diff options
Diffstat (limited to 'utils.cpp')
| -rw-r--r-- | utils.cpp | 174 |
1 files changed, 72 insertions, 102 deletions
@@ -240,61 +240,6 @@ std::map<std::string, int32_t> json_parse(const std::string & fname) { return result; } -std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) { - std::vector<std::string> words; - - // first split the text into words - { - std::string str = text; - std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"; - - std::regex re(pat); - std::smatch m; - - while (std::regex_search(str, m, re)) { - for (auto x : m) { - words.push_back(x); - } - str = m.suffix(); - } - } - - // find the longest tokens that form the words: - std::vector<gpt_vocab::id> tokens; - for (const auto & word : words) { - if (word.size() == 0) continue; - - int i = 0; - int n = word.size(); - while (i < n) { - int j = n; - while (j > i) { - auto it = vocab.token_to_id.find(word.substr(i, j-i)); - if (it != vocab.token_to_id.end()) { - tokens.push_back(it->second); - i = j; - break; - } - --j; - } - if (i == n) { - break; - } - if (j == i) { - auto sub = word.substr(i, 1); - if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) { - tokens.push_back(vocab.token_to_id.at(sub)); - } else { - fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data()); - } - ++i; - } - } - } - - return tokens; -} - static size_t utf8_len(char src) { const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; uint8_t highbits = static_cast<uint8_t>(src) >> 4; @@ -305,7 +250,8 @@ struct llama_sp_symbol { using index = int; index prev; index next; - std::string_view text; + const char * text; + size_t n; }; struct llama_sp_bigram { @@ -322,19 +268,23 @@ struct llama_sp_bigram { size_t size; }; +// original implementation: +// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4 struct llama_tokenizer { - llama_tokenizer(const gpt_vocab & vocab): vocab_(vocab) {} + llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {} - void tokenize(std::string_view text, std::vector<gpt_vocab::id> & output) { + void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) { // split string into utf8 chars int index = 0; - while (!text.empty()) { + size_t offs = 0; + while (offs < text.size()) { llama_sp_symbol sym; - size_t char_len = std::min(text.size(), utf8_len(text.data()[0])); - sym.text = std::string_view(text.data(), char_len); + size_t char_len = std::min(text.size() - offs, utf8_len(text[offs])); + sym.text = text.c_str() + offs; + sym.n = char_len; + offs += char_len; sym.prev = index - 1; - text.remove_prefix(char_len); - sym.next = text.empty() ? -1 : index + 1; + sym.next = offs == text.size() ? -1 : index + 1; index++; symbols_.emplace_back(std::move(sym)); } @@ -353,14 +303,16 @@ struct llama_tokenizer { auto & right_sym = symbols_[bigram.right]; // if one of the symbols already got merged, skip it. - if (left_sym.text.empty() || right_sym.text.empty() || - left_sym.text.size() + right_sym.text.size() != bigram.size) { + if (left_sym.n == 0 || right_sym.n == 0 || + left_sym.n + right_sym.n != bigram.size) { continue; } // merge the right sym into the left one - left_sym.text = std::string_view(left_sym.text.data(), left_sym.text.size() + right_sym.text.size()); - right_sym.text = std::string_view(""); + left_sym.n += right_sym.n; + right_sym.n = 0; + + //printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size); // remove the right sym from the chain left_sym.next = right_sym.next; @@ -374,13 +326,13 @@ struct llama_tokenizer { } for (int i = 0; i != -1; i = symbols_[i].next) { - auto& symbol = symbols_[i]; - auto token = vocab_.token_to_id.find(std::string(symbol.text)); + auto & symbol = symbols_[i]; + auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n)); if (token == vocab_.token_to_id.end()) { // output any symbols that did not form tokens as bytes. - for (int j = 0; j < symbol.text.size(); ++j) { - gpt_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3; + for (int j = 0; j < (int) symbol.n; ++j) { + llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3; output.push_back(token_id); } } else { @@ -395,8 +347,8 @@ private: return; } - std::string_view text(symbols_[left].text.data(), symbols_[left].text.size() + symbols_[right].text.size()); - auto token = vocab_.token_to_id.find(std::string(text)); + const std::string text = std::string(symbols_[left].text, symbols_[left].n + symbols_[right].n); + auto token = vocab_.token_to_id.find(text); if (token == vocab_.token_to_id.end()) { return; @@ -416,14 +368,52 @@ private: work_queue_.push(bigram); } - const gpt_vocab & vocab_; + const llama_vocab & vocab_; std::vector<llama_sp_symbol> symbols_; llama_sp_bigram::queue work_queue_; }; -std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, std::string_view text, bool bos) { +// TODO: temporary code duplication with llama.cpp +// will resolve after #77 is merged +bool llama_vocab_load(const std::string & fname, llama_vocab & vocab) { + std::ifstream fin(fname, std::ios::binary); + if (!fin.is_open()) { + return false; + } + + int n_vocab = 0; + fin.read((char *) &n_vocab, sizeof(n_vocab)); + + std::string word; + std::vector<char> tmp(64); + + for (int i = 0; i < n_vocab; i++) { + uint32_t len; + fin.read((char *) &len, sizeof(len)); + + word.resize(len); + if (len > 0) { + tmp.resize(len); + fin.read(tmp.data(), len); + word.assign(tmp.data(), len); + } else { + word.clear(); + } + + float score; + fin.read((char *) &score, sizeof(score)); + + vocab.token_to_id[word] = i; + vocab.id_to_token[i] = word; + vocab.score[i] = score; + } + + return true; +} + +std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) { llama_tokenizer tokenizer(vocab); - std::vector<gpt_vocab::id> output; + std::vector<llama_vocab::id> output; if (text.size() == 0) { return output; @@ -437,42 +427,22 @@ std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, std::string_v return output; } -bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) { - printf("%s: loading vocab from '%s'\n", __func__, fname.c_str()); - - vocab.token_to_id = ::json_parse(fname); - - for (const auto & kv : vocab.token_to_id) { - vocab.id_to_token[kv.second] = kv.first; - } - - printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size()); - - // print the vocabulary - //for (auto kv : vocab.token_to_id) { - // printf("'%s' -> %d\n", kv.first.data(), kv.second); - //} - - return true; -} - - -void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k) { +void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) { // find the top K tokens std::partial_sort( logits_id.begin(), logits_id.begin() + top_k, logits_id.end(), - [](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) { + [](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) { return a.first > b.first; }); logits_id.resize(top_k); } -gpt_vocab::id llama_sample_top_p_top_k( - const gpt_vocab & vocab, +llama_vocab::id llama_sample_top_p_top_k( + const llama_vocab & vocab, const float * logits, - std::vector<gpt_vocab::id> & last_n_tokens, + std::vector<llama_vocab::id> & last_n_tokens, double repeat_penalty, int top_k, double top_p, @@ -480,7 +450,7 @@ gpt_vocab::id llama_sample_top_p_top_k( std::mt19937 & rng) { int n_logits = vocab.id_to_token.size(); - std::vector<std::pair<double, gpt_vocab::id>> logits_id; + std::vector<std::pair<double, llama_vocab::id>> logits_id; logits_id.reserve(n_logits); { |