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mpt tokenizer: better special token handling

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closer to the behavior of huggingface `tokenizers`,
do not attempt to handle additional tokens as if they were part
of the original vocabulary as this cannot prevent them from being
split into smaller chunks - handle added tokens *before*
the regular tokenizing pass

note this is still necessary even with a "proper" tokenizer implementation
This commit is contained in:
Aaron Miller 2023-05-08 14:42:20 -07:00 committed by AT
parent 8c4b8f215f
commit 832720dd27
1 changed files with 37 additions and 17 deletions
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54
llmodel/mpt.cpp
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@ -223,7 +223,7 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
// TODO: this only kind-of works, the gpt_tokenize can still incorrectly // TODO: this only kind-of works, the gpt_tokenize can still incorrectly
// tokenize special tokens // tokenize special tokens
if(special) { if(special) {
vocab.add_special_token(regex_escape(word)); vocab.add_special_token(word);
} }
} }
} }
@ -648,7 +648,7 @@ bool mpt_eval(
return true; return true;
} }
std::vector<int> mpt_tokenize(const mpt_vocab & vocab, const std::string & text) { std::vector<int> mpt_tokenize_inner(const mpt_vocab & vocab, const std::string & text) {
// taken from stablelm example in ggml // taken from stablelm example in ggml
// they both use the gpt-neox tokenizer // they both use the gpt-neox tokenizer
// not sure if this entirely right? // not sure if this entirely right?
@ -659,21 +659,6 @@ std::vector<int> mpt_tokenize(const mpt_vocab & vocab, const std::string & text)
{ {
std::string str = text; std::string str = text;
std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"; std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
// Generate the subpattern from the special_tokens vector if it's not empty
if (!vocab.special_tokens.empty()) {
std::string special_tokens_subpattern;
for (const auto &token : vocab.special_tokens) {
if (!special_tokens_subpattern.empty()) {
special_tokens_subpattern += "|";
}
special_tokens_subpattern += token;
}
// Modify the regex pattern with the generated special tokens subpattern
pat = special_tokens_subpattern + "|" + pat;
}
std::regex re(pat); std::regex re(pat);
std::smatch m; std::smatch m;
@ -721,6 +706,41 @@ std::vector<int> mpt_tokenize(const mpt_vocab & vocab, const std::string & text)
return tokens; return tokens;
} }
std::vector<mpt_vocab::id> mpt_tokenize(const mpt_vocab & vocab, const std::string & text) {
// Generate the subpattern from the special_tokens vector if it's not empty
if (!vocab.special_tokens.empty()) {
std::vector<mpt_vocab::id> out;
std::vector<std::string> chunks;
std::string str = text;
std::string special_tokens_subpattern;
for (const auto &token : vocab.special_tokens) {
if (!special_tokens_subpattern.empty()) {
special_tokens_subpattern += "|";
}
special_tokens_subpattern += regex_escape(token);
}
std::regex re(special_tokens_subpattern);
std::smatch m;
while (std::regex_search(str, m, re)) {
auto tok = vocab.token_to_id.find(m.str());
if (tok != vocab.token_to_id.end()) {
auto tokid = tok->second;
auto pfxtoks = mpt_tokenize_inner(vocab, m.prefix());
out.insert(out.end(), pfxtoks.begin(), pfxtoks.end());
out.push_back(tokid);
str = m.suffix();
}
}
if (!str.empty()) {
auto tokrest = mpt_tokenize_inner(vocab, str);
out.insert(out.end(), tokrest.begin(), tokrest.end());
}
return out;
} else {
return mpt_tokenize_inner(vocab, text);
}
}
#define MPT_MAX_RNG_STATE 64*1024 #define MPT_MAX_RNG_STATE 64*1024
size_t mpt_get_state_size(const mpt_model &model) size_t mpt_get_state_size(const mpt_model &model)