""" 中文数据：clue superclue 英文数据：glue cnn_dailymail gigaword 代码数据: 数字： """ import json import os import sys import pandas as pd from datasets import load_dataset from utils.log_util import logger from vocab import load_tokener from vocab import all_tokenizers from typing import List, Optional, Union, Literal CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) common_units = ["g_bytes/b_tokens", "b_tokens/g_bytes", "t_bytes/t_tokens", "t_tokens/t_bytes", "n_chars/n_tokens", ] common_corpuses = sorted(["cc100-en", "cc100-zh-Hans", "cc100-es", "cc100-fr", "cc100-de", "cc100-ko", "cc100-fa", "cc100-ar", "cc100-ja"]) VALID_CODES_CC100 = [ "am", "ar", "as", "az", "be", "bg", "bn", "bn_rom", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gn", "gu", "ha", "he", "hi", "hi_rom", "hr", "ht", "hu", "hy", "id", "ig", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lg", "li", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "my_zaw", "ne", "nl", "no", "ns", "om", "or", "pa", "pl", "ps", "pt", "qu", "rm", "ro", "ru", "sa", "si", "sc", "sd", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "ta_rom", "te", "te_rom", "th", "tl", "tn", "tr", "ug", "uk", "ur", "ur_rom", "uz", "vi", "wo", "xh", "yi", "yo", "zh-Hans", "zh-Hant", "zu", ] # code: https://huggingface.co/datasets/codeparrot/github-code-clean python java c sql html # math: def get_n_bytes_of_string(string_text): n_bytes = len(string_text.encode("utf-8")) return n_bytes def unit_convertor(stat, unit): n_tokens = stat["n_tokens"] n_chars = stat["n_chars"] n_bytes = stat["n_bytes"] n_tokens_in_billion = n_tokens / (1000 * 1000 * 1000) n_tokens_in_trillion = n_tokens / (1000 * 1000 * 1000 * 1000) n_bytes_in_mb = n_bytes / (1024 * 1024) n_bytes_in_gb = n_bytes_in_mb / 1024 n_bytes_in_tb = n_bytes_in_gb / 1024 # n_chars_in_billion = n_chars / (1000 * 1000 * 1000) if unit == "n_tokens/n_bytes": value = n_tokens / n_bytes # the average number of characters per token elif unit in ["n_chars/n_tokens", "chars_per_token"]: # 重要：平均一个token包含多少个字符。 value = n_chars / n_tokens elif unit == "n_tokens/n_chars": # 一个中文汉字需要几个token？ value = n_tokens / n_chars elif unit == "g_bytes/b_tokens": value = n_bytes_in_gb / n_tokens_in_billion elif unit == "b_tokens/g_bytes": value = n_tokens_in_billion / n_bytes_in_gb elif unit == "t_bytes/t_tokens": # 重要： value = n_bytes_in_tb / n_tokens_in_trillion elif unit == "t_tokens/t_bytes": value = n_tokens_in_trillion / n_bytes_in_tb else: raise "measure not support" return round(value, 3) def to_dataframe(stats, units=None): if units is None: units = common_units elif not isinstance(units, list): units = [units] table = [] for tokenizer_name, stat in stats.items(): columns = {"tokenizer": tokenizer_name, "vocab_size": stat["vocab_size"]} for unit in units: if unit not in stat: columns[unit] = unit_convertor(stat, unit) else: logger.error(f"unit {unit} not support") table.append(columns) df = pd.DataFrame(table) return df cache = {} def tokenize_corpus( tokenizer_name: str, corpuses: List[str], cache_path: str = "stats/compress_rate.json" ) -> dict: """ 这个要独立的cache，因为速度慢。 :param tokenizer_name: :param corpuses: :param cache_path: :return: """ def _tokenize(tokenizer, datasets): n_tokens = 0 n_chars = 0 n_bytes = 0 for dataset in datasets: for item in dataset: text = item["text"] n_bytes += get_n_bytes_of_string(text) n_chars += len(text) encodings = tokenizer.encode(text) n_tokens += len(encodings) stat = { # "vocab_size": len(tokenizer.vocab_size, "vocab_size": len(tokenizer), "n_bytes": n_bytes, "n_tokens": n_tokens, "n_chars": n_chars, } return stat # load from cache cache_id = f"{tokenizer_name}.{'.'.join(corpuses)}" if not cache and os.path.exists(cache_path): with open(cache_path, "r", encoding="utf-8") as f_tmp: cache.update(json.load(f_tmp)) if cache_id in cache: logger.info(f"loading {cache_id} from in-memory cache") return cache[cache_id] # tokenize corpus tokenizer = load_tokener(tokenizer_name) datasets = [load_dataset("eson/cc100-samples", corpus.replace("cc100-", ""), split="train") for corpus in corpuses] stat = _tokenize(tokenizer, datasets) # save to cache len_before = len(cache) cache[cache_id] = stat len_after = len(cache) logger.info(f"saving {cache_id} to in-memory and file cache: {len_before}->{len_after}") with open(cache_path, "w", encoding="utf-8") as f_tmp: json.dump(cache, f_tmp, indent=2) return stat def get_compression_leaderboard( corpuses: List[str] = ['cc100-en'], unit: str = "b_tokens/g_bytes", tokenizer_filter: Optional[str] = None, return_type: Optional[Literal["dict", "dataframe"]] = "dataframe" ) -> Union[pd.DataFrame, dict]: """ ## TODO - search by organization, """ logger.info(f"corpuses: {corpuses}; unit: {unit}; tokenizer_filter: {tokenizer_filter}") stats = {} if tokenizer_filter is not None: tokenizers = [tokenizer_name for tokenizer_name in all_tokenizers if tokenizer_filter in tokenizer_name] else: tokenizers = all_tokenizers for lang in corpuses: for tokenizer_name in tokenizers: stat = tokenize_corpus(tokenizer_name, [lang]) stats[tokenizer_name] = stat if return_type == "dataframe": token_number_unit, file_size_unit = unit.split("/") reverse_unit = f"{file_size_unit}/{token_number_unit}" stats = to_dataframe(stats, [unit, reverse_unit, "n_chars/n_tokens"]) stats = stats.sort_values(unit) stats = stats.rename(columns={unit: f' ⬆️{unit}'}) return stats def update_compress_rate(): pass def test(): tokenizer_name = "gpt_4" tokenizer = load_tokener(tokenizer_name) stats = {tokenizer_name: tokenize_corpus(tokenizer, ["cc100-en", "cc100-zh-Hans"])} df = to_dataframe(stats) # print(df.to_markdown(index=False, tablefmt='fancy_grid')) logger.info(f"\n{df.to_markdown(index=False)}") def main(): if len(sys.argv) == 3: tokenizer_filter = [sys.argv[1]] corpuses = [sys.argv[2]] else: tokenizer_filter = None corpuses = common_corpuses df = get_compression_leaderboard(corpuses) # print(df.to_markdown(index=False, tablefmt='fancy_grid')) logger.info(f"\n{df.to_markdown(index=False)}") if __name__ == "__main__": main() # test()