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# Taken from llama code and lightly modified
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import os
from logging import getLogger
from typing import List
from sentencepiece import SentencePieceProcessor
TOKENIZER_MODEL = "tokenizer.model" # the llama sentencepiece tokenizer model
TOKENIZER_BIN = "tokenizer.bin" # binary version of the tokenizer for inference in C
class Tokenizer:
def __init__(self):
model_path = TOKENIZER_MODEL
assert os.path.isfile(model_path), model_path
self.sp_model = SentencePieceProcessor(model_file=model_path)
#print(f"Loaded SentencePiece model from {model_path}")
# BOS / EOS token IDs
self.n_words: int = self.sp_model.vocab_size()
self.bos_id: int = self.sp_model.bos_id()
self.eos_id: int = self.sp_model.eos_id()
self.pad_id: int = self.sp_model.pad_id()
#print(f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}")
assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
assert type(s) is str
t = self.sp_model.encode(s)
if bos:
t = [self.bos_id] + t
if eos:
t = t + [self.eos_id]
return t
def decode(self, t: List[int]) -> str:
return self.sp_model.decode(t)
def export(self):
tokens = []
for i in range(self.n_words):
# decode the token and light postprocessing
t = self.sp_model.id_to_piece(i)
if i == self.bos_id:
t = '\n<s>\n'
elif i == self.eos_id:
t = '\n</s>\n'
elif len(t) == 6 and t.startswith('<0x') and t.endswith('>'):
t = chr(int(t[3:5], 16)) # e.g. make '<0x01>' into '\x01'
t = t.replace('▁', ' ') # sentencepiece uses this as the whitespace
tokens.append(t)
with open(TOKENIZER_BIN, 'wb') as f:
for token in tokens:
bytes = token.encode('utf-8')
f.write((len(bytes)).to_bytes(4, 'little')) # write length of bytes
f.write(bytes) # write token bytes
if __name__ == "__main__":
t = Tokenizer()
t.export()
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