tokenization_qwen.py

# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""Tokenization classes for QWen."""

from __future__ import absolute_import, division, print_function, unicode_literals

import json
import logging
import os
import unicodedata
from io import open
import base64
import tiktoken
from typing import List, Optional, Tuple, Union

from transformers import PreTrainedTokenizer, AddedToken

logger = logging.getLogger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}


class QWenTokenizer(PreTrainedTokenizer):
    """QWen tokenizer."""

    """NOTE: This tokenizer will not handle special tokens to avoid injection attacks"""

    vocab_files_names = VOCAB_FILES_NAMES

    def __init__(
        self,
        vocab_file,
        errors="replace",
        max_len=None,
        unk_token="<|endoftext|>",
        bos_token="<|endoftext|>",
        eos_token="<|endoftext|>",
        pad_token=None,
        add_prefix_space=False,
        add_bos_token=False,
        add_more_sp_tokens=True,
        **kwargs,
    ):
        bos_token = (
            AddedToken(bos_token, lstrip=False, rstrip=False)
            if isinstance(bos_token, str)
            else bos_token
        )
        eos_token = (
            AddedToken(eos_token, lstrip=False, rstrip=False)
            if isinstance(eos_token, str)
            else eos_token
        )
        unk_token = (
            AddedToken(unk_token, lstrip=False, rstrip=False)
            if isinstance(unk_token, str)
            else unk_token
        )
        pad_token = (
            AddedToken(pad_token, lstrip=False, rstrip=False)
            if isinstance(pad_token, str)
            else pad_token
        )
        super().__init__(
            errors=errors,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            add_prefix_space=add_prefix_space,
            add_bos_token=add_bos_token,
        )
        self.add_bos_token = add_bos_token
        self.max_len = max_len if max_len is not None else int(1e12)

        self.errors = errors  # how to handle errors in decoding

        name = "QWen"
        ENDOFTEXT = "<|endoftext|>"
        IMSTART = "<|im_start|>"
        IMEND = "<|im_end|>"
        if add_more_sp_tokens:
            special_tokens = (
                ENDOFTEXT,
                IMSTART,
                IMEND,
                "<R>",
                "<S>",
                "<X>",
                "<mask>",
                "<sep>",
            ) + tuple([f"<extra_{i}>" for i in range(200)])
        else:
            special_tokens = (ENDOFTEXT, IMSTART, IMEND)

        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+"""

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

        mergeable_ranks = load_tiktoken_bpe(vocab_file)
        special_tokens = {
            token: index
            for index, token in enumerate(special_tokens, start=len(mergeable_ranks))
        }
        self.special_tokens = special_tokens
        enc = tiktoken.Encoding(
            name,
            pat_str=PAT_STR,
            mergeable_ranks=mergeable_ranks,
            special_tokens=special_tokens,
        )
        assert (
            len(mergeable_ranks) + len(special_tokens) == enc.n_vocab
        ), f"{len(mergeable_ranks) + len(special_tokens)} != {enc.n_vocab} in encoding"

        self.mergeable_ranks = mergeable_ranks
        self.encoder = self.mergeable_ranks
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.tokenizer = enc  # type: tiktoken.Encoding
        self.eod_id = self.tokenizer.eot_token
        self.im_start_id = special_tokens[IMSTART]
        self.im_end_id = special_tokens[IMEND]

    def __len__(self):
        return self.tokenizer.n_vocab

    def get_vocab(self):
        return self.mergeable_ranks

    def convert_tokens_to_ids(self, tokens):
        ids = []
        # Remove support for py2
        if isinstance(tokens, str):
            if tokens in self.special_tokens:
                return self.special_tokens[tokens]
            else:
                return self.encoder.get(tokens)
        for token in tokens:
            if token in self.special_tokens:
                ids.append(self.special_tokens[token])
            else:
                ids.append(self.encoder.get(token))
        if len(ids) > self.max_len:
            logger.warning(
                "Token indices sequence length is longer than the specified maximum "
                " sequence length for this OpenAI GPT model ({} > {}). Running this"
                " sequence through the model will result in indexing errors".format(
                    len(ids), self.max_len
                )
            )
        return ids

    def save_vocabulary(self, save_directory: str) -> Tuple[str]:
        """
        Save only the vocabulary of the tokenizer (vocabulary + added tokens).

        Returns:
            `Tuple(str)`: Paths to the files saved.
        """
        file_path = os.path.join(save_directory, "qwen.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, **kwargs) -> List[str]:
        """
        Converts a string in a sequence of tokens, replacing unknown tokens with the `unk_token`.

        Args:
            text (`str`):
                The sequence to be encoded.
            pair (`str`, *optional*):
                A second sequence to be encoded with the first.
            add_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not to add the special tokens associated with the corresponding model.
            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific encode method. See details in
                [`~PreTrainedTokenizerBase.__call__`]

        Returns:
            `List[str]`: The list of tokens.
        """
        tokens = []
        text = unicodedata.normalize("NFC", text)
        for t in self.tokenizer.encode_ordinary(text):
            tokens.append(self.decoder[t])
        return tokens

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """
        Converts a sequence of tokens in a single string. The most simple way to do it is `" ".join(tokens)` but we
        often want to remove sub-word tokenization artifacts at the same time.
        """
        text = "".join(tokens)
        text = bytearray([self.byte_decoder[c] for c in text]).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) -> str:
        raise NotImplementedError

    def _tokenize(self, text, **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,
        clean_up_tokenization_spaces: bool = None,
        **kwargs,
    ) -> str:
        if isinstance(token_ids, int):
            token_ids = [token_ids]
        return self.tokenizer.decode(token_ids)