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| from patcher import tiktoken_patch | |
| import tiktoken | |
| from transformers import AutoTokenizer, PreTrainedTokenizer | |
| from enum import Enum, auto | |
| from dataclasses import dataclass, field | |
| from utils.log_util import logger | |
| from typing import Dict, Any, Union | |
| """Interface: | |
| # https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils_base.py | |
| tokenizer.encode -> List[int]: Converts a string to a sequence of ids (integer) | |
| tokenizer.decode | |
| tokenizer.convert_tokens_to_string # gpt4 没有这个方法 | |
| tokenizer.convert_ids_to_tokens | |
| tokenizer.tokenize -> List[str]: Converts a string into a sequence of tokens -> | |
| tokenizer.parent = "" | |
| tokenizer.vocab_size | |
| tokenizer.get_vocab() # gpt-neox-20b, llama | |
| tokenizer.type = TokenizerType.ByteBPE.name | |
| tokenizer.implementation = TokenizerImpl.SentencePiece.name # https://github.com/facebookresearch/llama/blob/main/llama/tokenizer.py | |
| "HFGPT2Tokenizer", "HFTokenizer", "GPT2BPETokenizer", "CharLevelTokenizer", "TiktokenTokenizer", "SPMTokenizer", https://github.com/EleutherAI/gpt-neox/blob/main/tools/preprocess_data.py | |
| tokenizer.comments = "split all numbers into individual digits, " \ | |
| "and fallback to bytes to decompose unknown UTF-8 characters" | |
| tokenizer.all_special_tokens # baichuan | |
| tokenizer.special_tokens_set # gpt3.5_turbo | |
| tokenizer.special_tokens_map | |
| """ | |
| class TokenizerImpl(Enum): | |
| """ | |
| - https://github.com/huggingface/tokenizers/blob/main/bindings/python/py_src/tokenizers/implementations/__init__.py | |
| - https://huggingface.co/docs/transformers/tokenizer_summary | |
| - https://github.com/EleutherAI/gpt-neox/blob/main/megatron/tokenizer/tokenizer.py | |
| ## google/BertTokenizer | |
| - https://github.com/huggingface/tokenizers/blob/main/bindings/python/py_src/tokenizers/implementations/bert_wordpiece.py | |
| - 特征 | |
| - 算法：BERT的编码器是 BPE-WordPiece，将单词拆分成多个前缀符号（比如BERT中的##）最小单元 | |
| - 词典：有##开头的token，表示subword， | |
| - 中文采用char粒度分词 | |
| - 英文采用 WordPiece | |
| ## google/sentencepiece | |
| - https://github.com/google/sentencepiece/blob/3863f7648e5d8edb571ac592f3ac4f5f0695275a/src/sentencepiece_model.proto#L48 | |
| - 支持 sentencepiece 和 wordpiece | |
| - sentencepiece 有byte-bpe吗？ | |
| - UNIGRAM = 1; // Unigram language model with dynamic algorithm | |
| - BPE = 2; // Byte Pair Encoding | |
| - WORD = 3; // Delimitered by whitespace. | |
| - CHAR = 4; // tokenizes into character sequence | |
| - wordpiece | |
| - 特征： | |
| - 训练: spm_train --model_type unigram/bpe/char/word | |
| - 特殊符号： Ġ | |
| - 文件: *.sp_model 或 *.model (可选文件 .vocab，) spm简称 (其他格式比如 tokenizer.json是给hf_tokenizer兼容用的) | |
| - 实现: | |
| - 依赖: protobuf | |
| - 训练: `import sentencepiece as spm; spm.SentencePieceTrainer.train` 或 `spm_train` | |
