mirror of
https://github.com/huggingface/transformers.git
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GPTTokenizer dependency removed from deberta class (#19551)
* GPTTOkenizer dependency removed from deberta class Fixup made the Deberta Tokenizer fast independent of GPT-2 tokenizer Copied annotation added Done the dependency removal * Added some missing copied statement * Added some copied statements
This commit is contained in:
RamitPahwa
GitHub
@ -14,13 +14,19 @@
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# limitations under the License.
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""" Tokenization class for model DeBERTa."""
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from typing import List, Optional
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import json
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import os
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from typing import TYPE_CHECKING, List, Optional, Tuple
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from ...tokenization_utils import AddedToken
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import regex as re
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import logging
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from ..gpt2.tokenization_gpt2 import GPT2Tokenizer
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if TYPE_CHECKING:
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from transformers.pipelines.conversational import Conversation
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
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@ -63,30 +69,107 @@ PRETRAINED_INIT_CONFIGURATION = {
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}
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class DebertaTokenizer(GPT2Tokenizer):
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r"""
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Constructs a DeBERTa tokenizer, which runs end-to-end tokenization: punctuation splitting + wordpiece
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# Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode
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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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# Copied from transformers.models.gpt2.tokenization_gpt2.get_pairs
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def get_pairs(word):
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"""
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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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Construct a DeBERTa tokenizer. Based on byte-level Byte-Pair-Encoding.
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This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
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be encoded differently whether it is at the beginning of the sentence (without space) or not:
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```
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>>> from transformers import DebertaTokenizer
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>>> tokenizer = DebertaTokenizer.from_pretrained("microsoft/deberta-base")
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>>> tokenizer("Hello world")['input_ids']
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[15496, 995]
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>>> tokenizer(" Hello world")['input_ids']
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[18435, 995]
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```
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You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
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call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
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<Tip>
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When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
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</Tip>
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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File containing the vocabulary.
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether or not to lowercase the input when tokenizing.
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unk_token (`str`, *optional*, defaults to `"[UNK]"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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Path to the vocabulary file.
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merges_file (`str`):
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Path to the merges file.
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errors (`str`, *optional*, defaults to `"replace"`):
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Paradigm to follow when decoding bytes to UTF-8. See
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[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
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bos_token (`str`, *optional*, defaults to `"[CLS]"`):
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The beginning of sequence token.
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eos_token (`str`, *optional*, defaults to `"[SEP]"`):
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The end of sequence token.
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sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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pad_token (`str`, *optional*, defaults to `"[PAD]"`):
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The token used for padding, for example when batching sequences of different lengths.
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cls_token (`str`, *optional*, defaults to `"[CLS]"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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unk_token (`str`, *optional*, defaults to `"[UNK]"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"[PAD]"`):
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The token used for padding, for example when batching sequences of different lengths.
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mask_token (`str`, *optional*, defaults to `"[MASK]"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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add_prefix_space (`bool`, *optional*, defaults to `False`):
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Whether or not to add an initial space to the input. This allows to treat the leading word just as any
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other word. (Deberta tokenizer detect beginning of words by the preceding space).
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add_bos_token (`bool`, *optional*, defaults to `False`):
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Whether or not to add an initial <|endoftext|> to the input. This allows to treat the leading word just as
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any other word.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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@ -107,6 +190,7 @@ class DebertaTokenizer(GPT2Tokenizer):
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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
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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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@ -120,8 +204,6 @@ class DebertaTokenizer(GPT2Tokenizer):
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mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
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super().__init__(
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vocab_file=vocab_file,
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merges_file=merges_file,
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errors=errors,
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bos_token=bos_token,
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eos_token=eos_token,
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@ -131,8 +213,78 @@ class DebertaTokenizer(GPT2Tokenizer):
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pad_token=pad_token,
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mask_token=mask_token,
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add_prefix_space=add_prefix_space,
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add_bos_token=add_bos_token,
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**kwargs,
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)
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self.add_bos_token = add_bos_token
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with open(vocab_file, encoding="utf-8") as vocab_handle:
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self.encoder = json.load(vocab_handle)
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self.decoder = {v: k for k, v in self.encoder.items()}
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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 merges_handle:
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bpe_merges = merges_handle.read().split("\n")[1:-1]
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bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
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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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# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
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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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# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
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def vocab_size(self):
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return len(self.encoder)
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# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.get_vocab
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe
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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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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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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 build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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@ -216,8 +368,75 @@ class DebertaTokenizer(GPT2Tokenizer):
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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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# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize
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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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# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_token_to_id
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_id_to_token
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.decoder.get(index)
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# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.convert_tokens_to_string
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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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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# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.save_vocabulary
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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merge_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
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)
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with open(vocab_file, "w", encoding="utf-8") as f:
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f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
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index = 0
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with open(merge_file, "w", encoding="utf-8") as writer:
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writer.write("#version: 0.2\n")
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
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" Please check that the tokenizer is not corrupted!"
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)
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index = token_index
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writer.write(" ".join(bpe_tokens) + "\n")
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index += 1
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return vocab_file, merge_file
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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) and (len(text) > 0 and not text[0].isspace()):
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text = " " + text
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return (text, kwargs)
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# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._build_conversation_input_ids
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def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
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input_ids = []
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for is_user, text in conversation.iter_texts():
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input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id])
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if len(input_ids) > self.model_max_length:
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input_ids = input_ids[-self.model_max_length :]
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return input_ids
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@ -14,14 +14,21 @@
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# limitations under the License.
|
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""" Fast Tokenization class for model DeBERTa."""
