tokenization_sagelite.py

from functools import lru_cache
from typing import Any, Dict, List, Optional, Tuple

from transformers import PreTrainedTokenizer
import tiktoken


# Taken from
# https://github.com/huggingface/transformers/blob/8aca43bdb3cb9a5020f6d57589d85679dc873b1c/src/transformers/models/gpt2/tokenization_gpt2.py#L62-L84
@lru_cache()
def bytes_to_unicode():
    """Returns list of utf-8 byte and a mapping to unicode strings.
    We specifically avoids mapping to whitespace/control characters the bpe code
    barfs on.
    The reversible bpe codes work on unicode strings. This means you need a
    large # of unicode characters in your vocab if you want to avoid UNKs. When
    you're at something like a 10B token dataset you end up needing around 5K
    for decent coverage. This is a significant percentage of your normal, say,
    32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and
    unicode strings.
    """
    bs = (list(range(ord('!'),
                     ord('~') + 1)) + list(range(ord('¡'),
                                                 ord('¬') + 1)) +
          list(range(ord('®'),
                     ord('ÿ') + 1)))
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8 + n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


def add_special_tokens_to_tiktoken(base="cl100k_base", eos_token=None, pad_token=None):
    def include_dobf_tokens():
        dobf_tokens = [f"<dobf_special_{i}>" for i in range(18)]
        return dobf_tokens

    def include_vector_tokens():
        tokens = []
        tokens.append("<sep>")
        tokens.append("<mask>")
        tokens += [f"<dummy_{i}>" for i in reversed(range(20))]
        return tokens

    dobf_tokens = include_dobf_tokens()
    vector_tokens = include_vector_tokens()

    tokenizer = tiktoken.get_encoding(base)
    idx = tokenizer.n_vocab
    bpe_ranks = tokenizer._mergeable_ranks
    special_tokens = dict()

    # print(f"INIT TOKEN SIZE: {idx}, EOS TOKEN: {tokenizer._special_tokens[eos_token]}")
    if eos_token and eos_token not in tokenizer._special_tokens and eos_token not in special_tokens:
        special_tokens[eos_token] = idx
        idx += 1

    for sp in dobf_tokens:
        special_tokens[sp] = idx
        idx += 1
    for sp in vector_tokens:
        special_tokens[sp] = idx
        idx += 1

    if pad_token and pad_token not in tokenizer._special_tokens and pad_token not in special_tokens:
        special_tokens[pad_token] = idx
        idx += 1
        # print(f"PAD TOKEN ADDED: {pad_token}")
    # In production, load the arguments directly instead of accessing private attributes
    # See openai_public.py for examples of arguments for specific encodings
    enc = tiktoken.Encoding(
        # If you're changing the set of special tokens, make sure to use a different name
        # It should be clear from the name what behaviour to expect.
        name=base.replace("base", "im"),
        pat_str=tokenizer._pat_str,
        mergeable_ranks=bpe_ranks,
        special_tokens={
            **tokenizer._special_tokens,
            **special_tokens
        }
    )
    return enc


class SageLiteTokenizer(PreTrainedTokenizer):
    """A thin wrapper around tiktoken to make it compatible with Hugging Face.
    tokenizers.
    See HuggingFace for further documentation on general tokenizer methods.
    """

    model_input_names = ['input_ids', 'attention_mask']