| - 加载: `import sentencepiece as spm; spm.SentencePieceProcessor().Load(vocab_file)` | |
| - 方法: 是SentencePieceProcessor类型，sp_model.id_to_piece，有tokenizer.json tokenizer.model， | |
| - 分词: | |
| - pre_tokenizers.ByteLevel(add_prefix_space=True, use_regex=False) | |
| - 词典: 词典字符有 ▁ (U+2581) ，表示空格或句首。 | |
| - 示例：google-t5, llama，baichuan, orion, | |
| - llama: tokenizer.json(包含model.vocab model.merges) tokenizer.model | |
| - grok: 原始是 .model文件，后面转成了 tokenizer.json | |
| - google-t5: tokenizer.json, spiece.model | |
| - Skywork-13B-Math: tokenizer.model | |
| - xlm_roberta: sentencepiece.bpe.model | |
| - GPT2Tokenizer | |
| - tokenizer.json, vocab.json, merges.txt (https://huggingface.co/openai-community/gpt2) | |
| - vocab.bpe, encoder.json, dict.txt （fairseq版本，不常用，可以忽略这个版本） | |
| ## thu/icetk | |
| - icetk： sentencepiece的分支，支持image_tokenizer。 | |
| - glm, chatglm1, chatglm2 | |
| ## huggingface/tokenizers | |
| - https://github.com/huggingface/tokenizers | |
| - VS sentencepiece | |
| - 支持sentencepiece | |
| - .model转化为 (merges.txt + vocab.json) 或者 tokenizer.json | |
| - https://github.com/huggingface/tokenizers/blob/main/bindings/python/scripts/sentencepiece_extractor.py | |
| - 加载 merges.txt, vocab.json | |
| - SentencePieceBPETokenizer https://github.com/huggingface/tokenizers/blob/v0.19.1/bindings/python/py_src/tokenizers/implementations/sentencepiece_bpe.py#L10 | |
| - 在 sentencepiece基础上，hf_tokenizer支持pre-tokenization的正则表达式，对tab和换行支持更好，支持special token | |
| - 类型： 支持 BBPE, WordPiece or Unigram | |
| - 特征： | |
| - 文件: tokenizer.json(包含后两个文件的内容), merges.txt, vocab.json | |
| - added_tokens 在vocab中不一定存在。 | |
| - 实现: | |
| - 训练: `from tokenizers.trainers import BpeTrainer, UnigramTrainer, WordLevelTrainer, WordPieceTrainer` | |
| - 加载: | |
| - 方法: .model.from_file .model.save .model.token_to_id .model.tokenize | |
| - .model 是 tokenizer.models.BPE 类型 | |
| - 词典有 Ġ "\u0120" 开头 | |
| - 优势 | |
| - | |
| - 示例：gpt2, gpt_neox_20b, moss, bloom, qwen2 | |
| - 优势：相对sentence piece， | |
| - ss | |
| ## openai/tiktoken | |
| - 特征：空格就是空格， | |
| - 示例：gpt3.5 gpt4, qwen, | |
| """ | |
| """ 算法体系 https://www.huaxiaozhuan.com/%E5%B7%A5%E5%85%B7/huggingface_transformer/chapters/1_tokenizer.html | |
| - word-base tokenizer: | |
| - char-base tokenizer: | |
| - subword-based Tokenizer | |
| - BPE | |
| - byte-bpe: base vocabulary大小是256 | |
| - WordPiece: | |
| - 相比BPE，WordPiece 仅保存最终词表，而不保存学到的 merge rule | |
| - Unigram | |
| - SentencePiece | |
| """ | |
| # 分类体系：https://github.com/huggingface/tokenizers/blob/main/bindings/python/py_src/tokenizers/implementations/ | |
| BertTokenizer = "wordpiece.BertTokenizer" | |
| JapaneseTokenizer = ("wordpiece.MecabTokenizer", "https://github.com/polm/fugashi") # 常用日语包 ipadic，fugashi， | |
| ByteLevelBPETokenizer = "byte_level_bpe" # BBPE | |
