mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-15 07:19:53 +00:00
ff252ea48e
* sampling : add DRY sampler (post-refactor) * DRY: Trying to fix coauthors, removed unneeded line * DRY: Fixed redundant code * DRY: Fixed crash issue due to DRY being in chain but uninitialized --------- Co-authored-by: l3utterfly <gc.pthzfoldr@gmail.com> Co-authored-by: pi6am <34464159+pi6am@users.noreply.github.com>
1985 lines
76 KiB
C++
1985 lines
76 KiB
C++
#include "llama-vocab.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 <cfloat>
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#include <climits>
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#include <cstdarg>
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#include <cstring>
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#include <forward_list>
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#include <queue>
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#include <sstream>
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//
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// helpers
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//
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LLAMA_ATTRIBUTE_FORMAT(1, 2)
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static std::string format(const char * fmt, ...) {
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va_list ap;
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va_list ap2;
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va_start(ap, fmt);
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va_copy(ap2, ap);
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int size = vsnprintf(NULL, 0, fmt, ap);
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GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
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std::vector<char> buf(size + 1);
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int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
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GGML_ASSERT(size2 == size);
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va_end(ap2);
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va_end(ap);
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return std::string(buf.data(), size);
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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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// impl
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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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llama_vocab::~llama_vocab() {
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delete tokenizer;
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}
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int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
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GGML_ASSERT(token_left.find(' ') == std::string::npos);
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GGML_ASSERT(token_left.find('\n') == std::string::npos);
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GGML_ASSERT(token_right.find(' ') == std::string::npos);
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GGML_ASSERT(token_right.find('\n') == std::string::npos);
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auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
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if (it == bpe_ranks.end()) {
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return -1;
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}
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return it->second;
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}
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static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
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return vocab.type;
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}
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static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
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GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
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return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
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}
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static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
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GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
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return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
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}
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static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
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GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
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return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
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}
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static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
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GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
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return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
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}
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static bool llama_is_user_defined_token(const llama_vocab & vocab, llama_token id) {
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GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
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return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
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}
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static bool llama_is_unused_token(const llama_vocab & vocab, llama_token id) {
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GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
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return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNUSED;
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}
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static uint8_t llama_token_to_byte(const llama_vocab & vocab, llama_token id) {
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GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
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GGML_ASSERT(llama_is_byte_token(vocab, id));
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const auto & token_data = vocab.id_to_token.at(id);
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switch (llama_vocab_get_type(vocab)) {
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case LLAMA_VOCAB_TYPE_SPM:
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case LLAMA_VOCAB_TYPE_UGM: {
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auto buf = token_data.text.substr(3, 2);
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return strtol(buf.c_str(), NULL, 16);
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}
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case LLAMA_VOCAB_TYPE_BPE: {
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GGML_ABORT("fatal error");
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//return unicode_utf8_to_byte(token_data.text); // TODO: why is this here after GGML_ASSERT?
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}
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case LLAMA_VOCAB_TYPE_WPM: {
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GGML_ABORT("fatal error");
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}
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default:
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GGML_ABORT("fatal error");
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}
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}
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static void llama_escape_whitespace(std::string & text) {
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replace_all(text, " ", "\xe2\x96\x81");
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}
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static void llama_unescape_whitespace(std::string & word) {
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replace_all(word, "\xe2\x96\x81", " ");
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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*/) : llm_tokenizer() {}
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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_vocab::id> & 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_vocab::id> & output) {
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auto text = std::string(symbol.text, symbol.n);
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auto token = vocab.token_to_id.find(text);
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// Do we need to support is_unused?
