File size: 7,035 Bytes
9b0e299 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
import torch
@lru_cache
def default_bpe():
"""Returns the file path to the default BPE vocabulary file 'bpe_simple_vocab_16e6.txt.gz'."""
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@lru_cache
def bytes_to_unicode():
"""Returns list of utf-8 byte and a corresponding list of unicode strings.
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. And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
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
def basic_clean(text):
"""Clean text by fixing encoding issues and unescaping HTML entities, then stripping extraneous whitespace."""
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip()
def whitespace_clean(text):
"""Clean text by collapsing multiple whitespace characters into a single space and trimming leading/trailing
whitespace.
"""
text = re.sub(r"\s+", " ", text)
text = text.strip()
return text
class SimpleTokenizer:
"""Tokenizes text using byte pair encoding (BPE) and predefined tokenization rules for efficient text processing."""
def __init__(self, bpe_path: str = default_bpe()):
"""Initialize the SimpleTokenizer object with byte pair encoding (BPE) paths and set up encoders, decoders, and
patterns.
"""
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
merges = merges[1 : 49152 - 256 - 2 + 1]
merges = [tuple(merge.split()) for merge in merges]
vocab = list(bytes_to_unicode().values())
vocab += [f"{v}</w>" for v in vocab]
vocab.extend("".join(merge) for merge in merges)
vocab.extend(["<|startoftext|>", "<|endoftext|>"])
self.encoder = dict(zip(vocab, range(len(vocab))))
self.decoder = {v: k for k, v in self.encoder.items()}
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {"<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>"}
self.pat = re.compile(
r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
re.IGNORECASE,
)
self.sot_token_id = self.encoder["<|startoftext|>"]
self.eot_token_id = self.encoder["<|endoftext|>"]
self.context_length = 77
def bpe(self, token):
"""Apply byte pair encoding (BPE) to a given token and cache the result."""
if token in self.cache:
return self.cache[token]
word = (*tuple(token[:-1]), f"{token[-1]}</w>")
pairs = get_pairs(word)
if not pairs:
return f"{token}</w>"
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 Exception:
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 encode(self, text):
"""Converts input text to BPE tokens using byte-pair encoding and pre-defined tokenization rules."""
bpe_tokens = []
text = whitespace_clean(basic_clean(text)).lower()
for token in re.findall(self.pat, text):
token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
return bpe_tokens
def decode(self, tokens):
"""Decodes a list of BPE tokens into a UTF-8 string, replacing '</w>' with a space."""
text = "".join([self.decoder[token] for token in tokens])
return bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors="replace").replace("</w>", " ")
def __call__(self, texts: str | list[str], context_length: int | None = None) -> torch.LongTensor:
"""Returns the tokenized representation of given input string(s).
Args:
texts (Union[str, list[str]]): An input string or a list of input strings to tokenize.
context_length (int): The context length to use.
Returns:
(torch.Tensor): A two-dimensional tensor containing the resulting tokens, shape = [number of input strings,
context_length]
"""
if isinstance(texts, str):
texts = [texts]
context_length = context_length or self.context_length
assert context_length, "Please set a valid context length"
all_tokens = [[self.sot_token_id, *self.encode(text), self.eot_token_id] for text in texts]
result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
for i, tokens in enumerate(all_tokens):
if len(tokens) > context_length:
tokens = tokens[:context_length] # Truncate
tokens[-1] = self.eot_token_id
result[i, : len(tokens)] = torch.as_tensor(tokens, dtype=torch.long)
return result
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