96 lines
3 KiB
Python
96 lines
3 KiB
Python
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from __future__ import annotations
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import abc
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from typing import (
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List,
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Optional,
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Any,
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)
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import llama_cpp
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from llama_cpp.llama_types import List
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class BaseLlamaTokenizer(abc.ABC):
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@abc.abstractmethod
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def tokenize(
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self, text: bytes, add_bos: bool = True, special: bool = True
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) -> List[int]:
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raise NotImplementedError
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@abc.abstractmethod
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def detokenize(
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self, tokens: List[int], prev_tokens: Optional[List[int]] = None
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) -> bytes:
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raise NotImplementedError
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class LlamaTokenizer(BaseLlamaTokenizer):
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def __init__(self, llama: llama_cpp.Llama):
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self._model = llama._model # type: ignore
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def tokenize(
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self, text: bytes, add_bos: bool = True, special: bool = True
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) -> List[int]:
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return self._model.tokenize(text, add_bos=add_bos, special=special)
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def detokenize(
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self, tokens: List[int], prev_tokens: Optional[List[int]] = None
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) -> bytes:
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if prev_tokens is not None:
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return self._model.detokenize(tokens[len(prev_tokens) :])
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else:
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return self._model.detokenize(tokens)
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def encode(
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self, text: str, add_bos: bool = True, special: bool = True
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) -> List[int]:
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return self.tokenize(
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text.encode("utf-8", errors="ignore"), add_bos=add_bos, special=special
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)
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def decode(self, tokens: List[int]) -> str:
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return self.detokenize(tokens).decode("utf-8", errors="ignore")
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@classmethod
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def from_ggml_file(cls, path: str) -> "LlamaTokenizer":
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return cls(llama_cpp.Llama(model_path=path, vocab_only=True))
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class LlamaHFTokenizer(BaseLlamaTokenizer):
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def __init__(self, hf_tokenizer: Any):
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self.hf_tokenizer = hf_tokenizer
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def tokenize(
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self, text: bytes, add_bos: bool = True, special: bool = True
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) -> List[int]:
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return self.hf_tokenizer.encode(
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text.decode("utf-8", errors="ignore"), add_special_tokens=special
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)
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def detokenize(
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self, tokens: List[int], prev_tokens: Optional[List[int]] = None
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) -> bytes:
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if prev_tokens is not None:
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text = self.hf_tokenizer.decode(tokens).encode("utf-8", errors="ignore")
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prev_text = self.hf_tokenizer.decode(prev_tokens).encode(
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"utf-8", errors="ignore"
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)
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return text[len(prev_text) :]
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else:
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return self.hf_tokenizer.decode(tokens).encode("utf-8", errors="ignore")
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: str) -> "LlamaHFTokenizer":
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try:
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from transformers import AutoTokenizer
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except ImportError:
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raise ImportError(
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"The `transformers` library is required to use the `HFTokenizer`."
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"You can install it with `pip install transformers`."
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)
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hf_tokenizer = AutoTokenizer.from_pretrained(
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pretrained_model_name_or_path=pretrained_model_name_or_path
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)
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return cls(hf_tokenizer)
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