hf_wrapper.py

# Copied from
# https://huggingface.co/turboderp/dbrx-instruct-exl2/raw/1643e9fdeaaf9435862fce31d17789fac262df8d/tiktoken.py

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

from transformers import PreTrainedTokenizer
import tiktoken
import tiktoken_chatml


# 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))


class TiktokenTokenizerWrapper(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] = None,
        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] = None,
        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.
            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"`.
        """

        # 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)

        if model_name is not None and encoding_name is not None:
            raise ValueError(
                "You need to specify either model_name or encoding_name, not both."
            )

        self.model_name = model_name
        self.encoding_name = encoding_name

        if self.model_name is not None:
            self.encoding = tiktoken.encoding_for_model(  # type: ignore (thirdParty)
                self.model_name
            )
        elif self.encoding_name is not None:
            self.encoding = tiktoken_chatml.get_encoding(  # type: ignore (thirdParty)
                self.encoding_name
            )
        else:
            raise ValueError("You need to specify either model_name or encoding_name.")

        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

    @property
    def default_chat_template(self):
        """Chat ML Template for User/Assistant.
        Pinning default Chat ML template in case defaults change.
        """
        return tiktoken_chatml.DEFAULT_CHAT_TEMPLATE

    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)

    # @TODO: supoort more args
    # why overwriting this?
    # bc HF chat template is buggy :sob:
    def apply_chat_template(
        self,
        conversation: List[Dict[str, str]],
        add_generation_prompt: bool = False,
        tokenize: bool = False,
    ) -> str:
        if tokenize:
            raise NotImplementedError
        return self.encoding.apply_chat_template(
            msgs=conversation,
            add_generation_prompt=add_generation_prompt,
            tokenize=False,
        )