adding gpt2
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thomwolf
199
pytorch_pretrained_bert/tokenization_gpt2.py
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199
pytorch_pretrained_bert/tokenization_gpt2.py
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# coding=utf-8
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# Copyright 2018 The Open AI Team Authors and The HugginFace 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 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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import sys
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from io import open
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from functools import lru_cache
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from tqdm import tqdm
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from .file_utils import cached_path
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from .tokenization import BasicTokenizer
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logger = logging.getLogger(__name__)
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PRETRAINED_VOCAB_ARCHIVE_MAP = {
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'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
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}
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PRETRAINED_MERGES_ARCHIVE_MAP = {
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'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
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}
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PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
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'gpt2': 1024,
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}
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VOCAB_NAME = 'vocab.json'
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MERGES_NAME = 'merges.txt'
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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 corresponding list of unicode strings.
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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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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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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(object):
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"""
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GPT-2 BPE tokenizer. Peculiarities:
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- Byte-level BPE
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- argument special_tokens and function set_special_tokens:
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can be used to add additional symbols (ex: "__classify__") to a vocabulary.
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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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merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
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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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merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
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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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resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
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except EnvironmentError:
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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 {} and {} "
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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, merges_file))
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return None
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if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
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logger.info("loading vocabulary file {}".format(vocab_file))
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logger.info("loading merges file {}".format(merges_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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logger.info("loading merges file {} from cache at {}".format(
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merges_file, resolved_merges_file))
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if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
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# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
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# than the number of positional embeddings
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max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
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kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
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# Instantiate tokenizer.
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tokenizer = cls(resolved_vocab_file, resolved_merges_file, *inputs, **kwargs)
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return tokenizer
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def __init__(self, vocab_file, merges_file, errors='replace', max_len=None):
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self.max_len = max_len if max_len is not None else int(1e12)
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self.encoder = json.load(open(vocab_file))
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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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def __len__(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 encode(self, text):
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bpe_tokens = []
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for token in re.findall(self.pat, 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(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
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return bpe_tokens
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def decode(self, tokens):
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text = ''.join([self.decoder[token] for token in 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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