mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
84ab83cc0b
* initial jina-embeddings-v3 support * initial jina-embeddings-v3 support * initial jina-embeddings-v3 support * fix vocab parsing with only tokenizer.json * set mask token lstrip attribute * additional unk_token_id fallback just in case [no ci] * revert vocab_size() change [no ci] * merge tensor loading into general bert * rope * add lora embedding and loading (non-functional) * export separate lora ggufs instead * add adapter metadata api * use std::string * convert_hf_to_lora compatibility * fix assert * apply suggestions from review * apply suggestion from review
3793 lines
146 KiB
C++
3793 lines
146 KiB
C++
#include "llama-vocab.h"
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#include "ggml.h"
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#include "gguf.h"
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#include "llama-impl.h"
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#include "llama-model-loader.h"
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#include "unicode.h"
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#include <algorithm>
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#include <cassert>
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#include <cctype>
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#include <cfloat>
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#include <cmath>
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#include <cstdarg>
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#include <cstring>
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#include <forward_list>
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#include <limits>
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#include <map>
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#include <queue>
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#include <set>
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#include <unordered_map>
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//
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// helpers
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//
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struct naive_trie {
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naive_trie() : has_value(false), value(0) {
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}
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void insert(const char * key, size_t len, int32_t value = 0) {
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if (len == 0) {
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this->has_value = true;
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this->value = value;
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return;
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}
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char c = key[0];
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auto res = children.find(c);
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if (res != children.end()) {
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res->second.insert(key + 1, len - 1, value);
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} else {
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auto res = children.insert(std::make_pair(c, naive_trie()));
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res.first->second.insert(key + 1, len - 1, value);
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}
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}
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std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) const {
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if (len == 0 || offset == len) {
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return std::make_pair(key, offset);
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}
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char c = key[offset];
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auto res = children.find(c);
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if (res != children.end()) {
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return res->second.get_longest_prefix(key, len, offset + 1);
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}
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return std::make_pair(key, offset);
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}
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const struct naive_trie * traverse(const char c) const {
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auto res = children.find(c);
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if (res != children.end()) {
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return &res->second;
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}
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return NULL;
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}
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std::map<char, struct naive_trie> children;
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bool has_value;
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llama_token value;
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};
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//
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// tokenizers
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//
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struct llm_tokenizer {
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llm_tokenizer() {}
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virtual ~llm_tokenizer() = default;
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};
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struct llm_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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static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");
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//
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// SPM tokenizer
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// original implementation:
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// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
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//
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struct llm_bigram_spm {
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struct comparator {
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bool operator()(llm_bigram_spm & l, llm_bigram_spm & 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<llm_bigram_spm>;
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using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
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llm_symbol::index left;
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llm_symbol::index right;
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float score;
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size_t size;
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};
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struct llm_tokenizer_spm : llm_tokenizer {
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llm_tokenizer_spm(const llama_vocab & /*vocab*/) {}
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};
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struct llm_tokenizer_spm_session {
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llm_tokenizer_spm_session(const llama_vocab & vocab) : vocab(vocab) {}
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void tokenize(const std::string & text, std::vector<llama_token> & 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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llm_symbol sym;
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size_t len = unicode_len_utf8(text[offs]);
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sym.text = text.c_str() + offs;
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sym.n = std::min(len, text.size() - offs);
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offs += sym.n;
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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(sym);
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}
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// seed the work queue with all possible 2-character tokens.
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for (int i = 1; i < (int) 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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//LLAMA_LOG_INFO("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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resegment(symbol, output);
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}
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}
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private:
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void resegment(llm_symbol & symbol, std::vector<llama_token> & output) {
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auto text = std::string(symbol.text, symbol.n);
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auto token = vocab.text_to_token(text);
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// Do we need to support is_unused?
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if (token != LLAMA_TOKEN_NULL) {
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output.push_back(token);
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return;
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}
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const auto p = rev_merge.find(text);
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if (p == rev_merge.end()) {
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// output any symbols that did not form tokens as bytes.
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output.reserve(output.size() + symbol.n);
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for (int j = 0; j < (int)symbol.n; ++j) {
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llama_token id = vocab.byte_to_token(symbol.text[j]);
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output.push_back(id);
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}
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return;
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}
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resegment(symbols[p->second.first], output);
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resegment(symbols[p->second.second], output);
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}
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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.text_to_token(text);
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if (token == LLAMA_TOKEN_NULL) {
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return;
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}
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if (static_cast<uint32_t>(token) >= vocab.n_tokens()) {
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return;
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}
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const auto & tok_data = vocab.get_token_data(token);
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llm_bigram_spm bigram;
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bigram.left = left;
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bigram.right = right;
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bigram.score = tok_data.score;
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bigram.size = text.size();
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work_queue.push(bigram);
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// Do we need to support is_unused?
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rev_merge[text] = std::make_pair(left, right);
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}
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const llama_vocab & vocab;
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// currently unused
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// const llm_tokenizer_spm * spm_tokenizer;
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std::vector<llm_symbol> symbols;
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llm_bigram_spm::queue work_queue;
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std::map<std::string, std::pair<int, int>> rev_merge;
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};
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//
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// BPE tokenizer
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// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
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// tried to simplify unicode stuff, so most likely does not work 100% correctly!
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//
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// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused
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template<typename T, typename Container = std::vector<T>, typename Compare = std::less<typename Container::value_type>>
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class llama_priority_queue : public std::priority_queue<T, Container, Compare> {
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public:
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using std::priority_queue<T, Container, Compare>::priority_queue;
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T pop_move() {
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T item = std::move(this->c.front());
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std::pop_heap(this->c.begin(), this->c.end(), this->comp);
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this->c.pop_back();
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return item;
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}
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void pop() = delete;
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};
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struct llm_bigram_bpe {
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struct comparator {
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bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
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return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
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}
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};
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using queue_storage = std::vector<llm_bigram_bpe>;
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using queue = llama_priority_queue<llm_bigram_bpe, queue_storage, comparator>;
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llm_symbol::index left;
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llm_symbol::index right;
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std::string text;
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int rank;
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size_t size;
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};
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struct llm_tokenizer_bpe : llm_tokenizer {
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llm_tokenizer_bpe(const llama_vocab & vocab) {
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GGML_ASSERT(vocab.get_type() == LLAMA_VOCAB_TYPE_BPE);
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switch (vocab.get_pre_type()) {
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case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
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regex_exprs = {
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// original regex from tokenizer.json
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//"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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// adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
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"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_DBRX:
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case LLAMA_VOCAB_PRE_TYPE_SMAUG:
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regex_exprs = {
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// same as llama3
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"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM:
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regex_exprs = {
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"[\r\n]",
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"\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿDŽ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿff-stﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
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"\\s?[!-/:-~！-／：-～‘-‟ -。]+",
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"\\s+$",
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"[一-龥ࠀ-一가-]+",
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"\\p{N}+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM:
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case LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE:
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regex_exprs = {
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"\\p{N}{1,3}",
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"[一-龥-ゟ゠-ヿ]+",
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"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
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regex_exprs = {
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"[\r\n]",
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"\\s?\\p{L}+",
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"\\s?\\p{P}+",
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"[一-龥ࠀ-一가-]+",
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"\\p{N}",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_FALCON:
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regex_exprs = {
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"[\\p{P}\\$\\+<=>\\^~\\|`]+",
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"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
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"[0-9][0-9][0-9]",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_STARCODER:
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case LLAMA_VOCAB_PRE_TYPE_REFACT:
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case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
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case LLAMA_VOCAB_PRE_TYPE_SMOLLM:
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case LLAMA_VOCAB_PRE_TYPE_CODESHELL:
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case LLAMA_VOCAB_PRE_TYPE_EXAONE:
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case LLAMA_VOCAB_PRE_TYPE_MINERVA:
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regex_exprs = {
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"\\p{N}",
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"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_GPT2:
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case LLAMA_VOCAB_PRE_TYPE_MPT:
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case LLAMA_VOCAB_PRE_TYPE_OLMO:
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case LLAMA_VOCAB_PRE_TYPE_JAIS:
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case LLAMA_VOCAB_PRE_TYPE_TRILLION:
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regex_exprs = {
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"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
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case LLAMA_VOCAB_PRE_TYPE_QWEN2:
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case LLAMA_VOCAB_PRE_TYPE_HUNYUAN:
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regex_exprs = {
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// original regex from tokenizer.json
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// "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
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"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_PORO:
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case LLAMA_VOCAB_PRE_TYPE_BLOOM:
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case LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH:
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regex_exprs = {
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" ?[^(\\s|.,!?…。，、।۔،)]+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_CHATGLM4:
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regex_exprs = {
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"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_VIKING:
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regex_exprs = {
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" ?[^(\\s|.,!?…。，、।۔،)]+",
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"\\p{N}",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_TEKKEN:
