mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-02-09 12:41:52 +08:00
66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
from pathlib import Path
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from typing import Any, List, Union
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from transformers import AutoTokenizer, PreTrainedTokenizerBase
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from transformers.tokenization_utils_base import PreTrainedTokenizerBase
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TokenIdsTy = List[int]
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class TokenizerBase(PreTrainedTokenizerBase):
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''' This is a protocol for the tokenizer. Users can implement their own tokenizer by inheriting this class. '''
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class TransformersTokenizer(TokenizerBase):
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''' A wrapper for the Transformers' tokenizer.
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This is the default tokenizer for LLM. '''
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@classmethod
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def from_pretrained(cls, pretrained_model_dir: str, **kwargs):
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir,
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**kwargs)
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return TransformersTokenizer(tokenizer)
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def __init__(self, tokenizer):
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self.tokenizer = tokenizer
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def __call__(self, text: str, *args, **kwargs) -> Any:
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return self.tokenizer(text, *args, **kwargs)
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@property
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def eos_token_id(self) -> int:
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return self.tokenizer.eos_token_id
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@property
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def pad_token_id(self) -> int:
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return self.tokenizer.pad_token_id
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def encode(self, text: str, *args, **kwargs) -> TokenIdsTy:
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return self.tokenizer.encode(text, *args, **kwargs)
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def decode(self, token_ids: TokenIdsTy, *args, **kwargs) -> str:
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return self.tokenizer.decode(token_ids, *args, **kwargs)
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def batch_encode_plus(self, texts: List[str], *args, **kwargs) -> dict:
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return self.tokenizer.batch_encode_plus(texts, *args, **kwargs)
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def tokenizer_factory(
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obj: Union[str, Path, TokenizerBase, PreTrainedTokenizerBase, None],
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**kwargs) -> Union[TokenizerBase, PreTrainedTokenizerBase, None]:
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if obj is None:
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return None
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if isinstance(obj, (str, Path)):
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default_kwargs = {
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'legacy': False,
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'padding_side': 'left',
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'truncation_side': 'left',
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'trust_remote_code': True,
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'use_fast': True,
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}
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default_kwargs.update(kwargs)
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return AutoTokenizer.from_pretrained(obj, **kwargs)
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return obj
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