Source code for transformers.tokenization_utils_fast

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
 Tokenization classes for fast tokenizers (provided by HuggingFace's tokenizers library). For slow (python) tokenizers
 see tokenization_utils.py
"""

import json
import os
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, Union

from tokenizers import Encoding as EncodingFast
from tokenizers import Tokenizer as TokenizerFast
from tokenizers.decoders import Decoder as DecoderFast

from .convert_slow_tokenizer import convert_slow_tokenizer
from .file_utils import PaddingStrategy, add_end_docstrings
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_base import (
    INIT_TOKENIZER_DOCSTRING,
    AddedToken,
    BatchEncoding,
    PreTokenizedInput,
    PreTokenizedInputPair,
    PreTrainedTokenizerBase,
    TextInput,
    TextInputPair,
    TruncationStrategy,
)
from .utils import logging


logger = logging.get_logger(__name__)


# Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file
TOKENIZER_FILE = "tokenizer.json"
SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
TOKENIZER_CONFIG_FILE = "tokenizer_config.json"

# Slow tokenizers have an additional added tokens files
ADDED_TOKENS_FILE = "added_tokens.json"

INIT_TOKENIZER_DOCSTRING += """
        tokenizer_object (:class:`tokenizers.Tokenizer`):
            A :class:`tokenizers.Tokenizer` object from 🤗 tokenizers to instantiate from. See :doc:`Using tokenizers
            from 🤗 tokenizers <../fast_tokenizers>` for more information.
"""


