# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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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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# Unless required by applicable law or agreed to in writing, software
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from typing import Optional
from transformers import AutoTokenizer as AUTOTOKENIZER
from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
from nemo.utils import logging
__all__ = [
'AutoTokenizer',
]
[docs]class AutoTokenizer(TokenizerSpec):
'''
Wrapper of HuggingFace AutoTokenizer https://huggingface.co/transformers/model_doc/auto.html#autotokenizer.
'''
[docs] def __init__(
self,
pretrained_model_name: str,
vocab_file: Optional[str] = None,
merges_file: Optional[str] = None,
mask_token: Optional[str] = None,
bos_token: Optional[str] = None,
eos_token: Optional[str] = None,
pad_token: Optional[str] = None,
sep_token: Optional[str] = None,
cls_token: Optional[str] = None,
unk_token: Optional[str] = None,
use_fast: Optional[bool] = False,
):
"""
Args:
pretrained_model_name: corresponds to HuggingFace-AutoTokenizer's 'pretrained_model_name_or_path' input argument.
For more details please refer to https://huggingface.co/transformers/_modules/transformers/tokenization_auto.html#AutoTokenizer.from_pretrained.
The list of all supported models can be found here: ALL_PRETRAINED_CONFIG_ARCHIVE_MAP
vocab_file: path to file with vocabulary which consists
of characters separated by '\n'.
mask_token: mask token
bos_token: the beginning of sequence token
eos_token: the end of sequence token. Usually equal to sep_token
pad_token: token to use for padding
sep_token: token used for separating sequences
cls_token: class token. Usually equal to bos_token
unk_token: token to use for unknown tokens
use_fast: whether to use fast HuggingFace tokenizer
"""
try:
# this logic deals with different huggingface tokenizers having different positional args
if vocab_file is None:
self.tokenizer = AUTOTOKENIZER.from_pretrained(
pretrained_model_name_or_path=pretrained_model_name, use_fast=use_fast,
)
elif merges_file is None:
self.tokenizer = AUTOTOKENIZER.from_pretrained(
pretrained_model_name_or_path=pretrained_model_name, vocab_file=vocab_file, use_fast=use_fast,
)
else:
self.tokenizer = AUTOTOKENIZER.from_pretrained(
pretrained_model_name_or_path=pretrained_model_name,
vocab_file=vocab_file,
merges_file=merges_file,
use_fast=use_fast,
)
except Exception as e:
raise ValueError(
f'Unable to instantiate HuggingFace AUTOTOKENIZER for {pretrained_model_name}. Exception: {e}'
)
self.original_vocab_size = len(self.tokenizer)
special_tokens_dict = {}
# # setting special tokens, by default the default model's special tokens will be preserved
# # unless passes new values to the special tokens
if unk_token is not None:
special_tokens_dict["unk_token"] = unk_token
if mask_token is not None:
special_tokens_dict["mask_token"] = mask_token
if pad_token is not None:
special_tokens_dict["pad_token"] = pad_token
# if the model does not have eos_token but has sep_token,
# set eos_token = sep_token, and vice versa
if sep_token is not None:
special_tokens_dict["sep_token"] = sep_token
elif self.tokenizer.sep_token is None and self.tokenizer.eos_token:
special_tokens_dict["sep_token"] = self.tokenizer.eos_token
if eos_token is not None:
special_tokens_dict["eos_token"] = eos_token
elif self.tokenizer.eos_token is None and self.tokenizer.sep_token:
special_tokens_dict["eos_token"] = self.tokenizer.sep_token
# if the model does not have bos_token but has cls_token,
# set bos_token = cls_token, and vice versa
if bos_token is not None:
special_tokens_dict["bos_token"] = bos_token
elif self.tokenizer.bos_token is None and self.tokenizer.cls_token:
special_tokens_dict["bos_token"] = self.tokenizer.cls_token
if cls_token is not None:
special_tokens_dict["cls_token"] = cls_token
elif self.tokenizer.cls_token is None and self.tokenizer.bos_token:
special_tokens_dict["cls_token"] = self.tokenizer.bos_token
new_tokens_in_vocab = []
for token in [mask_token, bos_token, eos_token, pad_token, sep_token, cls_token, unk_token]:
if token is not None and token not in self.tokenizer.get_vocab():
new_tokens_in_vocab.append(token)
if len(new_tokens_in_vocab) > 0:
"""
Special tokens that were not previously included in the tokenizer's vocabulary file will be added to
the vocabulary and, as a result, the model should be resized, for example:
# define your model
pretrained_model_name = 'roberta-base'
model = nemo_nlp.modules.get_lm_model(pretrained_model_name=pretrained_model_name)
# define pretrained tokenizer
tokenizer_default = nemo_nlp.modules.get_tokenizer(tokenizer_name=pretrained_model_name)
special_tokens = {'bos_token': '<BOS>',
'cls_token': '<CSL>',
'additional_special_tokens': ['<MY_NER_TOKEN>', '<ANOTHER_TOKEN>']}
tokenizer_default.add_special_tokens(special_tokens_dict=special_tokens)
# resize your model so that the embeddings for newly added tokens are updated during training/finetuning
model.resize_token_embeddings(tokenizer_default.vocab_size)
See NLP_Tokenizers.ipynb for more details.
