tokenizer.py

import gzip
import html
import ftfy
import torch
import regex as re
import numpy as np
from typing import Union
from functools import lru_cache


@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

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 = "./vocab/bpe_simple_vocab_16e6.txt.gz"):

    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))
    vocab.extend(['<|startoftext|>', '<|endoftext|>'])
    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 = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
    self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)

  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 __call__(
    self, text: Union[str, list],
    context_length: Union[str, list],
    device = "cpu"
  ) -> torch.Tensor:
    """tokenize the text and return proper tensors

    Args:
        text (Union[str, list]): text as a string or list of strings
        context_length (Union[str, list]): sequence length for transformer
        device (torch.Device): device to load sequence on 

    Returns:
        (torch.Tensor): input ids for the model
    """
    # create tokens array
    if isinstance(text, list):
      text_tokens = [self.encode(t) for t in text]
      text_input = torch.zeros(len(text_tokens), context_length)
    else:
      text_tokens = self.encode(text)
      text_input = np.zeros((1, context_length))

    # convert to proper tokens ids
    start_token = self.encoder['<|startoftext|>']
    end_token = self.encoder['<|endoftext|>']
    if isinstance(text, list):
      for i, tokens in enumerate(text_tokens):
        tokens = [start_token] + tokens + [end_token]
        text_input[i, : len(tokens)] = torch.tensor(tokens)
    else:
      tokens = [start_token] + text_tokens + [end_token]
      text_input[0, :len(tokens)] = tokens
      text_input = torch.from_numpy(text_input)

    # move to correct device
    preprocessed_text = text_input.long().to(device)
    return preprocessed_text