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if (token != vocab.token_to_id.end()) {
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output.push_back((*token).second);
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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_vocab::id token_id = llama_byte_to_token_impl(vocab, symbol.text[j]);
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output.push_back(token_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.token_to_id.find(text);
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if (token == vocab.token_to_id.end()) {
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return;
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}
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if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
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return;
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}
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const auto & tok_data = vocab.id_to_token[(*token).second];
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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) : llm_tokenizer() {
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GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE);
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switch (vocab.type_pre) {
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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+$",
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"[一-龥ࠀ-一가-]+",
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"\\p{N}+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
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regex_exprs = {
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"[\r\n]",
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"\\s?\\p{L}+",
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"\\s?\\p{P}+",
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"[一-龥ࠀ-一가-]+",
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"\\p{N}",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_FALCON:
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regex_exprs = {
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"[\\p{P}\\$\\+<=>\\^~\\|`]+",
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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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regex_exprs = {
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"\\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) : vocab(vocab),
|
||
bpe_tokenizer(static_cast<const llm_tokenizer_bpe *>(vocab.tokenizer)) {}
|
||
|
||
static void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output) {
|
||
output.push_back(token_id);
|
||
}
|
||
|
||
bool append_bos(std::vector<llama_vocab::id> & output) const {
|
||
if (vocab.tokenizer_add_bos) {
|
||
GGML_ASSERT(vocab.special_bos_id != -1);
|
||
output.push_back(vocab.special_bos_id);
|
||
return true;
|
||
}
|
||
return false;
|
||
}
|
||
|
||
bool append_eos(std::vector<llama_vocab::id> & output) const {
|
||
if (vocab.tokenizer_add_eos) {
|
||
GGML_ASSERT(vocab.special_eos_id != -1);
|
||
output.push_back(vocab.special_eos_id);
|
||
return true;
|
||
}
|
||
return false;
|
||
}
|
||
|
||
void check_double_bos_eos(const std::vector<llama_vocab::id> & output) const {
|
||
if (vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.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 (vocab.tokenizer_add_eos && output.size() >= 2 && *(output.end()-2) == vocab.special_eos_id) {
|
||
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_vocab::id> & output) {
|
||
int final_prev_index = -1;
|
||
const auto word_collection = unicode_regex_split(text, bpe_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()) {
|
||
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.token_to_id.find(str);
|
||
|
||
if (token == vocab.token_to_id.end()) {
|
||
for (auto j = str.begin(); j != str.end(); ++j) {
|
||
std::string byte_str(1, *j);
|
||
auto token_multibyte = vocab.token_to_id.find(byte_str);
|
||
if (token_multibyte != vocab.token_to_id.end()) {
|
||
output.push_back(token_multibyte->second);
|
||
}
|
||
}
|
||
} else {
|
||
output.push_back((*token).second);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
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 * 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*/) : llm_tokenizer() {}
|
||
};
|
||
|
||
struct llm_tokenizer_wpm_session {
|
||
llm_tokenizer_wpm_session(const llama_vocab & vocab) : vocab(vocab) {}
|
||
|
||
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
|
||
const auto & token_map = vocab.token_to_id;
|
||
// 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 it = token_map.find(word1.substr(i, j - i));
|
||
if (it != token_map.end()) {
|
||
output.push_back(it->second);
|
||
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.special_unk_id);
|
||
}
|
||
}
|
||
}
|
||
|
||
// 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(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) : llm_tokenizer() {