| SentencePieceBPETokenizer = "sentencepiece_bpe" | |
| # 分类体系 | |
| # SentencePeice(BPE) | |
| SentencePiece = auto() # sentencepiece.bpe, sentencepiece.unigram, sentencepiece.char, sentencepiece.word, | |
| byte_level_bpe = auto() | |
| # HFTokenizer = auto() # , 支持 | |
| TikToken = auto() | |
| # subword-nmt | |
| # WordPiece | |
| # load_vocab_with_SPECIAL_TOKEN = True # 如果不包含会导致计算词典大小错误、overlap_token计算不一致。 | |
| class TokenizerConfig: | |
| """ | |
| https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/blob/main/src/leaderboard/read_evals.py | |
| """ | |
| name_or_path: str # org/model (path on hub), as unique id | |
| name_display: str = None # | |
| impl: TokenizerImpl = None # implementation, tokenizer_class/type | |
| org: str = None | |
| link: str = None # http://** | |
| desc: str = None # description | |
| meta: str = None | |
| level: str = None # char-level, word-level, byte-level | |
| lang: str = None | |
| init_kwargs: Dict[str, Any] = field(default_factory=dict, ) | |
| def __post_init__(self): | |
| if self.link is None: | |
| self.link = "https://huggingface.co/" + self.name_or_path # TODO + revision | |
| if self.name_display is None: | |
| self.name_display = self.name_or_path | |
| def init_from_json_file(cls, json_filepath: str) -> 'TokenizerConfig': | |
| pass | |
| def __eq__(self, other): | |
| if isinstance(other, self.__class__): | |
| return self.__dict__ == other.__dict__ | |
| else: | |
| return False | |
| def __hash__(self): | |
| return hash(self.name_or_path) | |
| # TODO: append link and description to the end of dropdown button. | |
| # Add tokenizer_class/type, comments | |
| _all_tokenizer_config = [ | |
| # bert style tokenizers | |
| TokenizerConfig("google-bert/bert-base-cased", impl=TokenizerImpl.BertTokenizer, org="Google", | |
| desc="first add whitespace around any CJK character, then perform wordpiece tokenization."), | |
| TokenizerConfig("google-bert/bert-base-uncased", impl=TokenizerImpl.BertTokenizer, org="Google", | |
| desc="first add whitespace around any CJK character, then perform wordpiece tokenization."), | |
| TokenizerConfig("google-bert/bert-base-chinese", impl=TokenizerImpl.BertTokenizer, org="Google", | |
| desc="first add whitespace around any CJK character, then perform wordpiece tokenization."), | |
| TokenizerConfig("google-bert/bert-base-german-cased", impl=TokenizerImpl.BertTokenizer, org="Google"), | |
| TokenizerConfig("dbmdz/bert-base-german-uncased", impl=TokenizerImpl.BertTokenizer, org="dbmdz"), | |
| TokenizerConfig("asafaya/bert-base-arabic", impl=TokenizerImpl.BertTokenizer, org="-"), | |
| TokenizerConfig("google-bert/bert-base-multilingual-uncased", impl=TokenizerImpl.BertTokenizer, org="Google"), | |
| TokenizerConfig("google-bert/bert-base-multilingual-cased", impl=TokenizerImpl.BertTokenizer, org="Google"), | |
