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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
import struct
import argparse
from typing import List
from sentencepiece import SentencePieceProcessor
TOKENIZER_MODEL = "tokenizer.model" # the llama sentencepiece tokenizer model
class Tokenizer:
def __init__(self, tokenizer_model=None):
model_path = tokenizer_model if tokenizer_model else TOKENIZER_MODEL
assert os.path.isfile(model_path), model_path
self.sp_model = SentencePieceProcessor(model_file=model_path)
self.model_path = 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):
# get all the tokens (postprocessed) and their scores as floats
tokens, scores = [], []
for i in range(self.n_words):
# decode the token and light postprocessing
t = self.sp_model.id_to_piece(i)
s = self.sp_model.get_score(i)
if i == self.bos_id:
t = '\n<s>\n'
elif i == self.eos_id:
t = '\n</s>\n'
t = t.replace('▁', ' ') # sentencepiece uses this character as whitespace
b = t.encode('utf-8') # bytes of this token, utf-8 encoded
tokens.append(b)
scores.append(s)
# record the max token length
max_token_length = max(len(t) for t in tokens)
# write to a binary file
# the tokenizer.bin file is the same as .model file, but .bin
tokenizer_bin = self.model_path.replace('.model', '.bin')
with open(tokenizer_bin, 'wb') as f:
f.write(struct.pack("I", max_token_length))
for bytes, score in zip(tokens, scores):
f.write(struct.pack("fI", score, len(bytes)))
f.write(bytes)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-t", "--tokenizer-model", type=str, help="optional path to custom tokenizer ")
args = parser.parse_args()
t = Tokenizer(args.tokenizer_model)
t.export()
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