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| import gradio as gr | |
| import json | |
| import pandas as pd | |
| import config | |
| from vocab import load_tokener | |
| from utils.character_util import iter_vocab | |
| from utils.log_util import logger | |
| from utils.compression_util import tokenize_corpus, unit_convertor | |
| from functools import lru_cache | |
| def tokenize(text, tokenizer_name, color_num=5): | |
| """ | |
| """ | |
| logger.info("param=" + json.dumps({"text": text, "tokenizer_type": tokenizer_name}, ensure_ascii=False)) | |
| pos_tokens = [] | |
| tokenizer = load_tokener(tokenizer_name) | |
| if config.ADD_SPECIAL_TOKEN: | |
| encoding = tokenizer.encode(text, add_special_tokens=True) | |
| else: | |
| encoding = tokenizer.encode(text, add_special_tokens=False) | |
| table = [] | |
| for idx, token_id in enumerate(encoding): | |
| decode_text = tokenizer.decode([token_id]) # 特殊字符解码后会统一变成 �，对应 "\ufffd" | |
| pos_tokens.extend([(decode_text, str(idx % color_num))]) | |
| # token "Byte": # 这是 utf-8编码吧？ | |
| token = tokenizer.convert_ids_to_tokens([token_id], skip_special_tokens=False)[0] | |
| if isinstance(token, bytes): | |
| try: | |
| token_str = token.decode("utf-8") | |
| except: | |
| token_str = token.decode("utf-8", errors="ignore") | |
| logger.error(f"{idx}: decode_error: " + json.dumps( # gpt_35_turbo 经常有token会decode error，这里用来记录一下 | |
| {"tokenizer_type": tokenizer_name, "token": str(token), "token_str": token_str}, | |
| ensure_ascii=False)) | |
| token_bytes = token | |
| # json_dumps = json.dumps(token_str) | |
| elif isinstance(token, str): | |
| token_str = token | |
| token_bytes = bytes(token_str, "utf-8") | |
| # json_dumps = json.dumps(token_str) | |
| else: | |
| logger.error(f"{idx}: wrong type for token {token_id} {type(token)} " + json.dumps({"text": text, "tokenizer_type": tokenizer_name}, ensure_ascii=False)) | |
| token_str = token | |
| token_bytes = token | |
| # continue | |
| # ⭐ | |
| # TODO: gpt3.5_turbo错误： 只有id和text是对的，token和 utf8都是错的。说明 convert_ids_to_tokens 出错了。 | |
| table.append( | |
| {"TokenID": token_id, | |
| "Token": token_str, # utf-8解码后的字符串，为什么有些是 <0xE7>，表示什么？比如llama | |
| "Text": decode_text, # | |
| # "Bytes": token_bytes, # bytes类型在gradio前端页面被解码成字符串，比如 b'\xe4\xb8\xad' 仍然显示成 "中"。因此 str(token_bytes) | |
| "UTF8 Bytes": str(token_bytes), | |
| # "Unicode": json_dumps # unicode, 如果是ascii码，就直接显示。如果不是ascii码，就显示unicode | |
| } | |
| ) | |
| table_df = pd.DataFrame(table) | |
| logger.info(f"tokenizer_type={tokenizer_name}, Tokens={table[:4]}") | |
| # print(table_df) | |
| return gr.update(value=pos_tokens, label=f"Tokens: {len(encoding)}"), table_df | |
| def tokenize_pair(text, tokenizer_type_1, tokenizer_type_2): | |
| """ | |
| input_text.change | |
| """ | |
| pos_tokens_1, table_df_1 = tokenize(text, tokenizer_type_1) | |
| pos_tokens_2, table_df_2 = tokenize(text, tokenizer_type_2) | |
| return pos_tokens_1, table_df_1, pos_tokens_2, table_df_2 | |
| def basic_count(tokenizer_name): | |
| stats = iter_vocab(tokenizer_name) | |
| return stats['vocab_size'], f'{stats["中文token数"]}' | |
