cb38ffcc5e
* push to show * small improvement * small improvement * Update src/transformers/feature_extraction_utils.py * Update src/transformers/feature_extraction_utils.py * implement base * add common tests * make all tests pass for wav2vec2 * make padding work & add more tests * finalize feature extractor utils * add call method to feature extraction * finalize feature processor * finish tokenizer * finish general processor design * finish tests * typo * remove bogus file * finish docstring * add docs * finish docs * small fix * correct docs * save intermediate * load changes * apply changes * apply changes to doc * change tests * apply surajs recommend * final changes * Apply suggestions from code review * fix typo * fix import * correct docstring
539 lines
22 KiB
Python
539 lines
22 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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"""
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Tokenization classes for fast tokenizers (provided by HuggingFace's tokenizers library). For slow (python) tokenizers
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see tokenization_utils.py
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"""
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import json
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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 import Tokenizer as TokenizerFast
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from tokenizers.decoders import Decoder as DecoderFast
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from .convert_slow_tokenizer import convert_slow_tokenizer
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from .file_utils import PaddingStrategy, add_end_docstrings
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from .tokenization_utils import PreTrainedTokenizer
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from .tokenization_utils_base import (
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INIT_TOKENIZER_DOCSTRING,
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AddedToken,
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BatchEncoding,
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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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from .utils import logging
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logger = logging.get_logger(__name__)
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# Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file
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TOKENIZER_FILE = "tokenizer.json"
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SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
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TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
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# Slow tokenizers have an additional added tokens files
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ADDED_TOKENS_FILE = "added_tokens.json"
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@add_end_docstrings(INIT_TOKENIZER_DOCSTRING)
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class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
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"""
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Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
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Inherits from :class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase`.
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Handles all the shared methods for tokenization and special tokens, as well as methods for
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downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary.
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This class also contains the added tokens in a unified way on top of all tokenizers so we don't have to handle the
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specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
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"""
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slow_tokenizer_class: PreTrainedTokenizer = None
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def __init__(self, *args, **kwargs):
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slow_tokenizer = kwargs.pop("__slow_tokenizer", None)
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fast_tokenizer_file = kwargs.pop("tokenizer_file", None)
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from_slow = kwargs.pop("from_slow", False)
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if from_slow and slow_tokenizer is None and self.slow_tokenizer_class is None:
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raise ValueError(
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"Cannot instantiate this tokenizer from a slow version. If it's based on sentencepiece, make sure you "
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"have sentencepiece installed."
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)
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if fast_tokenizer_file is not None and not from_slow:
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# We have a serialization from tokenizers which let us directly build the backend
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fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
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elif slow_tokenizer is not None:
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# We need to convert a slow tokenizer to build the backend
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fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
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elif self.slow_tokenizer_class is not None:
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# We need to create and convert a slow tokenizer to build the backend
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slow_tokenizer = self.slow_tokenizer_class(*args, **kwargs)
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fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
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else:
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raise ValueError(
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"Couldn't instantiate the backend tokenizer from one of: "
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"(1) a `tokenizers` library serialization file, "
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"(2) a slow tokenizer instance to convert or "
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"(3) an equivalent slow tokenizer class to instantiate and convert. "
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"You need to have sentencepiece installed to convert a slow tokenizer to a fast one."
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)
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self._tokenizer = fast_tokenizer
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if slow_tokenizer is not None:
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kwargs.update(slow_tokenizer.init_kwargs)
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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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"""
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:obj:`int`: Size of the base vocabulary (without the added tokens).
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"""
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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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@property
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def vocab(self) -> Dict[str, int]:
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return self.get_vocab()
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def get_added_vocab(self) -> Dict[str, int]:
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"""
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Returns the added tokens in the vocabulary as a dictionary of token to index.
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Returns:
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:obj:`Dict[str, int]`: The added tokens.
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"""
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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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"""
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Size of the full vocabulary with the added tokens.
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"""
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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) -> TokenizerFast:
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"""
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:obj:`tokenizers.implementations.BaseTokenizer`: The Rust tokenizer used as a backend.
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"""
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return self._tokenizer
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@property
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def decoder(self) -> DecoderFast:
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"""
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:obj:`tokenizers.decoders.Decoder`: The Rust decoder for this tokenizer.
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"""
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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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) -> Tuple[Dict[str, Any], List[EncodingFast]]:
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"""
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Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list
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of encodings, take care of building a batch from overflowing tokens.
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Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are
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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, encodings
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def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
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"""
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Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
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vocabulary.
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Args:
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tokens (:obj:`str` or :obj:`List[str]`): One or several token(s) to convert to token id(s).
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Returns:
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:obj:`int` or :obj:`List[int]`: The token id or list of token ids.
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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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"""
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Returns the number of added tokens when encoding a sequence with special tokens.
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.. note::
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This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not
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put this inside your training loop.
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Args:
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pair (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether the number of added tokens should be computed in the case of a sequence pair or a single
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sequence.
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Returns:
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:obj:`int`: Number of special tokens added to sequences.
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"""
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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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"""
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Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
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added tokens.
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Args:
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ids (:obj:`int` or :obj:`List[int]`):
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The token id (or token ids) to convert to tokens.
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skip_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to remove special tokens in the decoding.
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Returns:
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:obj:`str` or :obj:`List[str]`: The decoded token(s).
