[BIG] pytorch-transformers => transformers
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thomwolf
230
transformers/tokenization_gpt2.py
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230
transformers/tokenization_gpt2.py
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# coding=utf-8
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# Copyright 2018 The Open AI Team 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 OpenAI GPT."""
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import sys
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import json
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import logging
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import os
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import regex as re
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from io import open
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try:
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from functools import lru_cache
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except ImportError:
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# Just a dummy decorator to get the checks to run on python2
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# because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
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def lru_cache():
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return lambda func: func
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from .tokenization_utils import PreTrainedTokenizer
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logger = logging.getLogger(__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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PRETRAINED_VOCAB_FILES_MAP = {
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'vocab_file':
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{
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'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
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'gpt2-medium': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-vocab.json",
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'gpt2-large': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-vocab.json",
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},
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'merges_file':
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{
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'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
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'gpt2-medium': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-merges.txt",
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'gpt2-large': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-merges.txt",
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},
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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'gpt2': 1024,
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'gpt2-medium': 1024,
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'gpt2-large': 1024,
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}
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@lru_cache()
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a mapping to unicode strings.
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We specifically avoids mapping to whitespace/control characters the bpe code barfs on.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a signficant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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"""
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_chr = unichr if sys.version_info[0] == 2 else chr
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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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"""Return set of symbol pairs in a word.
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Word is represented as tuple of symbols (symbols being variable-length strings).
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"""
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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 GPT2Tokenizer(PreTrainedTokenizer):
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"""
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GPT-2 BPE tokenizer. Peculiarities:
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- Byte-level Byte-Pair-Encoding
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- Requires a space to start the input string => will add a space is there isn't.
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As a consequence, this tokenizer `encode` and `decode` method will not conserve
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the absence of a space at the beginning of a string: `tokenizer.decode(tokenizer.encode("Hello")) = " Hello"
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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def __init__(self, vocab_file, merges_file, errors='replace', unk_token="<|endoftext|>",
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bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs):
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super(GPT2Tokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
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self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
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self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
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self.encoder = json.load(open(vocab_file, encoding="utf-8"))
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.errors = errors # how to handle errors in decoding
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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_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
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bpe_merges = [tuple(merge.split()) for merge in bpe_data]
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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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# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
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self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
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@property
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def vocab_size(self):
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return len(self.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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new_word.extend(word[i:j])
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i = j
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except:
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new_word.extend(word[i:])
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break
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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, add_prefix_space=False):
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""" Tokenize a string.
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Args:
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- add_prefix_space (boolean, default False):
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Begin the sentence with at least one space toto get invariance to word order in GPT-2 (and RoBERTa) tokenizers.
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"""
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if add_prefix_space:
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text = ' ' + text
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bpe_tokens = []
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for token in re.findall(self.pat, text):
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if sys.version_info[0] == 2:
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token = ''.join(self.byte_encoder[ord(b)] for b in token) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
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else:
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token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
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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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""" Converts a token (str/unicode) in an id using the vocab. """
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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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"""Converts an index (integer) in a token (string/unicode) using the vocab."""
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return self.decoder.get(index)
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def convert_tokens_to_string(self, tokens):
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""" Converts a sequence of tokens (string) in a single string. """
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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 save_vocabulary(self, save_directory):
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"""Save the tokenizer vocabulary and merge files to a directory."""
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if not os.path.isdir(save_directory):
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logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
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return
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vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
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merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES['merges_file'])
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with open(vocab_file, 'w', encoding='utf-8') as f:
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f.write(json.dumps(self.encoder, ensure_ascii=False))
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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(u'#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("Saving vocabulary to {}: BPE merge indices are not consecutive."
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" Please check that the tokenizer is not corrupted!".format(merge_file))
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index = token_index
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writer.write(' '.join(bpe_tokens) + u'\n')
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index += 1
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return vocab_file, merge_file
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