# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """tokenization""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import unicodedata import six import os import json import codecs import regex as re from functools import lru_cache import numpy as np def convert_to_unicode(text): """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" if six.PY3: if isinstance(text, str): return text elif isinstance(text, bytes): try: return text.decode("utf-8", "ignore") except: return text.decode("gb18030", "ignore") else: raise ValueError("Unsupported string type: %s" % (type(text))) elif six.PY2: if isinstance(text, str): try: return text.decode("utf-8", "ignore") except: return text.decode("gb18030", "ignore") elif isinstance(text, unicode): return text else: raise ValueError("Unsupported string type: %s" % (type(text))) else: raise ValueError("Not running on Python2 or Python 3?") def printable_text(text): """Returns text encoded in a way suitable for print or `tf.logging`.""" # These functions want `str` for both Python2 and Python3, but in one case # it's a Unicode string and in the other it's a byte string. if six.PY3: if isinstance(text, str): return text elif isinstance(text, bytes): return text.decode("utf-8", "ignore") else: raise ValueError("Unsupported string type: %s" % (type(text))) elif six.PY2: if isinstance(text, str): return text elif isinstance(text, unicode): return text.encode("utf-8") else: raise ValueError("Unsupported string type: %s" % (type(text))) else: raise ValueError("Not running on Python2 or Python 3?") def load_vocab(vocab_file): """Loads a vocabulary file into a dictionary.""" vocab = collections.OrderedDict() fin = codecs.open(vocab_file, 'r', encoding='utf-8') for num, line in enumerate(fin): items = convert_to_unicode(line.strip()).split("\t") if len(items) > 2: break token = items[0] index = items[1] if len(items) == 2 else num token = token.strip() vocab[token] = int(index) return vocab def convert_by_vocab(vocab, items): """Converts a sequence of [tokens|ids] using the vocab.""" output = [] for item in items: output.append(vocab[item]) return output def convert_tokens_to_ids(vocab, tokens): """convert tokens to vocab ids""" return convert_by_vocab(vocab, tokens) def convert_ids_to_tokens(inv_vocab, ids): """convert vocab ids to tokens""" return convert_by_vocab(inv_vocab, ids) def whitespace_tokenize(text): """Runs basic whitespace cleaning and splitting on a peice of text.""" text = text.strip() if not text: return [] tokens = text.split() return tokens @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(33, 126 + 1)) + list(range(161, 172 + 1)) + list(range(174, 255 + 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 class Tokenizer(object): """RoBERTa Tokenizer""" def __init__(self, encoder, bpe_merges, errors='replace'): self.encoder = encoder self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") self.int_token = re.compile(r"^[0-9]+$") def bpe(self, token): """bpe tokenizing""" if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token 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 encoding""" bpe_tokens = [] 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): """bpe decoding""" decoded_tokens = [] for token in tokens: if self.int_token.match(str(token)): decoded_tokens.append(self.decoder[int(token)]) else: decoded_tokens.append(str(token)) text = ''.join(decoded_tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors) return text def decode_token(self, token): """decode one token""" if self.int_token.match(str(token)): token = self.decoder[int(token)] else: token = str(token) text = bytearray([self.byte_decoder[c] for c in token]).decode('utf-8', errors=self.errors) return text class GptBpeTokenizer(object): """GptBpeTokenizer""" def __init__(self, vocab_file=None, encoder_json_file=None, vocab_bpe_file=None, do_lower_case=True): if vocab_file is None: vocab_file = "./model_files/dict/unimo_en.vocab.txt" if encoder_json_file is None: encoder_json_file = "./model_files/dict/unimo_en.encoder.json" if vocab_bpe_file is None: vocab_bpe_file = "./model_files/dict/unimo_en.vocab.bpe" with codecs.open(encoder_json_file, 'r', encoding='utf-8') as f: encoder = json.load(f) with codecs.open(vocab_bpe_file, 