""" 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, )