Custom Tokenizer#
In addition to using tokenizers as specified in the “Tokenizer Initialization” section, the Model Zoo also supports the use of custom tokenizers.
To implement a custom tokenizer, ensure the configuration file is properly set up. Follow these steps:
1. In the processing section, make sure that you are using the custom_tokenizer parameter.
2. Specify the path to the custom token tokenizer class in the config file, in the custom_tokenizer param, within the setup section. If needed, pass in additional tokenizer_params (such as token, vocab_file, encoder_file and so on) as well.
This would look like:
custom_tokenizer: "<path/to/custom-tokenizer-class>"
tokenizer_params:
token: "<insert-token-here>"
vocab_file: "<insert-path-to-vocab-file>"
encoder_file: "<insert-path-to-encoder-file>"
Note
The custom_tokenizer path should be specified with the class name being separated with a
colon : from the module name, for the custom tokenizer class be instantiated correctly.
Example of a Custom Tokenizer#
Here is an example of custom LLama3 tokenizer which was implemented in Model Zoo:
Note
This tokenizer has a custom implementation because of a bug that is present on HuggingFace’s Llama3 tokenizer’s offset_mapping calculation. (see: https://github.com/huggingface/tokenizers/issues/1553). Once that is fixed, this tokenizer need not be used.
Also, the current preprocessing pipeline relies on the tokenizer’s offset_mapping to process semantic regions. Please use this implementation for Llama3.
from functools import wraps
from typing import Any, Dict, List, Tuple, Union
from transformers import AutoTokenizer
class CustomLlama3Tokenizer:
def __init__(
self,
pretrained_model_name_or_path: str,
eos_token_id: Union[int, None] = None,
pad_token_id: Union[int, None] = None,
**kwargs: Any,
):
"""
Custom implementation of Llama3 Tokenizer, which overrides compute_offsets
of the HuggingFace (which is buggy - https://github.com/huggingface/tokenizers/issues/1553).
Args:
pretrained_model_name_or_path (str): The pretrained model name or path.
eos_token_id (Union[int, None], optional): The ID of the end-of-sequence token. Defaults to None.
pad_token_id (Union[int, None], optional): The ID of the padding token. Defaults to None.
**kwargs (Any): Additional keyword arguments to be passed to AutoTokenizer.
Attributes:
tokenizer (AutoTokenizer): The AutoTokenizer instance for the given pretrained model.
eos_token_id (int): The ID of the end-of-sequence token.
pad_token_id (int): The ID of the padding token.
"""
self.tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs
)
self.eos_token_id = (
eos_token_id
if eos_token_id is not None
else self.tokenizer.eos_token_id
)
self.pad_token_id = (
pad_token_id if pad_token_id is not None else self.eos_token_id
)
def compute_offsets(
self, encoded: Dict[str, Any], return_offsets_mapping: bool = False
) -> List[Tuple[int, int]]:
"""
Compute offsets for the given encoded input.
Args:
encoded (Dict[str, Any]): The encoded input containing 'input_ids' and 'offset_mapping'.
return_offsets_mapping (bool, optional): Whether to return the offsets mapping. Defaults to False.
Returns:
List[Tuple[int, int]]: A list of tuples representing the start and end offsets for each token.
"""
input_ids = encoded['input_ids']
offset_mapping = encoded['offset_mapping']
for i, (input_id, (start, end)) in enumerate(
zip(input_ids, offset_mapping)
):
token = self.tokenizer.convert_ids_to_tokens(input_id)
if input_id not in self.tokenizer.all_special_ids:
offset_mapping[i] = (start, start + len(token))
return offset_mapping
def __call__(self, text: str, **kwargs: Any) -> Dict[str, Any]:
"""
Encode the given text into tokens and optionally return the offsets mapping.
Args:
text (str): The input text to tokenize.
**kwargs (Any): Additional keyword arguments for tokenization.
Returns:
Dict[str, Any]: The encoded result containing 'input_ids', 'attention_mask', and optionally 'offset_mapping'.
"""
return_offset_mapping = kwargs.get('return_offsets_mapping')
encoded = self.tokenizer(text, **kwargs)
if return_offset_mapping:
fixed_offsets = self.compute_offsets(
encoded, return_offsets_mapping=return_offset_mapping
)
encoded['offset_mapping'] = fixed_offsets
return encoded
def __getattr__(self, name: str) -> Any:
"""
Forward attribute access to the underlying tokenizer.
Args:
name (str): The name of the attribute to access.
Returns:
Any: The attribute value.
"""
attr = getattr(self.tokenizer, name)
if callable(attr):
@wraps(attr)
def wrapper(*args, **kwargs):
return attr(*args, **kwargs)
return wrapper
return attr
def __len__(self) -> int:
"""
Get the vocabulary size of the tokenizer.
Returns:
int: The vocabulary size.
"""
return self.tokenizer.vocab_size
The code for this is presen in the directory modelzoo/data_preparation/data_preprocessing/custom_tokenizer_example.