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// original regex from tokenizer.json
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// "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
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regex_exprs = {
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"[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_CHAMELEON:
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// Note: in theory, the special token (sentinel and image token) regex_exprs below
|
||
// are unnecessary, as they are split in `tokenizer_st_partition` anyway.
|
||
// However, since the upstream pre-tokenizer uses them, they are also
|
||
// included here (see https://huggingface.co/facebook/chameleon-7b).
|
||
regex_exprs = {
|
||
"<sentinel:[0-9]+>", // Sentinel tokens
|
||
"(IMGIMG)((A|B|C|D|E|F|G|H|I){1,4})Z", // Image tokens
|
||
"([\\t\\n]| | )", // directly from tokenizer.json
|
||
"\\p{N}", // Individual digits
|
||
"[\\p{P}!-/:-@\\[-`{-~]", // Punctuation, Isolated
|
||
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
|
||
};
|
||
break;
|
||
case LLAMA_VOCAB_PRE_TYPE_GPT4O:
|
||
regex_exprs = {
|
||
// original regex from tokenizer.json
|
||
// "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
|
||
"[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
|
||
};
|
||
break;
|
||
case LLAMA_VOCAB_PRE_TYPE_KIMI_K2:
|
||
regex_exprs = {
|
||
// K2 trigger pattern - this will activate the custom K2 handler in unicode.cpp
|
||
// The custom handler implements all K2 patterns with proper Han character exclusion
|
||
"\\p{Han}+",
|
||
};
|
||
break;
|
||
case LLAMA_VOCAB_PRE_TYPE_SUPERBPE:
|
||
regex_exprs = {
|
||
"\\p{N}+",
|
||
"(?=(\\d{3})+(?!\\d))",
|
||
};
|
||
break;
|
||
case LLAMA_VOCAB_PRE_TYPE_BAILINGMOE:
|
||
regex_exprs = {
|
||
// original regex from tokenizer.json
|
||
// "'(?i:[sdmt]|ll|ve|re)|[^\\r\\n\\p{L}\\p{N}]?+\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]++[\\r\\n]*|\\s*[\\r\\n]|\\s+(?!\\S)|\\s+"
|
||
// FIXME? Changed possessive quantifiers (?+ and ++) to greedy to avoid errors and imatrix hanging (tried atomic grouping but it's not supported?)
|
||
"'(?:[sSdDmMtT]|[lL][lL]|[vV][eE]|[rR][eE])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]|\\s+(?!\\S)|\\s+",
|
||
};
|
||
break;
|
||
case LLAMA_VOCAB_PRE_TYPE_SEED_CODER:
|
||
regex_exprs = {
|
||
// original regex from tokenizer.json
|
||
// "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1}| ?[^\\s\\p{L}\\p{N}\r\n]+|\\s*[\r\n]+|\\s+(?!\\S)|\\s+"
|
||
"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1}| ?[^\\s\\p{L}\\p{N}\\r\\n]+|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
|
||
};
|
||
break;
|
||
default:
|
||
// default regex for BPE tokenization pre-processing
|
||
regex_exprs = {
|
||
"[\\p{P}\\$\\+<=>\\^~\\|]+",
|
||
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
|
||
"\\p{N}+",
|
||
"[0-9][0-9][0-9]",
|
||
};
|
||
break;
|
||
}
|
||
}
|
||
|
||
std::vector<std::string> regex_exprs;
|
||
};
|
||
|
||
struct llm_tokenizer_bpe_session {
|
||
llm_tokenizer_bpe_session(const llama_vocab & vocab, const llm_tokenizer_bpe & tokenizer) : vocab(vocab), tokenizer(tokenizer) {}
|
||
|
||
static void append(const llama_token token_id, std::vector<llama_token> & output) {
|
||
output.push_back(token_id);
|
||
}
|
||
|
||
bool append_bos(std::vector<llama_token> & output) const {
|
||
if (vocab.get_add_bos()) {
|
||
GGML_ASSERT(vocab.token_bos() != LLAMA_TOKEN_NULL);
|
||
output.push_back(vocab.token_bos());
|
||
return true;
|
||
}
|
||
return false;
|
||
}
|
||
|
||
bool append_eos(std::vector<llama_token> & output) const {
|
||
if (vocab.get_add_eos()) {
|
||
GGML_ASSERT(vocab.token_eos() != LLAMA_TOKEN_NULL);
|
||
output.push_back(vocab.token_eos());
|
||
return true;
|
||
}
|
||
return false;
|
||
}
|
||
|
||
void check_double_bos_eos(const std::vector<llama_token> & output) const {
|
||
if (vocab.get_add_bos() && output.size() >= 2 && output[1] == vocab.token_bos()) {
|
||
LLAMA_LOG_WARN(
|
||
"%s: Added a BOS token to the prompt as specified by the model but the prompt "
|
||
"also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
|
||
"Are you sure this is what you want?\n", __FUNCTION__);
|
||
}
|
||
if (vocab.get_add_eos() && output.size() >= 2 && *(output.end()-2) == vocab.token_eos()) {
|
||
LLAMA_LOG_WARN(
|
||
"%s: Added a EOS token to the prompt as specified by the model but the prompt "
|
||
"also ends with a EOS token. So now the final prompt ends with 2 EOS tokens. "
|
||
"Are you sure this is what you want?\n", __FUNCTION__);
|
||
}
|
||
}
|
||
|
||
void tokenize(const std::string & text, std::vector<llama_token> & output) {
|
||
int final_prev_index = -1;
|
||
const auto word_collection = unicode_regex_split(text, tokenizer.regex_exprs);
|
||
|
||
symbols_final.clear();
|
||
|
||
for (const auto & word : word_collection) {
|
||
work_queue = llm_bigram_bpe::queue();
|
||
symbols.clear();
|
||
|
||
int index = 0;
|
||
size_t offset = 0;
|
||
|
||
//if (vocab.tokenizer_ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
|
||
if (vocab.get_ignore_merges() && vocab.text_to_token(word) != LLAMA_TOKEN_NULL) {
|
||
symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
|
||
offset = word.size();
|
||
}
|
||
|
||
while (offset < word.size()) {
|
||
llm_symbol sym;
|
||
size_t char_len = std::min(word.size() - offset, (size_t) unicode_len_utf8(word[offset]));
|
||
sym.text = word.c_str() + offset;
|
||
sym.n = char_len;
|
||
offset += sym.n;
|
||
sym.prev = index - 1;
|
||
sym.next = offset == word.size() ? -1 : index + 1;
|
||
index++;
|
||
symbols.emplace_back(sym);
|
||
}
|
||
for (int i = 1; i < (int) symbols.size(); ++i) {
|
||
add_new_bigram(i - 1, i);
|
||
}
|
||
|
||
// build token(s)
|
||
while (!work_queue.empty()) {
|
||
auto bigram = work_queue.pop_move();
|
||
|
||
auto & left_symbol = symbols[bigram.left];
|
||
auto & right_symbol = symbols[bigram.right];
|
||
|
||
if (left_symbol.n == 0 || right_symbol.n == 0) {
|
||
continue;
|
||
}
|
||
std::string left_token = std::string(left_symbol.text, left_symbol.n);
|
||
std::string right_token = std::string(right_symbol.text, right_symbol.n);
|
||
if (left_token + right_token != bigram.text) {
|
||
continue; // Skip this bigram if it's outdated
|
||
}
|
||
|
||
// merge the right sym into the left one
|
||
left_symbol.n += right_symbol.n;
|
||
right_symbol.n = 0;
|
||
|
||
// remove the right sym from the chain
|
||
left_symbol.next = right_symbol.next;
|
||
if (right_symbol.next >= 0) {
|
||
symbols[right_symbol.next].prev = bigram.left;
|
||
}
|
||
|
||
add_new_bigram(left_symbol.prev, bigram.left); // left side of current symbol
|
||
add_new_bigram(bigram.left, left_symbol.next); // right side of current symbol
|
||
}
|
||
|
||
// add the finished tokens to the final list keeping correct order for next and prev
|
||
for (auto & sym : symbols) {
|
||
if (sym.n > 0) {
|
||
sym.prev = final_prev_index;
|
||
sym.next = -1;
|
||
if (final_prev_index != -1) {
|
||
symbols_final[final_prev_index].next = symbols_final.size();
|
||
}
|
||
symbols_final.emplace_back(sym);
|
||
final_prev_index = symbols_final.size() - 1;
|
||
}
|
||
}
|
||
}
|
||
|
||
symbols = symbols_final;
|
||
|
||
if (!symbols.empty()) {
|
||
for (int i = 0; i != -1; i = symbols[i].next) {
|
||
auto & symbol = symbols[i];
|
||
if (symbol.n == 0) {
|
||
continue;
|
||
}
|
||
|
||
const std::string str = std::string(symbol.text, symbol.n);
|
||
const auto token = vocab.text_to_token(str);
|
||
|
||
if (token == LLAMA_TOKEN_NULL) {
|
||
for (auto j = str.begin(); j != str.end(); ++j) {
|
||
std::string byte_str(1, *j);
|
||
auto token_multibyte = vocab.text_to_token(byte_str);
|
||
if (token_multibyte != LLAMA_TOKEN_NULL) {
|
||
output.push_back(token_multibyte);
|
||
}
|
||
}
|
||
} else {
|
||
output.push_back(token);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
private:
|
||
void add_new_bigram(int left, int right) {
|
||
if (left == -1 || right == -1) {
|
||
return;
|
||
}
|
||
std::string left_token = std::string(symbols[left].text, symbols[left].n);
|
||
std::string right_token = std::string(symbols[right].text, symbols[right].n);
|
||
|
||
int rank_found = -1;
|
||
|
||
rank_found = vocab.find_bpe_rank(left_token, right_token);
|
||
|
||
if (rank_found < 0) {
|
||
return;
|
||
}
|
||
|
||
llm_bigram_bpe bigram;
|
||
|
||
bigram.left = left;
|
||
bigram.right = right;
|
||
bigram.text = left_token + right_token;
|
||
bigram.size = left_token.size() + right_token.size();
|
||
bigram.rank = rank_found;
|
||
|
||
work_queue.push(bigram);
|
||
}
|
||
|
||
const llama_vocab & vocab;
|
||
const llm_tokenizer_bpe & tokenizer;
|
||
|
||
std::vector<llm_symbol> symbols;
|
||
std::vector<llm_symbol> symbols_final;
|
||
llm_bigram_bpe::queue work_queue;
|
||
};
|
||
|
||
//
|
||
// WPM tokenizer
|
||
//
|
||
|
||
struct llm_tokenizer_wpm : llm_tokenizer {
|
||
llm_tokenizer_wpm(const llama_vocab & /*vocab*/) {}
|
||
};
|
||
|
||
struct llm_tokenizer_wpm_session {
|
||
llm_tokenizer_wpm_session(const llama_vocab & vocab) : vocab(vocab) {}
|
||
|
||
void tokenize(const std::string & text, std::vector<llama_token> & output) {
|
||
// normalize and split by whitespace
|
||
std::vector<std::string> words = preprocess(text);
|
||
// bos token prepended already
|
||
|
||
// find the longest tokens that form the words
|
||
for (const std::string & word : words) {
|
||
// skip empty words
|
||
if (word.size() == 0) {
|
||
continue;
|
||
}
|
||
|
||
// prepend phantom space
|
||
const std::string word1 = "\xe2\x96\x81" + word;
|
||
const int n = word1.size();
|
||
|
||
const size_t current_tokens = output.size();
|
||
|
||
// we're at the start of a new word
|
||
// move through character position in word
|
||
for (int i = 0; i < n; ++i) {
|
||
// loop through possible match length
|
||
bool match = false;
|
||
for (int j = std::min(n, i + vocab.max_token_len() + 1); j > i; j--) {
|
||
auto id = vocab.text_to_token(word1.substr(i, j - i));
|
||
if (id != LLAMA_TOKEN_NULL) {
|
||
output.push_back(id);
|
||
match = true;
|
||
i = j - 1;
|
||
break;
|
||
}
|
||
}
|
||
|
||
if (!match) { // discard all
|
||
output.resize(current_tokens);
|
||
break; // and discard next tokens
|
||
}
|
||
}
|
||
|
||
// we didn't find any matches for this word
|
||
if (current_tokens == output.size()) {
|
||
output.push_back(vocab.token_unk());
|
||
}
|
||
}
|
||
}
|
||
|
||
// TODO: reduce string copies by using cpts_offs array
|
||
static std::vector<std::string> preprocess(const std::string & text) {
|
||
const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
|
||
std::vector<std::string> words(1, "");
|
||
|
||
for (const uint32_t cpt : cpts_nfd) {
|
||
const auto flags = unicode_cpt_flags_from_cpt(cpt);
|
||
|
||
if (flags.is_whitespace) {
|
||
if (words.back().size()) { // finish previous word if any
|
||
words.emplace_back();
|
||
}
|
||
continue;
|
||
}
|
||
|
||
assert (!flags.is_separator);
|
||
if (cpt == 0 || cpt == 0xFFFD || flags.is_control) {
|
||
continue;
|
||
}
|
||
|
||
const std::string s = unicode_cpt_to_utf8(unicode_tolower(cpt));
|
||
if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) {
|
||
if (words.back().size()) { // finish previous word if any
|
||
words.emplace_back();
|
||
}
|
||
words.back() = s; // single char word
|
||
words.emplace_back(); // start a new word
|
||
} else {
|
||
words.back() += s; // append char to word
|
||
}
|
||
}
|
||
|
||
if (!words.back().size()) {
|
||
words.pop_back();
|
||
}
|
||
|
||
return words;
|
||
}
|
||
|
||
static bool is_chinese_char(uint32_t cpt) {
|
||
return
|
||
(cpt >= 0x04E00 && cpt <= 0x09FFF) ||
|
||
(cpt >= 0x03400 && cpt <= 0x04DBF) ||
|
||
(cpt >= 0x20000 && cpt <= 0x2A6DF) ||
|
||
(cpt >= 0x2A700 && cpt <= 0x2B73F) ||
|
||
(cpt >= 0x2B740 && cpt <= 0x2B81F) ||
|
||
(cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
|
||
(cpt >= 0x0F900 && cpt <= 0x0FAFF) ||
|
||
(cpt >= 0x2F800 && cpt <= 0x2FA1F);
|
||
//(cpt >= 0x3000 && cpt <= 0x303F) ||
|
||
//(cpt >= 0xFF00 && cpt <= 0xFFEF);
|
||
}
|
||
|
||
private:
|
||
const llama_vocab & vocab;
|
||
// currently unused
|
||
// const llm_tokenizer_wpm * wpm_tokenizer;
|
||
};
|
||
|
||
//
|
||
// UGM tokenizer
|
||
//
|
||
|
||
struct llm_tokenizer_ugm : llm_tokenizer {
|
||
llm_tokenizer_ugm(const llama_vocab & vocab, const std::vector<char> & precompiled_charsmap) {
|
||
if (precompiled_charsmap.size() > 0) {
|
||
size_t charsmap_offset = 0;
|
||
|
||
// First four bytes of precompiled_charsmap contains length of binary
|
||
// blob containing XOR-compressed compact double array (XCDA) entries
|
||
uint32_t xcda_blob_size = *(const uint32_t *) &precompiled_charsmap[0];
|
||
charsmap_offset += sizeof(xcda_blob_size);
|
||
if (xcda_blob_size + charsmap_offset >= precompiled_charsmap.size()) {
|
||
throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
|
||
}
|
||
|
||
// Next xcda_blob_size bytes contain entries of XOR-compressed compact
|
||
// double array (XCDA). Each entry is bit-packed into a 32-bit integer.
|
||
xcda_array = (const uint32_t *) &precompiled_charsmap[charsmap_offset];
|
||
xcda_array_size = xcda_blob_size / sizeof(uint32_t);
|
||
charsmap_offset += xcda_blob_size;
|
||
|
||
// Remaining bytes of precompiled charsmap contain null-terminated
|
||
// replacement strings for prefixes matched by the XCDA.
|
||
prefix_replacements = &precompiled_charsmap[charsmap_offset];
|
||
prefix_replacements_size = precompiled_charsmap.size() - charsmap_offset;
|
||
}
|
||
|
||
for (uint32_t id = 0; id < vocab.n_tokens(); ++id) {
|
||
const auto & token_data = vocab.get_token_data(id);
|
||
|
||
if (vocab.is_normal(id)) {
|
||
min_score = std::min<float>(min_score, token_data.score);
|
||
max_score = std::max<float>(max_score, token_data.score);
|
||
}
|
||
|
||
if (vocab.is_normal(id) ||
|
||
vocab.is_user_defined(id) ||
|
||
vocab.is_unused(id)) {
|
||
token_matcher.insert(token_data.text.data(), token_data.text.size(), id);
|
||
}
|
||
|
||
if (vocab.is_user_defined(id)) {
|
||
user_defined_token_matcher.insert(token_data.text.data(), token_data.text.size());
|
||
}
|
||
}
|
||
|
||
unknown_token_score = min_score - unknown_token_score_penalty;
|
||
}
|
||
|
||
// escaped space symbol - U+2581 (Lower One Eighth Block)
|
||
const std::string escaped_space = "\xE2\x96\x81";
|
||
|
||
const char * prefix_replacements = NULL;
|
||
size_t prefix_replacements_size = 0;
|
||
|
||
const uint32_t * xcda_array = NULL;
|
||
size_t xcda_array_size = 0;
|
||
|
||
struct naive_trie user_defined_token_matcher;
|
||
|
||
float min_score = FLT_MAX;
|
||
float max_score = -FLT_MAX;
|
||
|
||
float unknown_token_score_penalty = 10.0;
|
||
float unknown_token_score;
|
||
|
||
struct naive_trie token_matcher;
|
||
};
|
||
|
||
struct llm_tokenizer_ugm_session {
|
||
llm_tokenizer_ugm_session(const llama_vocab & vocab, const llm_tokenizer_ugm & tokenizer) : vocab(vocab), tokenizer(tokenizer) {}
|
||
|
||
/* This implementation is based on SentencePiece optimized Viterbi algorithm for
|
||
* unigram language models. The general idea is to:
|
||
* - move along the input sequence in steps of one UTF code point,
|
||
* - at each step find all possible tokenizations of the prefix by
|
||
* traversing the tokens trie,
|
||
* - for each tokenization store the best one so far (by higher score)
|
||
* - use the position in sequence after given token as an index to store
|
||
* results
|
||
* - if there was no valid tokenization of the current UTF code point
|
||
* then use unknown token with additional score penalty
|
||
* After processing the whole sequence we backtrack from the end to get
|
||
* the best tokenization.
|
||
*/
|
||
void tokenize(const std::string & text, std::vector<llama_token> & output) {
|
||
// get current size of output (for reversal later)
|
||
size_t output_size = output.size();
|
||
|
||
// normalize the input first
|
||
std::string normalized;
|
||
normalize(text, &normalized);
|
||
size_t input_len = normalized.size();
|
||
if (input_len == 0) {
|
||
return;
|
||
}
|
||
|
||
// initialize score_sum to -FLT_MAX so it will be always lower than sums of token scores
|
||
std::vector<struct best_tokenization> tokenization_results(input_len + 1, {vocab.token_unk(), 0, -DBL_MAX});
|
||
// at the beginning tokenization score is zero
|
||
tokenization_results[0] = { vocab.token_unk(), 0, 0 };
|
||
|
||
for (size_t input_offset = 0; input_offset < input_len;) {
|
||
size_t prefix_offset = input_offset;
|
||
// calculate how many code units are in the currently processed UTF code point
|
||
size_t n_utf8_code_units = std::min<size_t>(unicode_len_utf8(normalized[input_offset]), input_len - input_offset);
|
||
|
||
// traverse the token matcher trie to find a matching token
|
||
bool single_codepoint_token_found = false;
|
||
const struct best_tokenization & current_best = tokenization_results[input_offset];
|
||
const struct naive_trie * node = tokenizer.token_matcher.traverse(normalized[prefix_offset++]);
|
||
|
||
while (prefix_offset <= input_len && node != NULL) {
|
||
// check if we found valid token in prefix
|
||
if (node->has_value) {
|
||
// check if it corresponds to the whole UTF code point
|
||
if (prefix_offset - input_offset == n_utf8_code_units) {
|
||
single_codepoint_token_found = true;
|
||
}
|
||
llama_token token_id = node->value;
|
||
const auto & token_data = vocab.get_token_data(token_id);
|
||
|
||
// we set the user-defined token scores to 0 to make them more likely to be selected
|
||
// (normal token scores are log probabilities, so they are negative)
|
||
// score type is double here to make tokenization results exactly
|
||
// the same as in the HF tokenizer using SentencePiece
|
||
const double token_score = vocab.is_user_defined(token_id) ? 0.0 : token_data.score;
|
||
const double challenger_score = current_best.score_sum + token_score;
|
||
struct best_tokenization & current_champ = tokenization_results[prefix_offset];
|
||
if (challenger_score > current_champ.score_sum) {
|
||
struct best_tokenization challenger = { token_id, input_offset, challenger_score };
|
||
current_champ = challenger;
|
||
}
|
||
}
|
||
node = node->traverse(normalized[prefix_offset++]);
|
||
}
|
||
|
||
// if we didn't find a valid token corresponding to the whole UTF code point
|
||
// then use unknown token as the tokenization of this UTF code point
|
||
if (!single_codepoint_token_found) {
|
||
const double challenger_score = current_best.score_sum + tokenizer.unknown_token_score;
|
||
prefix_offset = input_offset + n_utf8_code_units;
|
||
struct best_tokenization & current_champ = tokenization_results[prefix_offset];
|
||
if (challenger_score > current_champ.score_sum) {
|
||
struct best_tokenization challenger = { vocab.token_unk(), input_offset, challenger_score };
|
||
current_champ = challenger;
|
||
}
|
||
}
|
||
|
||
// move to the next UTF code point
|
||
input_offset += n_utf8_code_units;
|
||
}
|
||
|
||
// now backtrack from the end to gather token ids of the best tokenization
|
||
// merge sequences of consecutive unknown tokens into single unknown tokens
|
||
bool is_prev_unknown = false;
|
||
for (struct best_tokenization & tokenization = tokenization_results[input_len]; ; tokenization = tokenization_results[tokenization.input_offset]) {
|
||
bool is_unknown = tokenization.token_id == vocab.token_unk();
|
||
if (!(is_prev_unknown && is_unknown)) {
|
||
output.push_back(tokenization.token_id);
|
||
}
|
||
if (tokenization.input_offset == 0) {
|
||
break;
|
||
}
|
||
is_prev_unknown = is_unknown;
|
||
}
|
||
|
||
// reverse the output since we added tokens starting from the end of the input
|
||
std::reverse(output.begin() + output_size, output.end());
|
||
}
|
||
|
||
private:
|
||
|
||
// helper structure for returning normalization results
|
||
struct normalization_result {
|
||
const char * normalized;
|
||
size_t normalized_len;
|
||
size_t consumed_input;
|
||
};
|
||
|
||
void normalize(const std::string& input, std::string * normalized) {
|
||
normalized->clear();
|
||
normalized->reserve(input.size() * 3);
|
||
|
||
const std::string space = vocab.get_escape_whitespaces() ? tokenizer.escaped_space : " ";
|
||
|
||
const bool shall_prepend_space = !vocab.get_treat_whitespace_as_suffix() && vocab.get_add_space_prefix();
|
||
const bool shall_append_space = vocab.get_treat_whitespace_as_suffix() && vocab.get_add_space_prefix();
|
||
const bool shall_merge_spaces = vocab.get_remove_extra_whitespaces();
|
||
|
||
bool is_space_prepended = false;
|
||
bool processing_non_ws = false;
|
||
|
||
size_t input_len = input.size();
|
||
|
||
for (size_t input_offset = 0; input_offset < input_len; ) {
|
||
auto norm_res = normalize_prefix(input, input_offset);
|
||
for (size_t i = 0; i < norm_res.normalized_len; i++) {
|
||
char c = norm_res.normalized[i];
|
||
if (c != ' ') {
|
||
if (!processing_non_ws) {
|
||
processing_non_ws = true;
|
||
if ((shall_prepend_space && !is_space_prepended) || shall_merge_spaces) {
|
||
normalized->append(space);
|
||
is_space_prepended = true;
|
||
}
|
||
}
|
||
normalized->push_back(c);
|
||
} else {
|
||
if (processing_non_ws) {
|
||
processing_non_ws = false;
|
||
}
|
||
if (!shall_merge_spaces) {
|
||
normalized->append(space);
|
||
}
|
||
}
|
||
}
|
||
|
||
input_offset += norm_res.consumed_input;
|
||
}
|
||
|
||
if (shall_append_space) {
|
||
normalized->append(space);
|
||
}
|
||
}
|
||
|
||
/*
|
||
* This structure is a view wrapper for XOR-compressed double array (XCDA)
|
||
* See Shunsuke Kanda (2018). Space- and Time-Efficient String Dictionaries.
