|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
import gzip |
|
import html |
|
import os |
|
from functools import lru_cache |
|
|
|
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') |
|
|
|
@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 significant 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 Tokenize: |
|
|
|
def __init__(self, tokenizer, max_seq_len=77, truncate=True): |
|
self.tokenizer = tokenizer |
|
self.max_seq_len = max_seq_len |
|
self.truncate = truncate |
|
|
|
def __call__(self, texts): |
|
expanded_dim = False |
|
if isinstance(texts, str): |
|
texts = [texts] |
|
expanded_dim = True |
|
|
|
sot_token = self.tokenizer.encoder['<|startoftext|>'] |
|
eot_token = self.tokenizer.encoder['<|endoftext|>'] |
|
all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts] |
|
result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long) |
|
|
|
for i, tokens in enumerate(all_tokens): |
|
if len(tokens) > self.max_seq_len: |
|
if self.truncate: |
|
tokens = tokens[:self.max_seq_len] |
|
tokens[-1] = eot_token |
|
else: |
|
raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}') |
|
result[i, :len(tokens)] = torch.tensor(tokens) |
|
|
|
if expanded_dim: |
|
return result[0] |
|
|
|
return result |
|
|
|
|
|
class SimpleTokenizer(object): |
|
|
|
def __init__(self, bpe_path: str = default_bpe()): |
|
self.byte_encoder = bytes_to_unicode() |
|
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} |
|
|
|
with open(bpe_path) as f: |
|
contents = f.readlines() |
|
merges = [] |
|
for cnt in contents: |
|
merges.append(cnt.split('\n')[0]) |
|
merges.append("") |
|
|
|
|
|
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 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 |