    def __init__(self,
                 model_name: Optional[str] = None,
                 encoding_name: Optional[str] = "cl100k_base",
                 add_bos_token: bool = False,
                 add_eos_token: bool = False,
                 unk_token: Optional[str] = '<|endoftext|>',
                 eos_token: Optional[str] = '<|endoftext|>',
                 bos_token: Optional[str] = '<|endoftext|>',
                 pad_token: Optional[str] = '<pad>',
                 errors: str = 'replace',
                 **kwargs: Any):
        """Constructor creates a tiktoken tokenizer to use as the underlying.
        tokenizer.
        Args:
            model_name (Optional[str], optional): The name of the model to load from tiktoken. Defaults to None.
                Either model_name or encoding_name must be set, but not both.
            encoding_name (Optional[str], optional): The name of the encoding to load from tiktoken. Defaults to None.
                Either model_name or encoding_name must be set, but not both.
            add_bos_token (bool, optional): Whether to add bos tokens. Defaults to False.
            add_eos_token (bool, optional): Whether to add eos tokens. Defaults to False.
            use_default_system_prompt (bool, optional): Use the default system prompt or not. Defaults to False.
            unk_token (Optional[str], optional): The unk token. Defaults to '<|endoftext|>'.
            eos_token (Optional[str], optional): The eos token. Defaults to '<|endoftext|>'.
            bos_token (Optional[str], optional): The bos token. Defaults to '<|endoftext|>'.
            pad_token (Optional[str], optional): The pad token. Defaults to None.
            errors (str, optional): Paradigm to follow when decoding bytes to UTF-8. See
                [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
                Defaults to `"replace"`.
        """
        try:
            import tiktoken
        except:
            raise ImportError(
                'You need to install tiktoken to use TiktokenTokenizerWrapper.')

        # Workaround to make tiktokenizer picklable.
        # https://github.com/huggingface/datasets/issues/5536#issuecomment-1682309347
        # There is an open PR from HF to add this to tiktoken: https://github.com/openai/tiktoken/pull/181
        import copyreg
        import functools

        from tiktoken import Encoding  # type: ignore (thirdParty)

        def pickle_Encoding(enc: Encoding):
            return (functools.partial(Encoding,
                                      enc.name,
                                      pat_str=enc._pat_str,
                                      mergeable_ranks=enc._mergeable_ranks,
                                      special_tokens=enc._special_tokens), ())

        copyreg.pickle(Encoding, pickle_Encoding)


        self.encoding = add_special_tokens_to_tiktoken(base=encoding_name, eos_token=eos_token, pad_token=pad_token)

        self.add_bos_token = add_bos_token
        self.add_eos_token = add_eos_token

        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        self.errors = errors

        self.decoder: Dict[int, str] = {}
        for i in range(self.encoding.n_vocab):
            try:
                self.encoding.decode_single_token_bytes(i)
            except KeyError:
                continue
            # Taken from
            # https://gist.github.com/xenova/a452a6474428de0182b17605a98631ee
            decoding = ''.join([
                bytes_to_unicode()[ord(char)] for char in
                self.encoding.decode_single_token_bytes(i).decode('latin-1')
            ])
            self.decoder[i] = decoding

        self.encoder: Dict[str, int] = {}
        for i in range(self.encoding.n_vocab):
            if i in self.decoder:
                self.encoder[self.decoder[i]] = i

        super().__init__(model_name=model_name,
                         encoding_name=encoding_name,
                         add_bos_token=add_bos_token,
                         add_eos_token=add_eos_token,
                         unk_token=unk_token,
                         eos_token=eos_token,
                         bos_token=bos_token,
                         pad_token=pad_token,
                         errors=errors,
                         **kwargs)

    @property
    def vocab_size(self) -> int:
        """Returns vocab size."""
        return self.encoding.n_vocab

    @property
    def is_fast(self) -> bool:
        return False

    def get_vocab(self) -> Dict[str, int]:
        """Returns vocab as a dict."""
        # As far as I can tell, we don't require get_vocab to completely work,
        # but when using additional_special_tokens, Hugging Face determines the next
        # token index to add with len(self.get_vocab()) so we need the _size_ of this dictionary to be correct.
        vocab_clone = self.encoder.copy()
        extra_id_index = 0
        candidate_extra_id = f'<extra_id_{extra_id_index}>'
        indices_to_fill_in = {i for i in range(self.vocab_size)} - set(
            vocab_clone.values())

        # Add enough indices to make get_vocab() the right length
        for index_to_add in indices_to_fill_in:
            # Make sure we don't overwrite a token that already exists
            while candidate_extra_id in vocab_clone:
                extra_id_index += 1
                candidate_extra_id = f'<extra_id_{extra_id_index}>'

