| | """ CLIP tokenizer
|
| |
|
| | Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
|
| | """
|
| | import gzip
|
| | import html
|
| | import os
|
| | from functools import lru_cache
|
| | from typing import Union, List
|
| |
|
| | import ftfy
|
| | import regex as re
|
| | import torch
|
| |
|
| |
|
| | @lru_cache()
|
| | def default_bpe():
|
| | 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 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))
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| | + list(range(ord("®"), ord("ÿ") + 1))
|
| | )
|
| | cs = bs[:]
|
| | n = 0
|
| | for b in range(2**8):
|
| | if b not in bs:
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| | bs.append(b)
|
| | cs.append(2**8 + n)
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| | 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):
|
| | text = ftfy.fix_text(text)
|
| | text = html.unescape(html.unescape(text))
|
| | return text.strip()
|
| |
|
| |
|
| | def whitespace_clean(text):
|
| | text = re.sub(r"\s+", " ", text)
|
| | text = text.strip()
|
| | return text
|
| |
|
| |
|
| | class SimpleTokenizer(object):
|
| | def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
|
| | 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 = vocab + [v + "</w>" for v in vocab]
|
| | for merge in merges:
|
| | vocab.append("".join(merge))
|
| | if not special_tokens:
|
| | special_tokens = ["<start_of_text>", "<end_of_text>"]
|
| | else:
|
| | special_tokens = ["<start_of_text>", "<end_of_text>"] + special_tokens
|
| | vocab.extend(special_tokens)
|
| | 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 = {t: t for t in special_tokens}
|
| | special = "|".join(special_tokens)
|
| | self.pat = re.compile(
|
| | special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
|
| | re.IGNORECASE,
|
| | )
|
| |
|
| | self.vocab_size = len(self.encoder)
|
| | self.all_special_ids = [self.encoder[t] for t in special_tokens]
|
| |
|
| | def bpe(self, token):
|
| | if token in self.cache:
|
| | return self.cache[token]
|
| | word = tuple(token[:-1]) + (token[-1] + "</w>",)
|
| | pairs = get_pairs(word)
|
| |
|
| | if not pairs:
|
| | return 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:
|
| | 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):
|
| | 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):
|
| | text = "".join([self.decoder[token] for token in tokens])
|
| | text = (
|
| | bytearray([self.byte_decoder[c] for c in text])
|
| | .decode("utf-8", errors="replace")
|
| | .replace("</w>", " ")
|
| | )
|
| | return text
|
| |
|
| |
|
| | _tokenizer = SimpleTokenizer()
|
| |
|
| |
|
| | def tokenize(
|
| | texts: Union[str, List[str]], context_length: int = 77
|
| | ) -> torch.LongTensor:
|
| | """
|
| | Returns the tokenized representation of given input string(s)
|
| |
|
| | Parameters
|
| | ----------
|
| | texts : Union[str, List[str]]
|
| | An input string or a list of input strings to tokenize
|
| | context_length : int
|
| | The context length to use; all CLIP models use 77 as the context length
|
| |
|
| | Returns
|
| | -------
|
| | A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
|
| | """
|
| | if isinstance(texts, str):
|
| | texts = [texts]
|
| |
|
| | sot_token = _tokenizer.encoder["<start_of_text>"]
|
| | eot_token = _tokenizer.encoder["<end_of_text>"]
|
| | all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] 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]
|
| | result[i, : len(tokens)] = torch.tensor(tokens)
|
| |
|
| | return result
|
| |
|
