450 lines
20 KiB
Python
450 lines
20 KiB
Python
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# coding=utf-8
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# Copyright 2018 T5 Authors and HuggingFace Inc. team.
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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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""" Tokenization class for model T5."""
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import os
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import re
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import warnings
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from shutil import copyfile
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...convert_slow_tokenizer import import_protobuf
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from ...tokenization_utils import PreTrainedTokenizer
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from ...tokenization_utils_base import AddedToken
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if TYPE_CHECKING:
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from ...tokenization_utils_base import TextInput
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
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# TODO(PVP) - this should be removed in Transformers v5
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SPIECE_UNDERLINE = "▁"
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class T5Tokenizer(PreTrainedTokenizer):
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"""
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Construct a T5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
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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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[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
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contains the vocabulary necessary to instantiate a tokenizer.
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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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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extra_ids (`int`, *optional*, defaults to 100):
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Add a number of extra ids added to the vocabulary for use as sentinels. These tokens are
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accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. These tokens can be
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retrieved by calling get_sentinel_tokens method and token ids can be by calling get_sentinel_token_ids
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method
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additional_special_tokens (`List[str]`, *optional*):
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Additional special tokens used by the tokenizer.
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sp_model_kwargs (`dict`, *optional*):
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Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
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SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
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to set:
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- `enable_sampling`: Enable subword regularization.
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- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
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- `nbest_size = {0,1}`: No sampling is performed.
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- `nbest_size > 1`: samples from the nbest_size results.
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- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
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using forward-filtering-and-backward-sampling algorithm.
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- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
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BPE-dropout.
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legacy (`bool`, *optional*):
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Whether or not the `legacy` behaviour of the tokenizer should be used. Legacy is before the merge of #24622
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and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple
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example:
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- `legacy=True`:
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```python
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>>> from transformers import T5Tokenizer
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>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=True)
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>>> tokenizer.encode("Hello <extra_id_0>.")
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[8774, 32099, 3, 5, 1]
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```
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- `legacy=False`:
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```python
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>>> from transformers import T5Tokenizer
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>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False)
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>>> tokenizer.encode("Hello <extra_id_0>.") # the extra space `[3]` is no longer here
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[8774, 32099, 5, 1]
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```
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Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
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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.
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Attributes:
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sp_model (`SentencePieceProcessor`):
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The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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vocab_file,
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eos_token="</s>",
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unk_token="<unk>",
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pad_token="<pad>",
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extra_ids=100,
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additional_special_tokens=None,
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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legacy=None,
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add_prefix_space=True,
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**kwargs,
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) -> None:
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pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
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unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
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eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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self.vocab_file = vocab_file
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self._extra_ids = extra_ids
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(vocab_file)
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if additional_special_tokens is not None:
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extra_tokens = [x for x in additional_special_tokens if "<extra_id_" in str(x)]
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if len(extra_tokens) < 1:
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additional_special_tokens += [f"<extra_id_{i}>" for i in range(extra_ids)]
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elif extra_ids > 0 and extra_ids != len(extra_tokens):
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raise ValueError(
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f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
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" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
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" tokens"
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)
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else:
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extra_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
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additional_special_tokens = extra_tokens
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# for legacy purpose, we keep this. Will be removed and tests updated. (when `added_tokens_decoder` is not passed as kwargs)
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self._added_tokens_decoder = {}
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for i in range(len(extra_tokens)):
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self._added_tokens_decoder[len(self.sp_model) - 1 + extra_ids - i] = AddedToken(
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f"<extra_id_{i}>", single_word=False, lstrip=True, rstrip=True, special=True, normalized=False
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)
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if legacy is None:
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logger.warning_once(
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f"You are using the default legacy behaviour of the {self.__class__}. This is"
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" expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
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" If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
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" means, and thoroughly read the reason why this was added as explained in"
