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"""
Byte pair encoding utilities adapted from:
https://github.com/openai/gpt-2/blob/master/src/encoder.py
"""
import gzip
import json
import os
from functools import lru_cache
from typing import List, Tuple
import regex as re
@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 signficant 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
class Encoder:
def __init__(self, encoder, bpe_merges, errors="replace"):
self.encoder = encoder
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()}
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+"""
)
@property
def n_vocab(self) -> int:
return len(self.encoder)
@property
def end_token(self) -> int:
return self.n_vocab - 1
def padded_tokens_and_mask(
self, tokens: List[int], text_ctx: int
) -> Tuple[List[int], List[bool]]:
tokens = tokens[:text_ctx]
padding = text_ctx - len(tokens)
padded_tokens = tokens + [self.end_token] * padding
mask = [True] * len(tokens) + [False] * padding
return padded_tokens, mask
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: # pylint: disable=bare-except
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):
text = text.lower()
bpe_tokens = []
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):
text = "".join([self.decoder[token] for token in tokens])
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
return text
def get_encoder():
root_dir = os.path.dirname(os.path.abspath(__file__))
with gzip.open(os.path.join(root_dir, "encoder.json.gz"), "r") as f:
encoder = json.load(f)
with gzip.open(os.path.join(root_dir, "vocab.bpe.gz"), "r") as f:
bpe_data = str(f.read(), "utf-8")
bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
return Encoder(
encoder=encoder,
bpe_merges=bpe_merges,
)
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