179 lines
6.6 KiB
Python
179 lines
6.6 KiB
Python
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"""Byte pair encoding utilities"""
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# Modified MIT License
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# Software Copyright (c) 2019 OpenAI
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# We don’t claim ownership of the content you create with GPT-2, so it is yours to do with as you please.
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# We only ask that you use GPT-2 responsibly and clearly indicate your content was created using GPT-2.
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# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
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# associated documentation files (the "Software"), to deal in the Software without restriction,
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# including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
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# subject to the following conditions:
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# The above copyright notice and this permission notice shall be included
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# in all copies or substantial portions of the Software.
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# The above copyright notice and this permission notice need not be included
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# with content created by the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
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# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
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# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
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# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
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# OR OTHER DEALINGS IN THE SOFTWARE.
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# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
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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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import json
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from functools import lru_cache
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import regex as re
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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 significant 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("!"),
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ord("~") + 1)) + list(range(
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ord("¡"),
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ord("¬") + 1)) + list(range(ord("®"),
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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 Encoder:
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def __init__(self, encoder, bpe_merges, errors='replace'):
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self.encoder = encoder
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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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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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self.cache = {}
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# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
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self.pat = re.compile(
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r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
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)
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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(
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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 +
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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]
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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]
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for c in text]).decode('utf-8', errors=self.errors)
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return text
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def batch_decode(self, output):
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ret = []
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for tokens in output:
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ret.append(self.decode(tokens))
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return ret
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def get_encoder(vocab_file, bpe_file):
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with open(vocab_file, 'r', encoding="utf-8") as f:
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encoder = json.load(f)
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with open(bpe_file, 'r', encoding="utf-8") as f:
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bpe_data = f.read()
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bpe_merges = [
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tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]
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]
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return Encoder(
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encoder=encoder,
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bpe_merges=bpe_merges,
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)
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