lavila/models/tokenizer.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

# Part of the code is from https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
# Modified by Yue Zhao
# The original code is under MIT License

import gzip
import html
import os
from functools import lru_cache

import ftfy
import regex as re
import torch

from transformers import (BertTokenizer, DistilBertTokenizer, GPT2Tokenizer)


@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))+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 = default_bpe()):
        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 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

    def __call__(self, texts, context_length=77):
        if isinstance(texts, str):
            texts = [texts]

        sot_token = self.encoder["<|startoftext|>"]
        eot_token = self.encoder["<|endoftext|>"]
        all_tokens = [[sot_token] + self.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):
            tokens = tokens[:context_length]
            result[i, :len(tokens)] = torch.tensor(tokens)

        if len(result) == 1:
            return result[0]
        return result


class MyBertTokenizer(object):
    def __init__(self, name=''):
        print('=> Initialize MyBertTokenizer ({})'.format(name))
        self.tokenizer = BertTokenizer.from_pretrained(name)
        self.bos_token_id, self.eos_token_id = self.tokenizer('').input_ids
        self.pad_token_id = 0

    def __call__(self, texts, context_length=77):
        if isinstance(texts, str):
            texts = [texts]
        result = torch.zeros(len(texts), context_length, dtype=torch.long)
        mask = torch.zeros(len(texts), context_length, dtype=torch.float32)
        for i, text in enumerate(texts):
            tokens = self.tokenizer(text)
            input_ids = tokens.input_ids[:context_length]
            attention_mask = tokens.attention_mask[:context_length]
            result[i, :len(input_ids)] = torch.tensor(input_ids)
            mask[i, :len(attention_mask)] = torch.tensor(attention_mask)

        if len(result) == 1:
            return result[0], mask[0]
        return result, mask


class MyDistilBertTokenizer(object):
    def __init__(self, name=''):
        print('=> Initialize MyDistilBertTokenizer ({})'.format(name))
        self.tokenizer = DistilBertTokenizer.from_pretrained(name)

    def __call__(self, texts, context_length=77):
        if isinstance(texts, str):
            texts = [texts]
        result = torch.zeros(len(texts), context_length, dtype=torch.long)
        mask = torch.zeros(len(texts), context_length, dtype=torch.float32)
        for i, text in enumerate(texts):
            tokens = self.tokenizer(text)
            input_ids = tokens.input_ids[:context_length]
            attention_mask = tokens.attention_mask[:context_length]
            result[i, :len(input_ids)] = torch.tensor(input_ids)
            mask[i, :len(attention_mask)] = torch.tensor(attention_mask)

        if len(result) == 1:
            return result[0], mask[0]
        return result, mask


class MyGPT2Tokenizer(object):
    def __init__(self, name='', add_bos=False):
        print('=> Initialize MyGPT2Tokenizer ({})'.format(name))
        self.tokenizer = GPT2Tokenizer.from_pretrained(name)
        self.bos_token_id, self.eos_token_id = self.tokenizer.bos_token_id, self.tokenizer.eos_token_id
        self.pad_token_id = 0
        self.add_bos = add_bos
        # num_added_tokens = self.tokenizer.add_special_tokens({'pad_token': "[PAD]"})
        # print('num_added_tokens={}'.format(len(num_added_tokens)))

    def __call__(self, texts, context_length=77):
        if isinstance(texts, str):
            texts = [texts]
        result = torch.zeros(len(texts), context_length, dtype=torch.long)
        for i, text in enumerate(texts):
            tokens = self.tokenizer(text)
            if not self.add_bos:
                input_ids = tokens.input_ids[:context_length - 1]
                input_ids = input_ids + [self.tokenizer.eos_token_id]  # add [EOS]
            else:
                input_ids = tokens.input_ids[:context_length - 2]
                input_ids = [self.tokenizer.bos_token_id] + input_ids + [self.tokenizer.eos_token_id]  # add [EOS]
            # attention_mask = tokens.attention_mask[:context_length]
            # attention_mask = attention_mask + [0.] * pad_length
            result[i, :len(input_ids)] = torch.tensor(input_ids)

        if len(result) == 1:
            return result[0]
        return result