|
||
* Each bit-packed entry contains:
|
||
* - BASE array value in bits 10-30
|
||
* - LCHECK array value in bits 0-7
|
||
* - LEAF array value in bit 9
|
||
* Entries containing indexes of replacement sequences have set bit 31
|
||
*/
|
||
struct xcda_array_view {
|
||
public:
|
||
xcda_array_view(const uint32_t * xcda_array, size_t xcda_array_size) : xcda_array(xcda_array), xcda_array_size(xcda_array_size) {
|
||
}
|
||
uint32_t get_base(size_t index) {
|
||
uint32_t packed_node = get_node(index);
|
||
return (packed_node >> 10) << ((packed_node & (1U << 9)) >> 6);
|
||
}
|
||
uint32_t get_lcheck(size_t index) {
|
||
uint32_t packed_node = get_node(index);
|
||
return packed_node & ((1U << 31) | 0xff);
|
||
}
|
||
bool get_leaf(size_t index) {
|
||
uint32_t packed_node = get_node(index);
|
||
return (packed_node >> 8) & 1;
|
||
}
|
||
uint32_t get_value(size_t index) {
|
||
uint32_t packed_node = get_node(index);
|
||
return packed_node & ((1U << 31) - 1);
|
||
}
|
||
private:
|
||
uint32_t get_node(size_t index) {
|
||
if (index > xcda_array_size) {
|
||
throw std::runtime_error("Index out of array bounds in XCDA array!");
|
||
}
|
||
return xcda_array[index];
|
||
}
|
||
const uint32_t * xcda_array;
|
||
size_t xcda_array_size;
|
||
};
|
||
|
||
// this structure stores the best tokenization so far at input_offset
|
||
struct best_tokenization {
|
||
llama_token token_id;
|
||
size_t input_offset;
|
||
double score_sum;
|
||
};
|
||
|
||
struct normalization_result normalize_prefix(const std::string & input, size_t input_offset) {
|
||
if (input_offset == input.size()) {
|
||
return { &input[input_offset], 0, 0 };
|
||
}
|
||
|
||
// if input prefix matches some user-defined token return this token as normalization result
|
||
auto user_defined_token_match =
|
||
tokenizer.user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
|
||
if (user_defined_token_match.second > 0) {
|
||
return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second };
|
||
}
|
||
|
||
size_t longest_prefix_length = 0;
|
||
size_t longest_prefix_offset = 0;
|
||
|
||
if (tokenizer.xcda_array_size > 0) {
|
||
struct xcda_array_view xcda_view(tokenizer.xcda_array, tokenizer.xcda_array_size);
|
||
|
||
// Find the longest normalized sequence matching the input prefix by walking
|
||
// the XOR-compressed compact double array (XCDA) starting from the root node
|
||
// We find the index of the next node by calculating BASE[s] ^ c where s is
|
||
// the index of the previous node and c is a numerical character value
|
||
uint32_t node_index = 0;
|
||
// get BASE of the root node
|
||
node_index = xcda_view.get_base(node_index);
|
||
for (size_t prefix_offset = input_offset; prefix_offset < input.size(); prefix_offset++) {
|
||
unsigned char c = input[prefix_offset];
|
||
if (c == 0) {
|
||
break;
|
||
}
|
||
node_index ^= c;
|
||
// if value of LCHECK is not c it means that this is not a child of
|
||
// the previous node, so we stop matching
|
||
if (xcda_view.get_lcheck(node_index) != c) {
|
||
break;
|
||
}
|
||
bool is_leaf = xcda_view.get_leaf(node_index);
|
||
// get BASE of the current node
|
||
node_index ^= xcda_view.get_base(node_index);
|
||
// if LEAF of the current node is true, it means that its BASE points to the node
|
||
// containing index of replacement sequence for currently matched input prefix
|
||
if (is_leaf)
|
||
{
|
||
longest_prefix_length = prefix_offset - input_offset + 1;
|
||
// get index of replacement sequence for currently matched input prefix
|
||
longest_prefix_offset = xcda_view.get_value(node_index);
|
||
}
|
||
}
|
||
}
|
||
|
||
if (longest_prefix_length > 0) {
|
||
// we have a match, so return the replacement sequence
|
||
if (longest_prefix_offset >= tokenizer.prefix_replacements_size) {
|
||
throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
|
||
}
|
||
const char * prefix_replacement = &(tokenizer.prefix_replacements)[longest_prefix_offset];
|
||
return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
|
||
}
|
||
|
||
// check if the input prefix contains a valid sequence of UTF-8 code units
|
||
try {
|
||
// if yes, return this sequence unmodified
|
||
size_t prefix_offset = input_offset;
|
||
unicode_cpt_from_utf8(input, prefix_offset);
|
||
return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset };
|
||
} catch (std::invalid_argument & /*ex*/) {
|
||
// if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER
|
||
return { "\xEF\xBF\xBD", 3, 1 };
|
||
}
|
||
}
|
||
|
||
const llama_vocab & vocab;
|
||
const llm_tokenizer_ugm & tokenizer;
|
||
};
|
||
|
||
//
|
||
// RWKV tokenizer
|
||
//
|
||
|
||
static std::vector<uint8_t> llama_unescape_rwkv_token(const std::string & escaped) {
|
||
std::vector<uint8_t> output;
|
||
output.reserve(escaped.size());
|
||
|
||
// Parser state
|
||
bool escaping = false;
|
||
uint8_t hex_remaining = 0;
|
||
uint8_t hex_acc = 0;
|
||
|
||
// Step through characters, performing parsing
|
||
for (const char & c : escaped) {
|
||
// If we're parsing a hex code, interpret the next character
|
||
if (hex_remaining != 0) {
|
||
uint8_t value = (c >= 'a') ? (c - 'a' + 10) : (c - '0');
|
||
hex_acc = (hex_acc << 4) + value;
|
||
|
||
hex_remaining -= 1;
|
||
if (hex_remaining == 0) {
|
||
output.push_back(hex_acc);
|
||
hex_acc = 0;
|
||
}
|
||
|
||
continue;
|
||
}
|
||
|
||
// If we got an escape character, interpret it
|
||
if (escaping) {
|
||
if (c == 't') {
|
||
output.push_back('\t');
|
||
} else if (c == 'n') {
|
||
output.push_back('\n');
|
||
} else if (c == 'r') {
|
||
output.push_back('\r');
|
||
} else if (c == 'x') {
|
||
hex_remaining = 2;
|
||
} else {
|
||
output.push_back(c);
|
||
}
|
||
|
||
escaping = false;
|
||
continue;
|
||
}
|
||
|
||
if (c == '\\') {
|
||
escaping = true;
|
||
continue;
|
||
}
|
||
|
||
output.push_back(c);
|
||
}
|
||
|
||
return output;
|
||
}
|
||
|
||
struct llm_tokenizer_rwkv : llm_tokenizer {
|
||
llm_tokenizer_rwkv(const llama_vocab & vocab) {
|
||
// RWKV supports arbitrary byte tokens, but the vocab struct only supports string tokens.
|
||
// For now, we decode the vocab here into the lookup we'll use for tokenization.
|
||
|
||
// build trie
|
||
for (uint32_t id = 0; id < vocab.n_tokens(); ++id) {
|
||
const auto & data = vocab.get_token_data(id);
|
||
const auto text = llama_unescape_rwkv_token(data.text);
|
||
token_matcher.insert((const char *) text.data(), text.size(), id);
|
||
}
|
||
}
|
||
|
||
struct naive_trie token_matcher;
|
||
};
|
||
|
||
struct llm_tokenizer_rwkv_session {
|
||
llm_tokenizer_rwkv_session(const llama_vocab & vocab, const llm_tokenizer_rwkv & tokenizer) : vocab(vocab), tokenizer(tokenizer) {}
|
||
|
||
void tokenize(const std::string & text, std::vector<llama_token> & output) {
|
||
uint32_t position = 0;
|
||
while (position < text.size()) {
|
||
const struct naive_trie * node = tokenizer.token_matcher.traverse(text[position]);
|
||
if (node == NULL) {
|
||
// no matching token found, add unknown token
|
||
output.push_back(vocab.token_unk());
|
||
position += 1;
|
||
continue;
|
||
}
|
||
|
||
// traverse the trie to find the longest matching token
|
||
uint32_t token_id = 0;
|
||
uint32_t token_length = 0;
|
||
while (node != NULL) {
|
||
if (node->has_value) {
|
||
token_id = node->value;
|
||
token_length = position + 1;
|
||
}
|
||
node = node->traverse(text[++position]);
|
||
}
|
||
|
||
// add the longest matching token
|
||
output.push_back(token_id);
|
||
position = token_length;
|
||
}
|
||
}
|
||
|
||
private:
|
||
const llama_vocab & vocab;
|
||
const llm_tokenizer_rwkv & tokenizer;
|
||
};
|
||
|
||
struct llm_tokenizer_plamo2 : llm_tokenizer {
|
||
llm_tokenizer_plamo2(const llama_vocab & vocab) {
|
||
build(vocab);
|
||
}
|
||
|
||
void build(const llama_vocab & vocab) {
|
||
// Reset internal structures
|
||
tokens_.clear();
|
||
bytes_.assign(256, 0);
|
||
to_suffix_id_.clear();
|
||
table_.clear();
|
||
|
||
// Build token list and byte mapping
|
||
std::unordered_map<std::string, float> suffix_to_score;
|
||
std::unordered_map<std::string, llama_token> token_to_id;
|
||
|
||
for (size_t token_id = 0; token_id < vocab.n_tokens(); ++token_id) {
|
||
const auto & entry = vocab.get_token_data(token_id);
|
||
tokens_.push_back(entry.text);
|
||
token_to_id[entry.text] = static_cast<llama_token>(token_id);
|
||
|
||
// Handle byte tokens
|
||
if (vocab.is_byte(token_id)) {
|
||
if (entry.text.length() == 6 && entry.text.substr(0, 3) == "<0x" && entry.text.back() == '>') {
|
||
std::string hex_str = entry.text.substr(3, 2);
|
||
int byte_val = std::stoi(hex_str, nullptr, 16);
|
||
bytes_[byte_val] = static_cast<llama_token>(token_id);
|
||
}
|
||
continue;
|
||
}
|
||
|
||
// Add token and all its suffixes to suffix_to_score
|
||
suffix_to_score[entry.text] = entry.score;
|
||
|
||
// Extract suffixes character by character (UTF-8 aware)
|
||
std::vector<uint32_t> cpts = unicode_cpts_from_utf8(entry.text);
|
||
for (size_t i = 1; i < cpts.size(); ++i) {
|
||
std::string suffix;
|
||
for (size_t j = i; j < cpts.size(); ++j) {
|
||
suffix += unicode_cpt_to_utf8(cpts[j]);
|
||
}
|
||
if (suffix_to_score.find(suffix) == suffix_to_score.end()) {
|
||
suffix_to_score[suffix] = std::numeric_limits<float>::quiet_NaN();
|
||
}
|
||
}
|
||
}
|
||
|
||
// Check that all byte tokens are set
|
||
for (int i = 0; i < 256; ++i) {
|
||
if (bytes_[i] == 0) {
|
||
throw std::runtime_error("Byte token for <0x" + std::to_string(i) + "> is not set");
|
||
}
|
||
}
|
||
|
||
// Build suffix list in lexicographical order of reversed strings
|
||
std::vector<std::string> suffixes;
|
||
for (const auto & pair : suffix_to_score) {
|
||
suffixes.push_back(pair.first);
|
||
}
|
||
suffixes.push_back(""); // Empty suffix
|
||
|
||
std::sort(suffixes.begin(), suffixes.end(), [](const std::string & a, const std::string & b) {
|
||
std::string rev_a(a.rbegin(), a.rend());
|
||
std::string rev_b(b.rbegin(), b.rend());
|
||
return rev_a < rev_b;
|
||
});
|
||
|
||
// Build suffix_to_id and to_suffix_id_
|
||
std::unordered_map<std::string, int32_t> suffix_to_id;
|
||
int32_t num_pieces = 0;
|
||
|
||
for (const auto & suffix : suffixes) {
|
||
suffix_to_id[suffix] = num_pieces;
|
||
if (!suffix.empty()) {
|
||
std::vector<uint32_t> cpts = unicode_cpts_from_utf8(suffix);
|
||
|
||
std::string remaining;
|
||
for (size_t i = 1; i < cpts.size(); ++i) {
|
||
remaining += unicode_cpt_to_utf8(cpts[i]);
|
||
}
|
||
|
||
int64_t piece_code = (static_cast<int64_t>(cpts[0]) << 32) | suffix_to_id[remaining];
|
||
to_suffix_id_[piece_code] = num_pieces;
|
||
|
||
// Count number of pieces for this suffix
|
||
int32_t pieces_for_suffix = 1; // sentinel row
|
||
for (int32_t piece_length = static_cast<int32_t>(cpts.size()); piece_length > 0; --piece_length) {
|
||
std::string piece;
|
||
for (int32_t i = 0; i < piece_length; ++i) {
|
||
piece += unicode_cpt_to_utf8(cpts[i]);
|
||
}
|
||
if (suffix_to_score.find(piece) != suffix_to_score.end()) {
|
||
pieces_for_suffix++;
|
||
}
|
||
}
|
||
num_pieces += pieces_for_suffix;
|
||
} else {
|
||
num_pieces++; // Empty suffix contributes one piece (sentinel row)
|
||
}
|
||
}
|
||
|
||
// Build flattened table
|
||
table_.resize(num_pieces, std::vector<int32_t>(4, 0));
|
||
int32_t table_idx = 0;
|
||
|
||
for (const auto & suffix : suffixes) {
|
||
// Add all prefixes of the suffix to the table (in decreasing order of length)
|
||
std::vector<uint32_t> cpts = unicode_cpts_from_utf8(suffix);
|
||
for (int32_t piece_length = static_cast<int32_t>(cpts.size()); piece_length > 0; --piece_length) {
|
||
std::string piece;
|
||
for (int32_t i = 0; i < piece_length; ++i) {
|
||
piece += unicode_cpt_to_utf8(cpts[i]);
|
||
}
|
||
|
||
auto score_it = suffix_to_score.find(piece);
|
||
if (score_it == suffix_to_score.end()) {
|
||
continue;
|
||
}
|
||
|
||
table_[table_idx][TABLE_PIECE_LENGTH] = piece_length;
|
||
auto token_it = token_to_id.find(piece);
|
||
table_[table_idx][TABLE_TOKEN_ID] = (token_it != token_to_id.end()) ? token_it->second : -1;
|
||
|
||
float score = score_it->second;
|
||
table_[table_idx][TABLE_SCORE] = std::isfinite(score) ?