| # return tokenizer.vocab_size, f'{stats["中文汉字数"]["中文单字"]}/{stats["中文汉字数"]["中文多字"]}' | |
| def get_compress_rate(tokenizer_type, all_corpus, unit): | |
| tokenizer = load_tokener(tokenizer_type) | |
| compress_rate_stats = tokenize_corpus(tokenizer, all_corpus) | |
| compress_rate = unit_convertor(compress_rate_stats, unit) | |
| return compress_rate | |
| # def get_all_compress_rate(corpuses, unit): | |
| # stats = {} | |
| # for lang in corpuses: | |
| # print("###" * 10 + lang) | |
| # for tokenizer_name in tokenizers: | |
| # tokenizer = load_tokener(tokenizer_name) | |
| # stat = tokenize_corpus(tokenizer, [lang]) | |
| # stats[tokenizer_name] = stat | |
| # pprint(stats) | |
| def get_overlap_token_size(tokenizer_type_1, tokenizer_type_2): | |
| tokenizer1 = load_tokener(tokenizer_type_1) | |
| tokenizer2 = load_tokener(tokenizer_type_2) | |
| vocab_set_1 = tokenizer1.get_vocab().keys() | |
| vocab_set_2 = tokenizer2.get_vocab().keys() | |
| token1 = next(iter(vocab_set_1)) | |
| token2 = next(iter(vocab_set_2)) | |
| if type(token1) != type(token2): # bytes str | |
| if isinstance(token1, str): | |
| vocab_set_1 = set([token.encode("utf-8") for token in vocab_set_1]) | |
| if isinstance(token2, str): | |
| vocab_set_2 = set([token.encode("utf-8") for token in vocab_set_2]) | |
| overlap_tokens = vocab_set_1 & vocab_set_2 | |
| overlap_token_size = len(overlap_tokens) | |
| logger.info( | |
| f"{overlap_token_size} OverlapTokens of {tokenizer_type_1} {tokenizer_type_2}: {list(overlap_tokens)[:10]}") | |
| return overlap_token_size, overlap_token_size | |
| def on_load(url_params, request: gr.Request): | |
| """ | |
| onLoad | |
| """ | |
| text = None | |
| tokenizer_type_1 = None | |
| tokenizer_type_2 = None | |
| try: | |
| url_params = json.loads(url_params) | |
| except: | |
| url_params = {} | |
| if request: | |
| logger.info(str(request.headers)) | |
| client_ip = request.client.host | |
| # local_ip = socket.gethostbyname(socket.gethostbyname("")) | |
| # headers = request.kwargs['headers'] | |
| # if headers and 'x-forwarded-for' in headers: | |
| # x_forwarded_for = headers['x-forwarded-for'] | |
| # client_ip = x_forwarded_for.split(' ')[0] if x_forwarded_for else "" | |
| # if "referer" in request.headers: # not work for huggingface-space | |
| # url_params = parse_qs(urlparse(request.headers["referer"]).query) | |
| # url_params = {k: v[0] for k, v in url_params.items() if len(v) > 0} | |
| tokenizer_type_1 = url_params.get("tokenizer1", config.default_tokenizer_type_1) | |
| tokenizer_type_2 = url_params.get("tokenizer2", config.default_tokenizer_type_2) | |
| text = url_params.get("text", config.default_user_input) | |
| logger.info(f"client_ip: {client_ip}; params: {url_params}") | |
| return text, tokenizer_type_1, tokenizer_type_2 | |
| def compress_rate_unit_change(unit): | |
| return gr.update(label=f"Compress Rate: {unit}"), gr.update(label=f"Compress Rate: {unit}"), | |
| def test_coding(): | |
| bytes1 = b'\xe4\xb8\xad' | |
| print(bytes1) # b'\xe4\xb8\xad' | |
| if __name__ == "__main__": | |
| print(get_overlap_token_size("gpt_35_turbo", "gpt_4")) | |
| # print(basic_count("internlm_chat_7b")) | |