            # Get an index to add and add the item
            vocab_clone[candidate_extra_id] = index_to_add

        return vocab_clone

    def _tokenize(self, text: str) -> List[str]:
        """Returns a tokenized string."""
        if not isinstance(text, str):
            raise ValueError(
                f'Expected a string input to _tokenize but got {type(text)}.')

        tokens = [
            self.decoder[t]
            for t in self.encoding.encode(text, allowed_special='all')
        ]

        return tokens

    def _convert_token_to_id(self, token: str) -> Optional[int]:
        """Converts a token (str) in an id using the vocab."""
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index: int) -> Optional[str]:
        """Converts an index (integer) in a token (str) using the vocab."""
        # For tokens in either the gap in ids in the tokenizer, or beyond the range of the tokenizer,
        # we return empty string. This matches the behavior of Hugging Face fast tokenizers,
        # but not slow tokenizers.
        return self.decoder.get(index, '')

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """Converts a sequence of tokens (string) in a single string."""
        text = ''.join(tokens)
        text = bytearray([self.byte_decoder[c] for c in text
                         ]).decode('utf-8', errors=self.errors)
        return text

    def build_inputs_with_special_tokens(
            self,
            token_ids_0: List[int],
            token_ids_1: Optional[List[int]] = None) -> List[int]:
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = bos_token_id + token_ids_0 + eos_token_id

        if token_ids_1 is not None:
            output = output + bos_token_id + token_ids_1 + eos_token_id

        return output

    def get_special_tokens_mask(
            self,
            token_ids_0: List[int],
            token_ids_1: Optional[List[int]] = None,
            already_has_special_tokens: bool = False) -> List[int]:
        """Retrieves sequence ids from a token list that has no special tokens.
        Function copied from
        https://github.com/huggingface/transformers/blob/e3a4bd2bee212a2d0fd9f03b27fe7bfc1debe42d/src/transformers/models/gpt2/tokenization_gpt2.py#L265-L295
        added. This method is called when adding special tokens using the
        tokenizer `prepare_for_model` or `encode_plus` methods.
        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.
        Returns:
            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0,
                token_ids_1=token_ids_1,
                already_has_special_tokens=True)

        bos_token_id = [1] if self.add_bos_token else []
        eos_token_id = [1] if self.add_eos_token else []

        if token_ids_1 is None:
            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
        return (bos_token_id + ([0] * len(token_ids_0)) + eos_token_id +
                bos_token_id + ([0] * len(token_ids_1)) + eos_token_id)

    def create_token_type_ids_from_sequences(
            self,
            token_ids_0: List[int],
            token_ids_1: Optional[List[int]] = None) -> List[int]:
        sep = [self.sep_token_id]

        if token_ids_1 is None:
            return len(token_ids_0 + sep) * [0]
        return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

    def save_vocabulary(self,
                        save_directory: str,
                        filename_prefix: Optional[str] = None) -> Tuple[str]:

        # ignore the below type to keep the original signature
        # we are knowingly breaking the signature here, although not 100% certain
        # it doesn't have side effects
        # There is some code in huggingface that calls this function to get the vocab files,
        # but it doesn't seem to access them (or at least checks for their existence
        # before accessing them)
        return (None, None)  # type: ignore

    def sanitize_special_tokens(self) -> int:
        """Make sure that all the special tokens attributes of the tokenizer.
        (`tokenizer.mask_token`, `tokenizer.cls_token`, etc.) are in the
        vocabulary.
        Add the missing ones to the vocabulary if needed.
        Return:
            `int`: The number of tokens added in the vocabulary during the operation.
        """
        actual_new_tokens = []
        for token in self.all_special_tokens_extended:
            encoded = self.encoding.encode(token, allowed_special='all')
            if len(encoded) > 1:
                actual_new_tokens.append(token)

        return self.add_tokens(actual_new_tokens, special_tokens=True)