|
||
static_cast<int32_t>(std::round(score * 1e4)) : INVALID_SCORE;
|
||
table_[table_idx][TABLE_PIECE_ID] = suffix_to_id[piece];
|
||
|
||
table_idx++;
|
||
}
|
||
|
||
// Add sentinel row
|
||
table_[table_idx][TABLE_PIECE_LENGTH] = 1;
|
||
table_[table_idx][TABLE_TOKEN_ID] = -1;
|
||
table_[table_idx][TABLE_SCORE] = UNKNOWN_SCORE;
|
||
table_idx++;
|
||
}
|
||
}
|
||
|
||
std::vector<llama_token> encode(const std::string & text) const {
|
||
std::vector<uint32_t> unicode_data = unicode_cpts_from_utf8(text);
|
||
// Skip the first code point if it is a BOM (Byte Order Mark)
|
||
if (!unicode_data.empty() && unicode_data[0] == 0xFEFF) {
|
||
unicode_data.erase(unicode_data.begin());
|
||
}
|
||
|
||
if (unicode_data.empty()) {
|
||
return {};
|
||
}
|
||
|
||
const size_t data_len = unicode_data.size();
|
||
|
||
// Initialize scores array (dynamic programming)
|
||
std::vector<int64_t> scores(data_len + 1, static_cast<int64_t>(1) << 60);
|
||
scores[data_len] = 0;
|
||
|
||
// Path array to track best tokenization
|
||
std::vector<std::vector<int32_t>> path(data_len + 1, std::vector<int32_t>(3, 0));
|
||
|
||
int32_t suffix_id = 0;
|
||
|
||
// Process from end to beginning
|
||
for (int i = static_cast<int>(data_len) - 1; i >= 0; --i) {
|
||
uint32_t c = unicode_data[i];
|
||
|
||
// Find next suffix ID
|
||
for (size_t p = suffix_id; p < table_.size(); ++p) {
|
||
int64_t piece_code = (static_cast<int64_t>(c) << 32) | table_[p][TABLE_PIECE_ID];
|
||
auto it = to_suffix_id_.find(piece_code);
|
||
suffix_id = (it != to_suffix_id_.end()) ? it->second : 0;
|
||
|
||
if (suffix_id > 0 || table_[p][TABLE_SCORE] == UNKNOWN_SCORE) {
|
||
break;
|
||
}
|
||
}
|
||
|
||
// Update best path
|
||
for (size_t p = suffix_id; p < table_.size(); ++p) {
|
||
int32_t score = table_[p][TABLE_SCORE];
|
||
if (score > INVALID_SCORE) {
|
||
int32_t piece_length = table_[p][TABLE_PIECE_LENGTH];
|
||
int64_t s = scores[i + piece_length] - score;
|
||
|
||
if (s < scores[i]) {
|
||
scores[i] = s;
|
||
path[i][PATH_TOKEN_LENGTH] = piece_length;
|
||
path[i][PATH_TOKEN_ID] = table_[p][TABLE_TOKEN_ID];
|
||
path[i][PATH_NUM_TOKENS] = path[i + piece_length][PATH_NUM_TOKENS] + 1;
|
||
|
||
if (score == UNKNOWN_SCORE) {
|
||
// Add UTF-8 byte count
|
||
path[i][PATH_NUM_TOKENS] += (c >= 0x80) + (c >= 0x800) + (c >= 0x10000);
|
||
}
|
||
}
|
||
}
|
||
|
||
if (score == UNKNOWN_SCORE) {
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
|
||
// Decode the best path
|
||
std::vector<llama_token> token_ids;
|
||
token_ids.reserve(path[0][PATH_NUM_TOKENS]);
|
||
|
||
int pos = 0;
|
||
while (pos < static_cast<int>(data_len)) {
|
||
if (path[pos][PATH_TOKEN_ID] >= 0) {
|
||
token_ids.push_back(path[pos][PATH_TOKEN_ID]);
|
||
} else {
|
||
// Fall back to byte tokens
|
||
uint32_t c = unicode_data[pos];
|
||
int s = 1 + (c >= 0x80) + (c >= 0x800) + (c >= 0x10000);
|
||
|
||
for (int i = 0; i < s; ++i) {
|
||
uint8_t b;
|
||
if (s == 1) {
|
||
b = c;
|
||
} else {
|
||
if (i == 0) {
|
||
b = (0xF00 >> s) & 0xFF;
|
||
} else {
|
||
b = 0x80;
|
||
}
|
||
}
|
||
token_ids.push_back(bytes_[b | ((c >> ((s - i - 1) * 6)) & 0x3F)]);
|
||
}
|
||
}
|
||
|
||
assert(path[pos][PATH_TOKEN_LENGTH] > 0);
|
||
pos += path[pos][PATH_TOKEN_LENGTH];
|
||
}
|
||
|
||
return token_ids;
|
||
}
|
||
private:
|
||
// Constants for table structure
|
||
static constexpr int32_t TABLE_PIECE_LENGTH = 0;
|
||
static constexpr int32_t TABLE_TOKEN_ID = 1;
|
||
static constexpr int32_t TABLE_SCORE = 2;
|
||
static constexpr int32_t TABLE_PIECE_ID = 3;
|
||
|
||
// Constants for path array
|
||
static constexpr int32_t PATH_TOKEN_LENGTH = 0;
|
||
static constexpr int32_t PATH_TOKEN_ID = 1;
|
||
static constexpr int32_t PATH_NUM_TOKENS = 2;
|
||
|
||
// Score constants
|
||
static constexpr int32_t INVALID_SCORE = -20000000;
|
||
static constexpr int32_t UNKNOWN_SCORE = -10000000;
|
||
|
||
// List of tokens in the vocabulary
|
||
std::vector<std::string> tokens_;
|
||
|
||
// Mapping from byte code point to token ID (for byte fallback)
|
||
std::vector<llama_token> bytes_;
|
||
|
||
// Mapping from piece code to suffix ID
|
||
std::unordered_map<int64_t, int32_t> to_suffix_id_;
|
||
|
||
// Flattened table representing the Trie structure
|
||
// Each row contains: [piece_length, token_id, score, piece_id]
|
||
std::vector<std::vector<int32_t>> table_;
|
||
};
|
||
|
||
struct llm_tokenizer_plamo2_session {
|
||
llm_tokenizer_plamo2_session(const llm_tokenizer_plamo2 & tokenizer) : tokenizer(tokenizer) {}
|
||
|
||
void tokenize(const std::string & text, std::vector<llama_token> & output) {
|
||
std::vector<llama_token> tokens = tokenizer.encode(text);
|
||
output.insert(output.end(), tokens.begin(), tokens.end());
|
||
}
|
||
|
||
private:
|
||
const llm_tokenizer_plamo2 & tokenizer;
|
||
};
|
||
|
||
//
|
||
// impl
|
||
//
|
||
|
||
typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
|
||
FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
|
||
FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
|
||
} FRAGMENT_BUFFER_VARIANT_TYPE;
|
||
|
||
struct fragment_buffer_variant {
|
||
fragment_buffer_variant(llama_token _token)
|
||
:
|
||
type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
|
||
token(_token),
|
||
raw_text(_dummy),
|
||
offset(0),
|
||
length(0) {}
|
||
|
||
fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
|
||
:
|
||
type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
|
||
token((llama_token) - 1),
|
||
raw_text(_raw_text),
|
||
offset(_offset),
|
||
length(_length){
|
||
GGML_ASSERT(_offset >= 0);
|
||
GGML_ASSERT(_length >= 1);
|
||
GGML_ASSERT(offset + length <= raw_text.length());
|
||
}
|
||
|
||
const FRAGMENT_BUFFER_VARIANT_TYPE type;
|
||
const llama_token token;
|
||
const std::string _dummy;
|
||
const std::string & raw_text;
|
||
const uint64_t offset;
|
||
const uint64_t length;
|
||
};
|
||
|
||
struct llama_vocab::impl {
|
||
uint32_t n_token_types = 0; // for BERT-style token types
|
||
|
||
std::string tokenizer_model;
|
||
std::string tokenizer_pre;
|
||
|
||
enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
|
||
enum llama_vocab_pre_type pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
|
||
|
||
int max_token_len = 0; // used for optimizing longest token search
|
||
|
||
// default LLaMA special tokens
|
||
// TODO: should we set all of these to LLAMA_TOKEN_NULL?
|
||
llama_token special_bos_id = 1;
|
||
llama_token special_eos_id = 2;
|
||
llama_token special_eot_id = LLAMA_TOKEN_NULL;
|
||
llama_token special_eom_id = LLAMA_TOKEN_NULL;
|
||
llama_token special_unk_id = 0;
|
||
llama_token special_sep_id = LLAMA_TOKEN_NULL;
|
||
llama_token special_pad_id = LLAMA_TOKEN_NULL;
|
||
llama_token special_mask_id = LLAMA_TOKEN_NULL;
|
||
|
||
llama_token linefeed_id = 13;
|
||
|
||
// fim tokens
|
||
llama_token special_fim_pre_id = LLAMA_TOKEN_NULL;
|
||
llama_token special_fim_suf_id = LLAMA_TOKEN_NULL;
|
||
llama_token special_fim_mid_id = LLAMA_TOKEN_NULL;
|
||
llama_token special_fim_pad_id = LLAMA_TOKEN_NULL;
|
||
llama_token special_fim_rep_id = LLAMA_TOKEN_NULL; // repo
|
||
llama_token special_fim_sep_id = LLAMA_TOKEN_NULL; // file separator
|
||
|
||
// tokenizer flags
|
||
bool add_space_prefix = false;
|
||
bool add_bos = false;
|
||
bool add_eos = false;
|
||
bool add_sep = false;
|
||
bool ignore_merges = false;
|
||
bool clean_spaces = false; // clean_up_tokenization_spaces
|
||
bool remove_extra_whitespaces = false;
|
||
bool escape_whitespaces = true;
|
||
bool treat_whitespace_as_suffix = false;
|
||
|
||
std::unordered_map<std::string, llama_token> token_to_id;
|
||
std::vector<token_data> id_to_token;
|
||
|
||
std::vector<llama_token> cache_special_tokens;
|
||
std::vector<std::string> cache_token_to_piece; // llama_token_to_piece(special = true);
|
||
struct pair_hash {
|
||
size_t operator()(const std::pair<std::string, std::string> & p) const {
|
||
return std::hash<std::string>{}(p.first) ^ //create some hash for pair
|
||
(std::hash<std::string>{}(p.second) << 1);
|
||
}
|
||
};
|
||
std::unordered_map<std::pair<std::string, std::string>, int, pair_hash> bpe_ranks;
|
||
|
||
// set of all tokens that cause "end of generation"
|
||
std::set<llama_token> special_eog_ids;
|
||
|
||
std::unique_ptr<llm_tokenizer> tokenizer;
|
||
|
||
std::vector<char> precompiled_charsmap;
|
||
|
||
impl(const llama_vocab & vocab) : vocab(vocab) {
|
||
}
|
||
|
||
~impl() = default;
|
||
|
||
void load(llama_model_loader & ml, const LLM_KV & kv);
|
||
|
||
enum llama_vocab_type get_type() const;
|
||
|
||
std::string type_name() const;
|
||
|
||
bool is_normal (llama_token id) const;
|
||
bool is_unknown (llama_token id) const;
|
||
bool is_control (llama_token id) const;
|
||
bool is_byte (llama_token id) const;
|
||
bool is_user_defined(llama_token id) const;
|
||
bool is_unused (llama_token id) const;
|
||
bool is_eog (llama_token id) const;
|
||
|
||
uint8_t token_to_byte(llama_token id) const;
|
||
|
||
llama_token_attr token_get_attr(llama_token id) const;
|
||
|
||
void init_tokenizer(enum llama_vocab_type type);
|
||
|
||
void tokenizer_st_partition(std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) const;
|
||
|
||
std::string token_to_piece_for_cache(
|
||
llama_token token,
|
||
bool special) const;
|
||
|
||
|
||
std::vector<llama_token> tokenize(
|
||
const std::string & raw_text,
|
||
bool add_special,
|
||
bool parse_special = false) const;
|
||
|
||
int32_t tokenize(
|
||
const char * text,
|
||
int32_t text_len,
|
||
llama_token * tokens,
|
||
int32_t n_tokens_max,
|
||
bool add_special,
|
||
bool parse_special) const;
|
||
|
||
// does not write null-terminator to buf
|
||
int32_t token_to_piece(
|
||
llama_token token,
|
||
char * buf,
|
||
int32_t length,
|
||
int32_t lstrip,
|
||
bool special) const;
|
||
|
||
// use cached data
|
||
const std::string & token_to_piece(llama_token token) const;
|
||
|
||
int32_t detokenize(
|
||
const llama_token * tokens,
|
||
int32_t n_tokens,
|
||
char * text,
|
||
int32_t text_len_max,
|
||
bool remove_special,
|
||
bool unparse_special) const;
|
||
|
||
std::string detokenize(
|
||
const std::vector<llama_token> & tokens,
|
||
bool special) const;
|
||
|
||
void print_info() const;
|
||
|
||
private:
|
||
const llama_vocab & vocab;
|
||
};
|
||
|
||
void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||
struct gguf_context * ctx = ml.meta.get();
|
||
|
||
// determine vocab type
|
||
{
|
||
ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
|
||
ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
|
||
|
||
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, n_token_types, false);
|
||
|
||
if (tokenizer_model == "no_vocab" || tokenizer_model == "none") {
|
||
type = LLAMA_VOCAB_TYPE_NONE;
|
||
|
||
// default special tokens
|
||
special_bos_id = LLAMA_TOKEN_NULL;
|
||
special_eos_id = LLAMA_TOKEN_NULL;
|
||
special_unk_id = LLAMA_TOKEN_NULL;
|
||
special_sep_id = LLAMA_TOKEN_NULL;
|
||
special_pad_id = LLAMA_TOKEN_NULL;
|
||
special_mask_id = LLAMA_TOKEN_NULL;
|
||
linefeed_id = LLAMA_TOKEN_NULL;
|
||
|
||
// read vocab size from metadata
|
||
uint32_t n_tokens = 0;
|
||
if (ml.get_key(LLM_KV_VOCAB_SIZE, n_tokens, false)) {
|
||
LLAMA_LOG_WARN("%s: adding %u dummy tokens\n", __func__, n_tokens);
|
||
id_to_token.resize(n_tokens);
|
||
}
|
||
|
||
return;
|
||
}
|
||
|
||
if (tokenizer_model == "llama") {
|
||
type = LLAMA_VOCAB_TYPE_SPM;
|
||
|
||
// default special tokens
|
||
special_bos_id = 1;
|
||
special_eos_id = 2;
|
||
special_unk_id = 0;
|
||
special_sep_id = LLAMA_TOKEN_NULL;
|
||
special_pad_id = LLAMA_TOKEN_NULL;
|
||
special_mask_id = LLAMA_TOKEN_NULL;
|
||
} else if (tokenizer_model == "bert") {
|
||
type = LLAMA_VOCAB_TYPE_WPM;
|
||
|
||
// default special tokens
|
||
special_bos_id = 101;
|
||
special_eos_id = LLAMA_TOKEN_NULL;
|
||
special_unk_id = 100;
|
||
special_sep_id = 102;
|
||
special_pad_id = 0;
|
||
special_mask_id = 103;
|
||
|
||
add_sep = true;
|
||
} else if (tokenizer_model == "gpt2") {
|
||
type = LLAMA_VOCAB_TYPE_BPE;
|
||
|
||
// read bpe merges and populate bpe ranks
|
||
const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
|
||
if (merges_keyidx == -1) {
|
||
throw std::runtime_error("cannot find tokenizer merges in model file\n");
|
||
}
|
||
|
||
const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
|
||
for (int i = 0; i < n_merges; i++) {
|
||
const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
|
||
//GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
|
||
|
||
std::string first;
|
||
std::string second;
|
||
|
||
const size_t pos = word.find(' ', 1);
|
||
|
||
if (pos != std::string::npos) {
|
||
first = word.substr(0, pos);
|
||
second = word.substr(pos + 1);
|
||
}
|
||
|
||
bpe_ranks.emplace(std::make_pair(first, second), i);
|
||
}
|
||
|
||
// default special tokens
|
||
special_bos_id = 11;
|
||
special_eos_id = 11;
|
||
special_unk_id = LLAMA_TOKEN_NULL;
|
||
special_sep_id = LLAMA_TOKEN_NULL;
|
||
special_pad_id = LLAMA_TOKEN_NULL;
|
||
special_mask_id = LLAMA_TOKEN_NULL;
|
||
} else if (tokenizer_model == "t5") {
|
||
type = LLAMA_VOCAB_TYPE_UGM;
|
||
|
||
// default special tokens
|
||
special_bos_id = LLAMA_TOKEN_NULL;
|
||
special_eos_id = 1;
|
||
special_unk_id = 2;
|
||
special_sep_id = LLAMA_TOKEN_NULL;
|
||
special_pad_id = 0;
|
||
special_mask_id = LLAMA_TOKEN_NULL;
|
||
|
||
const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
|
||
if (precompiled_charsmap_keyidx != -1) {
|
||
const gguf_type pc_type = gguf_get_arr_type(ctx, precompiled_charsmap_keyidx);
|
||
GGML_ASSERT(pc_type == GGUF_TYPE_INT8 || pc_type == GGUF_TYPE_UINT8);
|
||
|
||
const size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
|
||
const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
|
||
precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap);
|
||
#ifdef IS_BIG_ENDIAN
|
||
// correct endiannes of data in precompiled_charsmap binary blob
|
||
uint32_t * xcda_blob_size = (uint32_t *) &precompiled_charsmap[0];
|
||
*xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
|
||
assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
|
||
size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
|
||
uint32_t * xcda_array = (uint32_t *) &precompiled_charsmap[sizeof(uint32_t)];
|
||
for (size_t i = 0; i < xcda_array_size; ++i) {
|
||
xcda_array[i] = __builtin_bswap32(xcda_array[i]);
|
||
}
|
||
#endif
|
||
}
|
||
} else if (tokenizer_model == "rwkv") {
|
||
type = LLAMA_VOCAB_TYPE_RWKV;
|
||
|
||
// default special tokens
|
||
special_bos_id = LLAMA_TOKEN_NULL;
|
||
special_eos_id = LLAMA_TOKEN_NULL;
|
||
special_unk_id = LLAMA_TOKEN_NULL;
|
||
special_sep_id = LLAMA_TOKEN_NULL;
|
||
special_pad_id = LLAMA_TOKEN_NULL;
|
||
} else if (tokenizer_model == "plamo2") {
|
||
type = LLAMA_VOCAB_TYPE_PLAMO2;
|
||
|
||
// PLaMo-2 default special tokens (these will be overridden by model config)
|
||
special_bos_id = 1; // <|plamo:bos|>
|
||
special_eos_id = 2; // <|plamo:eos|>
|
||
special_unk_id = 0; // <|plamo:unk|>
|
||
special_sep_id = LLAMA_TOKEN_NULL;
|
||
special_pad_id = 3; // <|plamo:pad|>
|
||
special_mask_id = LLAMA_TOKEN_NULL;
|
||
} else {
|
||
throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
|
||
}
|
||
|
||
// for now, only BPE models have pre-tokenizers
|
||
if (type == LLAMA_VOCAB_TYPE_BPE) {
|
||
add_space_prefix = false;
|
||
clean_spaces = true;
|
||
if (tokenizer_pre.empty()) {
