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| """ | |
| 中文数据：clue superclue | |
| 英文数据：glue cnn_dailymail gigaword | |
| code: | |
| math： | |
| """ | |
| import json | |
| import os | |
| import sys | |
| import pandas as pd | |
| from datasets import load_dataset | |
| from utils.log_util import logger | |
| from vocab import tokenizer_factory, TokenizerConfig | |
| 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"] | |
| if n_tokens is None: | |
| return None | |
| 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 | |
| elif unit in ["char/token", "chars_per_token"]: # 重要：平均一个token包含多少个字符。 | |
| value = n_chars / n_tokens | |
| elif unit in ["token/char", "tokens_per_char"]: # 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 _merge_stats_by_corpus(stats_by_corpus, oov_threshold=0.3): | |
| """ | |
| """ | |
| all_stats = list(stats_by_corpus.values()) | |
| assert len(set([stats["tokenizer"] for stats in all_stats])) == 1 | |
| reversible = all(stat['reversible'] for stat in all_stats) | |
| is_support = all(stat['oov_ratio'] < oov_threshold for stat in all_stats) | |
| merged_stats = { | |
| "tokenizer": all_stats[0]["tokenizer"], | |
| "organization": all_stats[0]["organization"], | |
| "vocab_size": all_stats[0]["vocab_size"], | |
| "_n_bytes": 0, | |
| "_n_tokens": 0 if is_support else None, | |
| "_n_chars": 0, | |
| "_n_oov_chars": 0, | |
| "reversible": True, | |
| } | |
| for stats in all_stats: | |
| merged_stats["_n_bytes"] += stats["_n_bytes"] | |
| merged_stats["_n_chars"] += stats["_n_chars"] | |
| if is_support: # The number of tokens cannot be accurately counted, when there are too many UNKs. | |
| merged_stats["_n_tokens"] += stats["_n_tokens"] | |
| merged_stats["_n_oov_chars"] += stats["_n_oov_chars"] | |
| merged_stats["reversible"] &= stats['reversible'] | |
| merged_stats.update({ | |
| "oov_ratio": float("%.4g" % (stats["_n_oov_chars"] / stats["_n_chars"])), | |
| "reversible": reversible | |
| }) | |
| return merged_stats | |
| def to_dataframe(stats, units=None): | |
| if units is None: | |
| units = common_units | |
| elif not isinstance(units, list): | |
| units = [units] | |
| table = [] | |
| for stat in stats.values(): | |
| columns = {k: v for k, v in stat.items() if not k.startswith("_")} | |
| 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, # 可以免加载tokenizer直接出结果 | |
| corpuses: List[str], | |
| cache_dir: str = "stats" | |
| ) -> dict: | |
| """ | |
| 这个要独立的cache，因为速度慢。 | |
| :param tokenizer_config: 可以不加载就 | |
| :param corpuses: | |
| :param cache_path: | |
| :return: | |
| """ | |
| def _char_based_oov(src_text, decode_text): | |
| oov_chars = [] | |
| for char in src_text: | |
| if char not in decode_text: | |
| oov_chars.append(char) | |
| n_oov_chars = len(oov_chars) | |
| oov_charset = list(dict.fromkeys(oov_chars)) | |
| return n_oov_chars, oov_charset | |
| def _tokenize(tokenizer, datasets, detail_path=None): | |
| """ | |
| export_diff: true | false | |
| :param tokenizer: | |
| :param datasets: | |
| :param detail_path: | |
| :return: | |
| """ | |
| n_bytes = 0 | |
| n_tokens = 0 | |
| n_chars = 0 | |
| n_oov_chars = 0 | |
| diff_details = [] | |
| oov_charset = set() | |
| unk_token_id = None | |
| if hasattr(tokenizer, "unk_token"): | |
| unk_token_id = tokenizer.unk_token_id | |
| for dataset in datasets: | |
| for item in dataset: | |
| text = item["text"] | |
| n_bytes += get_n_bytes_of_string(text) | |
| n_chars += len(text) | |
| ids = tokenizer.encode(text, add_special_tokens=False) | |
| # detect oov | |
| decode_text = tokenizer.decode(ids) | |
| decode_text_without_unk = tokenizer.decode([token_id for token_id in ids if token_id != unk_token_id]) | |
| if decode_text != text: | |
| _n_oov_chars, _oov_charset = _char_based_oov(text, decode_text_without_unk) | |
| diff_details.append( | |
| { | |
| "text": text, | |
| "decode_text": decode_text, | |
| "decode_text_without_unk": decode_text_without_unk, | |
| "n_oov_chars": _n_oov_chars, | |
| 'oov_ratio': _n_oov_chars / len(text), | |
