315f464b0a
* avoid recursion in id checks for fast tokenizers * better typings and fix #5232 * align slow and fast tokenizers behaviors for Roberta and GPT2 * style and quality * fix tests - improve typings
449 lines
20 KiB
Python
449 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2020 The 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 classes for fast tokenizers (provided by HuggingFace's tokenizers library).
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For slow (python) tokenizers see tokenization_utils.py
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"""
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import logging
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import os
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from collections import defaultdict
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from typing import Any, Dict, List, Optional, Tuple, Union
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from tokenizers import Encoding as EncodingFast
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from tokenizers.decoders import Decoder as DecoderFast
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from tokenizers.implementations import BaseTokenizer as BaseTokenizerFast
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from .tokenization_utils_base import (
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AddedToken,
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BatchEncoding,
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PaddingStrategy,
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PreTokenizedInput,
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PreTokenizedInputPair,
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PreTrainedTokenizerBase,
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TextInput,
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TextInputPair,
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TruncationStrategy,
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)
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logger = logging.getLogger(__name__)
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class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
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""" Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
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Inherit from PreTrainedTokenizer.
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Handle all the shared methods for tokenization and special tokens as well as methods
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downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
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This class also contain the added tokens in a unified way on top of all tokenizers so we don't
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have to handle the specific vocabulary augmentation methods of the various underlying
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dictionary structures (BPE, sentencepiece...).
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Class attributes (overridden by derived classes):
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- ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file
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required by the model, and as associated values, the filename for saving the associated file (string).
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- ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys
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being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the
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`short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the
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associated pretrained vocabulary file.
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- ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained
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models, and as associated values, the maximum length of the sequence inputs of this model, or None if the
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model has no maximum input size.
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- ``pretrained_init_configuration``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the
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pretrained models, and as associated values, a dictionnary of specific arguments to pass to the
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``__init__``method of the tokenizer class for this pretrained model when loading the tokenizer with the
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``from_pretrained()`` method.
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Args:
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- ``tokenizer`` (`BaseTokenizerFast`): A Fast tokenizer from the HuggingFace tokenizer library (in low level Rust language)
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- ``model_max_length``: (`Optional`) int: the maximum length in number of tokens for the inputs to the transformer model.
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When the tokenizer is loaded with `from_pretrained`, this will be set to the value stored for the associated
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model in ``max_model_input_sizes`` (see above). If no value is provided, will default to VERY_LARGE_INTEGER (`int(1e30)`).
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no associated max_length can be found in ``max_model_input_sizes``.
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- ``padding_side``: (`Optional`) string: the side on which the model should have padding applied.
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Should be selected between ['right', 'left']
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- ``model_input_names``: (`Optional`) List[string]: the list of the forward pass inputs accepted by the
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model ("token_type_ids", "attention_mask"...).
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- ``bos_token``: (`Optional`) string: a beginning of sentence token.
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Will be associated to ``self.bos_token`` and ``self.bos_token_id``
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- ``eos_token``: (`Optional`) string: an end of sentence token.
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Will be associated to ``self.eos_token`` and ``self.eos_token_id``
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- ``unk_token``: (`Optional`) string: an unknown token.
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Will be associated to ``self.unk_token`` and ``self.unk_token_id``
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- ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence).
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Will be associated to ``self.sep_token`` and ``self.sep_token_id``
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- ``pad_token``: (`Optional`) string: a padding token.
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Will be associated to ``self.pad_token`` and ``self.pad_token_id``
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- ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence
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leveraging self-attention along the full depth of the model).
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Will be associated to ``self.cls_token`` and ``self.cls_token_id``
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- ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language
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modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``
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- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens.
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Adding all special tokens here ensure they won't be split by the tokenization process.
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Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
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.. automethod:: __call__
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"""
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def __init__(self, tokenizer: BaseTokenizerFast, **kwargs):
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if not isinstance(tokenizer, BaseTokenizerFast):
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raise ValueError(
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"Tokenizer should be an instance of a Tokenizer " "provided by HuggingFace tokenizers library."
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)
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self._tokenizer: BaseTokenizerFast = tokenizer
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# We call this after having initialized the backend tokenizer because we update it.
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super().__init__(**kwargs)
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@property
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def is_fast(self) -> bool:
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return True
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@property
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def vocab_size(self) -> int:
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return self._tokenizer.get_vocab_size(with_added_tokens=False)
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def get_vocab(self) -> Dict[str, int]:
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return self._tokenizer.get_vocab(with_added_tokens=True)
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def get_added_vocab(self) -> Dict[str, int]:
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base_vocab = self._tokenizer.get_vocab(with_added_tokens=False)
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full_vocab = self._tokenizer.get_vocab(with_added_tokens=True)
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added_vocab = dict((tok, index) for tok, index in full_vocab.items() if tok not in base_vocab)
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return added_vocab
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def __len__(self) -> int:
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return self._tokenizer.get_vocab_size(with_added_tokens=True)
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@property
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def backend_tokenizer(self) -> BaseTokenizerFast:
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return self._tokenizer
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@property
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def decoder(self) -> DecoderFast:
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return self._tokenizer._tokenizer.decoder
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def _convert_encoding(
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self,
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encoding: EncodingFast,
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return_token_type_ids: Optional[bool] = None,
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return_attention_mask: Optional[bool] = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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) -> Dict[str, Any]:
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""" Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict.
