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llama-vocab.cpp
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#include "llama-vocab.h"
#include "llama-impl.h"
#include "llama-model-loader.h"
#include "unicode.h"
#include <algorithm>
#include <cassert>
#include <cfloat>
#include <climits>
#include <cstdarg>
#include <cstring>
#include <forward_list>
#include <map>
#include <queue>
#include <set>
#include <unordered_map>
//
// helpers
//
struct naive_trie {
naive_trie() : has_value(false), value(0) {
}
void insert(const char * key, size_t len, int32_t value = 0) {
if (len == 0) {
this->has_value = true;
this->value = value;
return;
}
char c = key[0];
auto res = children.find(c);
if (res != children.end()) {
res->second.insert(key + 1, len - 1, value);
} else {
auto res = children.insert(std::make_pair(c, naive_trie()));
res.first->second.insert(key + 1, len - 1, value);
}
}
std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) const {
if (len == 0 || offset == len) {
return std::make_pair(key, offset);
}
char c = key[offset];
auto res = children.find(c);
if (res != children.end()) {
return res->second.get_longest_prefix(key, len, offset + 1);
}
return std::make_pair(key, offset);
}
const struct naive_trie * traverse(const char c) const {
auto res = children.find(c);
if (res != children.end()) {
return &res->second;
}
return NULL;
}
std::map<char, struct naive_trie> children;
bool has_value;
llama_token value;
};
//
// tokenizers
//
struct llm_tokenizer {
llm_tokenizer() {}
virtual ~llm_tokenizer() = default;
};
struct llm_symbol {
using index = int;
index prev;
index next;
const char * text;
size_t n;
};
static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");
//
// SPM tokenizer
// original implementation:
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
//
struct llm_bigram_spm {
struct comparator {
bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
return (l.score < r.score) || (l.score == r.score && l.left > r.left);
}
};
using queue_storage = std::vector<llm_bigram_spm>;
using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
llm_symbol::index left;
llm_symbol::index right;
float score;
size_t size;
};
struct llm_tokenizer_spm : llm_tokenizer {
llm_tokenizer_spm(const llama_vocab & /*vocab*/) {}
};
struct llm_tokenizer_spm_session {
llm_tokenizer_spm_session(const llama_vocab & vocab) : vocab(vocab) {}
void tokenize(const std::string & text, std::vector<llama_token> & output) {
// split string into utf8 chars
int index = 0;
size_t offs = 0;
while (offs < text.size()) {
llm_symbol sym;
size_t len = unicode_len_utf8(text[offs]);
sym.text = text.c_str() + offs;
sym.n = std::min(len, text.size() - offs);
offs += sym.n;
sym.prev = index - 1;
sym.next = offs == text.size() ? -1 : index + 1;
index++;
symbols.emplace_back(sym);
}
// seed the work queue with all possible 2-character tokens.
for (int i = 1; i < (int) symbols.size(); ++i) {
try_add_bigram(i - 1, i);
}
// keep substituting the highest frequency pairs for as long as we can.
while (!work_queue.empty()) {
auto bigram = work_queue.top();
work_queue.pop();
auto & left_sym = symbols[bigram.left];
auto & right_sym = symbols[bigram.right];
// if one of the symbols already got merged, skip it.
if (left_sym.n == 0 || right_sym.n == 0 ||
left_sym.n + right_sym.n != bigram.size) {
continue;
}
// merge the right sym into the left one
left_sym.n += right_sym.n;
right_sym.n = 0;
//LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
// remove the right sym from the chain
left_sym.next = right_sym.next;
if (right_sym.next >= 0) {
symbols[right_sym.next].prev = bigram.left;
}
// find more substitutions
try_add_bigram(left_sym.prev, bigram.left);
try_add_bigram(bigram.left, left_sym.next);
}
for (int i = 0; i != -1; i = symbols[i].next) {
auto & symbol = symbols[i];
resegment(symbol, output);
}
}
private:
void resegment(llm_symbol & symbol, std::vector<llama_token> & output) {
auto text = std::string(symbol.text, symbol.n);
auto token = vocab.text_to_token(text);
// Do we need to support is_unused?
if (token != LLAMA_TOKEN_NULL) {
output.push_back(token);
return;
}
const auto p = rev_merge.find(text);
if (p == rev_merge.end()) {
// output any symbols that did not form tokens as bytes.
