wehub-resource-sync
414 lines
15 KiB
Python
414 lines
15 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import shutil
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from functools import lru_cache
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import regex as re
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from .. import AddedToken, PretrainedTokenizer
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__all__ = [
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"DebertaTokenizer",
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]
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# false
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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"deberta-base": 512,
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}
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@lru_cache()
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
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characters the bpe code barfs on.
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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
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decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
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tables between utf-8 bytes and unicode strings.
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"""
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bs = (
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list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
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)
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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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class DebertaTokenizer(PretrainedTokenizer):
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"""
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Constructs a DeBERTa tokenizer based on byte-level Byte-Pair-Encoding.
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This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer`
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which contains most of the main methods. For more information regarding those methods,
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please refer to this superclass.
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Args:
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vocab_file (str):
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Path to the vocab file.
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The vocab file contains a mapping from vocabulary strings to indices.
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merges_file (str):
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Path to the merge file.
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The merge file is used to split the input sentence into "subword" units.
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The vocab file is then used to encode those units as intices.
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errors (str):
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Paradigm to follow when decoding bytes to UTF-8.
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Defaults to `'replace'`.
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max_len (int, optional):
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The maximum value of the input sequence length.
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Defaults to `None`.
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Examples:
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.. code-block::
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from paddlenlp.transformers import DebertaTokenizer
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tokenizer = DebertaTokenizer.from_pretrained('microsoft/deberta-base')
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print(tokenizer('Welcome to use PaddlePaddle and PaddleNLP'))
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'''
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{'input_ids': [1, 25194, 7, 304, 221, 33151, 510, 33151, 8, 221, 33151, 487, 21992, 2],
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'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
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'''
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"""
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resource_files_names = {"vocab_file": "vocab.json", "merges_file": "merges.txt"} # for save_pretrained
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pretrained_resource_files_map = {
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"vocab_file": {
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"deberta-base": "https://paddlenlp.bj.bcebos.com/models/community/microsoft/deberta-base/vocab.json",
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},
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"merges_file": {
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"deberta-base": "https://paddlenlp.bj.bcebos.com/models/community/microsoft/deberta-base/merges.txt",
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},
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}
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# TODO: Add pretrained init configuration
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pretrained_init_configuration = {
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"deberta-base": {"do_lower_case": True},
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}
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def __init__(
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self,
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vocab_file,
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merges_file,
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errors="replace",
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max_len=None,
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bos_token="[CLS]",
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eos_token="[SEP]",
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sep_token="[SEP]",
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cls_token="[CLS]",
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unk_token="[UNK]",
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pad_token="[PAD]",
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mask_token="[MASK]",
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add_prefix_space=False,
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add_bos_token=False,
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**kwargs # The token of newline.
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):
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bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
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eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
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sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
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cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
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pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
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eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
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unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
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mask_token = AddedToken(mask_token, lstrip=False, rstrip=False) if isinstance(mask_token, str) else mask_token
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self._build_special_tokens_map_extended(
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bos_token=bos_token,
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eos_token=eos_token,
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sep_token=sep_token,
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cls_token=cls_token,
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pad_token=pad_token,
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mask_token=mask_token,
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unk_token=unk_token,
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)
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self._vocab_file = vocab_file
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self._merges_file = merges_file
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self.max_len = max_len if max_len is not None else int(1e12)
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self.num_command_tokens = 2
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self.num_type_tokens = 2
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with open(vocab_file, "r", encoding="utf-8") as f:
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self.encoder = json.load(f)
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.num_tokens = len(self.encoder)
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self.num_text_tokens = self.num_tokens - 1
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self.errors = errors # how to handle errors in decoding
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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(merges_file, encoding="utf-8") as f:
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bpe_data = f.read().split("\n")[1:-1]
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bpe_merges = [tuple(merge.split()) for merge in bpe_data]
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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self.cache = {}
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self.add_prefix_space = add_prefix_space
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self.add_bos_token = add_bos_token
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self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
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@property
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def vocab_size(self):
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"""
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Returns the size of vocabulary.
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Returns:
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int: The sum of size of vocabulary and the size of special tokens.
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"""
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return len(self.encoder)
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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)
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pairs = get_pairs(word)
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if not pairs:
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return token
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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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# no
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def _tokenize(self, text):
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"""Tokenize a string."""
