41 lines
No EOL
1.7 KiB
Python
41 lines
No EOL
1.7 KiB
Python
from typing import Union
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from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
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Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
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NUM_SENTINEL_TOKENS: int = 100
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def adapt_tokenizer_for_denoising(tokenizer: Tokenizer):
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"""Adds sentinel tokens and padding token (if missing).
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Expands the tokenizer vocabulary to include sentinel tokens
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used in mixture-of-denoiser tasks as well as a padding token.
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All added tokens are added as special tokens. No tokens are
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added if sentinel tokens and padding token already exist.
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"""
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sentinels_to_add = [f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)]
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tokenizer.add_tokens(sentinels_to_add, special_tokens=True)
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if tokenizer.pad_token is None:
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tokenizer.add_tokens('<pad>', special_tokens=True)
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tokenizer.pad_token = '<pad>'
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assert tokenizer.pad_token_id is not None
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sentinels = ''.join([f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)])
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_sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids
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tokenizer.sentinel_token_ids = _sentinel_token_ids
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class AutoTokenizerForMOD(AutoTokenizer):
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"""AutoTokenizer + Adaptation for MOD.
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A simple wrapper around AutoTokenizer to make instantiating
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an MOD-adapted tokenizer a bit easier.
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MOD-adapted tokenizers have sentinel tokens (e.g., <extra_id_0>),
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a padding token, and a property to get the token ids of the
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sentinel tokens.
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"""
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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"""See `AutoTokenizer.from_pretrained` docstring."""
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tokenizer = super().from_pretrained(*args, **kwargs)
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adapt_tokenizer_for_denoising(tokenizer)
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return tokenizer |