| TokenizerConfig("tohoku-nlp/bert-base-japanese", impl=TokenizerImpl.BertTokenizer, org="Tohoku", | |
| desc="The texts are first tokenized by MeCab morphological parser with the IPA dictionary, " | |
| "then split into subwords by the WordPiece algorithm."), | |
| TokenizerConfig("clue/roberta_chinese_clue_tiny", name_display="clue/roberta-chinese-clue", | |
| impl=TokenizerImpl.BertTokenizer, org="CLUE", | |
| init_kwargs={"revision": "refs/pr/1"}, | |
| desc="", | |
| meta="去掉了繁体字, https://github.com/CLUEbenchmark/CLUEPretrainedModels/blob/master/README.md"), | |
| TokenizerConfig("eson/kplug-base-encoder", name_display="eson/kplug", impl=TokenizerImpl.BertTokenizer, org="JD"), | |
| TokenizerConfig("ckiplab/gpt2-base-chinese", impl=TokenizerImpl.BertTokenizer, org="SINICA"), # 台湾中央研究院 | |
| # WoBERT https://kexue.fm/archives/7758 | |
| # WoBERT Plus https://github.com/ZhuiyiTechnology/WoBERT | |
| # gpt2 style tokenizers | |
| TokenizerConfig("openai-community/gpt2", impl=TokenizerImpl.SentencePiece, org="OpenAI"), | |
| # byte-level BPE,没有byte，是unicode-level的吗？ | |
| TokenizerConfig("ClassCat/gpt2-base-french", impl=TokenizerImpl.SentencePiece, org="ClassCat"), | |
| TokenizerConfig("ClassCat/gpt2-base-spanish", impl=TokenizerImpl.SentencePiece, org="ClassCat"), | |
| TokenizerConfig("fnlp/moss-moon-003-sft", impl=TokenizerImpl.SentencePiece, init_kwargs={"revision": "refs/pr/6"}, | |
| org="Fudan", | |
| desc="This tokenizer has been trained to treat spaces like parts of the tokens " | |
| "(a bit like sentencepiece) so a word will be encoded differently whether " | |
| "it is at the beginning of the sentence (without space) or not", | |
| meta="在gpt2词典基础上，扩充了5万中文"), | |
| TokenizerConfig("bigscience/bloom", impl=TokenizerImpl.SentencePiece, org="BigScience", | |
| meta="比gpt_neox的词典 对中文支持更好。"), | |
| # ("bloomz_6b4_zh", | |
| # ("BelleGroup/BELLE-7B-2M", # 模型和词典都基于bloom | |
| # | |
| TokenizerConfig("EleutherAI/gpt-neox-20b", impl=TokenizerImpl.SentencePiece, org="EleutherAI"), # 5万 | |
| TokenizerConfig("cyberagent/open-calm-7b", impl=TokenizerImpl.SentencePiece, org="CyberAgent"), # GPTNeoXTokenizer | |
| TokenizerConfig("abeja/gpt-neox-japanese-2.7b", impl=TokenizerImpl.SentencePiece, org="ABEJA"), | |
| TokenizerConfig("rinna/bilingual-gpt-neox-4b", impl=TokenizerImpl.SentencePiece, org="ABEJA", lang="en/ja"), | |
| TokenizerConfig("Qwen/Qwen1.5-14B", impl=TokenizerImpl.SentencePiece, org="Alibaba"), # 15万，速度有点慢 | |
| TokenizerConfig("Qwen/Qwen1.5-110B", impl=TokenizerImpl.SentencePiece, org="Alibaba"), | |
| TokenizerConfig("Qwen/Qwen1.5-1.8B", impl=TokenizerImpl.SentencePiece, org="Alibaba"), | |
| TokenizerConfig("Qwen/Qwen2-0.5B", impl=TokenizerImpl.SentencePiece, org="Alibaba"), | |
| TokenizerConfig("Qwen/Qwen2-72B", impl=TokenizerImpl.SentencePiece, org="Alibaba"), | |
