tokenization_plamo.py

import os
import warnings
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging

VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
logger = logging.get_logger(__name__)


def _get_tokenizer_threads(default: int = -1) -> int:
    env_names = [
        "PLAMO_TOKENIZER_NUM_THREADS",
        "RAYON_NUM_THREADS",
    ]
    for name in env_names:
        v = os.environ.get(name, None)
        if v:
            try:
                return int(v)
            except ValueError:
                warnings.warn(
                    f"Value assigned to env `{name}` is not an integer. Current value is {v}",
                    category=RuntimeWarning,
                    stacklevel=2,
                )
    return default


class PlamoTokenizer(PreTrainedTokenizer):  # type: ignore
    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file: str,
        unk_token: str = "<unk>",
        bos_token: str = "<s>",
        eos_token: str = "</s>",
        pad_token: str = "<pad>",
        cls_token: str = "<cls>",
        sep_token: str = "<sep>",
        mask_token: str = "<mask>",
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        clean_up_tokenization_spaces: bool = False,
        num_threads: int = -1,
        **kwargs: Any,
    ) -> None:
        """Tokenizer for PLaMo.

        Args:
            vocab_file (str): Vocabrary file path.
            unk_token (str): Unknown token.
            bos_token (str): Beginning of sentence token.
            eos_token (str): End of sentence token.
            pad_token (str): Padding token.
            cls_token (str):
                Classification token, to extract a summary of an input sequence leveraging self-attention along the
                full depth of the model.
            sep_token (str): Separation token, to separate context and query in an input sequence.
            mask_token (str): Mask token, to use when training a model with masked-language modeling.
            sp_model_kwargs (Dict[atr, Any] or None): kwargs for sentencepiece model.
            clean_up_tokenization_spaces (bool): Whether or not to clean up the tokenization spaces.
            num_threads (int):
                Number of threads. This value will be ignored if one of `PLAMO_TOKENIZER_NUM_THREADS` or
                `RAYON_NUM_THREADS` is set as an environment variable.
        """
        if "add_bos_token" not in kwargs:
            kwargs["add_bos_token"] = False
        if "add_eos_token" not in kwargs:
            kwargs["add_eos_token"] = False
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Init(model_file=vocab_file, num_threads=_get_tokenizer_threads(num_threads))
        self.vocab_file = vocab_file
        self.add_bos_token = kwargs["add_bos_token"]
        self.add_eos_token = kwargs["add_eos_token"]

        super().__init__(
            vocab_file=vocab_file,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            cls_token=cls_token,
            sep_token=sep_token,
            mask_token=mask_token,
            sp_model_kwargs=sp_model_kwargs,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )

    # the functions below are copied from hf transformers LlamaTokenizer's implementation to fix the behaviour of the tokenizer
    # https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/models/llama/tokenization_llama.py

    def __getstate__(self) -> dict[str, Any]:
        state = self.__dict__.copy()
        state["sp_model"] = None
        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
        return state

    def __setstate__(self, d: dict[str, Any]) -> None:
        self.__dict__ = d
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

    @property
    def vocab_size(self) -> Any:
        """Returns vocab size"""
        return self.sp_model.get_piece_size()

    def get_vocab(self) -> dict[str, int]:
        """Returns vocab as a dict"""
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def convert_tokens_to_string(self, tokens: List[int]) -> str:
        """Converts a sequence of tokens (string) in a single string."""
        current_sub_tokens: List[int] = []
        out_string = ""
        prev_is_special = False
        for i, token in enumerate(tokens):
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special and i != 0:
                    out_string += " "
                out_string += self.sp_model.decode(current_sub_tokens) + token
                prev_is_special = True
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
                prev_is_special = False
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string

    def _tokenize(self, text: str) -> Any:
        """Returns a tokenized string."""
        return self.sp_model.encode(text, out_type=str)

    def _convert_token_to_id(self, token: str) -> Any:
        """Converts a token (str) in an id using the vocab."""
        return self.sp_model.piece_to_id(token)

    def _convert_id_to_token(self, index: int) -> Any:
        """Converts an index (integer) in a token (str) using the vocab."""
        token = self.sp_model.IdToPiece(index)
        return token

    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 save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Save the vocabulary and special tokens file to a directory.

        Args:
            save_directory (`str`):
                The directory in which to save the vocabulary.

        Returns:
            `Tuple(str)`: Paths to the files saved.
        """
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return ("",)
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (out_vocab_file,)