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" https://github.com/huggingface/transformers/pull/24565"
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)
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legacy = True
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self.legacy = legacy
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self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
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self.vocab_file = vocab_file
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self._extra_ids = extra_ids
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self.add_prefix_space = add_prefix_space
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super().__init__(
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eos_token=eos_token,
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unk_token=unk_token,
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pad_token=pad_token,
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extra_ids=extra_ids,
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additional_special_tokens=additional_special_tokens,
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sp_model_kwargs=self.sp_model_kwargs,
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legacy=legacy,
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add_prefix_space=add_prefix_space,
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**kwargs,
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)
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
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def get_spm_processor(self, from_slow=False):
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tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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if self.legacy or from_slow: # no dependency on protobuf
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tokenizer.Load(self.vocab_file)
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return tokenizer
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with open(self.vocab_file, "rb") as f:
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sp_model = f.read()
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model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
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model = model_pb2.ModelProto.FromString(sp_model)
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normalizer_spec = model_pb2.NormalizerSpec()
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normalizer_spec.add_dummy_prefix = False
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model.normalizer_spec.MergeFrom(normalizer_spec)
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sp_model = model.SerializeToString()
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tokenizer.LoadFromSerializedProto(sp_model)
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return tokenizer
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@staticmethod
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def _eventually_correct_t5_max_length(pretrained_model_name_or_path, max_model_length, init_max_model_length):
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if pretrained_model_name_or_path in T5Tokenizer.max_model_input_sizes:
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deprecated_max_model_length = T5Tokenizer.max_model_input_sizes[pretrained_model_name_or_path]
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if init_max_model_length is not None and init_max_model_length != max_model_length:
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return init_max_model_length
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elif init_max_model_length is None:
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warnings.warn(
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"This tokenizer was incorrectly instantiated with a model max length of"
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f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
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" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
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" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
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f" {pretrained_model_name_or_path} automatically truncating your input to"
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f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
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f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
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" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
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" instantiate this tokenizer with `model_max_length` set to your preferred value.",
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FutureWarning,
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)
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return max_model_length
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@property
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def vocab_size(self):
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return self.sp_model.get_piece_size()
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve 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 `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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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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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A 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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# normal case: some special tokens
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if token_ids_1 is None:
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return ([0] * len(token_ids_0)) + [1]
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return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
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def get_sentinel_tokens(self):
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return list(
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set(filter(lambda x: bool(re.search(r"<extra_id_\d+>", x)) is not None, self.additional_special_tokens))
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)
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def get_sentinel_token_ids(self):
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return [self.convert_tokens_to_ids(token) for token in self.get_sentinel_tokens()]
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def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
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"""Do not add eos again if user already added it."""
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if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
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warnings.warn(
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f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
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" eos tokens being added."
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)
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return token_ids
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else:
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return token_ids + [self.eos_token_id]
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
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use of token type ids, therefore a list of zeros is returned.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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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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Returns:
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`List[int]`: List of zeros.
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"""
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eos = [self.eos_token_id]
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if token_ids_1 is None:
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return len(token_ids_0 + eos) * [0]
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return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
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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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) -> List[int]:
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"""
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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. A sequence has the following format:
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- single sequence: `X </s>`
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- pair of sequences: `A </s> B </s>`
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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*):
|
||
|
|
Optional second list of IDs for sequence pairs.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
||
|
|
"""
|
||
|
|
token_ids_0 = self._add_eos_if_not_present(token_ids_0)
|
||
|
|
if token_ids_1 is None:
|
||
|
|
return token_ids_0
|
||
|
|
else:
|
||
|
|
token_ids_1 = self._add_eos_if_not_present(token_ids_1)
|
||
|
|
return token_ids_0 + token_ids_1
|
||
|
|
|
||
|
|
def __getstate__(self):
|
||
|
|
state = self.__dict__.copy()
|
||
|
|
state["sp_model"] = None
|
||
|
|
return state
|
||
|
|
|
||
|
|
def __setstate__(self, d):
|
||
|
|
self.__dict__ = d
|
||
|
|
|
||
|
|
# for backward compatibility
|
||
|
|
if not hasattr(self, "sp_model_kwargs"):
|
||
|
|
self.sp_model_kwargs = {}
|
||
|
|
|
||
|
|
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
||
|
|
self.sp_model.Load(self.vocab_file)
|
||
|
|
|
||
|
|
def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
|
||
|
|
"""
|
||
|
|
Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
|
||
|
|
first token is special.