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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, **kwargs) -> List[str]:
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return self.encode_plus(text=text, text_pair=pair, add_special_tokens=add_special_tokens, **kwargs).tokens()
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def set_truncation_and_padding(
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self,
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padding_strategy: PaddingStrategy,
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truncation_strategy: TruncationStrategy,
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max_length: int,
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stride: int,
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pad_to_multiple_of: Optional[int],
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):
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"""
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Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers
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library) and restore the tokenizer settings afterwards.
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The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a
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padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed
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section.
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Args:
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padding_strategy (:class:`~transformers.file_utils.PaddingStrategy`):
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The kind of padding that will be applied to the input
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truncation_strategy (:class:`~transformers.tokenization_utils_base.TruncationStrategy`):
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The kind of truncation that will be applied to the input
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max_length (:obj:`int`):
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The maximum size of a sequence.
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stride (:obj:`int`):
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The stride to use when handling overflow.
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pad_to_multiple_of (:obj:`int`, `optional`):
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If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
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the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
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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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pad_to_multiple_of=pad_to_multiple_of,
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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_split_into_words: bool = False,
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pad_to_multiple_of: Optional[int] = None,
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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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) -> BatchEncoding:
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if not isinstance(batch_text_or_text_pairs, list):
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raise TypeError(
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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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# 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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pad_to_multiple_of=pad_to_multiple_of,
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)
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encodings = self._tokenizer.encode_batch(
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batch_text_or_text_pairs,
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add_special_tokens=add_special_tokens,
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is_pretokenized=is_split_into_words,
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)
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# Convert encoding to dict
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# `Tokens` has type: Tuple[
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# List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
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# List[EncodingFast]
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# ]
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# with nested dimensions corresponding to batch, overflows, sequence length
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tokens_and_encodings = [
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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] and remove the additional overflows dimension
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# From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
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# (we say ~ because the number of overflow varies with the example in the batch)
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#
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# To match each overflowing sample with the original sample in the batch
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# we add an overflow_to_sample_mapping array (see below)
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sanitized_tokens = {}
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for key in tokens_and_encodings[0][0].keys():
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stack = [e for item, _ in tokens_and_encodings for e in item[key]]
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sanitized_tokens[key] = stack
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sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
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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, (toks, _) in enumerate(tokens_and_encodings):
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overflow_to_sample_mapping += [i] * len(toks["input_ids"])
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sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
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for input_ids in sanitized_tokens["input_ids"]:
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self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
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return BatchEncoding(sanitized_tokens, 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_split_into_words: bool = False,
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pad_to_multiple_of: Optional[int] = None,
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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_split_into_words=is_split_into_words,
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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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pad_to_multiple_of=pad_to_multiple_of,
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return_tensors=return_tensors,
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return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
# Return tensor is None, then we can remove the leading batch axis
|
|
# Overflowing tokens are returned as a batch of output so we keep them in this case
|
|
if return_tensors is None and not return_overflowing_tokens:
|
|
batched_output = BatchEncoding(
|
|
{
|
|
key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
|
|
for key, value in batched_output.items()
|
|
},
|
|
batched_output.encodings,
|
|
)
|
|
|
|
self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
|
|
|
|
return batched_output
|
|
|
|
def convert_tokens_to_string(self, tokens: List[str]) -> str:
|
|
return self.backend_tokenizer.decoder.decode(tokens)
|
|
|
|
def _decode(
|
|
self,
|
|
token_ids: Union[int, List[int]],
|
|
skip_special_tokens: bool = False,
|
|
clean_up_tokenization_spaces: bool = True,
|
|
**kwargs
|
|
) -> str:
|
|
if isinstance(token_ids, int):
|
|
token_ids = [token_ids]
|
|
text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
|
|
|
|
if clean_up_tokenization_spaces:
|
|
clean_text = self.clean_up_tokenization(text)
|
|
return clean_text
|
|
else:
|
|
return text
|
|
|
|
def _save_pretrained(
|
|
self,
|
|
save_directory: Union[str, os.PathLike],
|
|
file_names: Tuple[str],
|
|
legacy_format: bool = True,
|
|
filename_prefix: Optional[str] = None,
|
|
) -> Tuple[str]:
|
|
"""
|
|
Save a tokenizer using the slow-tokenizer/legacy format: vocabulary + added tokens.
|
|
|
|
Fast tokenizers can also be saved in a unique JSON file containing {config + vocab + added-tokens} using the
|
|
specific :meth:`~transformers.PreTrainedTokenizerFast._save_pretrained`
|
|
"""
|
|
save_directory = str(save_directory)
|
|
|
|
if legacy_format:
|
|
added_tokens_file = os.path.join(
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE
|
|
)
|
|
added_vocab = self.get_added_vocab()
|
|
if added_vocab:
|
|
with open(added_tokens_file, "w", encoding="utf-8") as f:
|
|
out_str = json.dumps(added_vocab, ensure_ascii=False)
|
|
f.write(out_str)
|
|
|
|
vocab_files = self.save_vocabulary(save_directory, filename_prefix=filename_prefix)
|
|
file_names = file_names + vocab_files + (added_tokens_file,)
|
|
else:
|
|
tokenizer_file = os.path.join(
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + TOKENIZER_FILE
|
|
)
|
|
self.backend_tokenizer.save(tokenizer_file)
|
|
file_names = file_names + (tokenizer_file,)
|
|
|
|
return file_names
|