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import gzip |
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import html |
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import os |
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from functools import lru_cache |
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import ftfy |
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import regex as re |
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import torch |
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@lru_cache() |
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def default_bpe(): |
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return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bpe_simple_vocab_16e6.txt') |
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@lru_cache() |
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def bytes_to_unicode(): |
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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. This means you need a large # of unicode characters in your vocab |
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if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent |
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coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables |
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between utf-8 bytes and unicode strings. 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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def basic_clean(text): |
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text = ftfy.fix_text(text) |
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text = html.unescape(html.unescape(text)) |
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return text.strip() |
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def whitespace_clean(text): |
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text = re.sub(r'\s+', ' ', text) |
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text = text.strip() |
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return text |
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class Tokenize: |
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def __init__(self, tokenizer, max_seq_len=77, truncate=True): |
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self.tokenizer = tokenizer |
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self.max_seq_len = max_seq_len |
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self.truncate = truncate |
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def __call__(self, texts): |
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expanded_dim = False |
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if isinstance(texts, str): |
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texts = [texts] |
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expanded_dim = True |
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sot_token = self.tokenizer.encoder['<|startoftext|>'] |
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eot_token = self.tokenizer.encoder['<|endoftext|>'] |
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all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts] |
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result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long) |
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for i, tokens in enumerate(all_tokens): |
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if len(tokens) > self.max_seq_len: |
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if self.truncate: |
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tokens = tokens[:self.max_seq_len] |
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tokens[-1] = eot_token |
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else: |
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raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}') |
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result[i, :len(tokens)] = torch.tensor(tokens) |
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if expanded_dim: |
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return result[0] |
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return result |
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class SimpleTokenizer(object): |
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def __init__(self, bpe_path: str = default_bpe()): |
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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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with open(bpe_path) as f: |
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contents = f.readlines() |
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merges = [] |
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for cnt in contents: |
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merges.append(cnt.split('\n')[0]) |
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merges.append("") |
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merges = merges[1:49152 - 256 - 2 + 1] |
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merges = [tuple(merge.split()) for merge in merges] |
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vocab = list(bytes_to_unicode().values()) |
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vocab = vocab + [v + '</w>' for v in vocab] |
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for merge in merges: |
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vocab.append(''.join(merge)) |
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vocab.extend(['<|startoftext|>', '<|endoftext|>']) |
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self.encoder = dict(zip(vocab, range(len(vocab)))) |
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self.decoder = {v: k for k, v in self.encoder.items()} |
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self.bpe_ranks = dict(zip(merges, range(len(merges)))) |
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self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} |
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self.pat = re.compile( |
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r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", |
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re.IGNORECASE) |
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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[:-1]) + (token[-1] + '</w>', ) |
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pairs = get_pairs(word) |
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if not pairs: |
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return token + '</w>' |
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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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text = whitespace_clean(basic_clean(text)).lower() |
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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='replace').replace('</w>', ' ') |
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return text |