'r', encoding="utf-8") as f: bpe_data = f.read() bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]] self.gptbpe_tokenizer = Tokenizer(encoder=encoder, bpe_merges=bpe_merges) self.vocab = load_vocab(vocab_file) self.inv_vocab = {v: k for k, v in self.vocab.items()} self.cls_token = '[CLS]' self.pad_token = '[PAD]' self.sep_token = '[SEP]' self.unk_token = '[UNK]' self.mask_token = '[MASK]' self.single_modal = 'madeupword0000' self.multi_modal = 'madeupword0001' self.cls_token_id = self.vocab['[CLS]'] self.pad_token_id = self.vocab['[PAD]'] self.sep_token_id = self.vocab['[SEP]'] self.unk_token_id = self.vocab['[UNK]'] self.mask_token_id = self.vocab['[MASK]'] self.single_modal_id = self.vocab['madeupword0000'] self.multi_modal_id = self.vocab['madeupword0001'] def tokenize(self, text): """tokenize text to a list of tokens""" return [str(token) for token in self.gptbpe_tokenizer.encode(text)] def convert_tokens_to_ids(self, tokens): """convert tokens to vocab ids""" return convert_by_vocab(self.vocab, tokens) def convert_token_to_id(self, token): """convert token to vocab id""" return self.vocab[token] def convert_ids_to_tokens(self, ids): """convert vocab ids to tokens""" return convert_by_vocab(self.inv_vocab, ids) def convert_id_to_token(self, id): """convert vocab id to token""" return self.inv_vocab[id] def vocab_size(self): """get the vocab size""" return len(self.vocab) class FullTokenizer(object): """Runs end-to-end tokenziation.""" def __init__(self, vocab_file, do_lower_case=True): self.vocab = load_vocab(vocab_file) self.inv_vocab = {v: k for k, v in self.vocab.items()} self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) def tokenize(self, text): """tokenize the text""" split_tokens = [] for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) return split_tokens def convert_tokens_to_ids(self, tokens): """convert tokens to vocab ids""" return convert_by_vocab(self.vocab, tokens) def convert_ids_to_tokens(self, ids): """convert vocab ids to tokens""" return convert_by_vocab(self.inv_vocab, ids) def merge_subword(self, tokens): """merge subwords""" ret = [] for token in tokens: if token.startswith("##"): real_token = token[2:] if len(ret): ret[-1] += real_token else: ret.append(real_token) else: ret.append(token) return ret class CharTokenizer(object): """Runs end-to-end tokenziation.""" def __init__(self, vocab_file, do_lower_case=True): self.vocab = load_vocab(vocab_file) self.inv_vocab = {v: k for k, v in self.vocab.items()} self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) def tokenize(self, text): """tokenize the text""" split_tokens = [] for token in text.lower().split(" "): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) return split_tokens def convert_tokens_to_ids(self, tokens): """convert tokens to vocab ids""" return convert_by_vocab(self.vocab, tokens) def convert_ids_to_tokens(self, ids): """convert vocab ids to tokens""" return convert_by_vocab(self.inv_vocab, ids) class BasicTokenizer(object): """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" def __init__(self, do_lower_case=True): """Constructs a BasicTokenizer. Args: do_lower_case: Whether to lower case the input. """ self.do_lower_case = do_lower_case def tokenize(self, text): """Tokenizes a piece of text.""" text = convert_to_unicode(text) text = self._clean_text(text) # This was added on November 1st, 2018 for the multilingual and Chinese # models. This is also applied to the English models now, but it doesn't # matter since the English models were not trained on any Chinese data # and generally don't have any Chinese data in them (there are Chinese # characters in the vocabulary because Wikipedia does have some Chinese # words in the English Wikipedia.). text = self._tokenize_chinese_chars(text) orig_tokens = whitespace_tokenize(text) split_tokens = [] for token in orig_tokens: if self.do_lower_case: token = token.lower() token = self._run_strip_accents(token) split_tokens.extend(self._run_split_on_punc(token)) output_tokens = whitespace_tokenize(" ".join(split_tokens)) return output_tokens def _run_strip_accents(self, text): """Strips accents from a piece of text.""" text = unicodedata.normalize("NFD", text) output = [] for char in text: cat = unicodedata.category(char) if cat == "Mn": continue output.append(char) return "".join(output) def _run_split_on_punc(self, text): """Splits punctuation on a piece of text.""" chars = list(text) i = 0 start_new_word = True output = [] while i < len(chars): char = chars[i] if _is_punctuation(char): output.append([char]) start_new_word = True else: if start_new_word: output.append([]) start_new_word = False output[-1].append(char) i += 1 return ["".join(x) for x in output] def _tokenize_chinese_chars(self, text): """Adds whitespace around any CJK character.""" output = [] for char in text: cp = ord(char) if self._is_chinese_char(cp): output.append(" ") output.append(char) output.append(" ") else: output.append(char) return "".join(output) def _is_chinese_char(self, cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ((cp >= 0x4E00 and cp <= 0x9FFF) or # (cp >= 0x3400 and cp <= 0x4DBF) or # (cp >= 0x20000 and cp <= 0x2A6DF) or # (cp >= 0x2A700 and cp <= 0x2B73F) or # (cp >= 0x2B740 and cp <= 0x2B81F) or # (cp >= 0x2B820 and cp <= 0x2CEAF) or (cp >= 0xF900 and cp <= 0xFAFF) or # (cp >= 0x2F800 and cp <= 0x2FA1F)): # return True return False def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] for char in text: cp = ord(char) if cp == 0 or cp == 0xfffd or _is_control(char): continue if _is_whitespace(char): output.append(" ") else: output.append(char) return "".join(output) class WordpieceTokenizer(object): """Runs WordPiece tokenziation.""" def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100): self.vocab = vocab self.unk_token = unk_token self.max_input_chars_per_word = max_input_chars_per_word def tokenize(self, text): """Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. For example: input = "unaffable" output = ["un", "##aff", "##able"] Args: text: A single token or whitespace separated tokens. This should have already been passed through `BasicTokenizer. Returns: A list of wordpiece tokens. """ text = convert_to_unicode(text) output_tokens = [] for token in whitespace_tokenize(text): chars = list(token) if len(chars) > self.max_input_chars_per_word: output_tokens.append(self.unk_token) continue is_bad = False start = 0 sub_tokens = [] while start < len(chars): end = len(chars) cur_substr = None while start < end: substr = "".join(chars[start:end]) if start > 0: substr = "##" + substr if substr in self.vocab: cur_substr = substr break end -= 1 if cur_substr is None: is_bad = True break sub_tokens.append(cur_substr) start = end if is_bad: output_tokens.append(self.unk_token) else: output_tokens.extend(sub_tokens) return output_tokens def _is_whitespace(char): """Checks whether `chars` is a whitespace character.""" # \t, \n, and \r are technically contorl characters but we treat them # as whitespace since they are generally considered as such. if char == " " or char == "\t" or char == "\n" or char == "\r": return True cat = unicodedata.category(char) if cat == "Zs": return True return False def _is_control(char): """Checks whether `chars` is a control character.""" # These are technically control characters but we count them as whitespace # characters. if char == "\t" or char == "\n" or char == "\r": return False cat = unicodedata.category(char) if cat.startswith("C"): return True return False def _is_punctuation(char): """Checks whether `chars` is a punctuation character.""" cp = ord(char) # We treat all non-letter/number ASCII as punctuation. # Characters such as "^", "$", and "`" are not in the Unicode # Punctuation class but we treat them as punctuation anyways, for # consistency. if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): return True cat = unicodedata.category(char) if cat.startswith("P"): return True return False def build_id2is_full_token(tokenizer, dtype="float32"): """build id full token list""" vocab_sz = tokenizer.vocab_size() is_full_token = [0.0] * vocab_sz token_strs = [] for token_id in range(vocab_sz): token = tokenizer.convert_id_to_token(token_id) token_str = tokenizer.gptbpe_tokenizer.decode_token(token) token_strs.append(token_str) if token_str.startswith(' '): is_full_token[token_id] = 1.0 is_full_token = np.array(is_full_token, dtype=dtype) print(is_full_token[500:520]) print(token_strs[500:520]) if __name__ == "__main__": pass