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""" |
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Byte pair encoding utilities from GPT-2. |
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Original source: https://github.com/openai/gpt-2/blob/master/src/encoder.py |
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Original license: MIT |
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""" |
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import json |
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from functools import lru_cache |
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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 signficant 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 = ( |
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list(range(ord("!"), ord("~") + 1)) |
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+ list(range(ord("¡"), ord("¬") + 1)) |
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+ list(range(ord("®"), ord("ÿ") + 1)) |
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) |
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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 |
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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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try: |
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import regex as re |
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self.re = re |
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except ImportError: |
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raise ImportError("Please install regex with: pip install regex") |
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self.pat = self.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(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 self.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( |
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self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ") |
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) |
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return bpe_tokens |
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def decode(self, tokens): |
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text = "".join([self.decoder.get(token, token) for token in tokens]) |
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text = bytearray([self.byte_decoder[c] for c in text]).decode( |
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"utf-8", errors=self.errors |
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) |
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return text |
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def get_encoder(encoder_json_path, vocab_bpe_path): |
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with open(encoder_json_path, "r") as f: |
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encoder = json.load(f) |
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with open(vocab_bpe_path, "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( |
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encoder=encoder, |
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bpe_merges=bpe_merges, |
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) |
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