tokenization_transnormerllm.py

# CREDITS: tiktoken @openai
# https://github.com/openai/tiktoken
#
#    Copyright 2024 OpenNLPLab
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.

# coding=utf-8
import base64
import logging
import os
from typing import Collection, Dict, List, Set, Tuple, Union
import unicodedata

import tiktoken
from transformers import AddedToken, AutoTokenizer, PreTrainedTokenizer

logger = logging.getLogger(__name__)


VOCAB_FILES_NAMES = {"vocab_file": "transnormer_100k.tiktoken"}
PAT_STR = r"""(?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+"""
SPECIAL_TOKENS_DICT = {'<|endoftext|>': 100257, '<|fim_prefix|>': 100258, '<|fim_middle|>': 100259, '<|fim_suffix|>': 100260, '<|endofprompt|>': 100276, '<|J2PM|>': 100256, '<s>': 100261, '<pad>': 100262, '<unk>': 100263, '<mask>': 100264}
SPECIAL_TOKENS_SET = set(SPECIAL_TOKENS_DICT.keys())

# as the default behavior is changed to allow special tokens in
# regular texts, the surface forms of special tokens need to be
# as different as possible to minimize the impact
# changed to use actual index to avoid misconfiguration with vocabulary expansion

def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
    with open(tiktoken_bpe_file, "rb") as f:
        contents = f.read()
    return {
        base64.b64decode(token): int(rank)
        for token, rank in (line.split() for line in contents.splitlines() if line)
    }


class GPT4Tokenizer(PreTrainedTokenizer):
    vocab_files_names = VOCAB_FILES_NAMES
    def __init__(
        self,
        vocab_file,
        errors="replace",
        extra_vocab_file=None,
        **kwargs,
    ):
        super().__init__(**kwargs)

        # how to handle errors in decoding UTF-8 byte sequences
        # use ignore if you are in streaming inference
        self.errors = errors  

        self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)  # type: Dict[bytes, int]
        self.special_tokens = SPECIAL_TOKENS_DICT
 
        enc = tiktoken.Encoding(
            "transnormer_100k",
            pat_str=PAT_STR,
            mergeable_ranks=self.mergeable_ranks,
            special_tokens=self.special_tokens,
        )
        self.decoder = {
            v: k for k, v in self.mergeable_ranks.items()
        }  # type: dict[int, bytes|str]
        self.decoder.update({v: k for k, v in self.special_tokens.items()})

        self.tokenizer = enc  # type: tiktoken.Encoding

        self.eod_id = self.tokenizer.eot_token
        self.pad_token_id = 100262
        self.bos_token_id = 100261
        self.eos_token_id = self.eod_id

    def __getstate__(self):
        # for pickle lovers
        state = self.__dict__.copy()
        del state["tokenizer"]
        return state

    def __setstate__(self, state):
        # tokenizer is not python native; don't pass it; rebuild it
        self.__dict__.update(state)
        enc = tiktoken.Encoding(
            "transnormer_100k",
            pat_str=PAT_STR,
            mergeable_ranks=self.mergeable_ranks,
            special_tokens=self.special_tokens,
        )
        self.tokenizer = enc

    def __len__(self) -> int:
        return self.tokenizer.n_vocab

    def get_vocab(self) -> Dict[bytes, int]:
        return self.mergeable_ranks

    def convert_tokens_to_ids(
        self, tokens: Union[bytes, str, List[Union[bytes, str]]]
    ) -> List[int]:
        ids = []
        if isinstance(tokens, (str, bytes)):
            if tokens in self.special_tokens:
                return self.special_tokens[tokens]
            else:
                return self.mergeable_ranks.get(tokens)
        for token in tokens:
            if token in self.special_tokens:
                ids.append(self.special_tokens[token])
            else:
                ids.append(self.mergeable_ranks.get(token))
        return ids

    def _add_tokens(
        self,
        new_tokens: Union[List[str], List[AddedToken]],
        special_tokens: bool = False,
    ) -> int:
        if not special_tokens and new_tokens:
            raise ValueError("Adding regular tokens is not supported")
        for token in new_tokens:
            surface_form = token.content if isinstance(token, AddedToken) else token
            if surface_form not in SPECIAL_TOKENS_SET:
                raise ValueError("Adding unknown special tokens is not supported")
        return 0

    def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
        """
        Save only the vocabulary of the tokenizer (vocabulary).
        Returns:
            `Tuple(str)`: Paths to the files saved.
        """
        file_path = os.path.join(save_directory, "transnormer_100k.tiktoken")
        with open(file_path, "w", encoding="utf8") as w:
            for k, v in self.mergeable_ranks.items():
                line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
                w.write(line)
        return (file_path,)

    def tokenize(
        self,
        text: str,
        allowed_special: Union[Set, str] = "all",
        disallowed_special: Union[Collection, str] = (),
        **kwargs,
    ) -> List[Union[bytes, str]]:
        """
        Converts a string in a sequence of tokens.
        Args:
            text (`str`):
                The sequence to be encoded.
            allowed_special (`Literal["all"]` or `set`):
                The surface forms of the tokens to be encoded as special tokens in regular texts.
                Default to "all".
            disallowed_special (`Literal["all"]` or `Collection`):
                The surface forms of the tokens that should not be in regular texts and trigger errors.
                Default to an empty tuple.
            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific encode method.
        Returns:
            `List[bytes|str]`: The list of tokens.
        """
        tokens = []

        # this implementation takes a detour: text -> token id -> token surface forms
        for t in self.tokenizer.encode(
            text, allowed_special=allowed_special, disallowed_special=disallowed_special
        ):
            tokens.append(self.decoder[t])
        return tokens

    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
        """
        Converts a sequence of tokens in a single string.
        """
        text = ""
        temp = b""
        for t in tokens:
            if isinstance(t, str):
                if temp:
                    text += temp.decode("utf-8", errors=self.errors)
                    temp = b""
                text += t
            elif isinstance(t, bytes):
                temp += t
            else:
                raise TypeError("token should only be of type types or str")
        if temp:
            text += temp.decode("utf-8", errors=self.errors)
        return text

    @property
    def vocab_size(self):
        return self.tokenizer.n_vocab

    def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
        """Converts an id to a token, special tokens included"""
        if index in self.decoder:
            return self.decoder[index]
        raise ValueError("unknown ids")

    def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
        """Converts a token to an id using the vocab, special tokens included"""
        if token in self.special_tokens:
            return self.special_tokens[token]
        if token in self.mergeable_ranks:
            return self.mergeable_ranks[token]
        raise ValueError("unknown token")

    def _tokenize(self, text: str, **kwargs):
        """
        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
        Do NOT take care of added tokens.
        """
        raise NotImplementedError

    def _decode(
        self,
        token_ids: Union[int, List[int]],
        skip_special_tokens: bool = False,
        errors: str = None,
        **kwargs,
    ) -> str:
        if isinstance(token_ids, int):
            token_ids = [token_ids]
        if skip_special_tokens:
            token_ids = [i for i in token_ids if i < self.eod_id]
        return self.tokenizer.decode(token_ids, errors=errors or self.errors)