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bpe_tokens = []
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for token in re.findall(self.pat, text):
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token = "".join(
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self.byte_encoder[b] for b in token.encode("utf-8")
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) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
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bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
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return bpe_tokens
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def _convert_token_to_id(self, token):
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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return self.decoder[index]
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def convert_ids_to_string(self, ids):
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"""
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Converts a single index or a sequence of indices to texts.
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Args:
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ids (int|List[int]):
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The token id (or token ids) to be converted to text.
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Returns:
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str: The decoded text.
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Example:
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.. code-block::
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from paddlenlp.transformers import DebertaTokenizer
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tokenizer = DebertaTokenizer.from_pretrained('deberta-base')
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print(tokenizer.convert_ids_to_string(tokenizer.convert_ids_to_string([14618, 284, 779, 350, 37382, 47, 37382, 290, 350, 37382, 45, 19930]))
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# 'Welcome to use PaddlePaddle and PaddleNLP'
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"""
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text = "".join([self.decoder[id] for id in ids])
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text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
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return text
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def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
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r"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task.
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A ERNIE sequence pair mask has the following format:
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::
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
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| first sequence | second sequence |
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If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
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Args:
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token_ids_0 (List[int]):
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A list of `inputs_ids` for the first sequence.
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token_ids_1 (List[int], optional):
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Optional second list of IDs for sequence pairs.
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Defaults to `None`.
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Returns:
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List[int]: List of token_type_id according to the given sequence(s).
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"""
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_sep = [self.sep_token_id]
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_cls = [self.cls_token_id]
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if token_ids_1 is None:
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return len(_cls + token_ids_0 + _sep) * [0]
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return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 + _sep) * [1]
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def save_resources(self, save_directory):
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"""
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Saves `SentencePiece <https://github.com/google/sentencepiece>`__ file
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(ends with '.spm') under `save_directory`.
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Args:
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save_directory (str): Directory to save files into.
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"""
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for name, file_name in self.resource_files_names.items():
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source_path = getattr(self, "_%s" % name)
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save_path = os.path.join(save_directory, file_name)
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if os.path.abspath(source_path) != os.path.abspath(save_path):
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shutil.copyfile(source_path, save_path)
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def convert_tokens_to_string(self, tokens):
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"""
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Converts a sequence of tokens (string) in a single string.
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"""
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text = "".join(tokens)
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text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
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return text
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
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add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
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if is_split_into_words or add_prefix_space:
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text = " " + text
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return (text, kwargs)
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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r"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens.
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- single sequence: ``[CLS] X [SEP]``
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- pair of sequences: ``[CLS] A [SEP] B [SEP]``
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Args:
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token_ids_0 (List[int]):
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List of IDs to which the special tokens will be added.
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token_ids_1 (List[int], optional):
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Optional second list of IDs for sequence pairs.
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Defaults to `None`.
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Returns:
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List[int]: List of input_id with the appropriate special tokens.
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"""
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if token_ids_1 is None:
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
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_cls = [self.cls_token_id]
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_sep = [self.sep_token_id]
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return _cls + token_ids_0 + _sep + token_ids_1 + _sep
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def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
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r"""
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Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer ``encode`` methods.
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Args:
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token_ids_0 (List[int]):
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List of ids of the first sequence.
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token_ids_1 (List[int], optional):
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Optional second list of IDs for sequence pairs.
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Defaults to `None`.
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already_has_special_tokens (str, optional):
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Whether or not the token list is already formatted with special tokens for the model.
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Defaults to `False`.
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Returns:
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List[int]:
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The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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if token_ids_1 is None:
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return [1] + ([0] * len(token_ids_0)) + [1]
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
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def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
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"""
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Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
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A BERT offset_mapping has the following format:
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- single sequence: ``(0,0) X (0,0)``
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- pair of sequences: ``(0,0) A (0,0) B (0,0)``
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Args:
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offset_mapping_ids_0 (List[tuple]):
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List of wordpiece offsets to which the special tokens will be added.
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offset_mapping_ids_1 (List[tuple], optional):
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Optional second list of wordpiece offsets for offset mapping pairs. Defaults to None.
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Returns:
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List[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens.
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"""
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if offset_mapping_1 is None:
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return [(0, 0)] + offset_mapping_0 + [(0, 0)]
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return [(0, 0)] + offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]
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