|
||
LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
|
||
LLAMA_LOG_WARN("%s: \n", __func__);
|
||
LLAMA_LOG_WARN("%s: ************************************ \n", __func__);
|
||
LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED! \n", __func__);
|
||
LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL \n", __func__);
|
||
LLAMA_LOG_WARN("%s: ************************************ \n", __func__);
|
||
LLAMA_LOG_WARN("%s: \n", __func__);
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
|
||
} else if (tokenizer_pre == "default") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
|
||
} else if (
|
||
tokenizer_pre == "llama3" ||
|
||
tokenizer_pre == "llama-v3" ||
|
||
tokenizer_pre == "llama-bpe"||
|
||
tokenizer_pre == "falcon3" ||
|
||
tokenizer_pre == "falcon-h1" ||
|
||
tokenizer_pre == "pixtral" ||
|
||
tokenizer_pre == "midm-2.0" ||
|
||
tokenizer_pre == "lfm2") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
|
||
ignore_merges = true;
|
||
add_bos = true;
|
||
} else if (
|
||
tokenizer_pre == "deepseek-llm") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "deepseek-coder") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "deepseek-v3") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "falcon") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_FALCON;
|
||
} else if (
|
||
tokenizer_pre == "mpt") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_MPT;
|
||
} else if (
|
||
tokenizer_pre == "starcoder") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_STARCODER;
|
||
} else if (
|
||
tokenizer_pre == "gpt-2" ||
|
||
tokenizer_pre == "phi-2" ||
|
||
tokenizer_pre == "jina-es" ||
|
||
tokenizer_pre == "jina-de" ||
|
||
tokenizer_pre == "gigachat" ||
|
||
tokenizer_pre == "jina-v2-es" ||
|
||
tokenizer_pre == "jina-v2-de" ||
|
||
tokenizer_pre == "a.x-4.0" ||
|
||
tokenizer_pre == "mellum") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
|
||
} else if (
|
||
tokenizer_pre == "jina-v1-en" ||
|
||
tokenizer_pre == "jina-v2-code" ||
|
||
tokenizer_pre == "roberta-bpe") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
|
||
add_sep = true;
|
||
} else if (
|
||
tokenizer_pre == "refact") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_REFACT;
|
||
} else if (
|
||
tokenizer_pre == "command-r") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "qwen2" ||
|
||
tokenizer_pre == "deepseek-r1-qwen") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "stablelm2") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
|
||
} else if (
|
||
tokenizer_pre == "olmo") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_OLMO;
|
||
} else if (
|
||
tokenizer_pre == "dbrx") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DBRX;
|
||
} else if (
|
||
tokenizer_pre == "smaug-bpe") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_SMAUG;
|
||
} else if (
|
||
tokenizer_pre == "poro-chat") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_PORO;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "glm4" ||
|
||
tokenizer_pre == "chatglm-bpe") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
|
||
special_bos_id = LLAMA_TOKEN_NULL;
|
||
} else if (
|
||
tokenizer_pre == "viking") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_VIKING;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "jais") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_JAIS;
|
||
} else if (
|
||
tokenizer_pre == "tekken") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_TEKKEN;
|
||
clean_spaces = false;
|
||
ignore_merges = true;
|
||
add_bos = true;
|
||
} else if (
|
||
tokenizer_pre == "smollm") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_SMOLLM;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "codeshell") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_CODESHELL;
|
||
} else if (
|
||
tokenizer_pre == "bloom") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_BLOOM;
|
||
} else if (
|
||
tokenizer_pre == "gpt3-finnish") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH;
|
||
} else if (
|
||
tokenizer_pre == "exaone") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_EXAONE;
|
||
} else if (
|
||
tokenizer_pre == "exaone4") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
|
||
} else if (
|
||
tokenizer_pre == "chameleon") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_CHAMELEON;
|
||
add_bos = true;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "minerva-7b") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_MINERVA;
|
||
} else if (
|
||
tokenizer_pre == "megrez") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
|
||
} else if (
|
||
tokenizer_pre == "gpt-4o" ||
|
||
tokenizer_pre == "llama4") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_GPT4O;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "superbpe") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_SUPERBPE;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "trillion") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_TRILLION;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "bailingmoe") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_BAILINGMOE;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "seed-coder") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_SEED_CODER;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "hunyuan") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "hunyuan-dense") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE;
|
||
clean_spaces = false;
|
||
} else if (
|
||
tokenizer_pre == "kimi-k2") {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_KIMI_K2;
|
||
clean_spaces = false;
|
||
} else {
|
||
throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
|
||
}
|
||
} else if (type == LLAMA_VOCAB_TYPE_SPM) {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
|
||
add_space_prefix = true;
|
||
clean_spaces = false;
|
||
add_bos = true;
|
||
add_eos = false;
|
||
} else if (type == LLAMA_VOCAB_TYPE_WPM) {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
|
||
add_space_prefix = false;
|
||
clean_spaces = true;
|
||
add_bos = true;
|
||
add_eos = false;
|
||
add_sep = true;
|
||
} else if (type == LLAMA_VOCAB_TYPE_UGM) {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
|
||
add_bos = false;
|
||
add_eos = true;
|
||
} else if (type == LLAMA_VOCAB_TYPE_RWKV) {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
|
||
add_space_prefix = false;
|
||
clean_spaces = false;
|
||
add_bos = false;
|
||
add_eos = false;
|
||
} else {
|
||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
|
||
}
|
||
|
||
ml.get_key(LLM_KV_TOKENIZER_ADD_PREFIX, add_space_prefix, false);
|
||
ml.get_key(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, remove_extra_whitespaces, false);
|
||
}
|
||
|
||
const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
|
||
if (token_idx == -1) {
|
||
throw std::runtime_error("cannot find tokenizer vocab in model file\n");
|
||
}
|
||
|
||
const float * scores = nullptr;
|
||
const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
|
||
if (score_idx != -1) {
|
||
scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
|
||
}
|
||
|
||
const int * toktypes = nullptr;
|
||
const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
|
||
if (toktype_idx != -1) {
|
||
toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
|
||
}
|
||
|
||
uint32_t n_tokens = gguf_get_arr_n(ctx, token_idx);
|
||
id_to_token.resize(n_tokens);
|
||
|
||
for (uint32_t i = 0; i < n_tokens; i++) {
|
||
std::string word = gguf_get_arr_str(ctx, token_idx, i);
|
||
if (word.empty()) {
|
||
LLAMA_LOG_WARN("%s: empty token at index %u\n", __func__, i);
|
||
word = "[EMPTY_" + std::to_string(i) + "]";
|
||
}
|
||
|
||
token_to_id[word] = i;
|
||
max_token_len = std::max(max_token_len, (int) word.size());
|
||
|
||
auto & token_data = id_to_token[i];
|
||
token_data.text = std::move(word);
|
||
token_data.score = scores ? scores[i] : 0.0f;
|
||
token_data.attr = LLAMA_TOKEN_ATTR_NORMAL;
|
||
|
||
if (toktypes) { //TODO: remove, required until per token attributes are available from GGUF file
|
||
switch(toktypes[i]) {
|
||
case LLAMA_TOKEN_TYPE_UNKNOWN: token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN; break;
|
||
case LLAMA_TOKEN_TYPE_UNUSED: token_data.attr = LLAMA_TOKEN_ATTR_UNUSED; break;
|
||
case LLAMA_TOKEN_TYPE_NORMAL: token_data.attr = LLAMA_TOKEN_ATTR_NORMAL; break;
|
||
case LLAMA_TOKEN_TYPE_CONTROL: token_data.attr = LLAMA_TOKEN_ATTR_CONTROL; break;
|
||
case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break;
|
||
case LLAMA_TOKEN_TYPE_BYTE: token_data.attr = LLAMA_TOKEN_ATTR_BYTE; break;
|
||
case LLAMA_TOKEN_TYPE_UNDEFINED: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break;
|
||
default: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break;
|
||
}
|
||
}
|
||
}
|
||
GGML_ASSERT(id_to_token.size() == token_to_id.size());
|
||
|
||
init_tokenizer(type);
|
||
|
||
// determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
|
||
if (type == LLAMA_VOCAB_TYPE_SPM) {
|
||
try {
|
||
linefeed_id = vocab.byte_to_token('\n');
|
||
} catch (const std::exception & e) {
|
||
LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
|
||
linefeed_id = special_pad_id;
|
||
}
|
||
} else if (type == LLAMA_VOCAB_TYPE_WPM) {
|
||
linefeed_id = special_pad_id;
|
||
} else if (type == LLAMA_VOCAB_TYPE_RWKV) {
|
||
const std::vector<int> ids = tokenize("\n", false);
|
||
GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
|
||
linefeed_id = ids[0];
|
||
} else {
|
||
const std::vector<int> ids = tokenize("\n", false);
|
||
|
||
//GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
|
||
if (ids.empty()) {
|
||
LLAMA_LOG_WARN("%s: model vocab missing newline token, using special_pad_id instead\n", __func__);
|
||
linefeed_id = special_pad_id;
|
||
} else {
|
||
linefeed_id = ids[0];
|
||
}
|
||
}
|
||
|
||
// special tokens
|
||
{
|
||
const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
|
||
{ LLM_KV_TOKENIZER_BOS_ID, special_bos_id },
|
||
{ LLM_KV_TOKENIZER_EOS_ID, special_eos_id },
|
||
{ LLM_KV_TOKENIZER_EOT_ID, special_eot_id },
|
||
{ LLM_KV_TOKENIZER_EOM_ID, special_eom_id },
|
||
{ LLM_KV_TOKENIZER_UNK_ID, special_unk_id },
|
||
{ LLM_KV_TOKENIZER_SEP_ID, special_sep_id },
|
||
{ LLM_KV_TOKENIZER_PAD_ID, special_pad_id },
|
||
{ LLM_KV_TOKENIZER_MASK_ID, special_mask_id },
|
||
{ LLM_KV_TOKENIZER_FIM_PRE_ID, special_fim_pre_id },
|
||
{ LLM_KV_TOKENIZER_FIM_SUF_ID, special_fim_suf_id },
|
||
{ LLM_KV_TOKENIZER_FIM_MID_ID, special_fim_mid_id },
|
||
{ LLM_KV_TOKENIZER_FIM_PAD_ID, special_fim_pad_id },
|
||
{ LLM_KV_TOKENIZER_FIM_REP_ID, special_fim_rep_id },
|
||
{ LLM_KV_TOKENIZER_FIM_SEP_ID, special_fim_sep_id },
|
||
|
||
// deprecated
|
||
{ LLM_KV_TOKENIZER_PREFIX_ID, special_fim_pre_id },
|
||
{ LLM_KV_TOKENIZER_SUFFIX_ID, special_fim_suf_id },
|
||
{ LLM_KV_TOKENIZER_MIDDLE_ID, special_fim_mid_id },
|
||
};
|
||
|
||
for (const auto & it : special_token_types) {
|
||
const std::string & key = kv(std::get<0>(it));
|
||
int32_t & id = std::get<1>(it);
|
||
|
||
uint32_t new_id;
|
||
if (!ml.get_key(std::get<0>(it), new_id, false)) {
|
||
continue;
|
||
}
|
||
if (new_id >= id_to_token.size()) {
|
||
LLAMA_LOG_WARN("%s: bad special token: '%s' = %u, using default id %d\n",
|
||
__func__, key.c_str(), new_id, id);
|
||
} else {
|
||
id = new_id;
|
||
}
|
||
}
|
||
|
||
// Handle add_bos, add_eos and add_sep
|
||
{
|
||
bool temp = true;
|
||
|
||
if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
|
||
add_bos = temp;
|
||
}
|
||
if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
|
||
add_eos = temp;
|
||
}
|
||
if (ml.get_key(LLM_KV_TOKENIZER_ADD_SEP, temp, false)) {
|
||
add_sep = temp;
|
||
}
|
||
}
|
||
|
||
// auto-detect special tokens by text
|
||
// TODO: convert scripts should provide these tokens through the KV metadata LLM_KV_TOKENIZER_...
|
||
// for now, we apply this workaround to find the tokens based on their text
|
||
|
||
for (const auto & t : token_to_id) {
|
||
// find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
|
||
if (special_eot_id == LLAMA_TOKEN_NULL) {
|
||
if (false
|
||
|| t.first == "<|eot_id|>"
|
||
|| t.first == "<|im_end|>"
|
||
|| t.first == "<|end|>"
|
||
|| t.first == "<end_of_turn>"
|
||
|| t.first == "<|endoftext|>"
|
||
|| t.first == "<EOT>"
|
||
|| t.first == "_<EOT>"
|
||
|| t.first == "<｜end▁of▁sentence｜>" // DeepSeek
|
||
|| t.first == "<end_of_utterance>" // smoldocling
|
||
) {
|
||
special_eot_id = t.second;
|
||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||
__func__, t.second, t.first.c_str());
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
}
|
||
}
|
||
|
||
// find EOM token: "<|eom_id|>"
|
||
if (special_eom_id == LLAMA_TOKEN_NULL) {
|
||
if (false
|
||
|| t.first == "<|eom_id|>"
|
||
) {
|
||
special_eom_id = t.second;
|
||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||
__func__, t.second, t.first.c_str());
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
}
|
||
}
|
||
|
||
// find FIM_PRE token: "<|fim_prefix|>", "<fim-prefix>", "<PRE>", etc.
|
||
if (special_fim_pre_id == LLAMA_TOKEN_NULL) {
|
||
if (false
|
||
|| t.first == "<|fim_prefix|>" // Qwen
|
||
|| t.first == "<fim-prefix>"
|
||
|| t.first == "<fim_prefix>" // Granite
|
||
|| t.first == "<｜fim▁begin｜>" // DeepSeek
|
||
|| t.first == "<PRE>"
|
||
|| t.first == "▁<PRE>" // CodeLlama
|
||
|| t.first == "<|code_prefix|>" // GLM-4.5
|
||
) {
|
||
special_fim_pre_id = t.second;
|
||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||
__func__, t.second, t.first.c_str());
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
}
|
||
}
|
||
|
||
// find FIM_SUF token: "<|fim_suffix|>", "<fim-suffix>", "<SUF>", etc.
|
||
if (special_fim_suf_id == LLAMA_TOKEN_NULL) {
|
||
if (false
|
||
|| t.first == "<|fim_suffix|>" // Qwen
|
||
|| t.first == "<fim-suffix>"
|
||
|| t.first == "<fim_suffix>" // Granite
|
||
|| t.first == "<｜fim▁hole｜>" // DeepSeek
|
||
|| t.first == "<SUF>"
|
||
|| t.first == "▁<SUF>" // CodeLlama
|
||
|| t.first == "<|code_suffix|>" // GLM-4.5
|
||
) {
|
||
special_fim_suf_id = t.second;
|
||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||
__func__, t.second, t.first.c_str());
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
}
|
||
}
|
||
|
||
// find FIM_MID token: "<|fim_middle|>", "<fim-middle>", "<MID>", etc.
|
||
if (special_fim_mid_id == LLAMA_TOKEN_NULL) {
|
||
if (false
|
||
|| t.first == "<|fim_middle|>" // Qwen
|
||
|| t.first == "<fim-middle>"
|
||
|| t.first == "<fim_middle>" // Granite
|
||
|| t.first == "<｜fim▁end｜>" // DeepSeek
|
||
|| t.first == "<MID>"
|
||
|| t.first == "▁<MID>" // CodeLlama
|
||
|| t.first == "<|code_middle|>" // GLM-4.5
|
||
) {
|
||
special_fim_mid_id = t.second;
|
||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||
__func__, t.second, t.first.c_str());
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
}
|
||
}
|
||
|
||
// find FIM_PAD token: "<|fim_pad|>", "<fim-pad>", "<PAD>", etc.
|
||
if (special_fim_pad_id == LLAMA_TOKEN_NULL) {
|
||
if (false
|
||
|| t.first == "<|fim_pad|>" // Qwen
|
||
|| t.first == "<fim-pad>"
|
||
|| t.first == "<fim_pad>" // Granite
|
||
|| t.first == "<PAD>"
|
||
) {
|
||
special_fim_pad_id = t.second;
|
||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||
__func__, t.second, t.first.c_str());
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
}
|
||
}
|
||
|
||
// find FIM_REP token: "<|fim_repo|>", "<fim-repo>", "<REP>", etc.
