mirror of https://github.com/commaai/tinygrad.git
cleanup clip tokenizer
This commit is contained in:
George Hotz
parent
ff0da4c802
commit
801ecd4a07
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@ -490,112 +490,112 @@ class CLIPTextTransformer:
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# Clip tokenizer, taken from https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py (MIT license)
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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.gz")
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return os.path.join(os.path.dirname(os.path.abspath(__file__)), "../weights/bpe_simple_vocab_16e6.txt.gz")
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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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"""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 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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text = re.sub(r'\s+', ' ', text)
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text = text.strip()
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return text
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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 = 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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"""
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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 = 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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class ClipTokenizer(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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merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
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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.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
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self.pat = self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[^\s]+""", re.IGNORECASE)
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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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merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
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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.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
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self.pat = self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[^\s]+""", 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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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 Exception:
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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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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(text.strip()).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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# Truncation, keeping two slots for start and end tokens.
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if len(bpe_tokens) > 75:
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bpe_tokens = bpe_tokens[:75]
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return [49406] + bpe_tokens + [49407] * (77 - len(bpe_tokens) - 1)
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def encode(self, text):
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bpe_tokens = []
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text = whitespace_clean(text.strip()).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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# Truncation, keeping two slots for start and end tokens.
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if len(bpe_tokens) > 75:
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bpe_tokens = bpe_tokens[:75]
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return [49406] + bpe_tokens + [49407] * (77 - len(bpe_tokens) - 1)
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class StableDiffusion:
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def __init__(self):
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@ -650,6 +650,9 @@ if __name__ == "__main__":
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tokenizer = ClipTokenizer()
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phrase = tokenizer.encode("a horse sized cat eating a bagel")
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_phrase = [49406, 320, 4558, 9832, 2368, 4371, 320, 28777, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407]
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assert tuple(phrase) == tuple(_phrase)
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# phrase = tokenizer.encode("penguin with fire extinguisher")
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context = model.cond_stage_model.transformer.text_model(phrase).realize()
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