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Overflowing tokens are converted to additional examples (like batches) so the output values of
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the dict are lists (overflows) of lists (tokens).
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Output shape: (overflows, sequence length)
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"""
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if return_token_type_ids is None:
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return_token_type_ids = "token_type_ids" in self.model_input_names
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if return_attention_mask is None:
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return_attention_mask = "attention_mask" in self.model_input_names
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if return_overflowing_tokens and encoding.overflowing is not None:
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encodings = [encoding] + encoding.overflowing
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else:
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encodings = [encoding]
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encoding_dict = defaultdict(list)
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for e in encodings:
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encoding_dict["input_ids"].append(e.ids)
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if return_token_type_ids:
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encoding_dict["token_type_ids"].append(e.type_ids)
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if return_attention_mask:
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encoding_dict["attention_mask"].append(e.attention_mask)
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if return_special_tokens_mask:
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encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
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if return_offsets_mapping:
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encoding_dict["offset_mapping"].append(e.offsets)
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if return_length:
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encoding_dict["length"].append(len(e.ids))
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return encoding_dict
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def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
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""" Converts a token string (or a sequence of tokens) in a single integer id
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(or a sequence of ids), using the vocabulary.
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"""
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if tokens is None:
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return None
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if isinstance(tokens, str):
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return self._convert_token_to_id_with_added_voc(tokens)
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ids = []
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for token in tokens:
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ids.append(self._convert_token_to_id_with_added_voc(token))
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return ids
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def _convert_token_to_id_with_added_voc(self, token: str) -> int:
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index = self._tokenizer.token_to_id(token)
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if index is None:
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return self.unk_token_id
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return index
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def _convert_id_to_token(self, index: int) -> Optional[str]:
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return self._tokenizer.id_to_token(int(index))
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def _add_tokens(self, new_tokens: List[Union[str, AddedToken]], special_tokens=False) -> int:
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if special_tokens:
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return self._tokenizer.add_special_tokens(new_tokens)
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return self._tokenizer.add_tokens(new_tokens)
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def num_special_tokens_to_add(self, pair: bool = False) -> int:
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return self._tokenizer.num_special_tokens_to_add(pair)
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def convert_ids_to_tokens(
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self, ids: Union[int, List[int]], skip_special_tokens: bool = False
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) -> Union[str, List[str]]:
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""" Converts a single index or a sequence of indices (integers) in a token "
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(resp.) a sequence of tokens (str), using the vocabulary and added tokens.
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Args:
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skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False
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"""
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if isinstance(ids, int):
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return self._tokenizer.id_to_token(ids)
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tokens = []
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for index in ids:
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index = int(index)
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if skip_special_tokens and index in self.all_special_ids:
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continue
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tokens.append(self._tokenizer.id_to_token(index))
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return tokens
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def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False) -> List[str]:
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return self._tokenizer.encode(text, pair, add_special_tokens=add_special_tokens).tokens
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def set_truncation_and_padding(
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self, padding_strategy: PaddingStrategy, truncation_strategy: TruncationStrategy, max_length: int, stride: int,
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):
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""" This contextmanager is in charge of defining the truncation and the padding strategies for fast tokenizers
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(provided by HuggingFace tokenizers library) and restore the tokenizer settings afterwards.
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This contextmanager assumes the provider tokenizer has no padding / truncation strategy
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before the managed section. If your tokenizer set a padding / truncation strategy before,
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then it will be reset to no padding/truncation when exiting the managed section.
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Args:
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tokenizer (BaseTokenizerFast): The tokenizer which will be used
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max_length (int): The maximum size of the sequence
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stride (int): The stride to use when handling overflow
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strategy (str): Overflowing logic to use
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pad_to_max_length (bool): Boolean indicating if the output needs to be padded up to max_length
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padding_side (str): "left" or "right" indicating the direction the output sequence will be padded
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pad_token_id (int): The integer representation of the padding token to use
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pad_token_type_id (int): The integer representation of the padding token type to use
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pad_token (str): The string representation of the padding token to use
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"""
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# Set truncation and padding on the backend tokenizer
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if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
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self._tokenizer.enable_truncation(max_length, stride=stride, strategy=truncation_strategy.value)
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else:
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self._tokenizer.no_truncation()
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if padding_strategy != PaddingStrategy.DO_NOT_PAD:
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self._tokenizer.enable_padding(
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length=max_length if padding_strategy == PaddingStrategy.MAX_LENGTH else None,
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direction=self.padding_side,
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pad_id=self.pad_token_id,
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pad_type_id=self.pad_token_type_id,
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pad_token=self.pad_token,
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)
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else:
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self._tokenizer.no_padding()
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def _batch_encode_plus(
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self,
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batch_text_or_text_pairs: Union[
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List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
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],
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add_special_tokens: bool = True,
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padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
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truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
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max_length: Optional[int] = None,
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stride: int = 0,
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is_pretokenized: bool = False,
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return_tensors: Optional[str] = None,
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return_token_type_ids: Optional[bool] = None,
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return_attention_mask: Optional[bool] = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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**kwargs
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) -> BatchEncoding:
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if not isinstance(batch_text_or_text_pairs, list):
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raise ValueError(
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"batch_text_or_text_pairs has to be a list (got {})".format(type(batch_text_or_text_pairs))
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)
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if kwargs:
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raise ValueError(f"Keyword arguments {kwargs} not recognized.")