|
||
if (special_fim_rep_id == LLAMA_TOKEN_NULL) {
|
||
if (false
|
||
|| t.first == "<|fim_repo|>" // Qwen
|
||
|| t.first == "<|repo_name|>"
|
||
|| t.first == "<fim-repo>"
|
||
|| t.first == "<REPO>"
|
||
|| t.first == "<reponame>" // Granite
|
||
) {
|
||
special_fim_rep_id = t.second;
|
||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||
__func__, t.second, t.first.c_str());
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
}
|
||
}
|
||
|
||
// find FIM_SEP token: "<|file_sep|>"
|
||
if (special_fim_sep_id == LLAMA_TOKEN_NULL) {
|
||
if (false
|
||
|| t.first == "<|file_sep|>" // Qwen
|
||
) {
|
||
special_fim_sep_id = t.second;
|
||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||
__func__, t.second, t.first.c_str());
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// maintain a list of tokens that cause end-of-generation
|
||
// this is currently determined based on the token text, which is obviously not ideal
|
||
// ref: https://github.com/ggerganov/llama.cpp/issues/9606
|
||
special_eog_ids.clear();
|
||
|
||
if (special_fim_pad_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_pad_id) == 0) {
|
||
special_eog_ids.insert(special_fim_pad_id);
|
||
}
|
||
|
||
if (special_fim_rep_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_rep_id) == 0) {
|
||
special_eog_ids.insert(special_fim_rep_id);
|
||
}
|
||
|
||
if (special_fim_sep_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_sep_id) == 0) {
|
||
special_eog_ids.insert(special_fim_sep_id);
|
||
}
|
||
|
||
for (const auto & t : token_to_id) {
|
||
if (false
|
||
|| t.first == "<|eot_id|>"
|
||
|| t.first == "<|im_end|>"
|
||
|| t.first == "<|end|>"
|
||
|| t.first == "<|return|>" // o200k_harmony
|
||
|| t.first == "<|call|>" // o200k_harmony
|
||
|| t.first == "<end_of_turn>"
|
||
|| t.first == "<|endoftext|>"
|
||
|| t.first == "<|eom_id|>"
|
||
|| t.first == "<EOT>"
|
||
|| t.first == "_<EOT>"
|
||
|| t.first == "<|end_of_text|>"
|
||
|| t.first == "<end_of_utterance>" // smoldocling
|
||
) {
|
||
special_eog_ids.insert(t.second);
|
||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||
__func__, t.second, t.first.c_str());
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
} else {
|
||
// token is control, but not marked as EOG -> print a debug log
|
||
if (id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && special_eog_ids.count(t.second) == 0) {
|
||
LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n",
|
||
__func__, t.second, t.first.c_str());
|
||
}
|
||
}
|
||
}
|
||
|
||
// @ngxson : quick hack for gpt-oss, always render these tokens
|
||
for (const auto & t : token_to_id) {
|
||
if (t.first == "<|channel|>" || t.first == "<|message|>" || t.first == "<|start|>" || t.first == "<|constrain|>") {
|
||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
|
||
}
|
||
}
|
||
|
||
// sanity checks
|
||
if (special_eos_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_eos_id) == 0) {
|
||
special_eog_ids.insert(special_eos_id);
|
||
LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
|
||
}
|
||
|
||
if (special_eot_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_eot_id) == 0) {
|
||
special_eog_ids.insert(special_eot_id);
|
||
LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
|
||
}
|
||
|
||
if (special_eom_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_eom_id) == 0) {
|
||
special_eog_ids.insert(special_eom_id);
|
||
LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
|
||
}
|
||
|
||
// TODO: workaround for o200k_harmony tokenizer: the "<|end|>" token should not be EOG
|
||
// we don't have a good way to detect this, so for now, if we have "<|return|>" and "<|call|>" tokens,
|
||
// we remove the "<|end|>" token from the EOG list
|
||
{
|
||
bool has_return = false;
|
||
bool has_call = false;
|
||
bool has_end = false;
|
||
|
||
llama_token end_id = LLAMA_TOKEN_NULL;
|
||
|
||
LLAMA_LOG_INFO("%s: printing all EOG tokens:\n", __func__);
|
||
for (auto tid : special_eog_ids) {
|
||
LLAMA_LOG_INFO("%s: - %d ('%s')\n", __func__, tid, id_to_token[tid].text.c_str());
|
||
|
||
if (id_to_token[tid].text == "<|return|>") {
|
||
has_return = true;
|
||
} else if (id_to_token[tid].text == "<|call|>") {
|
||
has_call = true;
|
||
} else if (id_to_token[tid].text == "<|end|>") {
|
||
has_end = true;
|
||
end_id = tid;
|
||
}
|
||
}
|
||
|
||
if (has_return && has_call && has_end) {
|
||
special_eog_ids.erase(end_id);
|
||
id_to_token[end_id].attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
|
||
LLAMA_LOG_WARN("%s: special_eog_ids contains both '<|return|>' and '<|call|>' tokens, removing '<|end|>' token from EOG list\n", __func__);
|
||
}
|
||
}
|
||
}
|
||
|
||
// build special tokens cache
|
||
{
|
||
for (llama_token id = 0; id < (llama_token) n_tokens; ++id) {
|
||
if (id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
|
||
cache_special_tokens.push_back(id);
|
||
}
|
||
}
|
||
|
||
std::sort(cache_special_tokens.begin(), cache_special_tokens.end(),
|
||
[&] (const llama_token a, const llama_token b) {
|
||
return id_to_token[a].text.size() > id_to_token[b].text.size();
|
||
}
|
||
);
|
||
|
||
LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t) cache_special_tokens.size());
|
||
}
|
||
|
||
// build token to piece cache
|
||
{
|
||
size_t size_cache = 0;
|
||
|
||
std::vector<std::string> cache(n_tokens);
|
||
|
||
for (uint32_t id = 0; id < n_tokens; ++id) {
|
||
cache[id] = token_to_piece_for_cache(id, true);
|
||
|
||
size_cache += cache[id].size();
|
||
}
|
||
|
||
std::swap(cache_token_to_piece, cache);
|
||
|
||
LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
|
||
}
|
||
|
||
// Handle per token attributes
|
||
//NOTE: Each model customizes per token attributes.
|
||
//NOTE: Per token attributes are missing from the GGUF file.
|
||
//TODO: Extract attributes from GGUF file.
|
||
{
|
||
auto _contains_any = [] (const std::string & str, const std::vector<std::string_view> & substrs) -> bool {
|
||
for (const auto & substr : substrs) {
|
||
if (str.find(substr) != std::string::npos) {
|
||
return true;
|
||
}
|
||
}
|
||
return false;
|
||
};
|
||
|
||
auto _set_tokenid_attr = [&] (const llama_token id, llama_token_attr attr, bool value) {
|
||
uint32_t current = id_to_token.at(id).attr;
|
||
current = value ? (current | attr) : (current & ~attr);
|
||
id_to_token[id].attr = (llama_token_attr) current;
|
||
};
|
||
|
||
auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
|
||
_set_tokenid_attr(token_to_id.at(token), attr, value);
|
||
};
|
||
|
||
std::string model_name;
|
||
std::string tokenizer_pre;
|
||
std::string general_arch;
|
||
|
||
ml.get_key(LLM_KV_GENERAL_NAME, model_name, false);
|
||
ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
|
||
ml.get_key(LLM_KV_GENERAL_ARCHITECTURE, general_arch, false);
|
||
|
||
// model name to lowercase
|
||
std::transform(model_name.begin(), model_name.end(), model_name.begin(),
|
||
[] (const std::string::value_type x) {
|
||
return std::tolower(x);
|
||
}
|
||
);
|
||
|
||
// set attributes by model/tokenizer/architecture name
|
||
if (false
|
||
|| _contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})
|
||
|| _contains_any(general_arch, {"nomic-bert-moe", "jina-bert-v3"})
|
||
) {
|
||
if (token_to_id.count("<mask>") == 0) {
|
||
LLAMA_LOG_WARN("%s: Mask token is missing in vocab, please reconvert model!\n", __func__);
|
||
} else {
|
||
_set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
|
||
}
|
||
} else if (_contains_any(model_name, {"phi-3", "phi3"})) {
|
||
for (auto id : cache_special_tokens) {
|
||
_set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
|
||
}
|
||
for (const auto * token : {"</s>"}) {
|
||
_set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
|
||
}
|
||
for (const auto * token : {"<unk>", "<s>", "<|endoftext|>"}) {
|
||
_set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
enum llama_vocab_type llama_vocab::impl::get_type() const {
|
||
return type;
|
||
}
|
||
|
||
std::string llama_vocab::impl::type_name() const{
|
||
switch (type) {
|
||
case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
|
||
case LLAMA_VOCAB_TYPE_SPM: return "SPM";
|
||
case LLAMA_VOCAB_TYPE_BPE: return "BPE";
|
||
case LLAMA_VOCAB_TYPE_WPM: return "WPM";
|
||
case LLAMA_VOCAB_TYPE_UGM: return "UGM";
|
||
case LLAMA_VOCAB_TYPE_RWKV: return "RWKV";
|
||
case LLAMA_VOCAB_TYPE_PLAMO2: return "PLaMo2";
|
||
default: return "unknown";
|
||
}
|
||
}
|
||
|
||
bool llama_vocab::impl::is_normal(llama_token id) const {
|
||
GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
|
||
return id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
|
||
}
|
||
|
||
bool llama_vocab::impl::is_unknown(llama_token id) const {
|
||
GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
|
||
return id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
|
||
}
|
||
|
||
bool llama_vocab::impl::is_control(llama_token id) const {
|
||
GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
|
||
return id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
|
||
}
|
||
|
||
bool llama_vocab::impl::is_byte(llama_token id) const {
|
||
GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
|
||
return id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
|
||
}
|
||
|
||
bool llama_vocab::impl::is_user_defined(llama_token id) const {
|
||
GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
|
||
return id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
|
||
}
|
||
|
||
bool llama_vocab::impl::is_unused(llama_token id) const {
|
||
GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
|
||
return id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNUSED;
|
||
}
|
||
|
||
bool llama_vocab::impl::is_eog(llama_token id) const {
|
||
return id != LLAMA_TOKEN_NULL && special_eog_ids.count(id) > 0;
|
||
}
|
||
|
||
uint8_t llama_vocab::impl::token_to_byte(llama_token id) const {
|
||
GGML_ASSERT(get_type() != LLAMA_VOCAB_TYPE_NONE);
|
||
GGML_ASSERT(is_byte(id));
|
||
const auto & token_data = id_to_token.at(id);
|
||
switch (get_type()) {
|
||
case LLAMA_VOCAB_TYPE_SPM:
|
||
case LLAMA_VOCAB_TYPE_UGM: {
|
||
auto buf = token_data.text.substr(3, 2);
|
||
return strtol(buf.c_str(), NULL, 16);
|
||
}
|
||
case LLAMA_VOCAB_TYPE_BPE: {
|
||
GGML_ABORT("fatal error");
|
||
}
|
||
case LLAMA_VOCAB_TYPE_WPM: {
|
||
GGML_ABORT("fatal error");
|
||
}
|
||
default:
|
||
GGML_ABORT("fatal error");
|
||
}
|
||
}
|
||
|
||
llama_token_attr llama_vocab::impl::token_get_attr(llama_token id) const {
|
||
GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
|
||
return id_to_token.at(id).attr;
|
||
}
|
||
|
||
void llama_vocab::impl::init_tokenizer(enum llama_vocab_type type) {
|
||
LLAMA_LOG_DEBUG("%s: initializing tokenizer for type %d\n", __func__, type);
|
||
|
||
switch (type) {
|
||
case LLAMA_VOCAB_TYPE_SPM:
|
||
tokenizer = std::make_unique<llm_tokenizer_spm>(vocab);
|
||
break;
|
||
case LLAMA_VOCAB_TYPE_BPE:
|
||
tokenizer = std::make_unique<llm_tokenizer_bpe>(vocab);
|
||
break;
|
||
case LLAMA_VOCAB_TYPE_WPM:
|
||
tokenizer = std::make_unique<llm_tokenizer_wpm>(vocab);
|
||
break;
|
||
case LLAMA_VOCAB_TYPE_UGM:
|
||
tokenizer = std::make_unique<llm_tokenizer_ugm>(vocab, precompiled_charsmap);
|
||
break;
|
||
case LLAMA_VOCAB_TYPE_RWKV:
|
||
tokenizer = std::make_unique<llm_tokenizer_rwkv>(vocab);
|
||
break;
|
||
case LLAMA_VOCAB_TYPE_PLAMO2:
|
||
tokenizer = std::make_unique<llm_tokenizer_plamo2>(vocab);
|
||
break;
|
||
default:
|
||
GGML_ABORT("unsupported vocab type");
|
||
}
|
||
}
|
||
|
||
//
|
||
// (de-) tokenize
|
||
//
|
||
|
||
// #define PRETOKENIZERDEBUG
|
||
|
||
void llama_vocab::impl::tokenizer_st_partition(std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) const {
|
||
// for each special token
|
||
for (const llama_token special_id : cache_special_tokens) {
|
||
const auto & data = vocab.get_token_data(special_id);
|
||
const auto & text = data.text;
|
||
|
||
if (!parse_special && (data.attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_UNKNOWN))) {
|
||
// Ignore control and unknown tokens when parse_special == false
|
||
continue;
|
||
// User-defined tokens are still pre-tokenized before everything else
|
||
// ref: https://github.com/huggingface/tokenizers/blob/fdd26ba9a3f0c133427aab0423888cbde91362d7/tokenizers/src/tokenizer/mod.rs#L726
|
||
// This is mostly relevant for neox-style tokenizers (mpt, olmo, stablelm, etc.)
|
||
}
|
||
|
||
// for each text fragment
|
||
std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
|
||
while (it != buffer.end()) {
|
||
auto & fragment = (*it);
|
||
|
||
// if a fragment is text ( not yet processed )
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
const auto & raw_text = fragment.raw_text;
|
||
|
||
auto raw_text_base_offset = fragment.offset;
|
||
auto raw_text_base_length = fragment.length;
|
||
|
||
// loop over the text
|
||
while (true) {
|
||
// find the first occurrence of a given special token in this fragment
|
||
// passing offset argument only limit the "search area" but match coordinates
|
||
// are still relative to the source full raw_text
|
||
// string_view begins at pos 0 for the same reason
|
||
auto match = std::string_view(raw_text.data(), raw_text_base_offset + raw_text_base_length).find(text, raw_text_base_offset);
|
||
|
||
// no occurrences found, stop processing this fragment for a given special token
|
||
if (match == std::string::npos) break;
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
|
||
#endif
|
||
auto source = std::distance(buffer.begin(), it);
|
||
|
||
// if match is further than base offset
|
||
// then we have some text to the left of it
|
||
if (match > raw_text_base_offset) {
|
||
// left
|
||
const int64_t left_reminder_offset = raw_text_base_offset + 0;
|
||
int64_t left_reminder_length = match - raw_text_base_offset;
|
||
|
||
if (data.attr & LLAMA_TOKEN_ATTR_LSTRIP) {
|
||
while (left_reminder_length > 0 && isspace(raw_text[left_reminder_offset + left_reminder_length - 1])) {
|
||
left_reminder_length--;
|
||
