mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-02 09:40:42 +00:00
316 lines
9.8 KiB
C++
316 lines
9.8 KiB
C++
#include "utils.h"
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#include <fstream>
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#include <regex>
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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_inner(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::string regex_escape(const std::string &s) {
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static const std::regex metacharacters(R"([\.\^\$\-\+\(\)\[\]\{\}\|\?\*])");
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return std::regex_replace(s, metacharacters, "\\$&");
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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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// Generate the subpattern from the special_tokens vector if it's not empty
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if (!vocab.special_tokens.empty()) {
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std::vector<gpt_vocab::id> out;
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std::vector<std::string> chunks;
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std::string str = text;
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std::string special_tokens_subpattern;
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for (const auto &token : vocab.special_tokens) {
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if (!special_tokens_subpattern.empty()) {
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special_tokens_subpattern += "|";
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}
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special_tokens_subpattern += regex_escape(token);
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}
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std::regex re(special_tokens_subpattern);
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std::smatch m;
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while (std::regex_search(str, m, re)) {
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auto tok = vocab.token_to_id.find(m.str());
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if (tok != vocab.token_to_id.end()) {
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auto tokid = tok->second;
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auto pfxtoks = gpt_tokenize_inner(vocab, m.prefix());
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out.insert(out.end(), pfxtoks.begin(), pfxtoks.end());
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out.push_back(tokid);
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str = m.suffix();
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}
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}
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if (!str.empty()) {
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auto tokrest = gpt_tokenize_inner(vocab, str);
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out.insert(out.end(), tokrest.begin(), tokrest.end());
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}
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return out;
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} else {
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return gpt_tokenize_inner(vocab, text);
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}
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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 size_t actualVocabSize,
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const int32_t * last_n_tokens_data,
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int last_n_tokens_size,
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const std::vector<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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float repeat_penalty,
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std::mt19937 & rng) {
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int n_logits = actualVocabSize;
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const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_size);
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const auto * plogits = logits.data() + logits.size() - n_logits;
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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 float scale = 1.0f/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 (plogits[i] < 0.0f) {
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logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
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} else {
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logits_id.push_back(std::make_pair(plogits[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(plogits[i]*scale, i));
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}
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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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} |