| TokenizerConfig("HuggingFaceH4/starchat-alpha", impl=TokenizerImpl.SentencePiece, org="-"), | |
| ####### google/sentencepiece tokenizer: | |
| # T5 llama internlm | |
| TokenizerConfig("google-t5/t5-large", name_display="google-t5/t5", impl=TokenizerImpl.SentencePiece, org="Google"), | |
| # t5_small, t5_base, t5_large, flan_t5_base, | |
| # ("t5_base", "", "sentencepiece"), | |
| # TokenizerConfig("google/flan-t5-base", impl=TokenizerImpl.SentencePiece, ), | |
| TokenizerConfig("lmsys/fastchat-t5-3b-v1.0", impl=TokenizerImpl.SentencePiece, | |
| org="LMSYS", | |
| init_kwargs={"use_fast": False} # 解决 pyo3_runtime.PanicException: AddedVocabulary bad split | |
| ), | |
| TokenizerConfig("CohereForAI/aya-101", org="Cohere For AI"), # "tokenizer_class": "T5Tokenizer", | |
| TokenizerConfig("ClueAI/ChatYuan-large-v2", impl=TokenizerImpl.SentencePiece, org="CLUE"), | |
| TokenizerConfig("ClueAI/PromptCLUE-base", impl=TokenizerImpl.SentencePiece, org="CLUE"), | |
| # byte-level BPE | |
| # '中文单字': 700, '中文多字': 0 meta-llama/Meta-Llama-3.1-405B | |
| TokenizerConfig("meta-llama/Meta-Llama-3.1-405B", name_display="Meta/llama3.1", impl=TokenizerImpl.SentencePiece, | |
| org="Meta"), | |
| TokenizerConfig("NousResearch/Hermes-3-Llama-3.1-405B", impl=TokenizerImpl.SentencePiece, | |
| org="NousResearch"), | |
| TokenizerConfig("gradientai/Llama-3-8B-Instruct-Gradient-1048k", name_display="Meta/llama3", | |
| impl=TokenizerImpl.SentencePiece, org="Meta", | |
| desc="llama split all numbers into individual digits, and fallback to bytes to decompose unknown UTF-8 characters"), | |
| TokenizerConfig("NousResearch/Llama-2-7b-chat-hf", name_display="Meta/llama2", impl=TokenizerImpl.SentencePiece, | |
| org="Meta"), | |
| TokenizerConfig("huggyllama/llama-7b", name_display="Meta/llama", impl=TokenizerImpl.SentencePiece, org="Meta"), | |
| TokenizerConfig("hpcai-tech/grok-1", name_display="xai-org/grok-1", impl=TokenizerImpl.SentencePiece, org="xAI"), | |
| # 由.model文件转化为了 | |
| TokenizerConfig("hfl/chinese-llama-lora-7b", impl=TokenizerImpl.SentencePiece, org="-", | |
| meta="向原始LLaMA的词汇表中添加2w个中文词汇，针对原版LLaMA模型扩充了中文词表， 提升了中文编解码效率"), | |
| # | |
| TokenizerConfig("hfl/chinese-llama-2-7b", impl=TokenizerImpl.SentencePiece, org="-", | |
| meta="重新设计了新词表（大小：55296），进一步提升了中文字词的覆盖程度"), # | |
| TokenizerConfig("hfl/llama-3-chinese-8b", impl=TokenizerImpl.SentencePiece, org="-"), | |
| TokenizerConfig("hfl/chinese-alpaca-lora-7b", impl=TokenizerImpl.SentencePiece, org="-"), | |
| # 中文Alpaca模型在上述中文LLaMA模型的基础上进一步使用了指令数据进行精调。 "比chinese_llama词典多一个`[PAD]`，请勿混用" | |
| # | |
| # ("belle_llama_ext_7b", | |
| # ("alpaca_7b", | |
| TokenizerConfig("baichuan-inc/Baichuan-7B", name_display="baichuan-inc/baichuan", | |
| impl=TokenizerImpl.SentencePiece, | |
| level="byte-level", org="Baichuan"), | |
| TokenizerConfig("baichuan-inc/Baichuan2-7B-Chat", name_display="baichuan-inc/baichuan2", | |
