# coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for OpenAI GPT (BPE)."""
import json
import logging
import os
from functools import lru_cache
from io import open
from typing import List, Optional
import regex as re
from .tokenization_base import PreTrainedTokenizer
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json"},
"merges_file": {"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt"},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"gpt2": 1024,
}
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping
to whitespace/control characters the bpe code barfs on.
The reversible bpe codes work on unicode strings. This means you need a large # of unicode
characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token
dataset you end up needing around 5K for decent coverage. This is a significant percentage
of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8
bytes and unicode strings.
"""
bs = (
list(range(ord("!"), ord("~") + 1))
+ list(range(ord("¡"), ord("¬") + 1))
+ list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2 ** 8):
if b not in bs:
bs.append(b)
cs.append(2 ** 8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
[docs]class GPT2Tokenizer(PreTrainedTokenizer):
"""
Construct a GPT-2 tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (:obj:`str`):
Path to the vocabulary file.
merges_file (:obj:`str`):
Path to the merges file.
errors (:obj:`str`, `optional`, defaults to :obj:`"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See `bytes.decode
<https://docs.python.org/3/library/stdtypes.html#bytes.decode>`__ for more information.
unk_token (:obj:`str`, `optional`, defaults to :obj:`<|endoftext|>`):
The unknown token. A token that is not in the vocabulary cannot be
converted to an ID and is set to be this token instead.
bos_token (:obj:`str`, `optional`, defaults to :obj:`<|endoftext|>`):
The beginning of sequence token.
eos_token (:obj:`str`, `optional`, defaults to :obj:`<|endoftext|>`):
The end of sequence token.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
merges_file,
errors="replace",
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_bos_token=False,
**kwargs,
):
super(GPT2Tokenizer, self).__init__(
bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs
)
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
bpe_data = open(merges_file, encoding="utf-8").read().split("\n")[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
# Should haved added re.IGNORECASE so BPE merges can happen for
# capitalized versions of contractions
self.pat = re.compile(
r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
)
self.add_bos_token = add_bos_token
@property
def vocab_size(self):
return len(self.encoder)
[docs] def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except: # noqa
new_word.extend(word[i:])
break
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = " ".join(word)
self.cache[token] = word
return word
def _tokenize(self, text):
"""Tokenize a string."""
bpe_tokens = []
for token in re.findall(self.pat, text):
# Maps all our bytes to unicode strings, avoiding control tokens
# of the BPE (spaces in our case)
token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
return bpe_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
[docs] def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) to a single string."""
text = "".join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
return text
[docs] def save_vocabulary(self, save_directory, filename_prefix=None):
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"],
)
merge_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"],
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!"
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return (vocab_file, merge_file)