|
||
|
|
"""
|
||
|
|
if self.legacy or len(text) == 0:
|
||
|
|
return super().tokenize(text, **kwargs)
|
||
|
|
|
||
|
|
text = text.replace(SPIECE_UNDERLINE, " ")
|
||
|
|
if self.add_prefix_space:
|
||
|
|
text = SPIECE_UNDERLINE + text
|
||
|
|
|
||
|
|
tokens = super().tokenize(text, **kwargs)
|
||
|
|
|
||
|
|
if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
|
||
|
|
tokens = tokens[1:]
|
||
|
|
return tokens
|
||
|
|
|
||
|
|
@property
|
||
|
|
def unk_token_length(self):
|
||
|
|
return len(self.sp_model.encode(str(self.unk_token)))
|
||
|
|
|
||
|
|
def _tokenize(self, text, **kwargs):
|
||
|
|
"""
|
||
|
|
Returns a tokenized string.
|
||
|
|
|
||
|
|
We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
|
||
|
|
SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
|
||
|
|
`['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
|
||
|
|
`unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
|
||
|
|
`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
|
||
|
|
"""
|
||
|
|
tokens = self.sp_model.encode(text, out_type=str)
|
||
|
|
if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
|
||
|
|
return tokens
|
||
|
|
|
||
|
|
# 1. Encode string + prefix ex: "<unk> Hey"
|
||
|
|
tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
|
||
|
|
# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
|
||
|
|
return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
|
||
|
|
|
||
|
|
def _convert_token_to_id(self, token):
|
||
|
|
"""Converts a token (str) in an id using the vocab."""
|
||
|
|
return self.sp_model.piece_to_id(token)
|
||
|
|
|
||
|
|
def _convert_id_to_token(self, index):
|
||
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
||
|
|
token = self.sp_model.IdToPiece(index)
|
||
|
|
return token
|
||
|
|
|
||
|
|
def convert_tokens_to_string(self, tokens):
|
||
|
|
"""Converts a sequence of tokens (string) in a single string."""
|
||
|
|
# since we manually add the prefix space, we have to remove it when decoding
|
||
|
|
if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
|
||
|
|
tokens[0] = tokens[0][1:]
|
||
|
|
|
||
|
|
current_sub_tokens = []
|
||
|
|
out_string = ""
|
||
|
|
prev_is_special = False
|
||
|
|
for token in tokens:
|
||
|
|
# make sure that special tokens are not decoded using sentencepiece model
|
||
|
|
if token in self.all_special_tokens:
|
||
|
|
if not prev_is_special:
|
||
|
|
out_string += " "
|
||
|
|
out_string += self.sp_model.decode(current_sub_tokens) + token
|
||
|
|
prev_is_special = True
|
||
|
|
current_sub_tokens = []
|
||
|
|
else:
|
||
|
|
current_sub_tokens.append(token)
|
||
|
|
prev_is_special = False
|
||
|
|
out_string += self.sp_model.decode(current_sub_tokens)
|
||
|
|
return out_string.strip()
|
||
|
|
|
||
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||
|
|
if not os.path.isdir(save_directory):
|
||
|
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||
|
|
return
|
||
|
|
out_vocab_file = os.path.join(
|
||
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||
|
|
)
|
||
|
|
|
||
|
|
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
||
|
|
copyfile(self.vocab_file, out_vocab_file)
|
||
|
|
elif not os.path.isfile(self.vocab_file):
|
||
|
|
with open(out_vocab_file, "wb") as fi:
|
||
|
|
content_spiece_model = self.sp_model.serialized_model_proto()
|
||
|
|
fi.write(content_spiece_model)
|
||
|
|
|
||
|
|
return (out_vocab_file,)
|