| impl=TokenizerImpl.SentencePiece, org="Baichuan", | |
| desc="expand the vocabulary size from 64000 in Baichuan1 to 125696"), | |
| TokenizerConfig("internlm/internlm-chat-7b", impl=TokenizerImpl.SentencePiece, org="Shanghai AI Lab"), | |
| # 上海AI实验室 + 商汤 | |
| TokenizerConfig("internlm/internlm2-chat-7b", impl=TokenizerImpl.SentencePiece, org="Shanghai AI Lab"), | |
| TokenizerConfig("internlm/internlm2-math-7b", impl=TokenizerImpl.SentencePiece, org="Shanghai AI Lab"), | |
| TokenizerConfig("internlm/internlm-xcomposer-7b", impl=TokenizerImpl.SentencePiece, org="Shanghai AI Lab"), | |
| TokenizerConfig("tiiuae/falcon-7b", impl=TokenizerImpl.SentencePiece, org="TII"), | |
| TokenizerConfig("tiiuae/falcon-180b", impl=TokenizerImpl.SentencePiece, org="TII"), | |
| TokenizerConfig("Skywork/Skywork-13B-base", impl=TokenizerImpl.SentencePiece, org="Kunlun"), | |
| TokenizerConfig("Skywork/Skywork-13B-Math", impl=TokenizerImpl.SentencePiece, org="Kunlun"), # 文件：tokenizer.model | |
| TokenizerConfig("FacebookAI/xlm-roberta-base", impl=TokenizerImpl.SentencePiece, org="Facebook"), | |
| # 这个的tokenizer.json 为什么没有merges? vocab里为什么有概率值？ | |
| # "goat", | |
| # ##### glm系列 | |
| # "glm_chinese",), | |
| TokenizerConfig("THUDM/chatglm-6b", impl=TokenizerImpl.SentencePiece, org="Tsinghua", | |
| meta=f"num_image_tokens: {12}; num_image_tokens: {34} ", | |
| init_kwargs={"revision": "refs/pr/100"}), | |
| TokenizerConfig("THUDM/chatglm2-6b", impl=TokenizerImpl.SentencePiece, org="Tsinghua", ), | |
| TokenizerConfig("THUDM/chatglm3-6b", impl=TokenizerImpl.SentencePiece, org="Tsinghua", ), | |
| TokenizerConfig("thu-coai/CharacterGLM-6B", impl=TokenizerImpl.SentencePiece, org="Tsinghua", ), | |
| # tiktoken 系列 | |
| TokenizerConfig("openai/text-davinci-003", impl=TokenizerImpl.TikToken, org="OpenAI", | |
| link="https://github.com/openai/tiktoken"), | |
| # | |
| TokenizerConfig("openai/code-davinci-002", impl=TokenizerImpl.TikToken, org="OpenAI", | |
| link="https://github.com/openai/tiktoken"), | |
| TokenizerConfig("openai/gpt-3.5-turbo", impl=TokenizerImpl.TikToken, org="OpenAI", | |
| link="https://github.com/openai/tiktoken", | |
| desc="tiktoken is a fast BPE tokeniser for use with OpenAI's models. There are 16 tokens KeyError"), | |
| TokenizerConfig("openai/gpt-4", impl=TokenizerImpl.TikToken, org="OpenAI", | |
| link="https://github.com/openai/tiktoken", ), | |
| TokenizerConfig("openai/gpt-4o", impl=TokenizerImpl.TikToken, org="OpenAI", | |
| link="https://github.com/openai/tiktoken", ), | |
| TokenizerConfig("Qwen/Qwen-7B-Chat", name_display="Qwen/Qwen", impl=TokenizerImpl.TikToken, org="Alibaba", | |
| init_kwargs={"revision": "refs/pr/56"}, | |
| meta="在gpt4词典基础上，删除了100个多数字token，增加10000中文词token；并优化了special_token的分词"), | |
| # https://huggingface.co/Qwen/Qwen-7B-Chat#%E6%A8%A1%E5%9E%8B%E7%BB%86%E8%8A%82%EF%BC%88model%EF%BC%89 | |
