
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llama_sp_bigram bigram;
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bigram.left = left;
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bigram.right = right;
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bigram.score = tok_score.score;
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bigram.size = text.size();
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work_queue_.push(bigram);
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}
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const llama_vocab & vocab_;
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std::vector<llama_sp_symbol> symbols_;
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llama_sp_bigram::queue work_queue_;
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};
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// TODO: temporary code duplication with llama.cpp
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// will resolve after #77 is merged
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bool llama_vocab_load(const std::string & fname, llama_vocab & vocab) {
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std::ifstream fin(fname, std::ios::binary);
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if (!fin.is_open()) {
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return false;
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}
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int n_vocab = 0;
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fin.read((char *) &n_vocab, sizeof(n_vocab));
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std::string word;
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std::vector<char> tmp(64);
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vocab.id_to_token.resize(n_vocab);
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for (int i = 0; i < n_vocab; i++) {
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uint32_t len;
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fin.read((char *) &len, sizeof(len));
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word.resize(len);
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if (len > 0) {
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tmp.resize(len);
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fin.read(tmp.data(), len);
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word.assign(tmp.data(), len);
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} else {
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word.clear();
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}
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float score;
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fin.read((char *) &score, sizeof(score));
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vocab.token_to_id[word] = i;
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auto &tok_score = vocab.id_to_token[i];
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tok_score.tok = word;
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tok_score.score = score;
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}
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return true;
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}
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std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) {
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llama_tokenizer tokenizer(vocab);
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std::vector<llama_vocab::id> output;
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if (text.size() == 0) {
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return output;
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}
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if (bos) {
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output.push_back(1);
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}
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tokenizer.tokenize(text, output);
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return output;
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}
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void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
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// find the top K tokens
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std::partial_sort(
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logits_id.begin(),
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logits_id.begin() + top_k, logits_id.end(),
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[](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
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return a.first > b.first;
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});
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logits_id.resize(top_k);
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}
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llama_vocab::id llama_sample_top_p_top_k(
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const llama_vocab & vocab,
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const float * logits,
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std::vector<llama_vocab::id> & last_n_tokens,