|
||
if (vocab.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 *) &vocab.precompiled_charsmap[0];
|
||
charsmap_offset += sizeof(xcda_blob_size);
|
||
if (xcda_blob_size + charsmap_offset >= vocab.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 *) &vocab.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 = &vocab.precompiled_charsmap[charsmap_offset];
|
||
prefix_replacements_size = vocab.precompiled_charsmap.size() - charsmap_offset;
|
||
}
|
||
|
||
for (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
|
||
const auto &token_data = vocab.id_to_token[id];
|
||
|
||
if (llama_is_normal_token(vocab, id)) {
|
||
min_score = std::min<float>(min_score, token_data.score);
|
||
max_score = std::max<float>(max_score, token_data.score);
|
||
}
|
||
|
||
if (llama_is_normal_token(vocab, id) ||
|
||
llama_is_user_defined_token(vocab, id) ||
|
||
llama_is_unused_token(vocab, id)) {
|
||
token_matcher.insert(token_data.text.data(), token_data.text.size(), id);
|
||
}
|
||
|
||
if (llama_is_user_defined_token(vocab, 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) : vocab(vocab),
|
||
ugm_tokenizer(static_cast<const llm_tokenizer_ugm *>(vocab.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_vocab::id> & 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.special_unk_id, 0, -FLT_MAX});
|
||
// at the beginning tokenization score is zero
|
||
tokenization_results[0] = { vocab.special_unk_id, 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 = ugm_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.id_to_token[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 = llama_is_user_defined_token(vocab, 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 + ugm_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.special_unk_id, 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.special_unk_id;
|
||
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.tokenizer_escape_whitespaces ? ugm_tokenizer->escaped_space : " ";
|
||
|
||
bool shall_prepend_space = !vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
|
||
bool shall_append_space = vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
|
||
bool shall_merge_spaces = vocab.tokenizer_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 =
|
||
ugm_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 (ugm_tokenizer->xcda_array_size > 0) {
|
||
struct xcda_array_view xcda_view(ugm_tokenizer->xcda_array, ugm_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 >= ugm_tokenizer->prefix_replacements_size) {
|
||
throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
|
||
}
|
||
const char * prefix_replacement = &(ugm_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 * 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) : llm_tokenizer() {
|
||
// 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 (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
|
||
const auto & token = vocab.id_to_token[id];
|
||
const auto data = llama_unescape_rwkv_token(token.text);
|
||
token_matcher.insert((const char *) data.data(), data.size(), id);
|
||
}
|
||
}
|
||
|
||
struct naive_trie token_matcher;
|
||
};
|
||
|
||
struct llm_tokenizer_rwkv_session {
|
||
llm_tokenizer_rwkv_session(const llama_vocab & vocab) : vocab(vocab),
|
||
rwkv_tokenizer(static_cast<const llm_tokenizer_rwkv &>(*vocab.tokenizer)) {}
|
||
|
||
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
|
||
uint32_t position = 0;
|
||
while (position < text.size()) {
|
||
const struct naive_trie * node = rwkv_tokenizer.token_matcher.traverse(text[position]);
|
||
if (node == NULL) {
|
||
// no matching token found, add unknown token
|
||
output.push_back(vocab.special_unk_id);
|
||
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 & rwkv_tokenizer;
|
||
};
|
||
|
||
void llama_vocab::init_tokenizer() {
|
||
switch (type) {
|
||
case LLAMA_VOCAB_TYPE_SPM:
|
||
tokenizer = new llm_tokenizer_spm(*this);
|
||
break;
|
||
case LLAMA_VOCAB_TYPE_BPE:
|
||
tokenizer = new llm_tokenizer_bpe(*this);
|
||
break;
|
||
case LLAMA_VOCAB_TYPE_WPM:
|
||
tokenizer = new llm_tokenizer_wpm(*this);
|
||
break;
|
||
case LLAMA_VOCAB_TYPE_UGM:
|
||
tokenizer = new llm_tokenizer_ugm(*this);
|
||
break;
|
||
case LLAMA_VOCAB_TYPE_RWKV:
|
||
tokenizer = new llm_tokenizer_rwkv(*this);
|
||
break;
|
||
default:
|
||
GGML_ABORT("unsupported vocab type");
|
||
}
|
||
}
|
||
|
||
//
|
||
// (de-) tokenize
|
||
//
|
||
|
||
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_vocab::id _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_vocab::id) - 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_vocab::id token;
|
||
const std::string _dummy;
|
||
const std::string & raw_text;
|
||
const uint64_t offset;
|
||
const uint64_t length;
|
||
};
|
||
|
||
// #define PRETOKENIZERDEBUG
|
||
|
||
static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) {
|
||
// for each special token
|
||
for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
|
||
const auto & data = vocab.id_to_token[special_id];
