300 lines
12 KiB
Python
300 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Tokenization classes for XLNet model."""
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import json
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import logging
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import os
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import sys
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from shutil import copyfile
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from io import open
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import unicodedata
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import six
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from .file_utils import cached_path
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logger = logging.getLogger(__name__)
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PRETRAINED_VOCAB_ARCHIVE_MAP = {
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'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-spiece.model",
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}
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VOCAB_NAME = 'spiece.model'
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SPECIAL_TOKENS_NAME = 'special_tokens.txt'
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SPIECE_UNDERLINE = u'▁'
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# Tokens
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special_symbols = {
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"<unk>" : 0,
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"<s>" : 1,
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"</s>" : 2,
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"<cls>" : 3,
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"<sep>" : 4,
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"<pad>" : 5,
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"<mask>" : 6,
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"<eod>" : 7,
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"<eop>" : 8,
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}
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VOCAB_SIZE = 32000
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UNK_ID = special_symbols["<unk>"]
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CLS_ID = special_symbols["<cls>"]
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SEP_ID = special_symbols["<sep>"]
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MASK_ID = special_symbols["<mask>"]
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EOD_ID = special_symbols["<eod>"]
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# Segments (not really needed)
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SEG_ID_A = 0
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SEG_ID_B = 1
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SEG_ID_CLS = 2
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SEG_ID_SEP = 3
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SEG_ID_PAD = 4
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class XLNetTokenizer(object):
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"""
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SentencePiece based tokenizer. Peculiarities:
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- requires SentencePiece: https://github.com/google/sentencepiece
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"""
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
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"""
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Instantiate a PreTrainedBertModel from a pre-trained model file.
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Download and cache the pre-trained model file if needed.
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"""
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if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
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vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
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special_tokens_file = None
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if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True):
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logger.warning("The pre-trained model you are loading is a cased model but you have not set "
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"`do_lower_case` to False. We are setting `do_lower_case=False` for you but "
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"you may want to check this behavior.")
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kwargs['do_lower_case'] = False
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elif '-cased' not in pretrained_model_name_or_path and not kwargs.get('do_lower_case', True):
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logger.warning("The pre-trained model you are loading is an uncased model but you have set "
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"`do_lower_case` to False. We are setting `do_lower_case=True` for you "
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"but you may want to check this behavior.")
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kwargs['do_lower_case'] = True
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else:
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vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
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special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME)
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if not os.path.exists(special_tokens_file):
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special_tokens_file = None
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else:
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logger.info("loading special tokens file {}".format(special_tokens_file))
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# redirect to the cache, if necessary
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try:
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resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
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except EnvironmentError:
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if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
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logger.error(
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"Couldn't reach server at '{}' to download vocabulary.".format(
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vocab_file))
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else:
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logger.error(
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"Model name '{}' was not found in model name list ({}). "
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"We assumed '{}' was a path or url but couldn't find files {}"
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"at this path or url.".format(
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pretrained_model_name_or_path,
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', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
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pretrained_model_name_or_path,
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vocab_file))
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return None
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if resolved_vocab_file == vocab_file:
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logger.info("loading vocabulary file {}".format(vocab_file))
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else:
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logger.info("loading vocabulary file {} from cache at {}".format(
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vocab_file, resolved_vocab_file))
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# Instantiate tokenizer.
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if special_tokens_file and 'special_tokens' not in kwargs:
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special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
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else:
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special_tokens = kwargs.pop('special_tokens', [])
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tokenizer = cls(resolved_vocab_file, special_tokens=special_tokens, *inputs, **kwargs)
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return tokenizer
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def __init__(self, vocab_file, special_tokens=None, max_len=None,
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do_lower_case=False, remove_space=True, keep_accents=False):
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try:
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import sentencepiece as spm
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except ImportError:
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logger.warning("You need to install SentencePiece to use XLNetTokenizer: https://github.com/google/sentencepiece"
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"pip install sentencepiece")
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self.max_len = max_len if max_len is not None else int(1e12)
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self.do_lower_case = do_lower_case
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self.remove_space = remove_space
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self.keep_accents = keep_accents
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self.vocab_file = vocab_file
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self.sp_model = spm.SentencePieceProcessor()
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self.sp_model.Load(vocab_file)
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self.special_tokens = {}
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self.special_tokens_decoder = {}
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self.set_special_tokens(special_tokens)
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def __len__(self):
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return len(self.encoder) + len(self.special_tokens)
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def set_special_tokens(self, special_tokens):
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""" Add a list of additional tokens to the encoder.
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The additional tokens are indexed starting from the last index of the
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current vocabulary in the order of the `special_tokens` list.
