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580 lines
18 KiB
580 lines
18 KiB
#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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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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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 == "-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 == "--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 == "-h" || arg == "--help") {
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gpt_print_usage(argc, argv, params);
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exit(0);
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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, " -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: random)\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, " --temp N temperature (default: %.1f)\n", params.temp);
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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, " -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::map<std::string, int32_t> json_parse(const std::string & fname) {
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std::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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std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
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std::vector<std::string> words;
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// first split the text into words
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{
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std::string str = text;
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std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
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std::regex re(pat);
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std::smatch m;
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while (std::regex_search(str, m, re)) {
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for (auto x : m) {
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words.push_back(x);
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}
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str = m.suffix();
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}
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}
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// find the longest tokens that form the words:
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std::vector<gpt_vocab::id> tokens;
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for (const auto & word : words) {
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if (word.size() == 0) continue;
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int i = 0;
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int n = word.size();
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while (i < n) {
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int j = n;
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while (j > i) {
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auto it = vocab.token_to_id.find(word.substr(i, j-i));
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if (it != vocab.token_to_id.end()) {
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tokens.push_back(it->second);
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i = j;
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break;
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}
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--j;
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}
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if (i == n) {
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break;
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}
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if (j == i) {
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auto sub = word.substr(i, 1);
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if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
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tokens.push_back(vocab.token_to_id.at(sub));
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} else {
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fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
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}
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++i;
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}
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}
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}
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return tokens;
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}
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std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, const std::string & text, bool bos) {
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//auto res = gpt_tokenize(vocab, text);
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//if (bos) {
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// res.insert(res.begin(), 1); // TODO: replace with vocab.bos
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//}
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std::vector<gpt_vocab::id> res;
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if (bos) {
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res.push_back(1); // TODO: replace with vocab.bos
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}
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//find the longest token that matches the text
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int pos = 0;
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while (true) {
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int l = 0;
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int t = 0;
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for (const auto & kv : vocab.id_to_token) {
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if (kv.second.size() < l) continue;
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if (kv.second.size() > text.size() - pos) continue;
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if (text.substr(pos, kv.second.size()) == kv.second) {
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l = kv.second.size();
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t = kv.first;
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}
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}
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if (l == 0) {
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break;
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}
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res.push_back(t);
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pos += l;
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}
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return res;
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}
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bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
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printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
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vocab.token_to_id = ::json_parse(fname);
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for (const auto & kv : vocab.token_to_id) {
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vocab.id_to_token[kv.second] = kv.first;
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}
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printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
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// print the vocabulary
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//for (auto kv : vocab.token_to_id) {
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// printf("'%s' -> %d\n", kv.first.data(), kv.second);
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//}
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return true;
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}
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gpt_vocab::id gpt_sample_top_k_top_p(
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const gpt_vocab & vocab,
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const float * logits,
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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();
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std::vector<std::pair<double, gpt_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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{
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const double scale = 1.0/temp;
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for (int i = 0; i < n_logits; ++i) {
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logits_id.push_back(std::make_pair(logits[i]*scale, i));
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}
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}
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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, gpt_vocab::id> & a, const std::pair<double, gpt_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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double maxl = -INFINITY;
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for (const auto & kv : logits_id) {
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maxl = std::max(maxl, kv.first);
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}
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// compute probs for the top K tokens
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std::vector<double> probs;
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probs.reserve(logits_id.size());
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double sum = 0.0;
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for (const auto & kv : logits_id) {
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double p = exp(kv.first - maxl);
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probs.push_back(p);
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sum += p;
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}
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// normalize the probs
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for (auto & p : probs) {
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p /= sum;
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}