| 'oov_charset': json.dumps(_oov_charset, ensure_ascii=False), | |
| } | |
| ) | |
| n_oov_chars += _n_oov_chars | |
| oov_charset.update(_oov_charset) | |
| n_tokens += len(ids) | |
| stat = { | |
| "_n_bytes": n_bytes, | |
| "_n_tokens": n_tokens, | |
| "_n_chars": n_chars, | |
| "_n_oov_chars": n_oov_chars, | |
| "oov_ratio": n_oov_chars / n_chars, | |
| '_oov_charset': json.dumps(list(oov_charset), ensure_ascii=False), | |
| "reversible": len(diff_details) == 0 | |
| } | |
| if detail_path and diff_details: | |
| logger.info(f"saving tokenization detail to '{detail_path}'") | |
| with open(detail_path, "w", encoding="utf-8") as f: | |
| f.write(json.dumps(diff_details, ensure_ascii=False, indent=2)) | |
| # print(f"{tokenizer_config.name_or_path}, {infer_tokenizer_type(tokenizer_config)}\n" | |
| # f"reversible: false; unk_token: {get_unk(tokenizer_config)}," | |
| # f" unk_ratio: {unk_count / len(encoding):.4f}; oov: []") | |
| # for diff_detail in diff_details: | |
| # # print(f"text[{i}] = {str(bytes(text[i:], 'utf-8'))}\n" | |
| # # f"decoding[{i}] = {str(bytes(decoding[i:], 'utf-8'))}") | |
| # f.write(f"text= {json.dumps(text[i:], ensure_ascii=False)}, \n" | |
| # f"decoding[{i}] = {json.dumps(decoding[i:], ensure_ascii=False)}") | |
| return stat | |
| # load from cache | |
| cache_id = f"{tokenizer_name} @ {'.'.join(corpuses)}" | |
| cache_path = os.path.join(cache_dir, "compression_rate.json") | |
| 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 = tokenizer_factory.get_tokenizer(tokenizer_name) | |
| datasets = [load_dataset("eson/cc100-samples", corpus.replace("cc100/", ""), split="train") for corpus in corpuses] | |
| stat = { | |
| "tokenizer": tokenizer_factory.get_name_with_hyperlink(tokenizer_name), | |
| "organization": tokenizer_factory.get_tokenizer_config(tokenizer_name).org, | |
| "vocab_size": len(tokenizer), | |
| } | |
| tokenize_detail_dir = os.path.join(cache_dir, "compression_rate") | |
| os.makedirs(tokenize_detail_dir, exist_ok=True) | |
| tokenize_detail_path = os.path.join(tokenize_detail_dir, cache_id.replace("/", ".") + ".diff.json") | |
| stat.update(_tokenize(tokenizer, datasets, detail_path=tokenize_detail_path)) | |
| # add basic info | |
| # save to cache | |
| len_before = len(cache) | |
| cache[cache_id] = stat | |
| len_after = len(cache) | |
| logger.info(f"saving '{cache_id}' to memory and file cache '{cache_path}': {len_before}->{len_after}") | |
| with open(cache_path, "w", encoding="utf-8") as f_tmp: | |
| json.dump(cache, f_tmp, ensure_ascii=False, 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]: | |
| """ | |
| """ | |
| logger.info(f"corpuses: {corpuses}; unit: {unit}; tokenizer_filter: {tokenizer_filter}") | |
| stats = {} | |
| if tokenizer_filter is not None: | |
| tokenizer_names = [tokenizer_name for tokenizer_name in tokenizer_factory.all_tokenizer_names | |
| if tokenizer_filter.lower() in tokenizer_name.lower()] | |
| else: | |
| tokenizer_names = tokenizer_factory.all_tokenizer_names | |
| for tokenizer_name in tokenizer_names: | |
| stats_by_corpus = {} | |
| for corpus in corpuses: | |
| stats_by_corpus[corpus] = tokenize_corpus(tokenizer_name, [corpus]) | |
| stats[tokenizer_name] = _merge_stats_by_corpus(stats_by_corpus) | |
| 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, "char/token"]) | |
| stats = stats.sort_values(["oov_ratio", unit], ascending=[True, True]) | |
| stats = stats.rename(columns={"oov_ratio": f' ⬆️oov_ratio'}).rename(columns={unit: f' ⬆️{unit}'}) # ⬇ | |
| return stats | |
| def main(): | |
| if len(sys.argv) == 3: | |
| tokenizer_filter = [sys.argv[1]] | |
| corpuses = [sys.argv[2]] | |
| else: | |
| tokenizer_filter = None | |
| corpuses = common_corpuses | |
| # tokenizer_filter = "openai" | |
| # corpuses = ["cc100/en", "cc100/zh-Hans"] | |
| df = get_compression_leaderboard(corpuses, tokenizer_filter=tokenizer_filter) | |
| # print(df.to_markdown(index=False, tablefmt='fancy_grid')) | |
| logger.info(f"\n{df.to_markdown(index=False)}") | |
| if __name__ == "__main__": | |
| main() | |