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# Set the truncation and padding strategy and restore the initial configuration
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self.set_truncation_and_padding(
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padding_strategy=padding_strategy,
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truncation_strategy=truncation_strategy,
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max_length=max_length,
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stride=stride,
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)
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# Avoid thread overhead if only one example.
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if len(batch_text_or_text_pairs) == 1:
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if isinstance(batch_text_or_text_pairs[0], tuple):
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# We got a Tuple with a pair of sequences
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encodings = self._tokenizer.encode(
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*batch_text_or_text_pairs[0],
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add_special_tokens=add_special_tokens,
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is_pretokenized=is_pretokenized,
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)
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else:
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# We got a single sequence
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encodings = self._tokenizer.encode(
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batch_text_or_text_pairs[0],
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add_special_tokens=add_special_tokens,
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is_pretokenized=is_pretokenized,
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)
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encodings = [encodings]
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else:
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encodings = self._tokenizer.encode_batch(
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batch_text_or_text_pairs, add_special_tokens=add_special_tokens, is_pretokenized=is_pretokenized
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)
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# Convert encoding to dict
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# `Tokens` has type: List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]]
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# with nested dimensions corresponding to batch, overflows, sequence length
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tokens = [
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self._convert_encoding(
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encoding=encoding,
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return_token_type_ids=return_token_type_ids,
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return_attention_mask=return_attention_mask,
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return_overflowing_tokens=return_overflowing_tokens,
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return_special_tokens_mask=return_special_tokens_mask,
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return_offsets_mapping=return_offsets_mapping,
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return_length=return_length,
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verbose=verbose,
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)
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for encoding in encodings
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]
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# Convert the output to have dict[list] from list[dict]
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sanitized = {}
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for key in tokens[0].keys():
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# To List[List[List[int]]] of shape (batch, overflows, sequence length)
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stack = [e for item in tokens for e in item[key]]
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sanitized[key] = stack
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# If returning overflowing tokens, we need to return a mapping
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# from the batch idx to the original sample
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if return_overflowing_tokens:
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overflow_to_sample_mapping = []
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for i, enc in enumerate(tokens):
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overflow_to_sample_mapping += [i] * len(enc["input_ids"])
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sanitized["overflow_to_sample_mapping"] = overflow_to_sample_mapping
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return BatchEncoding(sanitized, encodings, tensor_type=return_tensors)
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def _encode_plus(
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self,
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text: Union[TextInput, PreTokenizedInput],
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text_pair: Optional[Union[TextInput, PreTokenizedInput]] = None,
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add_special_tokens: bool = True,
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padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
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truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
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max_length: Optional[int] = None,
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stride: int = 0,
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is_pretokenized: bool = False,
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return_tensors: Optional[bool] = None,
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return_token_type_ids: Optional[bool] = None,
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return_attention_mask: Optional[bool] = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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**kwargs
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) -> BatchEncoding:
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batched_input = [(text, text_pair)] if text_pair else [text]
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batched_output = self._batch_encode_plus(
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batched_input,
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is_pretokenized=is_pretokenized,
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add_special_tokens=add_special_tokens,
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padding_strategy=padding_strategy,
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truncation_strategy=truncation_strategy,
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max_length=max_length,
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stride=stride,
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return_tensors=return_tensors,
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return_token_type_ids=return_token_type_ids,
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return_attention_mask=return_attention_mask,
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return_overflowing_tokens=return_overflowing_tokens,
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return_special_tokens_mask=return_special_tokens_mask,
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return_offsets_mapping=return_offsets_mapping,
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return_length=return_length,
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verbose=verbose,
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**kwargs,
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)
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# Return tensor is None, then we can remove the leading batch axis
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# Overfolwing tokens are returned as a batch of output so we keep them in this case
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if return_tensors is None and not return_overflowing_tokens:
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batched_output = BatchEncoding(
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{
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key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
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for key, value in batched_output.items()
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},
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batched_output.encodings,
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)
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return batched_output
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def decode(
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self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True
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) -> str:
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text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
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if clean_up_tokenization_spaces:
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clean_text = self.clean_up_tokenization(text)
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return clean_text
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else:
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return text
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def save_vocabulary(self, save_directory: str) -> Tuple[str]:
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if os.path.isdir(save_directory):
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files = self._tokenizer.save_model(save_directory)
|
|
else:
|
|
folder, file = os.path.split(os.path.abspath(save_directory))
|
|
files = self._tokenizer.save_model(folder, name=file)
|
|
|
|
return tuple(files)
|