Andrej Karpathy
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
# Taken from llama code and lightly modified
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
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import os
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from logging import getLogger
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from typing import List
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from sentencepiece import SentencePieceProcessor
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TOKENIZER_MODEL = "tokenizer.model" # the llama sentencepiece tokenizer model
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class Tokenizer:
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def __init__(self):
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model_path = TOKENIZER_MODEL
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assert os.path.isfile(model_path), model_path
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self.sp_model = SentencePieceProcessor(model_file=model_path)
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print(f"Loaded SentencePiece model from {model_path}")
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# BOS / EOS token IDs
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self.n_words: int = self.sp_model.vocab_size()
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self.bos_id: int = self.sp_model.bos_id()
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self.eos_id: int = self.sp_model.eos_id()
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self.pad_id: int = self.sp_model.pad_id()
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print(
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f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
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)
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assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
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def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
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assert type(s) is str
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t = self.sp_model.encode(s)
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if bos:
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t = [self.bos_id] + t
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if eos:
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t = t + [self.eos_id]
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return t
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def decode(self, t: List[int]) -> str:
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return self.sp_model.decode(t)
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