}
|
||
}
|
||
|
||
if (left_reminder_length > 0) {
|
||
buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length);
|
||
it++;
|
||
}
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
|
||
#endif
|
||
}
|
||
|
||
// special token
|
||
buffer.emplace_after(it, special_id);
|
||
it++;
|
||
|
||
// right
|
||
if (match + text.length() < raw_text_base_offset + raw_text_base_length) {
|
||
int64_t right_reminder_offset = match + text.length();
|
||
int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + text.length());
|
||
|
||
if (data.attr & LLAMA_TOKEN_ATTR_RSTRIP) {
|
||
while (right_reminder_length > 0 && isspace(raw_text[right_reminder_offset])) {
|
||
right_reminder_offset++;
|
||
right_reminder_length--;
|
||
}
|
||
}
|
||
|
||
if (right_reminder_length > 0) {
|
||
buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length);
|
||
it++;
|
||
}
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
|
||
#endif
|
||
|
||
if (source == 0) {
|
||
buffer.erase_after(buffer.before_begin());
|
||
} else {
|
||
buffer.erase_after(std::next(buffer.begin(), (source - 1)));
|
||
}
|
||
|
||
// repeat for the right side
|
||
raw_text_base_offset = right_reminder_offset;
|
||
raw_text_base_length = right_reminder_length;
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
|
||
#endif
|
||
} else {
|
||
if (source == 0) {
|
||
buffer.erase_after(buffer.before_begin());
|
||
} else {
|
||
buffer.erase_after(std::next(buffer.begin(), (source - 1)));
|
||
}
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
it++;
|
||
}
|
||
}
|
||
}
|
||
|
||
// NOTE: avoid ever using this except for building the token_to_piece caches
|
||
std::string llama_vocab::impl::token_to_piece_for_cache(llama_token token, bool special) const {
|
||
std::string piece;
|
||
piece.resize(piece.capacity()); // using string internal cache
|
||
const int n_chars = vocab.token_to_piece(token, &piece[0], piece.size(), 0, special);
|
||
if (n_chars < 0) {
|
||
piece.resize(-n_chars);
|
||
int check = vocab.token_to_piece(token, &piece[0], piece.size(), 0, special);
|
||
GGML_ASSERT(check == -n_chars);
|
||
}
|
||
else {
|
||
piece.resize(n_chars);
|
||
}
|
||
|
||
return piece;
|
||
}
|
||
|
||
static void llama_escape_whitespace(std::string & text) {
|
||
replace_all(text, " ", "\xe2\x96\x81");
|
||
}
|
||
|
||
static void llama_unescape_whitespace(std::string & word) {
|
||
replace_all(word, "\xe2\x96\x81", " ");
|
||
}
|
||
|
||
static std::string llama_decode_text(const std::string & text) {
|
||
std::string decoded_text;
|
||
|
||
const auto cpts = unicode_cpts_from_utf8(text);
|
||
for (const auto cpt : cpts) {
|
||
const auto utf8 = unicode_cpt_to_utf8(cpt);
|
||
try {
|
||
decoded_text += unicode_utf8_to_byte(utf8);
|
||
} catch (const std::out_of_range & /*e*/) {
|
||
decoded_text += "[UNK_BYTE_0x";
|
||
for (const auto c : utf8) {
|
||
decoded_text += format("%02x", (uint8_t) c);
|
||
}
|
||
decoded_text += text + "]";
|
||
}
|
||
}
|
||
|
||
return decoded_text;
|
||
}
|
||
|
||
std::vector<llama_token> llama_vocab::impl::tokenize(
|
||
const std::string & raw_text,
|
||
bool add_special,
|
||
bool parse_special) const {
|
||
GGML_ASSERT(tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
|
||
|
||
std::vector<llama_token> output;
|
||
std::forward_list<fragment_buffer_variant> fragment_buffer;
|
||
|
||
if (!raw_text.empty()) {
|
||
fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
|
||
tokenizer_st_partition(fragment_buffer, parse_special);
|
||
}
|
||
|
||
switch (get_type()) {
|
||
case LLAMA_VOCAB_TYPE_SPM:
|
||
{
|
||
// OG tokenizer behavior:
|
||
//
|
||
// tokenizer.encode('', add_special_tokens=True) returns [1]
|
||
// tokenizer.encode('', add_special_tokens=False) returns []
|
||
|
||
bool is_prev_special = true; // prefix with space if first token
|
||
|
||
if (add_special && add_bos) {
|
||
GGML_ASSERT(special_bos_id != LLAMA_TOKEN_NULL);
|
||
output.push_back(special_bos_id);
|
||
is_prev_special = true;
|
||
}
|
||
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
std::string text;
|
||
|
||
// prefix with space if previous is special
|
||
if (add_space_prefix && is_prev_special) {
|
||
text = ' ';
|
||
}
|
||
|
||
text += fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
|
||
#endif
|
||
llama_escape_whitespace(text);
|
||
llm_tokenizer_spm_session session(vocab);
|
||
session.tokenize(text, output);
|
||
is_prev_special = false;
|
||
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
||
output.push_back(fragment.token);
|
||
is_prev_special = true;
|
||
}
|
||
}
|
||
|
||
if (add_special && add_bos && output.size() >= 2 && output[1] == special_bos_id) {
|
||
LLAMA_LOG_WARN(
|
||
"%s: Added a BOS token to the prompt as specified by the model but the prompt "
|
||
"also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
|
||
"Are you sure this is what you want?\n", __FUNCTION__);
|
||
}
|
||
|
||
if (add_special && add_eos) {
|
||
GGML_ASSERT(special_eos_id != LLAMA_TOKEN_NULL);
|
||
output.push_back(special_eos_id);
|
||
}
|
||
} break;
|
||
case LLAMA_VOCAB_TYPE_BPE:
|
||
{
|
||
llm_tokenizer_bpe_session session(vocab, *static_cast<const llm_tokenizer_bpe *>(tokenizer.get()));
|
||
// it calls some other methods that are not exist in llm_tokenizer,
|
||
// here just cast it to bpe tokenizer object
|
||
if (add_special) {
|
||
session.append_bos(output);
|
||
}
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
|
||
#endif
|
||
session.tokenize(text, output);
|
||
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
||
session.append(fragment.token, output);
|
||
}
|
||
}
|
||
|
||
if (add_special) {
|
||
session.append_eos(output);
|
||
session.check_double_bos_eos(output);
|
||
}
|
||
} break;
|
||
case LLAMA_VOCAB_TYPE_WPM:
|
||
{
|
||
if (add_special) {
|
||
GGML_ASSERT(special_bos_id != LLAMA_TOKEN_NULL);
|
||
output.push_back(special_bos_id);
|
||
}
|
||
|
||
llm_tokenizer_wpm_session session(vocab);
|
||
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
|
||
#endif
|
||
session.tokenize(text, output);
|
||
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
||
output.push_back(fragment.token);
|
||
}
|
||
}
|
||
|
||
if (add_special) {
|
||
GGML_ASSERT(special_sep_id != LLAMA_TOKEN_NULL);
|
||
output.push_back(special_sep_id);
|
||
}
|
||
} break;
|
||
case LLAMA_VOCAB_TYPE_UGM:
|
||
{
|
||
if (add_special && add_bos) {
|
||
GGML_ASSERT(special_bos_id != LLAMA_TOKEN_NULL);
|
||
output.push_back(special_bos_id);
|
||
}
|
||
llm_tokenizer_ugm_session session(vocab, *static_cast<const llm_tokenizer_ugm *>(tokenizer.get()));
|
||
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
|
||
#endif
|
||
session.tokenize(text, output);
|
||
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
||
output.push_back(fragment.token);
|
||
}
|
||
}
|
||
|
||
if (add_special && add_bos && output.size() >= 2 && output[1] == special_bos_id) {
|
||
LLAMA_LOG_WARN(
|
||
"%s: Added a BOS token to the prompt as specified by the model but the prompt "
|
||
"also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
|
||
"Are you sure this is what you want?\n", __FUNCTION__);
|
||
}
|
||
|
||
if (add_special && add_eos) {
|
||
GGML_ASSERT(special_eos_id != LLAMA_TOKEN_NULL);
|
||
output.push_back(special_eos_id);
|
||
}
|
||
} break;
|
||
case LLAMA_VOCAB_TYPE_RWKV:
|
||
{
|
||
llm_tokenizer_rwkv_session session(vocab, *static_cast<const llm_tokenizer_rwkv *>(tokenizer.get()));
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
|
||
#endif
|
||
|
||
session.tokenize(text, output);
|
||
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
||
output.push_back(fragment.token);
|
||
}
|
||
}
|
||
} break;
|
||
case LLAMA_VOCAB_TYPE_PLAMO2:
|
||
{
|
||
llm_tokenizer_plamo2_session session(*static_cast<const llm_tokenizer_plamo2 *>(tokenizer.get()));
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
|
||
#endif
|
||
|
||
session.tokenize(text, output);
|
||
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
||
output.push_back(fragment.token);
|
||
}
|
||
}
|
||
} break;
|
||
case LLAMA_VOCAB_TYPE_NONE:
|
||
GGML_ABORT("fatal error");
|
||
}
|
||
|
||
return output;
|
||
}
|
||
|
||
int32_t llama_vocab::impl::token_to_piece(llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) const {
|
||
// ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843
|
||
static const int attr_special = LLAMA_TOKEN_ATTR_UNKNOWN | LLAMA_TOKEN_ATTR_CONTROL;
|
||
const llama_token_attr attr = token_get_attr(token);
|
||
if (!special && (attr & attr_special)) {
|
||
return 0;
|
||
}
|
||
|
||
// copy piece chars to output text buffer
|
||
// skip up to 'lstrip' leading spaces before copying
|
||
auto _try_copy = [=] (const char * token, size_t size) -> int32_t {
|
||
if (size >= static_cast<size_t>(std::numeric_limits<int32_t>::max())) {
|
||
GGML_ABORT("invalid token size: %zu exceeds int32_t limit", size);
|
||
}
|
||
|
||
for (int32_t i = 0; i < lstrip && size && *token == ' '; ++i) {
|
||
token++;
|
||
size--;
|
||
}
|
||
if (length < (int32_t)size) {
|
||
return -(int32_t) size;
|
||
}
|
||
memcpy(buf, token, size);
|
||
return (int32_t) size;
|
||
};
|
||
|
||
// if we have a cache - use it
|
||
{
|
||
const auto & cache = cache_token_to_piece;
|
||
|
||
if (!cache.empty()) {
|
||
const auto & result = cache.at(token);
|
||
return _try_copy(result.data(), result.size());
|
||
}
|
||
}
|
||
|
||
if (0 <= token && token < (int32_t) id_to_token.size()) {
|
||
const std::string & token_text = id_to_token[token].text;
|
||
switch (get_type()) {
|
||
case LLAMA_VOCAB_TYPE_WPM:
|
||
case LLAMA_VOCAB_TYPE_SPM:
|
||
case LLAMA_VOCAB_TYPE_UGM: {
|
||
// NOTE: we accept all unsupported token types,
|
||
// suppressing them like CONTROL tokens.
|
||
if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
|
||
return _try_copy(token_text.data(), token_text.size());
|
||
}
|
||
if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
|
||
std::string result = token_text;
|
||
llama_unescape_whitespace(result);
|
||
return _try_copy(result.data(), result.size());
|
||
}
|
||
if (attr & LLAMA_TOKEN_ATTR_BYTE) {
|
||
char byte = (char) token_to_byte(token);
|
||
return _try_copy((char*) &byte, 1);
|
||
}
|
||
break;
|
||
}
|
||
case LLAMA_VOCAB_TYPE_BPE: {
|
||
// NOTE: we accept all unsupported token types,
|
||
// suppressing them like CONTROL tokens.
|
||
if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
|
||
return _try_copy(token_text.data(), token_text.size());
|
||
}
|
||
if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
|
||
std::string result = llama_decode_text(token_text);
|
||
return _try_copy(result.data(), result.size());
|
||
}
|
||
break;
|
||
}
|
||
case LLAMA_VOCAB_TYPE_RWKV: {
|
||
std::vector<uint8_t> result = llama_unescape_rwkv_token(token_text);
|
||
|
||
// If we don't have enough space, return an error
|
||
if (result.size() > (size_t)length) {
|
||
return -(int)result.size();
|
||
}
|
||
|
||
memcpy(buf, result.data(), result.size());
|
||
return (int)result.size();
|
||
}
|
||
case LLAMA_VOCAB_TYPE_PLAMO2: {
|
||
// PLaMo-2 uses similar token handling as BPE/SPM
|
||
if (vocab.is_byte(token)) {
|
||
// Handle byte tokens like <0xXX>
|
||
if (token_text.length() == 6 && token_text.substr(0, 3) == "<0x" && token_text.back() == '>') {
|
||
int hex_val = std::stoi(token_text.substr(3, 2), nullptr, 16);
|
||
if (length < 1) {
|
||
return -1;
|
||
}
|
||
buf[0] = static_cast<char>(hex_val);
|
||
return 1;
|
||
}
|
||
}
|
||
|
||
// Normal token - just copy the text
|
||
std::string result = token_text;
|
||
return _try_copy(result.data(), result.size());
|
||
}
|
||
default:
|
||
GGML_ABORT("fatal error");
|
||
}
|
||
}
|
||
|
||
return 0;
|
||
}
|
||
|
||
const std::string & llama_vocab::impl::token_to_piece(llama_token token) const {
|
||
return cache_token_to_piece.at(token);
|
||
}
|
||
|
||
int32_t llama_vocab::impl::detokenize(
|
||
const llama_token * tokens,
|
||
int32_t n_tokens,
|
||
char * text,
|
||
int32_t text_len_max,
|
||
bool remove_special,
|
||
bool unparse_special) const {
|
||
if (type == LLAMA_VOCAB_TYPE_NONE) {
|
||
return 0;
|
||
}
|
||
|
||
GGML_ASSERT(tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
|
||
|
||
int32_t avail = text_len_max;
|
||
int32_t total = 0;
|
||
|
||
// remove the leading space
|
||
bool remove_space = add_space_prefix;
|
||
|
||
if (remove_special && add_bos) {
|
||
if (n_tokens > 0 && tokens[0] == special_bos_id) {
|
||
remove_space = false;
|
||
n_tokens--;
|
||
tokens++;
|
||
}
|
||
}
|
||
|
||
if (remove_special && add_eos) {
|
||
if (n_tokens > 0 && tokens[n_tokens - 1] == special_eos_id) {
|
||
n_tokens--;
|
||
}
|
||
}
|
||
|
||
for (int32_t i = 0; i < n_tokens; ++i) {
|
||
GGML_ASSERT(avail >= 0);
|
||
int32_t n_chars = token_to_piece(tokens[i], text, avail, remove_space, unparse_special);
|
||
remove_space = false;
|
||
if (n_chars < 0) {
|
||
avail = 0;
|
||
total -= n_chars;
|
||
} else if (n_chars > 0) {
|
||
avail -= n_chars;
|
||
text += n_chars;
|
||
total += n_chars;
|
||
}
|
||
}
|
||
|
||
if (total > text_len_max) {
|
||
return -total;
|
||
}
|
||
|
||
if (clean_spaces) {
|
||
text -= total; // restart text
|
||
|
||
// first pass: characters ?!., //TODO: where do these characters come from?
|
||
const int32_t total1 = total;
|
||
total = total ? 1 : 0;
|
||
for (int32_t i = 1; i < total1; ++i) {
|
||
const char x = text[i];
|
||
if (text[i - 1] == ' ') {
|
||
if (x == '?' || x == '!' || x == '.' || x == ',') { // " ?", " !", " .", " ,"
|
||
total--; // remove space
|
||
}
|
||
}
|
||
text[total++] = x;
|
||
}
|
||
|
||
// second pass: strip single apostrophe between spaces
|
||
const int32_t total2 = total;
|
||
total = total ? 1 : 0;
|
||
for (int32_t i = 1; i < total2; ++i) {
|
||
const char x = text[i];
|
||
if (x == '\'' && i + 1 < total2 && text[i - 1] == ' ' && text[i + 1] == ' ') { // " ' "
|
||
total--; // remove prev space
|
||
text[++i] = '\0'; // remove next space
|
||
}
|
||
text[total++] = x;
|
||
}
|
||
|
||
// third pass: apostrophe contractions //NOTE: this makes sense?
