Andrej Karpathy
66 lines
2.3 KiB
Python
66 lines
2.3 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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TOKENIZER_BIN = "tokenizer.bin" # binary version of the tokenizer for inference in C
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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(f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}")
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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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def export(self):
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tokens = []
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for i in range(self.n_words):
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# decode the token and light postprocessing
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t = self.sp_model.id_to_piece(i)
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if i == self.bos_id:
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t = '\n<s>\n'
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elif i == self.eos_id:
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t = '\n</s>\n'
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elif len(t) == 6 and t.startswith('<0x') and t.endswith('>'):
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t = chr(int(t[3:5], 16)) # e.g. make '<0x01>' into '\x01'
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t = t.replace('▁', ' ') # sentencepiece uses this as the whitespace
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tokens.append(t)
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with open(TOKENIZER_BIN, 'wb') as f:
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for token in tokens:
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bytes = token.encode('utf-8')
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f.write((len(bytes)).to_bytes(4, 'little')) # write length of bytes
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f.write(bytes) # write token bytes
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if __name__ == "__main__":
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t = Tokenizer()
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t.export()
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