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if (top_p < 1.0f) {
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double cumsum = 0.0f;
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for (int i = 0; i < top_k; i++) {
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cumsum += probs[i];
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if (cumsum >= top_p) {
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top_k = i + 1;
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probs.resize(top_k);
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logits_id.resize(top_k);
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break;
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}
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}
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cumsum = 1.0/cumsum;
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for (int i = 0; i < (int) probs.size(); i++) {
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probs[i] *= cumsum;
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}
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}
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//printf("\n");
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//for (int i = 0; i < (int) probs.size(); i++) {
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// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
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//}
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//exit(0);
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std::discrete_distribution<> dist(probs.begin(), probs.end());
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int idx = dist(rng);
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return logits_id[idx].second;
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}
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gpt_vocab::id llama_sample_top_p(
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const gpt_vocab & vocab,
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const float * logits,
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std::vector<gpt_vocab::id> & last_n_tokens,
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double repeat_penalty,
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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();
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std::vector<std::pair<double, gpt_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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{
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const double scale = 1.0/temp;
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for (int i = 0; i < n_logits; ++i) {
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// repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
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// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
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if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
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// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
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if (logits[i] < 0.0) {
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logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
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} else {
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logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
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}
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} else {
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logits_id.push_back(std::make_pair(logits[i]*scale, i));
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}
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}
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}
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std::sort(
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logits_id.begin(),
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logits_id.end(),
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[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
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return a.first > b.first;
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});
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double maxl = -INFINITY;
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for (const auto & kv : logits_id) {
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maxl = std::max(maxl, kv.first);
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}
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// compute probs for the top K tokens
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std::vector<double> probs;
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probs.reserve(logits_id.size());
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double sum = 0.0;
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for (const auto & kv : logits_id) {
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double p = exp(kv.first - maxl);
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probs.push_back(p);
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sum += p;
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}
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// normalize the probs
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for (auto & p : probs) {
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p /= sum;
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}
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if (top_p < 1.0f) {
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double cumsum = 0.0f;
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for (int i = 0; i < (int) probs.size(); i++) {
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cumsum += probs[i];
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if (cumsum >= top_p) {
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probs.resize(i + 1);
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logits_id.resize(i + 1);
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break;
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}
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}
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cumsum = 1.0/cumsum;
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for (int i = 0; i < (int) probs.size(); i++) {
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probs[i] *= cumsum;
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}
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}
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//printf("\n");
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//for (int i = 0; i < (int) 10; i++) {
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// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
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//}
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//printf("\n\n");
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//exit(0);
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std::discrete_distribution<> dist(probs.begin(), probs.end());
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int idx = dist(rng);
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return logits_id[idx].second;
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}
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size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
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const int nb = k / qk;
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const size_t bs = (sizeof(float) + sizeof(uint8_t)*qk/2);
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const size_t row_size = nb*bs;
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assert(k % qk == 0);
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uint8_t pp[qk/2];
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char * pdst = (char *) dst;
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for (int j = 0; j < n; j += k) {
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uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
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uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
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for (int i = 0; i < nb; i++) {
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float amax = 0.0f; // absolute max
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{
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for (int l = 0; l < qk; l++) {
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const float v = src[j + i*qk + l];
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amax = std::max(amax, fabsf(v));
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}
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const float d = amax / ((1 << 3) - 1);
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const float id = d ? 1.0f/d : 0.0f;
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|
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*(float *) pd = d;
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|
pd += bs;
|
|
|
|
for (int l = 0; l < qk; l += 2) {
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const float v0 = (src[j + i*qk + l + 0])*id;
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const float v1 = (src[j + i*qk + l + 1])*id;
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|
|
|
const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
|
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const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
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|
|
|
assert(vi0 >= 0 && vi0 < 16);
|
|
assert(vi1 >= 0 && vi1 < 16);
|
|
|
|
hist[vi0]++;
|
|
hist[vi1]++;
|
|
|
|
pp[l/2] = vi0 | (vi1 << 4);
|
|
}
|
|
|
|
memcpy(pb, pp, sizeof(pp));
|
|
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 row_size = nb*(2*sizeof(float) + sizeof(uint8_t)*qk/2);
|
|
|
|
assert(k % qk == 0);
|
|
|
|
uint8_t pp[qk/2];
|
|
|
|
char * pdst = (char *) dst;
|
|
|
|
for (int j = 0; j < n; j += k) {
|
|
float * pm = (float *) (pdst + (j/k)*row_size);
|
|
float * pd = (float *) (pm + nb);
|
|
uint8_t * pb = (uint8_t *) (pd + nb);
|
|
|
|
//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;
|
|
|
|
pm[i] = min;
|
|
pd[i] = d;
|
|
|
|
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 + i*qk/2, pp, sizeof(pp));
|
|
}
|
|
}
|
|
}
|
|
|
|
return (n/k)*row_size;
|
|
}
|