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double repeat_penalty,
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int top_k,
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double top_p,
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double temp,
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std::mt19937 & rng) {
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int n_logits = vocab.id_to_token.size();
|
|
|
|
std::vector<std::pair<double, llama_vocab::id>> logits_id;
|
|
logits_id.reserve(n_logits);
|
|
|
|
{
|
|
const double scale = 1.0/temp;
|
|
for (int i = 0; i < n_logits; ++i) {
|
|
// repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
|
|
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
|
|
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
|
|
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
|
|
if (logits[i] < 0.0) {
|
|
logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
|
|
} else {
|
|
logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
|
|
}
|
|
} else {
|
|
logits_id.push_back(std::make_pair(logits[i]*scale, i));
|
|
}
|
|
}
|
|
}
|
|
|
|
sample_top_k(logits_id, top_k);
|
|
|
|
double maxl = -INFINITY;
|
|
for (const auto & kv : logits_id) {
|
|
maxl = std::max(maxl, kv.first);
|
|
}
|
|
|
|
// compute probs for the top K tokens
|
|
std::vector<double> probs;
|
|
probs.reserve(logits_id.size());
|
|
|
|
double sum = 0.0;
|
|
for (const auto & kv : logits_id) {
|
|
double p = exp(kv.first - maxl);
|
|
probs.push_back(p);
|
|
sum += p;
|
|
}
|
|
|
|
// normalize the probs
|
|
for (auto & p : probs) {
|
|
p /= sum;
|
|
}
|
|
|
|
if (top_p < 1.0f) {
|
|
double cumsum = 0.0f;
|
|
for (int i = 0; i < (int) probs.size(); i++) {
|
|
cumsum += probs[i];
|
|
if (cumsum >= top_p) {
|
|
probs.resize(i + 1);
|
|
logits_id.resize(i + 1);
|
|
break;
|
|
}
|
|
}
|
|
|
|
cumsum = 1.0/cumsum;
|
|
for (int i = 0; i < (int) probs.size(); i++) {
|
|
probs[i] *= cumsum;
|
|
}
|
|
}
|
|
|
|
//printf("\n");
|
|
//for (int i = 0; i < (int) 10; i++) {
|
|
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
|
|
//}
|
|
//printf("\n\n");
|
|
//exit(0);
|
|
|
|
std::discrete_distribution<> dist(probs.begin(), probs.end());
|
|
int idx = dist(rng);
|
|
|
|
return logits_id[idx].second;
|
|
}
|
|
|
|
|
|
size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
|
|
const int nb = k / qk;
|
|
const size_t bs = (sizeof(float) + sizeof(uint8_t)*qk/2);
|
|
const size_t row_size = nb*bs;
|
|
|
|
assert(k % qk == 0);
|
|
|
|
const size_t pp_size = qk / 2;
|
|
uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
|
|
|
|
char * pdst = (char *) dst;
|
|
|
|
for (int j = 0; j < n; j += k) {
|
|
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
|
|
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
|
|
|
|
for (int i = 0; i < nb; i++) {
|
|
float amax = 0.0f; // absolute max
|
|
|
|
{
|
|
for (int l = 0; l < qk; l++) {
|
|
const float v = src[j + i*qk + l];
|
|
amax = std::max(amax, fabsf(v));
|
|
}
|
|
|
|
const float d = amax / ((1 << 3) - 1);
|
|
const float id = d ? 1.0f/d : 0.0f;
|
|
|
|
*(float *) pd = d;
|
|
pd += bs;
|
|
|
|
for (int l = 0; l < qk; l += 2) {
|
|
const float v0 = (src[j + i*qk + l + 0])*id;
|
|
const float v1 = (src[j + i*qk + l + 1])*id;
|
|
|
|
const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
|
|
const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
|
|
|
|
assert(vi0 >= 0 && vi0 < 16);
|
|
assert(vi1 >= 0 && vi1 < 16);
|
|
|
|
hist[vi0]++;
|
|
hist[vi1]++;
|
|
|
|
pp[l/2] = vi0 | (vi1 << 4);
|
|
}
|
|
|
|
memcpy(pb, pp, pp_size);
|
|
pb += bs;
|
|
}
|
|
}
|
|
}
|
|
|
|
return (n/k)*row_size;
|
|
}
|
|
|
|
size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
|
|
const int nb = k / qk;
|
|
const size_t bs = (2*sizeof(float) + sizeof(uint8_t)*qk/2);
|
|
const size_t row_size = nb*bs;
|
|
|
|
assert(k % qk == 0);
|
|
|
|
const size_t pp_size = qk / 2;
|
|
uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
|
|
|
|
char * pdst = (char *) dst;
|
|
|
|
for (int j = 0; j < n; j += k) {
|
|
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
|
|
uint8_t * pm = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
|
|
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + 2*sizeof(float));
|
|
|
|
//printf("n = %d, k = %d, nb = %d, row_size = %d, j = %d, pm = %p, pd = %p, pb = %p\n", n, k, nb, row_size, j, pm, pd, pb);
|
|
|
|
for (int i = 0; i < nb; i++) {
|
|
float min = std::numeric_limits<float>::max();
|
|
float max = std::numeric_limits<float>::min();
|
|
|
|
{
|
|
for (int l = 0; l < qk; l++) {
|
|
const float v = src[j + i*qk + l];
|
|
if (v < min) min = v;
|
|
if (v > max) max = v;
|
|
}
|
|
|
|
const float d = (max - min) / ((1 << 4) - 1);
|
|
const float id = d ? 1.0f/d : 0.0f;
|
|
|
|
*(float *) pd = d;
|
|
*(float *) pm = min;
|
|
pd += bs;
|
|
pm += bs;
|
|
|
|
for (int l = 0; l < qk; l += 2) {
|
|
const float v0 = (src[j + i*qk + l + 0] - min)*id;
|
|
const float v1 = (src[j + i*qk + l + 1] - min)*id;
|
|
|
|
const uint8_t vi0 = round(v0);
|
|
const uint8_t vi1 = round(v1);
|
|
|
|
assert(vi0 >= 0 && vi0 < 16);
|
|
assert(vi1 >= 0 && vi1 < 16);
|
|
|
|
hist[vi0]++;
|
|
hist[vi1]++;
|
|
|
|
pp[l/2] = vi0 | (vi1 << 4);
|
|
}
|
|
|
|
memcpy(pb, pp, pp_size);
|
|
pb += bs;
|
|
}
|
|
}
|
|
}
|
|
|
|
return (n/k)*row_size;
|
|
}
|