"""
logging.warning(
f'{new_tokens_in_vocab} \n will be added to the vocabulary.\n'
f'Please resize your model accordingly, '
f'see NLP_Tokenizers.ipynb for more details.'
)
self.add_special_tokens(special_tokens_dict)
@property
def vocab_size(self):
return len(self.tokenizer)
[docs] def add_special_tokens(self, special_tokens_dict: dict) -> int:
"""
Adds a dictionary of special tokens (eos, pad, cls...). If special tokens are NOT in the vocabulary, they are added
to it (indexed starting from the last index of the current vocabulary).
Args:
special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
[``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
``additional_special_tokens``].
Tokens are only added if they are not already in the vocabulary.
Returns:
Number of tokens added to the vocabulary.
"""
num_tokens_added = self.tokenizer.add_special_tokens(special_tokens_dict)
if num_tokens_added > 0:
logging.info(f'{num_tokens_added} special tokens added, resize your model accordingly.')
for k in self.tokenizer.SPECIAL_TOKENS_ATTRIBUTES:
setattr(self, k, getattr(self.tokenizer, k, None))
return num_tokens_added
@property
def additional_special_tokens_ids(self):
"""Returns a list of the additional special tokens (excluding bos, eos, pad, unk). Used to return sentinel tokens for e.g. T5."""
return [self.token_to_id(token) for token in self.additional_special_tokens]
[docs] def text_to_tokens(self, text):
tokens = self.tokenizer.tokenize(text)
return tokens
[docs] def tokens_to_text(self, tokens):
text = self.tokenizer.convert_tokens_to_string(tokens)
return text
[docs] def token_to_id(self, token):
return self.tokens_to_ids([token])[0]
[docs] def tokens_to_ids(self, tokens):
ids = self.tokenizer.convert_tokens_to_ids(tokens)
return ids
[docs] def ids_to_tokens(self, ids):
tokens = self.tokenizer.convert_ids_to_tokens(ids)
return tokens
[docs] def text_to_ids(self, text):
tokens = self.text_to_tokens(text)
ids = self.tokens_to_ids(tokens)
return ids
[docs] def ids_to_text(self, ids):
tokens = self.ids_to_tokens(ids)
tokens_clean = [t for t in tokens if t not in self.tokenizer.all_special_tokens]
text = self.tokens_to_text(tokens_clean)
return text
@property
def vocab(self):
id2vocab = {v: k for k, v in self.tokenizer.vocab.items()}
return [id2vocab[i] for i in range(len(id2vocab))]
@property
def pad_id(self):
return self.tokens_to_ids([getattr(self, 'pad_token')])[0]
@property
def bos_id(self):
return self.tokens_to_ids([getattr(self, 'bos_token')])[0]
@property
def eos_id(self):
return self.tokens_to_ids([getattr(self, 'eos_token')])[0]
@property
def sep_id(self):
return self.tokens_to_ids([getattr(self, 'sep_token')])[0]
@property
def cls_id(self):
return self.tokens_to_ids([getattr(self, 'cls_token')])[0]
@property
def unk_id(self):
return self.tokens_to_ids([getattr(self, 'unk_token')])[0]
@property
def mask_id(self):
return self.tokens_to_ids([getattr(self, 'mask_token')])[0]
@property
def name(self):
return type(self.tokenizer).__name__
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: str = None):
"""Saves tokenizer's vocabulary and other artifacts to the specified directory"""
return self.tokenizer.save_vocabulary(save_directory=save_directory, filename_prefix=filename_prefix)
