kopia lustrzana https://github.com/jaymody/picoGPT
121 wiersze
4.2 KiB
Python
121 wiersze
4.2 KiB
Python
"""Byte pair encoding utilities.
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Copied from: https://github.com/openai/gpt-2/blob/master/src/encoder.py.
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"""
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import json
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import os
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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("!"), 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 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(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 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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def get_encoder(model_name, models_dir):
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with open(os.path.join(models_dir, model_name, "encoder.json"), "r") as f:
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encoder = json.load(f)
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with open(os.path.join(models_dir, model_name, "vocab.bpe"), "r", encoding="utf-8") as f:
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bpe_data = f.read()
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bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
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return Encoder(encoder=encoder, bpe_merges=bpe_merges)
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