output.reserve(output.size() + symbol.n);
for (int j = 0; j < (int)symbol.n; ++j) {
llama_token id = vocab.byte_to_token(symbol.text[j]);
output.push_back(id);
}
return;
}
resegment(symbols[p->second.first], output);
resegment(symbols[p->second.second], output);
}
void try_add_bigram(int left, int right) {
if (left == -1 || right == -1) {
return;
}
const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
auto token = vocab.text_to_token(text);
if (token == LLAMA_TOKEN_NULL) {
return;
}
if (static_cast<uint32_t>(token) >= vocab.n_tokens()) {
return;
}
const auto & tok_data = vocab.get_token_data(token);
llm_bigram_spm bigram;
bigram.left = left;
bigram.right = right;
bigram.score = tok_data.score;
bigram.size = text.size();
work_queue.push(bigram);
// Do we need to support is_unused?
rev_merge[text] = std::make_pair(left, right);
}
const llama_vocab & vocab;
// currently unused
// const llm_tokenizer_spm * spm_tokenizer;
std::vector<llm_symbol> symbols;
llm_bigram_spm::queue work_queue;
std::map<std::string, std::pair<int, int>> rev_merge;
};
//
// BPE tokenizer
// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
// tried to simplify unicode stuff, so most likely does not work 100% correctly!
//
// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused
template<typename T, typename Container = std::vector<T>, typename Compare = std::less<typename Container::value_type>>
class llama_priority_queue : public std::priority_queue<T, Container, Compare> {
public:
using std::priority_queue<T, Container, Compare>::priority_queue;
T pop_move() {
T item = std::move(this->c.front());
std::pop_heap(this->c.begin(), this->c.end(), this->comp);
this->c.pop_back();
return item;
}
void pop() = delete;
};
struct llm_bigram_bpe {
struct comparator {
bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
}
};
using queue_storage = std::vector<llm_bigram_bpe>;
using queue = llama_priority_queue<llm_bigram_bpe, queue_storage, comparator>;
llm_symbol::index left;
llm_symbol::index right;
std::string text;
int rank;
size_t size;
};
struct llm_tokenizer_bpe : llm_tokenizer {
llm_tokenizer_bpe(const llama_vocab & vocab) {
GGML_ASSERT(vocab.get_type() == LLAMA_VOCAB_TYPE_BPE);
switch (vocab.get_pre_type()) {
case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
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}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
// adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
"(?:'[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+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_DBRX:
case LLAMA_VOCAB_PRE_TYPE_SMAUG:
regex_exprs = {
// same as llama3
"(?:'[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+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM:
regex_exprs = {
"[\r\n]",
"\\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]+",
"\\s?[!-/:-~！-／：-～‘-‟ -。]+",
"\\s+$",
"[一-龥ࠀ-一가-]+",
"\\p{N}+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM:
regex_exprs = {
"\\p{N}{1,3}",
"[一-龥-ゟ゠-ヿ]+",
"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][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+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
regex_exprs = {
"[\r\n]",
"\\s?\\p{L}+",
"\\s?\\p{P}+",
"[一-龥ࠀ-一가-]+",
"\\p{N}",
};
break;
case LLAMA_VOCAB_PRE_TYPE_FALCON:
regex_exprs = {
"[\\p{P}\\$\\+<=>\\^~\\|`]+",
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
"[0-9][0-9][0-9]",
};
break;
case LLAMA_VOCAB_PRE_TYPE_STARCODER:
case LLAMA_VOCAB_PRE_TYPE_REFACT:
case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
case LLAMA_VOCAB_PRE_TYPE_SMOLLM:
case LLAMA_VOCAB_PRE_TYPE_CODESHELL:
case LLAMA_VOCAB_PRE_TYPE_EXAONE:
case LLAMA_VOCAB_PRE_TYPE_MINERVA:
regex_exprs = {
"\\p{N}",
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
};
break;
case LLAMA_VOCAB_PRE_TYPE_GPT2:
case LLAMA_VOCAB_PRE_TYPE_MPT:
case LLAMA_VOCAB_PRE_TYPE_OLMO:
case LLAMA_VOCAB_PRE_TYPE_JAIS:
regex_exprs = {
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
};
break;
case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
case LLAMA_VOCAB_PRE_TYPE_QWEN2:
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}| ?[^\\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}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_PORO:
case LLAMA_VOCAB_PRE_TYPE_BLOOM:
case LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH:
regex_exprs = {
" ?[^(\\s|.,!?…。，、।۔،)]+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_CHATGLM4:
regex_exprs = {
"(?:'[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+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_VIKING:
regex_exprs = {
" ?[^(\\s|.,!?…。，、।۔،)]+",
"\\p{N}",
};
break;
case LLAMA_VOCAB_PRE_TYPE_TEKKEN:
// 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}]+|[^\\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+"
regex_exprs = {
"[^\\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+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_CHAMELEON:
// 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;
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, -FLT_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, (float) 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, (float) 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;
float 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