|
||
const auto & special_token = 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
|
||
auto match = raw_text.find(special_token, raw_text_base_offset);
|
||
|
||
// no occurrences found, stop processing this fragment for a given special token
|
||
if (match == std::string::npos) break;
|
||
|
||
// check if match is within bounds of offset <-> length
|
||
if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) 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 + special_token.length() < raw_text_base_offset + raw_text_base_length) {
|
||
int64_t right_reminder_offset = match + special_token.length();
|
||
int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.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++;
|
||
}
|
||
}
|
||
}
|
||
|
||
std::vector<llama_vocab::id> llama_tokenize_internal(
|
||
const llama_vocab & vocab,
|
||
std::string raw_text,
|
||
bool add_special,
|
||
bool parse_special) {
|
||
GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
|
||
|
||
std::vector<llama_vocab::id> 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(vocab, fragment_buffer, parse_special);
|
||
}
|
||
|
||
switch (vocab.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 && vocab.tokenizer_add_bos) {
|
||
GGML_ASSERT(vocab.special_bos_id != -1);
|
||
output.push_back(vocab.special_bos_id);
|
||
is_prev_special = true;
|
||
}
|
||
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
|
||
// prefix with space if previous is special
|
||
if (vocab.tokenizer_add_space_prefix && is_prev_special) {
|
||
raw_text = " " + raw_text;
|
||
}
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
|
||
#endif
|
||
llama_escape_whitespace(raw_text);
|
||
llm_tokenizer_spm_session session(vocab);
|
||
session.tokenize(raw_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 && vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.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 && vocab.tokenizer_add_eos) {
|
||
GGML_ASSERT(vocab.special_eos_id != -1);
|
||
output.push_back(vocab.special_eos_id);
|
||
}
|
||
} break;
|
||
case LLAMA_VOCAB_TYPE_BPE:
|
||
{
|
||
llm_tokenizer_bpe_session session(vocab);
|
||
// 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) {
|
||
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
|
||
#endif
|
||
session.tokenize(raw_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(vocab.special_cls_id != -1);
|
||
output.push_back(vocab.special_cls_id);
|
||
}
|
||
|
||
llm_tokenizer_wpm_session session(vocab);
|
||
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
|
||
#endif
|
||
session.tokenize(raw_text, output);
|
||
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
||
output.push_back(fragment.token);
|
||
}
|
||
}
|
||
|
||
if (add_special) {
|
||
GGML_ASSERT(vocab.special_sep_id != -1);
|
||
output.push_back(vocab.special_sep_id);
|
||
}
|
||
} break;
|
||
case LLAMA_VOCAB_TYPE_UGM:
|
||
{
|
||
if (add_special && vocab.tokenizer_add_bos) {
|
||
GGML_ASSERT(vocab.special_bos_id != -1);
|
||
output.push_back(vocab.special_bos_id);
|
||
}
|
||
llm_tokenizer_ugm_session session(vocab);
|
||
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
|
||
#endif
|
||
session.tokenize(raw_text, output);
|
||
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
||
output.push_back(fragment.token);
|
||
}
|
||
}
|
||
|
||
if (add_special && vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.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 && vocab.tokenizer_add_eos) {
|
||
GGML_ASSERT(vocab.special_eos_id != -1);
|
||
output.push_back(vocab.special_eos_id);
|
||
}
|
||
} break;
|
||
case LLAMA_VOCAB_TYPE_RWKV:
|
||
{
|
||
llm_tokenizer_rwkv_session session(vocab);
|
||
for (const auto & fragment : fragment_buffer) {
|
||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||
|
||
#ifdef PRETOKENIZERDEBUG
|
||
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
|
||
#endif
|
||
|
||
session.tokenize(raw_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;
|
||
}
|
||
|
||
llama_token llama_byte_to_token_impl(const llama_vocab & vocab, uint8_t ch) {
|
||
GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
|
||
static const char * hex = "0123456789ABCDEF";
|
||
switch (llama_vocab_get_type(vocab)) {
|
||
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 = vocab.token_to_id.find(buf);
|
||
if (token != vocab.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 vocab.token_to_id.at(buf2);
|
||
}
|
||
case LLAMA_VOCAB_TYPE_WPM:
|
||
case LLAMA_VOCAB_TYPE_BPE: {
|
||
return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
|
||
}
|
||
default:
|
||
GGML_ABORT("fatal error");
|
||
}
|
||
}
|
||
|
||
const char * llama_token_get_text_impl(const struct llama_vocab & vocab, llama_token token) {
|
||
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
||
return vocab.id_to_token[token].text.c_str();
|
||
}
|
||
|
||
float llama_token_get_score_impl(const struct llama_vocab & vocab, llama_token token) {
|
||
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
||
return vocab.id_to_token[token].score;
