File size: 3,876 Bytes
5d62acd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
from __future__ import annotations
import abc
from typing import (
List,
Optional,
Any,
)
import llama_cpp
from llama_cpp.llama_types import List
class BaseLlamaTokenizer(abc.ABC):
@abc.abstractmethod
def tokenize(
self, text: bytes, add_bos: bool = True, special: bool = True
) -> List[int]:
"""Tokenize the text into tokens.
Args:
text: The utf-8 encoded string to tokenize.
add_bos: Whether to add a beginning of sequence token.
special: Whether to tokenize special tokens.
"""
raise NotImplementedError
@abc.abstractmethod
def detokenize(
self,
tokens: List[int],
prev_tokens: Optional[List[int]] = None,
special: bool = False,
) -> bytes:
"""Detokenize the tokens into text.
Args:
tokens: The list of tokens to detokenize.
prev_tokens: The list of previous tokens. Offset mapping will be performed if provided.
special: Whether to detokenize special tokens.
"""
raise NotImplementedError
class LlamaTokenizer(BaseLlamaTokenizer):
def __init__(self, llama: llama_cpp.Llama):
self._model = llama._model # type: ignore
def tokenize(
self, text: bytes, add_bos: bool = True, special: bool = True
) -> List[int]:
return self._model.tokenize(text, add_bos=add_bos, special=special)
def detokenize(
self,
tokens: List[int],
prev_tokens: Optional[List[int]] = None,
special: bool = False,
) -> bytes:
return self._model.detokenize(tokens, special=special)
def encode(
self, text: str, add_bos: bool = True, special: bool = True
) -> List[int]:
return self.tokenize(
text.encode("utf-8", errors="ignore"), add_bos=add_bos, special=special
)
def decode(self, tokens: List[int]) -> str:
return self.detokenize(tokens).decode("utf-8", errors="ignore")
@classmethod
def from_ggml_file(cls, path: str) -> "LlamaTokenizer":
return cls(llama_cpp.Llama(model_path=path, vocab_only=True))
class LlamaHFTokenizer(BaseLlamaTokenizer):
def __init__(self, hf_tokenizer: Any):
self.hf_tokenizer = hf_tokenizer
def tokenize(
self, text: bytes, add_bos: bool = True, special: bool = True
) -> List[int]:
return self.hf_tokenizer.encode(
text.decode("utf-8", errors="ignore"), add_special_tokens=special
)
def detokenize(
self,
tokens: List[int],
prev_tokens: Optional[List[int]] = None,
special: bool = False,
) -> bytes:
skip_special_tokens = not special
if prev_tokens is not None:
text = self.hf_tokenizer.decode(
prev_tokens + tokens, skip_special_tokens=skip_special_tokens
).encode("utf-8", errors="ignore")
prev_text = self.hf_tokenizer.decode(
prev_tokens, skip_special_tokens=skip_special_tokens
).encode("utf-8", errors="ignore")
return text[len(prev_text) :]
else:
return self.hf_tokenizer.decode(
tokens, skip_special_tokens=skip_special_tokens
).encode("utf-8", errors="ignore")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str) -> "LlamaHFTokenizer":
try:
from transformers import AutoTokenizer
except ImportError:
raise ImportError(
"The `transformers` library is required to use the `HFTokenizer`."
"You can install it with `pip install transformers`."
)
hf_tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path=pretrained_model_name_or_path
)
return cls(hf_tokenizer)
|