vocab/gpt_neox_chinese_v1/build_tokenizer_chinese_2.py

"""
merge 是干嘛的？

## 结果

共merge 4357 个 token
"""

import json
from tokenizers import Tokenizer
from data_sample.oov_base import jd_vocab_tokens
from zhon.hanzi import punctuation as zh_punc

def load_base_tokenizer():
    old_vocab_path = "../gpt_nexo_20b/20B_tokenizer.json"
    data = json.load(open(old_vocab_path, "r", encoding="utf-8"))
    tokenizer = Tokenizer.from_file(old_vocab_path)
    print("vocab_size with added_tokens:", )
    return data, tokenizer

data, base_tokenizer = load_base_tokenizer()
vocab = data["model"]["vocab"]
merges = data["model"]["merges"]
vocab_size = base_tokenizer.get_vocab_size(with_added_tokens=True)


"""
方式一：原有的added_tokens保持id不变。方式二：原有的added_tokens进行id移位。
以下采用方式一。
"""
new_added_tokens = set()
for word in jd_vocab_tokens + list(zh_punc):
    if len(word) > 1 or word in new_added_tokens:
        continue
    encoding = base_tokenizer.encode(word)
    # if len(encoding.ids) > 1:
    if len(encoding.ids) == 2:  # 3个的，怎么处理？
        tokens = [base_tokenizer.id_to_token(token_id) for token_id in encoding.ids]
        print("merging", vocab_size, word, json.dumps(tokens))
        vocab["".join(tokens)] = vocab_size
        vocab_size += 1
        merges.append(" ".join(tokens))
        new_added_tokens.add(word)


print("共merge %d 个 token" % (len(new_added_tokens)))

f_out = open("20B_tokenizer_chinese_2.json", "w", encoding="utf-8")

json.dump(data, f_out, indent=2)