|
||
}
|
||
|
||
llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, llama_token token) {
|
||
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
||
return vocab.id_to_token[token].attr;
|
||
}
|
||
|
||
bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token) {
|
||
return token != -1 && vocab.special_eog_ids.count(token) > 0;
|
||
}
|
||
|
||
bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token) {
|
||
return llama_is_control_token(vocab, token);
|
||
}
|
||
|
||
llama_token llama_token_bos_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_bos_id;
|
||
}
|
||
|
||
llama_token llama_token_eos_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_eos_id;
|
||
}
|
||
|
||
llama_token llama_token_eot_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_eot_id;
|
||
}
|
||
|
||
llama_token llama_token_eom_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_eom_id;
|
||
}
|
||
|
||
llama_token llama_token_cls_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_cls_id;
|
||
}
|
||
|
||
llama_token llama_token_sep_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_sep_id;
|
||
}
|
||
|
||
llama_token llama_token_nl_impl(const struct llama_vocab & vocab) {
|
||
return vocab.linefeed_id;
|
||
}
|
||
|
||
llama_token llama_token_pad_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_pad_id;
|
||
}
|
||
|
||
bool llama_add_bos_token_impl(const struct llama_vocab & vocab) {
|
||
return vocab.tokenizer_add_bos;
|
||
}
|
||
|
||
bool llama_add_eos_token_impl(const struct llama_vocab & vocab) {
|
||
return vocab.tokenizer_add_eos;
|
||
}
|
||
|
||
llama_token llama_token_prefix_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_fim_pre_id;
|
||
}
|
||
|
||
llama_token llama_token_middle_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_fim_mid_id;
|
||
}
|
||
|
||
llama_token llama_token_suffix_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_fim_suf_id;
|
||
}
|
||
|
||
llama_token llama_token_fim_pre_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_fim_pre_id;
|
||
}
|
||
|
||
llama_token llama_token_fim_suf_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_fim_suf_id;
|
||
}
|
||
|
||
llama_token llama_token_fim_mid_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_fim_mid_id;
|
||
}
|
||
|
||
llama_token llama_token_fim_pad_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_fim_pad_id;
|
||
}
|
||
|
||
llama_token llama_token_fim_rep_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_fim_rep_id;
|
||
}
|
||
|
||
llama_token llama_token_fim_sep_impl(const struct llama_vocab & vocab) {
|
||
return vocab.special_fim_sep_id;
|
||
}
|
||
|
||
int32_t llama_tokenize_impl(
|
||
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) {
|
||
auto res = llama_tokenize_internal(vocab, std::string(text, text_len), add_special, parse_special);
|
||
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();
|
||
}
|
||
|
||
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;
|
||
}
|
||
|
||
// does not write null-terminator to buf
|
||
int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) {
|
||
// 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 = llama_token_get_attr_impl(vocab, 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 {
|
||
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 = vocab.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) vocab.id_to_token.size()) {
|
||
const std::string & token_text = vocab.id_to_token[token].text;
|
||
switch (llama_vocab_get_type(vocab)) {
|
||
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) llama_token_to_byte(vocab, 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();
|
||
}
|
||
default:
|
||
GGML_ABORT("fatal error");
|
||
}
|
||
}
|
||
|
||
return 0;
|
||
}
|
||
|
||
int32_t llama_detokenize_impl(
|
||
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) {
|
||
GGML_ASSERT(vocab.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 = vocab.tokenizer_add_space_prefix;
|
||
|
||
if (remove_special && vocab.tokenizer_add_bos) {
|
||
if (n_tokens > 0 && tokens[0] == vocab.special_bos_id) {
|
||
remove_space = false;
|
||
n_tokens--;
|
||
tokens++;
|
||
}
|
||
}
|
||
|
||
if (remove_special && vocab.tokenizer_add_eos) {
|
||
if (n_tokens > 0 && tokens[n_tokens-1] == vocab.special_eos_id) {
|
||
n_tokens--;
|
||
}
|
||
}
|
||
|
||
for (int32_t i = 0; i < n_tokens; ++i) {
|
||
GGML_ASSERT(avail >= 0);
|
||
int32_t n_chars = llama_token_to_piece_impl(vocab, 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 (vocab.tokenizer_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;
|
||
}
|
||
|
||
std::string llama_detokenize(const struct llama_vocab & vocab, const std::vector<llama_token> & tokens, bool special) {
|
||
std::string text;
|
||
text.resize(std::max(text.capacity(), tokens.size()));
|
||
int32_t n_chars = llama_detokenize_impl(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
|
||
if (n_chars < 0) {
|
||
text.resize(-n_chars);
|
||
n_chars = llama_detokenize_impl(vocab, 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;
|
||
}
|