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"""
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if not special_tokens:
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self.special_tokens = {}
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self.special_tokens_decoder = {}
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return
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self.special_tokens = dict((tok, len(self.sp_model) + i) for i, tok in enumerate(special_tokens))
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self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()}
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logger.info("Special tokens: %s", str(self.special_tokens))
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def preprocess_text(self, inputs):
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if self.remove_space:
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outputs = ' '.join(inputs.strip().split())
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else:
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outputs = inputs
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outputs = outputs.replace("``", '"').replace("''", '"')
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if six.PY2 and isinstance(outputs, str):
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outputs = outputs.decode('utf-8')
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if not self.keep_accents:
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outputs = unicodedata.normalize('NFKD', outputs)
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outputs = ''.join([c for c in outputs if not unicodedata.combining(c)])
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if self.do_lower_case:
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outputs = outputs.lower()
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return outputs
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def tokenize(self, text, return_unicode=True, sample=False):
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""" Tokenize a string.
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return_unicode is used only for py2
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"""
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text = self.preprocess_text(text)
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# note(zhiliny): in some systems, sentencepiece only accepts str for py2
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if six.PY2 and isinstance(text, unicode):
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text = text.encode('utf-8')
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if not sample:
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pieces = self.sp_model.EncodeAsPieces(text)
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else:
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pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1)
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new_pieces = []
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for piece in pieces:
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if len(piece) > 1 and piece[-1] == ',' and piece[-2].isdigit():
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cur_pieces = self.sp_model.EncodeAsPieces(
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piece[:-1].replace(SPIECE_UNDERLINE, ''))
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if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
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if len(cur_pieces[0]) == 1:
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cur_pieces = cur_pieces[1:]
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else:
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cur_pieces[0] = cur_pieces[0][1:]
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cur_pieces.append(piece[-1])
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new_pieces.extend(cur_pieces)
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else:
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new_pieces.append(piece)
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# note(zhiliny): convert back to unicode for py2
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if six.PY2 and return_unicode:
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ret_pieces = []
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for piece in new_pieces:
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if isinstance(piece, str):
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piece = piece.decode('utf-8')
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ret_pieces.append(piece)
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new_pieces = ret_pieces
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return new_pieces
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def convert_tokens_to_ids(self, tokens, sample=False):
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""" Converts a sequence of tokens into ids using the vocab. """
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ids = []
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if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)):
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if tokens in self.special_tokens:
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return self.special_tokens[tokens]
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else:
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return self.sp_model.PieceToId(tokens)
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for token in tokens:
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if token in self.special_tokens:
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ids.append(self.special_tokens[token])
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else:
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ids.append(self.sp_model.PieceToId(token))
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if len(ids) > self.max_len:
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logger.warning(
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"Token indices sequence length is longer than the specified maximum "
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" sequence length for this XLNet model ({} > {}). Running this"
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" sequence through the model will result in indexing errors".format(len(ids), self.max_len)
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)
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return ids
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def convert_ids_to_tokens(self, ids, return_unicode=True, skip_special_tokens=False):
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"""Converts a sequence of ids in tokens."""
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tokens = []
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for i in ids:
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if i in self.special_tokens_decoder:
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if not skip_special_tokens:
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tokens.append(self.special_tokens_decoder[i])
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else:
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tokens.append(self.sp_model.IdToPiece(i))
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if six.PY2 and return_unicode:
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ret_pieces = []
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for piece in tokens:
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if isinstance(piece, str):
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piece = piece.decode('utf-8')
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ret_pieces.append(piece)
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tokens = ret_pieces
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return tokens
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def encode(self, text, sample=False):
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return self.convert_tokens_to_ids(self.tokenize(text, sample=sample))
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def decode(self, ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
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"""Converts a sequence of ids in a string."""
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tokens = self.convert_ids_to_tokens(ids, skip_special_tokens=skip_special_tokens)
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out_string = ''.join(tokens)
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if clean_up_tokenization_spaces:
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out_string = out_string.strip().replace('<unk>', '')
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out_string = out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ','
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).replace(" ' ", "'").replace(" n't", "n't").replace(" 'm", "'m").replace(" do not", " don't"
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).replace(" 's", "'s").replace(" 've", "'ve").replace(" 're", "'re")
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return out_string
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def save_vocabulary(self, vocab_path):
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""" Save the sentencepiece vocabulary (copy original file) and special tokens file
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to a directory.
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"""
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if not os.path.isdir(vocab_path):
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logger.error("Vocabulary path ({}) should be a directory".format(vocab_path))
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return
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out_vocab_file = os.path.join(vocab_path, VOCAB_NAME)
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special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
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copyfile(self.vocab_file, out_vocab_file)
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index = len(self.sp_model)
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with open(special_tokens_file, 'w', encoding='utf-8') as writer:
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for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive."
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" Please check that the tokenizer is not corrupted!".format(special_tokens_file))
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index = token_index
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writer.write(token + u'\n')
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index += 1
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return out_vocab_file, special_tokens_file
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