|
||
const int32_t total3 = total;
|
||
total = total ? 1 : 0;
|
||
for (int32_t i = 1; i < total3; ++i) {
|
||
const char x = text[i];
|
||
if (text[i - 1] == ' ') {
|
||
if (x == '\'' && i + 1 < total3) {
|
||
const char x1 = text[i + 1];
|
||
if (x1 == 't' || x1 == 'd') { // " 't", " 'd"
|
||
//total--; // remove space
|
||
} else if (x1 == 's' || x1 == 'm') { // " 's", " 'm"
|
||
total--; // remove space
|
||
} else if (i + 2 < total3) {
|
||
const char x2 = text[i + 2];
|
||
if ((x1 == 'l' && x2 == 'l')) { // " 'll"
|
||
//total--; // remove space
|
||
} else if ((x1 == 'r' && x2 == 'e') || (x1 == 'v' && x2 == 'e')) { // " 're", " 've"
|
||
total--; // remove space
|
||
} else {
|
||
//total--; // remove space
|
||
}
|
||
} else {
|
||
//total--; // remove space
|
||
}
|
||
}
|
||
}
|
||
text[total++] = x;
|
||
}
|
||
}
|
||
|
||
return total <= text_len_max ? total : -total;
|
||
}
|
||
|
||
void llama_vocab::impl::print_info() const {
|
||
LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, type_name().c_str());
|
||
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, vocab.n_tokens());
|
||
LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (uint32_t) bpe_ranks.size());
|
||
|
||
// special tokens
|
||
if (special_bos_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, special_bos_id, id_to_token.at(special_bos_id).text.c_str() ); }
|
||
if (special_eos_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, special_eos_id, id_to_token.at(special_eos_id).text.c_str() ); }
|
||
if (special_eot_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, special_eot_id, id_to_token.at(special_eot_id).text.c_str() ); }
|
||
if (special_eom_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: EOM token = %d '%s'\n", __func__, special_eom_id, id_to_token.at(special_eom_id).text.c_str() ); }
|
||
if (special_unk_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, special_unk_id, id_to_token.at(special_unk_id).text.c_str() ); }
|
||
if (special_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, special_sep_id, id_to_token.at(special_sep_id).text.c_str() ); }
|
||
if (special_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, special_pad_id, id_to_token.at(special_pad_id).text.c_str() ); }
|
||
if (special_mask_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, special_mask_id, id_to_token.at(special_mask_id).text.c_str() ); }
|
||
|
||
if (linefeed_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, linefeed_id, id_to_token.at(linefeed_id).text.c_str() ); }
|
||
|
||
if (special_fim_pre_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PRE token = %d '%s'\n", __func__, special_fim_pre_id, id_to_token.at(special_fim_pre_id).text.c_str() ); }
|
||
if (special_fim_suf_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SUF token = %d '%s'\n", __func__, special_fim_suf_id, id_to_token.at(special_fim_suf_id).text.c_str() ); }
|
||
if (special_fim_mid_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM MID token = %d '%s'\n", __func__, special_fim_mid_id, id_to_token.at(special_fim_mid_id).text.c_str() ); }
|
||
if (special_fim_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PAD token = %d '%s'\n", __func__, special_fim_pad_id, id_to_token.at(special_fim_pad_id).text.c_str() ); }
|
||
if (special_fim_rep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM REP token = %d '%s'\n", __func__, special_fim_rep_id, id_to_token.at(special_fim_rep_id).text.c_str() ); }
|
||
if (special_fim_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SEP token = %d '%s'\n", __func__, special_fim_sep_id, id_to_token.at(special_fim_sep_id).text.c_str() ); }
|
||
|
||
for (const auto & id : special_eog_ids) {
|
||
LLAMA_LOG_INFO( "%s: EOG token = %d '%s'\n", __func__, id, id_to_token.at(id).text.c_str() );
|
||
}
|
||
|
||
LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, max_token_len);
|
||
}
|
||
|
||
llama_vocab::llama_vocab() : pimpl(new impl(*this)) {
|
||
}
|
||
|
||
llama_vocab::~llama_vocab() {
|
||
}
|
||
|
||
void llama_vocab::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||
pimpl->load(ml, kv);
|
||
}
|
||
|
||
std::string llama_vocab::get_tokenizer_model() const {
|
||
return pimpl->tokenizer_model;
|
||
}
|
||
|
||
std::string llama_vocab::get_tokenizer_pre() const {
|
||
return pimpl->tokenizer_pre;
|
||
}
|
||
|
||
enum llama_vocab_type llama_vocab::get_type() const {
|
||
return pimpl->type;
|
||
}
|
||
|
||
enum llama_vocab_pre_type llama_vocab::get_pre_type() const {
|
||
return pimpl->pre_type;
|
||
}
|
||
|
||
uint32_t llama_vocab::n_tokens() const {
|
||
return (uint32_t) pimpl->id_to_token.size();
|
||
}
|
||
|
||
uint32_t llama_vocab::n_token_types() const {
|
||
return (uint32_t) pimpl->n_token_types;
|
||
}
|
||
|
||
std::string llama_vocab::type_name() const{
|
||
return pimpl->type_name();
|
||
}
|
||
|
||
bool llama_vocab::is_normal(llama_token id) const {
|
||
return pimpl->is_normal(id);
|
||
}
|
||
|
||
bool llama_vocab::is_unknown(llama_token id) const {
|
||
return pimpl->is_unknown(id);
|
||
}
|
||
|
||
bool llama_vocab::is_control(llama_token id) const {
|
||
return pimpl->is_control(id);
|
||
}
|
||
|
||
bool llama_vocab::is_byte(llama_token id) const {
|
||
return pimpl->is_byte(id);
|
||
}
|
||
|
||
bool llama_vocab::is_user_defined(llama_token id) const {
|
||
return pimpl->is_user_defined(id);
|
||
}
|
||
|
||
bool llama_vocab::is_unused(llama_token id) const {
|
||
return pimpl->is_unused(id);
|
||
}
|
||
|
||
bool llama_vocab::is_eog(llama_token id) const {
|
||
return pimpl->is_eog(id);
|
||
}
|
||
|
||
uint8_t llama_vocab::token_to_byte(llama_token id) const {
|
||
return pimpl->token_to_byte(id);
|
||
}
|
||
|
||
llama_token llama_vocab::byte_to_token(uint8_t ch) const {
|
||
GGML_ASSERT(get_type() != LLAMA_VOCAB_TYPE_NONE);
|
||
static const char * hex = "0123456789ABCDEF";
|
||
switch (get_type()) {
|
||
case LLAMA_VOCAB_TYPE_SPM:
|
||
case LLAMA_VOCAB_TYPE_UGM: {
|
||
const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
|
||
auto token = pimpl->token_to_id.find(buf);
|
||
if (token != pimpl->token_to_id.end()) {
|
||
return (*token).second;
|
||
}
|
||
// Try to fall back to just the byte as a string
|
||
const char buf2[2] = { (char)ch, 0 };
|
||
return pimpl->token_to_id.at(buf2);
|
||
}
|
||
case LLAMA_VOCAB_TYPE_WPM:
|
||
case LLAMA_VOCAB_TYPE_BPE: {
|
||
return pimpl->token_to_id.at(unicode_byte_to_utf8(ch));
|
||
}
|
||
case LLAMA_VOCAB_TYPE_PLAMO2: {
|
||
// PLaMo-2 uses byte tokens in format <0xXX>
|
||
char hex_str[8];
|
||
snprintf(hex_str, sizeof(hex_str), "<0x%02X>", ch);
|
||
return pimpl->token_to_id.at(hex_str);
|
||
}
|
||
default:
|
||
GGML_ABORT("fatal error");
|
||
}
|
||
}
|
||
|
||
llama_token llama_vocab::text_to_token(const std::string & text) const {
|
||
GGML_ASSERT(pimpl->type != LLAMA_VOCAB_TYPE_NONE);
|
||
auto it = pimpl->token_to_id.find(text);
|
||
if (it != pimpl->token_to_id.end()) {
|
||
return (*it).second;
|
||
}
|
||
return LLAMA_TOKEN_NULL;
|
||
}
|
||
|
||
const llama_vocab::token_data & llama_vocab::get_token_data(llama_token id) const {
|
||
GGML_ASSERT(pimpl->type != LLAMA_VOCAB_TYPE_NONE);
|
||
return pimpl->id_to_token.at(id);
|
||
}
|
||
|
||
const char * llama_vocab::token_get_text(llama_token id) const {
|
||
GGML_ASSERT(pimpl->type != LLAMA_VOCAB_TYPE_NONE);
|
||
return pimpl->id_to_token.at(id).text.c_str();
|
||
}
|
||
|
||
float llama_vocab::token_get_score(llama_token id) const {
|
||
GGML_ASSERT(pimpl->type != LLAMA_VOCAB_TYPE_NONE);
|
||
return pimpl->id_to_token.at(id).score;
|
||
}
|
||
|
||
llama_token_attr llama_vocab::token_get_attr(llama_token id) const {
|
||
return pimpl->token_get_attr(id);
|
||
}
|
||
|
||
llama_token llama_vocab::token_bos() const {
|
||
return pimpl->special_bos_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_eos() const {
|
||
return pimpl->special_eos_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_eot() const {
|
||
return pimpl->special_eot_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_eom() const {
|
||
return pimpl->special_eom_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_unk() const {
|
||
return pimpl->special_unk_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_sep() const {
|
||
return pimpl->special_sep_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_nl() const {
|
||
return pimpl->linefeed_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_pad() const {
|
||
return pimpl->special_pad_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_prefix() const {
|
||
return pimpl->special_fim_pre_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_middle() const {
|
||
return pimpl->special_fim_mid_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_suffix() const {
|
||
return pimpl->special_fim_suf_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_fim_pre() const {
|
||
return pimpl->special_fim_pre_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_fim_suf() const {
|
||
return pimpl->special_fim_suf_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_fim_mid() const {
|
||
return pimpl->special_fim_mid_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_fim_pad() const {
|
||
return pimpl->special_fim_pad_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_fim_rep() const {
|
||
return pimpl->special_fim_rep_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_fim_sep() const {
|
||
return pimpl->special_fim_sep_id;
|
||
}
|
||
|
||
llama_token llama_vocab::token_mask() const {
|
||
return pimpl->special_mask_id;
|
||
}
|
||
|
||
bool llama_vocab::get_add_space_prefix() const {
|
||
return pimpl->add_space_prefix;
|
||
}
|
||
|
||
bool llama_vocab::get_add_bos() const {
|
||
return pimpl->add_bos;
|
||
}
|
||
|
||
bool llama_vocab::get_add_eos() const {
|
||
return pimpl->add_eos;
|
||
}
|
||
|
||
bool llama_vocab::get_add_sep() const {
|
||
return pimpl->add_sep;
|
||
}
|
||
|
||
bool llama_vocab::get_ignore_merges() const {
|
||
return pimpl->ignore_merges;
|
||
}
|
||
|
||
bool llama_vocab::get_clean_spaces() const {
|
||
return pimpl->clean_spaces;
|
||
}
|
||
|
||
bool llama_vocab::get_remove_extra_whitespaces() const {
|
||
return pimpl->remove_extra_whitespaces;
|
||
}
|
||
|
||
bool llama_vocab::get_escape_whitespaces() const {
|
||
return pimpl->escape_whitespaces;
|
||
}
|
||
|
||
bool llama_vocab::get_treat_whitespace_as_suffix() const {
|
||
return pimpl->treat_whitespace_as_suffix;
|
||
}
|
||
|
||
int llama_vocab::max_token_len() const {
|
||
return pimpl->max_token_len;
|
||
}
|
||
|
||
int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
|
||
GGML_ASSERT(token_left.find(' ') == std::string::npos);
|
||
GGML_ASSERT(token_left.find('\n') == std::string::npos);
|
||
GGML_ASSERT(token_right.find(' ') == std::string::npos);
|
||
GGML_ASSERT(token_right.find('\n') == std::string::npos);
|
||
|
||
auto it = pimpl->bpe_ranks.find(std::make_pair(token_left, token_right));
|
||
if (it == pimpl->bpe_ranks.end()) {
|
||
return -1;
|
||
}
|
||
|
||
return it->second;
|
||
}
|
||
|
||
std::vector<std::string> llama_vocab::get_bpe_merges() const {
|
||
std::vector<std::string> result(pimpl->bpe_ranks.size());
|
||
|
||
for (const auto & pair : pimpl->bpe_ranks) {
|
||
result[pair.second] = pair.first.first + " " + pair.first.second;
|
||
}
|
||
|
||
return result;
|
||
}
|
||
|
||
std::vector<char> llama_vocab::get_precompiled_charsmap() const {
|
||
return pimpl->precompiled_charsmap;
|
||
}
|
||
|
||
int32_t llama_vocab::tokenize(
|
||
const char * text,
|
||
int32_t text_len,
|
||
llama_token * tokens,
|
||
int32_t n_tokens_max,
|
||
bool add_special,
|
||
bool parse_special) const {
|
||
auto res = tokenize(std::string(text, text_len), add_special, parse_special);
|
||
if (res.size() >= static_cast<size_t>(std::numeric_limits<int32_t>::max())) {
|
||
LLAMA_LOG_ERROR("%s: tokenization result size %zu exceeds int32_t limit\n", __func__, res.size());
|
||
return std::numeric_limits<int32_t>::min();
|
||
}
|
||
|
||
if (n_tokens_max < (int) res.size()) {
|
||
// LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
|
||
return -((int) res.size());
|
||
}
|
||
|
||
for (size_t i = 0; i < res.size(); i++) {
|
||
tokens[i] = res[i];
|
||
}
|
||
|
||
return res.size();
|
||
}
|
||
|
||
std::vector<llama_token> llama_vocab::tokenize(
|
||
const std::string & raw_text,
|
||
bool add_special,
|
||
bool parse_special) const {
|
||
return pimpl->tokenize(raw_text, add_special, parse_special);
|
||
}
|
||
|
||
const std::string & llama_vocab::token_to_piece(llama_token token) const {
|
||
return pimpl->token_to_piece(token);
|
||
}
|
||
|
||
int32_t llama_vocab::token_to_piece(llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) const {
|
||
return pimpl->token_to_piece(token, buf, length, lstrip, special);
|
||
}
|
||
|
||
int32_t llama_vocab::detokenize(
|
||
const llama_token * tokens,
|
||
int32_t n_tokens,
|
||
char * text,
|
||
int32_t text_len_max,
|
||
bool remove_special,
|
||
bool unparse_special) const {
|
||
return pimpl->detokenize(tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
|
||
}
|
||
|
||
std::string llama_vocab::detokenize(const std::vector<llama_token> & tokens, bool special) const {
|
||
std::string text;
|
||
text.resize(std::max(text.capacity(), tokens.size()));
|
||
int32_t n_chars = detokenize(tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
|
||
if (n_chars < 0) {
|
||
text.resize(-n_chars);
|
||
n_chars = detokenize(tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
|
||
GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization
|
||
}
|
||
|
||
text.resize(n_chars);
|
||
|
||
// NOTE: the original tokenizer decodes bytes after collecting the pieces.
|
||
return text;
|
||
}
|
||
|
||
void llama_vocab::print_info() const {
|
||
pimpl->print_info();
|
||
}
|
||
|
||
//
|
||
// interface implementation
|
||
//
|
||
|
||
int32_t llama_vocab_n_tokens(const struct llama_vocab * vocab) {
|
||
return vocab->n_tokens();
|
||
}
|
||
|
||
// deprecated
|
||
int32_t llama_n_vocab(const struct llama_vocab * vocab) {
|
||
return llama_vocab_n_tokens(vocab);
|
||
}
|
||
|
||
enum llama_vocab_type llama_vocab_type(const struct llama_vocab * vocab) {
|
||
return vocab->get_type();
|
||
}
|
||
|
||
const char * llama_vocab_get_text(const struct llama_vocab * vocab, llama_token token) {
|
||
return vocab->token_get_text(token);
|
||
}
|
||
|
||
float llama_vocab_get_score(const struct llama_vocab * vocab, llama_token token) {
|
||
return vocab->token_get_score(token);
|
||
}
|
||
|
||
enum llama_token_attr llama_vocab_get_attr(const struct llama_vocab * vocab, llama_token token) {
|
||
return vocab->token_get_attr(token);
|
||
}
|
||
|
||
bool llama_vocab_is_eog(const struct llama_vocab * vocab, llama_token token) {
|
||
return vocab->is_eog(token);
|
||
}
|
||
|
||
bool llama_vocab_is_control(const struct llama_vocab * vocab, llama_token token) {
|
||
return vocab->is_control(token);
|
||
}
|
||
|
||
llama_token llama_vocab_bos(const struct llama_vocab * vocab) {
|
||
return vocab->token_bos();
|
||
}
|
||
|
||
llama_token llama_vocab_eos(const struct llama_vocab * vocab) {
|
||
return vocab->token_eos();
|
||
}
|
||
|
||
llama_token llama_vocab_eot(const struct llama_vocab * vocab) {
|
||
return vocab->token_eot();
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_vocab_cls(const struct llama_vocab * vocab) {
|
||
return vocab->token_bos();
|
||
}
|
||
|
||
llama_token llama_vocab_sep(const struct llama_vocab * vocab) {
|
||
return vocab->token_sep();
|
||
}
|
||
|
||
llama_token llama_vocab_nl (const struct llama_vocab * vocab) {
|
||
return vocab->token_nl();
|
||
}
|
||
|
||
llama_token llama_vocab_pad(const struct llama_vocab * vocab) {
|
||
return vocab->token_pad();
|
||
}
|
||
|
||
bool llama_vocab_get_add_bos(const struct llama_vocab * vocab) {
|
||
return vocab->get_add_bos();
|
||
}
|
||
|
||
bool llama_vocab_get_add_eos(const struct llama_vocab * vocab) {
|
||
return vocab->get_add_eos();
|
||
}
|
||
|
||
bool llama_vocab_get_add_sep(const struct llama_vocab * vocab) {
|
||
return vocab->get_add_sep();
|
||
}
|
||
|
||
llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab) {
|
||
return vocab->token_fim_pre();
|
||
}
|
||
|
||
llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab) {
|
||
return vocab->token_fim_suf();
|
||
}
|
||
|
||
llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab) {
|
||
return vocab->token_fim_mid();
|
||
}
|
||
|
||
llama_token llama_vocab_fim_pad(const struct llama_vocab * vocab) {
|
||
return vocab->token_fim_pad();
|
||
}
|
||
|
||
llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab) {
|
||
return vocab->token_fim_rep();
|
||
}
|
||
|
||
llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab) {
|
||
return vocab->token_fim_sep();
|
||
}
|
||
|
||
llama_token llama_vocab_mask(const struct llama_vocab* vocab) {
|
||
return vocab->token_mask();
|
||
}
|
||
|
||
// deprecated
|
||
const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token) {
|
||
return llama_vocab_get_text(vocab, token);
|
||
}
|
||
|
||
// deprecated
|
||
float llama_token_get_score(const struct llama_vocab * vocab, llama_token token) {
|
||
return llama_vocab_get_score(vocab, token);
|
||
}
|
||
|
||
// deprecated
|
||
enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token) {
|
||
return llama_vocab_get_attr(vocab, token);
|
||
}
|
||
|
||
// deprecated
|
||
bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token) {
|
||
return llama_vocab_is_eog(vocab, token);
|
||
}
|
||
|
||
// deprecated
|
||
bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token) {
|
||
return llama_vocab_is_control(vocab, token);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_bos(const struct llama_vocab * vocab) {
|
||
return llama_vocab_bos(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_eos(const struct llama_vocab * vocab) {
|
||
return llama_vocab_eos(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_eot(const struct llama_vocab * vocab) {
|
||
return llama_vocab_eot(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_cls(const struct llama_vocab * vocab) {
|
||
//return llama_vocab_cls(vocab);
|
||
return llama_vocab_bos(vocab); // avoid deprecation warning
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_sep(const struct llama_vocab * vocab) {
|
||
return llama_vocab_sep(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_nl (const struct llama_vocab * vocab) {
|
||
return llama_vocab_nl(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_pad(const struct llama_vocab * vocab) {
|
||
return llama_vocab_pad(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
bool llama_add_bos_token(const struct llama_vocab * vocab) {
|
||
return llama_vocab_get_add_bos(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
bool llama_add_eos_token(const struct llama_vocab * vocab) {
|
||
return llama_vocab_get_add_eos(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_fim_pre(const struct llama_vocab * vocab) {
|
||
return llama_vocab_fim_pre(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_fim_suf(const struct llama_vocab * vocab) {
|
||
return llama_vocab_fim_suf(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_fim_mid(const struct llama_vocab * vocab) {
|
||
return llama_vocab_fim_mid(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_fim_pad(const struct llama_vocab * vocab) {
|
||
return llama_vocab_fim_pad(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_fim_rep(const struct llama_vocab * vocab) {
|
||
return llama_vocab_fim_rep(vocab);
|
||
}
|
||
|
||
// deprecated
|
||
llama_token llama_token_fim_sep(const struct llama_vocab * vocab) {
|
||
return llama_vocab_fim_sep(vocab);
|
||
}
|
||
|
||
//
|
||
// tokenization
|
||
//
|
||
|
||
int32_t llama_tokenize(
|
||
const struct llama_vocab * vocab,
|
||
const char * text,
|
||
int32_t text_len,
|
||
llama_token * tokens,
|
||
int32_t n_tokens_max,
|
||
bool add_special,
|
||
bool parse_special) {
|
||
return vocab->tokenize(text, text_len, tokens, n_tokens_max, add_special, parse_special);
|
||
}
|
||
|
||
int32_t llama_token_to_piece(
|
||
const struct llama_vocab * vocab,
|
||
llama_token token,
|
||
char * buf,
|
||
int32_t length,
|
||
int32_t lstrip,
|
||
bool special) {
|
||
return vocab->token_to_piece(token, buf, length, lstrip, special);
|
||
}
|
||
|
||
int32_t llama_detokenize(
|
||
const struct llama_vocab * vocab,
|
||
const llama_token * tokens,
|
||
int32_t n_tokens,
|
||
char * text,
|
||
int32_t text_len_max,
|
||
bool remove_special,
|
||
bool unparse_special) {
|
||
return vocab->detokenize(tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
|
||
}
|