248 lines
8.0 KiB
Python
248 lines
8.0 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# Copyright @2024 AI. Inspur Inc.
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#
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# @author: sunxian <sunxian@inspur.com>
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# @date: 2024/08/08
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#
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import json
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import os
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import unicodedata
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from functools import lru_cache
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from typing import Optional
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from typing import Tuple
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import regex as re
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from transformers.tokenization_utils import AddedToken
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from transformers.tokenization_utils import PreTrainedTokenizer
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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MAX_MODEL_INPUT_SIZES = {"hairuo/hairuo-tokenizer": 32768}
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@lru_cache()
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def bytes_to_unicode():
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bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8 + n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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def get_pairs(word):
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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class HairuoTokenizer(PreTrainedTokenizer):
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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_auto_class = "AutoTokenizer"
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def __init__(
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self,
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vocab_file,
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merges_file,
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errors="replace",
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unk_token="<|end_of_text|>",
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bos_token=None,
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eos_token="<|end_of_text|>",
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pad_token="<|end_of_text|>",
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clean_up_tokenization_spaces=False,
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split_special_tokens=False,
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**kwargs,
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):
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bos_token = (
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AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(bos_token, str)
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else bos_token
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)
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eos_token = (
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AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(eos_token, str)
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else eos_token
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)
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unk_token = (
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AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(unk_token, str)
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else unk_token
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)
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pad_token = (
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AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(pad_token, str)
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else pad_token
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)
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with open(vocab_file, encoding="utf-8") as vocab_handle:
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self.encoder = json.load(vocab_handle)
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.errors = errors
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self.byte_encoder = bytes_to_unicode()
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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bpe_merges = []
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with open(merges_file, encoding="utf-8") as merges_handle:
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for i, line in enumerate(merges_handle):
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line = line.strip()
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if (i == 0 and line.startswith("#version:")) or not line:
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continue
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bpe_merges.append(tuple(line.split()))
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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self.cache = {}
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self.pat = re.compile(
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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+"""
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)
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if kwargs.get("add_prefix_space", False):
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logger.warning_once(
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f"{self.__class__.__name} does not support `add_prefix_space`, setting it to True has no effect."
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)
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super().__init__(
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errors=errors,
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bos_token=bos_token,
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eos_token=eos_token,
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pad_token=pad_token,
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unk_token=unk_token,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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split_special_tokens=split_special_tokens,
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**kwargs,
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)
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@property
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def vocab_size(self) -> int:
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return len(self.encoder)
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def bpe(self, token):
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if token in self.cache:
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return self.cache[token]
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word = tuple(token)
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pairs = get_pairs(word)
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if not pairs:
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return token
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = " ".join(word)
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self.cache[token] = word
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return word
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def _tokenize(self, text, **kwargs):
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bpe_tokens = []
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for token in self.pat.findall(text):
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token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
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bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
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return bpe_tokens
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def _convert_token_to_id(self, token):
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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return self.decoder.get(index)
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def convert_tokens_to_string(self, tokens):
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text = "".join(tokens)
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text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
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return text
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def decode(
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self,
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token_ids,
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skip_special_tokens: bool = False,
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clean_up_tokenization_spaces: Optional[bool] = False,
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spaces_between_special_tokens: bool = False,
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**kwargs,
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) -> str:
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return super().decode(
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token_ids,
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skip_special_tokens=skip_special_tokens,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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spaces_between_special_tokens=spaces_between_special_tokens,
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**kwargs,
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)
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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merge_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
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)
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with open(vocab_file, "w", encoding="utf-8") as f:
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f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
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index = 0
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with open(merge_file, "w", encoding="utf-8") as writer:
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writer.write("#version: 0.2\n")
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
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" Please check that the tokenizer is not corrupted!"
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)
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index = token_index
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writer.write(" ".join(bpe_tokens) + "\n")
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index += 1
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return vocab_file, merge_file
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def prepare_for_tokenization(self, text, **kwargs):
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text = unicodedata.normalize("NFC", text)
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return text, kwargs
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