[docs]@add_end_docstrings(INIT_TOKENIZER_DOCSTRING) class PreTrainedTokenizerFast(PreTrainedTokenizerBase): """ Base class for all fast tokenizers (wrapping HuggingFace tokenizers library). Inherits from :class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase`. Handles all the shared methods for tokenization and special tokens, as well as methods for downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary. This class also contains the added tokens in a unified way on top of all tokenizers so we don't have to handle the specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...). """ slow_tokenizer_class: PreTrainedTokenizer = None def __init__(self, *args, **kwargs): tokenizer_object = kwargs.pop("tokenizer_object", None) slow_tokenizer = kwargs.pop("__slow_tokenizer", None) fast_tokenizer_file = kwargs.pop("tokenizer_file", None) from_slow = kwargs.pop("from_slow", False) if from_slow and slow_tokenizer is None and self.slow_tokenizer_class is None: raise ValueError( "Cannot instantiate this tokenizer from a slow version. If it's based on sentencepiece, make sure you " "have sentencepiece installed." ) if tokenizer_object is not None: fast_tokenizer = tokenizer_object elif fast_tokenizer_file is not None and not from_slow: # We have a serialization from tokenizers which let us directly build the backend fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file) elif slow_tokenizer is not None: # We need to convert a slow tokenizer to build the backend fast_tokenizer = convert_slow_tokenizer(slow_tokenizer) elif self.slow_tokenizer_class is not None: # We need to create and convert a slow tokenizer to build the backend slow_tokenizer = self.slow_tokenizer_class(*args, **kwargs) fast_tokenizer = convert_slow_tokenizer(slow_tokenizer) else: raise ValueError( "Couldn't instantiate the backend tokenizer from one of: \n" "(1) a `tokenizers` library serialization file, \n" "(2) a slow tokenizer instance to convert or \n" "(3) an equivalent slow tokenizer class to instantiate and convert. \n" "You need to have sentencepiece installed to convert a slow tokenizer to a fast one." ) self._tokenizer = fast_tokenizer if slow_tokenizer is not None: kwargs.update(slow_tokenizer.init_kwargs) self._decode_use_source_tokenizer = False # We call this after having initialized the backend tokenizer because we update it. super().__init__(**kwargs) @property def is_fast(self) -> bool: return True @property def vocab_size(self) -> int: """ :obj:`int`: Size of the base vocabulary (without the added tokens). """ return self._tokenizer.get_vocab_size(with_added_tokens=False) def get_vocab(self) -> Dict[str, int]: return self._tokenizer.get_vocab(with_added_tokens=True) @property def vocab(self) -> Dict[str, int]: return self.get_vocab()
[docs] def get_added_vocab(self) -> Dict[str, int]: """ Returns the added tokens in the vocabulary as a dictionary of token to index. Returns: :obj:`Dict[str, int]`: The added tokens. """ base_vocab = self._tokenizer.get_vocab(with_added_tokens=False) full_vocab = self._tokenizer.get_vocab(with_added_tokens=True) added_vocab = dict((tok, index) for tok, index in full_vocab.items() if tok not in base_vocab) return added_vocab
def __len__(self) -> int: """ Size of the full vocabulary with the added tokens. """ return self._tokenizer.get_vocab_size(with_added_tokens=True) @property def backend_tokenizer(self) -> TokenizerFast: """ :obj:`tokenizers.implementations.BaseTokenizer`: The Rust tokenizer used as a backend. """ return self._tokenizer @property def decoder(self) -> DecoderFast: """ :obj:`tokenizers.decoders.Decoder`: The Rust decoder for this tokenizer. """ return self._tokenizer._tokenizer.decoder def _convert_encoding( self, encoding: EncodingFast, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, ) -> Tuple[Dict[str, Any], List[EncodingFast]]: """ Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list of encodings, take care of building a batch from overflowing tokens. Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are lists (overflows) of lists (tokens). Output shape: (overflows, sequence length) """ if return_token_type_ids is None: return_token_type_ids = "token_type_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names if return_overflowing_tokens and encoding.overflowing is not None: encodings = [encoding] + encoding.overflowing else: encodings = [encoding] encoding_dict = defaultdict(list) for e in encodings: encoding_dict["input_ids"].append(e.ids) if return_token_type_ids: encoding_dict["token_type_ids"].append(e.type_ids) if return_attention_mask: encoding_dict["attention_mask"].append(e.attention_mask) if return_special_tokens_mask: encoding_dict["special_tokens_mask"].append(e.special_tokens_mask) if return_offsets_mapping: encoding_dict["offset_mapping"].append(e.offsets) if return_length: encoding_dict["length"].append(len(e.ids)) return encoding_dict, encodings
[docs] def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]: """ Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the vocabulary. Args: tokens (:obj:`str` or :obj:`List[str]`): One or several token(s) to convert to token id(s). Returns: :obj:`int` or :obj:`List[int]`: The token id or list of token ids. """ if tokens is None: return None if isinstance(tokens, str): return self._convert_token_to_id_with_added_voc(tokens) ids = [] for token in tokens: ids.append(self._convert_token_to_id_with_added_voc(token)) return ids
def _convert_token_to_id_with_added_voc(self, token: str) -> int: index = self._tokenizer.token_to_id(token) if index is None: return self.unk_token_id return index def _convert_id_to_token(self, index: int) -> Optional[str]: return self._tokenizer.id_to_token(int(index)) def _add_tokens(self, new_tokens: List[Union[str, AddedToken]], special_tokens=False) -> int: if special_tokens: return self._tokenizer.add_special_tokens(new_tokens) return self._tokenizer.add_tokens(new_tokens)
[docs] def num_special_tokens_to_add(self, pair: bool = False) -> int: """ Returns the number of added tokens when encoding a sequence with special tokens. .. note:: This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put this inside your training loop. Args: pair (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether the number of added tokens should be computed in the case of a sequence pair or a single sequence. Returns: :obj:`int`: Number of special tokens added to sequences. """ return self._tokenizer.num_special_tokens_to_add(pair)
[docs] def convert_ids_to_tokens( self, ids: Union[int, List[int]], skip_special_tokens: bool = False ) -> Union[str, List[str]]: """ Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and added tokens. Args: ids (:obj:`int` or :obj:`List[int]`): The token id (or token ids) to convert to tokens. skip_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to remove special tokens in the decoding. Returns: :obj:`str` or :obj:`List[str]`: The decoded token(s). """ if isinstance(ids, int): return self._tokenizer.id_to_token(ids) tokens = [] for index in ids: index = int(index) if skip_special_tokens and index in self.all_special_ids: continue tokens.append(self._tokenizer.id_to_token(index)) return tokens
[docs] def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]: return self.encode_plus(text=text, text_pair=pair, add_special_tokens=add_special_tokens, **kwargs).tokens()
[docs] def set_truncation_and_padding( self, padding_strategy: PaddingStrategy, truncation_strategy: TruncationStrategy, max_length: int, stride: int, pad_to_multiple_of: Optional[int], ): """ Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers library) and restore the tokenizer settings afterwards. The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed section. Args: padding_strategy (:class:`~transformers.file_utils.PaddingStrategy`): The kind of padding that will be applied to the input truncation_strategy (:class:`~transformers.tokenization_utils_base.TruncationStrategy`): The kind of truncation that will be applied to the input max_length (:obj:`int`): The maximum size of a sequence. stride (:obj:`int`): The stride to use when handling overflow. pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ # Set truncation and padding on the backend tokenizer if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE: self._tokenizer.enable_truncation(max_length, stride=stride, strategy=truncation_strategy.value) else: self._tokenizer.no_truncation() if padding_strategy != PaddingStrategy.DO_NOT_PAD: self._tokenizer.enable_padding( length=max_length if padding_strategy == PaddingStrategy.MAX_LENGTH else None, direction=self.padding_side, pad_id=self.pad_token_id, pad_type_id=self.pad_token_type_id, pad_token=self.pad_token, pad_to_multiple_of=pad_to_multiple_of, ) else: self._tokenizer.no_padding()
def _batch_encode_plus( self, batch_text_or_text_pairs: Union[ List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair] ], add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, is_split_into_words: bool = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: if not isinstance(batch_text_or_text_pairs, list): raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})") # Set the truncation and padding strategy and restore the initial configuration self.set_truncation_and_padding( padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, ) encodings = self._tokenizer.encode_batch( batch_text_or_text_pairs, add_special_tokens=add_special_tokens, is_pretokenized=is_split_into_words, ) # Convert encoding to dict # `Tokens` has type: Tuple[ # List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]], # List[EncodingFast] # ] # with nested dimensions corresponding to batch, overflows, sequence length tokens_and_encodings = [ self._convert_encoding( encoding=encoding, 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, ) for encoding in encodings ] # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length) # (we say ~ because the number of overflow varies with the example in the batch) # # To match each overflowing sample with the original sample in the batch # we add an overflow_to_sample_mapping array (see below) sanitized_tokens = {} for key in tokens_and_encodings[0][0].keys(): stack = [e for item, _ in tokens_and_encodings for e in item[key]] sanitized_tokens[key] = stack sanitized_encodings = [e for _, item in tokens_and_encodings for e in item] # If returning overflowing tokens, we need to return a mapping # from the batch idx to the original sample if return_overflowing_tokens: overflow_to_sample_mapping = [] for i, (toks, _) in enumerate(tokens_and_encodings): overflow_to_sample_mapping += [i] * len(toks["input_ids"]) sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping for input_ids in sanitized_tokens["input_ids"]: self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose) return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors) def _encode_plus( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[Union[TextInput, PreTokenizedInput]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, is_split_into_words: bool = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[bool] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: batched_input = [(text, text_pair)] if text_pair else [text] batched_output = self._batch_encode_plus( batched_input, is_split_into_words=is_split_into_words, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, 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
[docs] 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: self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False) 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