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from typing import List, Optional
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import json
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from typing import TYPE_CHECKING, List, Optional, Tuple
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from ...tokenization_utils_base import AddedToken
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from tokenizers import pre_tokenizers
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from ...tokenization_utils_base import AddedToken, BatchEncoding
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from ...tokenization_utils_fast import PreTrainedTokenizerFast
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from ...utils import logging
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from ..gpt2.tokenization_gpt2_fast import GPT2TokenizerFast
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from .tokenization_deberta import DebertaTokenizer
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if TYPE_CHECKING:
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from transformers.pipelines.conversational import Conversation
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
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@ -64,31 +71,67 @@ PRETRAINED_INIT_CONFIGURATION = {
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}
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class DebertaTokenizerFast(GPT2TokenizerFast):
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class DebertaTokenizerFast(PreTrainedTokenizerFast):
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"""
|
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Constructs a "fast" DeBERTa tokenizer, which runs end-to-end tokenization: punctuation splitting + wordpiece. It is
|
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backed by HuggingFace's *tokenizers* library.
|
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Construct a "fast" DeBERTa tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
|
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Byte-Pair-Encoding.
|
||||
|
||||
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
|
||||
be encoded differently whether it is at the beginning of the sentence (without space) or not:
|
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|
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```
|
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>>> from transformers import DebertaTokenizerFast
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>>> tokenizer = DebertaTokenizerFast.from_pretrained("microsoft/deberta-base")
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>>> tokenizer("Hello world")['input_ids']
|
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[15496, 995]
|
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>>> tokenizer(" Hello world")['input_ids']
|
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[18435, 995]
|
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```
|
||||
|
||||
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
|
||||
the model was not pretrained this way, it might yield a decrease in performance.
|
||||
|
||||
<Tip>
|
||||
|
||||
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
|
||||
|
||||
</Tip>
|
||||
|
||||
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
|
||||
refer to this superclass for more information regarding those methods.
|
||||
|
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Args:
|
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vocab_file (`str`):
|
||||
File containing the vocabulary.
|
||||
do_lower_case (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to lowercase the input when tokenizing.
|
||||
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
|
||||
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
||||
token instead.
|
||||
Path to the vocabulary file.
|
||||
merges_file (`str`):
|
||||
Path to the merges file.
|
||||
tokenizer_file (`str`, *optional*):
|
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The path to a tokenizer file to use instead of the vocab file.
|
||||
errors (`str`, *optional*, defaults to `"replace"`):
|
||||
Paradigm to follow when decoding bytes to UTF-8. See
|
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[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
|
||||
bos_token (`str`, *optional*, defaults to `"[CLS]"`):
|
||||
The beginning of sequence token.
|
||||
eos_token (`str`, *optional*, defaults to `"[SEP]"`):
|
||||
The end of sequence token.
|
||||
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
|
||||
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
|
||||
sequence classification or for a text and a question for question answering. It is also used as the last
|
||||
token of a sequence built with special tokens.
|
||||
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
|
||||
The token used for padding, for example when batching sequences of different lengths.
|
||||
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
|
||||
The classifier token which is used when doing sequence classification (classification of the whole sequence
|
||||
instead of per-token classification). It is the first token of the sequence when built with special tokens.
|
||||
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
|
||||
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
||||
token instead.
|
||||
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
|
||||
The token used for padding, for example when batching sequences of different lengths.
|
||||
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
|
||||
The token used for masking values. This is the token used when training this model with masked language
|
||||
modeling. This is the token which the model will try to predict.
|
||||
add_prefix_space (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
|
||||
other word. (Deberta tokenizer detect beginning of words by the preceding space).
|
||||
"""
|
||||
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
@ -129,6 +172,15 @@ class DebertaTokenizerFast(GPT2TokenizerFast):
|
||||
add_prefix_space=add_prefix_space,
|
||||
**kwargs,
|
||||
)
|
||||
self.add_bos_token = kwargs.pop("add_bos_token", False)
|
||||
|
||||
pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
|
||||
if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
|
||||
pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
|
||||
pre_tok_state["add_prefix_space"] = add_prefix_space
|
||||
self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
|
||||
|
||||
self.add_prefix_space = add_prefix_space
|
||||
|
||||
@property
|
||||
def mask_token(self) -> str:
|
||||
@ -209,3 +261,40 @@ class DebertaTokenizerFast(GPT2TokenizerFast):
|
||||
if token_ids_1 is None:
|
||||
return len(cls + token_ids_0 + sep) * [0]
|
||||
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
|
||||
|
||||
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast._batch_encode_plus
|
||||
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
|
||||
is_split_into_words = kwargs.get("is_split_into_words", False)
|
||||
assert self.add_prefix_space or not is_split_into_words, (
|
||||
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
|
||||
"to use it with pretokenized inputs."
|
||||
)
|
||||
|
||||
return super()._batch_encode_plus(*args, **kwargs)
|
||||
|
||||
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast._encode_plus
|
||||
def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
|
||||
is_split_into_words = kwargs.get("is_split_into_words", False)
|
||||
|
||||
assert self.add_prefix_space or not is_split_into_words, (
|
||||
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
|
||||
"to use it with pretokenized inputs."
|
||||
)
|
||||
|
||||
return super()._encode_plus(*args, **kwargs)
|
||||
|
||||
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast.save_vocabulary
|
||||
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||||
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
|
||||
return tuple(files)
|
||||
|
||||
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast._build_conversation_input_ids
|
||||
def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
|
||||
"""This corresponds to DialoGPT variants of models."""
|
||||
input_ids = []
|
||||
for is_user, text in conversation.iter_texts():
|
||||
input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id])
|
||||
|
||||
if len(input_ids) > self.model_max_length:
|
||||
input_ids = input_ids[-self.model_max_length :]
|
||||
return input_ids
|
||||
|
||||
Reference in New Issue
Block a user