| # 该词表在GPT-4使用的BPE词表cl100k_base基础上，对中文、多语言进行了优化，在对中、英、代码数据的高效编解码的基础上， | |
| # 对部分多语言更加友好，方便用户在不扩展词表的情况下对部分语种进行能力增强。 词表对数字按单个数字位切分。 | |
| # TokenizerConfig("Qwen/Qwen-72B-Chat", impl=TokenizerImpl.TikToken), | |
| # 未分类 | |
| # ("amber", ""), | |
| TokenizerConfig("LLM360/CrystalCoder", org="MBZUAI"), | |
| TokenizerConfig("apple/DCLM-7B", org="Apple"), | |
| TokenizerConfig("mistralai/Mistral-7B-v0.1", org="Mistral"), | |
| TokenizerConfig("mistralai/Mixtral-8x7B-v0.1", org="Mistral"), | |
| TokenizerConfig("mistralai/Mistral-Large-Instruct-2407", org="Mistral"), | |
| TokenizerConfig("mistralai/Mistral-Nemo-Instruct-2407", org="Mistral"), | |
| TokenizerConfig("paust/pko-t5-large", org="PAUST"), | |
| TokenizerConfig("01-ai/Yi-6B", org="Yi"), | |
| TokenizerConfig("01-ai/Yi-34B", org="Yi"), | |
| TokenizerConfig("01-ai/Yi-VL-34B", org="Yi"), | |
| TokenizerConfig("01-ai/Yi-1.5-34B", org="Yi"), | |
| TokenizerConfig("OrionStarAI/Orion-14B-Chat", org="OrionStar"), | |
| TokenizerConfig("microsoft/phi-1", org="Microsoft"), | |
| TokenizerConfig("microsoft/phi-2", org="Microsoft"), | |
| TokenizerConfig("microsoft/Phi-3-mini-4k-instruct", org="Microsoft", meta="即llama vocab"), | |
| TokenizerConfig("Upstage/SOLAR-10.7B-v1.0", org="-"), | |
| TokenizerConfig("google/mobilebert-uncased", org="Google"), | |
| # ("google/mobilenet_v2_1.0_224",), # error | |
| TokenizerConfig("google/switch-c-2048", org="Google"), | |
| TokenizerConfig("google/byt5-small", org="Google"), | |
| TokenizerConfig("google/mt5-large", org="Google"), | |
| TokenizerConfig("WizardLM/WizardCoder-Python-7B-V1.0", org="Microsoft"), | |
| TokenizerConfig("WizardLM/WizardCoder-15B-V1.0", org="Microsoft"), | |
| TokenizerConfig("WizardLM/WizardLM-7B-V1.0", org="Microsoft"), | |
| TokenizerConfig("WizardLM/WizardMath-70B-V1.0", org="Microsoft"), | |
| TokenizerConfig("TigerResearch/tigerbot-70b-chat-v4-4k", org="Tigerobo"), | |
| TokenizerConfig("TigerResearch/tigerbot-13b-chat-v2", org="Tigerobo"), | |
| TokenizerConfig("deepseek-ai/deepseek-coder-33b-instruct", org="DeepSeek"), | |
| TokenizerConfig("deepseek-ai/deepseek-llm-7b-base", org="DeepSeek"), | |
| TokenizerConfig("deepseek-ai/DeepSeek-V2", org="DeepSeek"), | |
| TokenizerConfig("google/gemma-7b", org="Google"), | |
| TokenizerConfig("google/gemma-2-9b", org="Google"), | |
| TokenizerConfig("allenai/OLMo-7B-hf", org="Allen AI"), | |
| TokenizerConfig("HuggingFaceH4/zephyr-7b-beta", org="HuggingFace"), | |
| TokenizerConfig("ai21labs/Jamba-v0.1", org="AI21"), | |
| TokenizerConfig("databricks/dbrx-instruct", org="Databricks"), | |
| # TokenizerConfig("nvidia/Nemotron-4-340B-Instruct", org="Nvidia"), | |
| # ("claude",), | |
| # https://github.com/Duxiaoman-DI/XuanYuan | |
| # https://huggingface.co/apple/OpenELM-3B-Instruct https://huggingface.co/apple/OpenELM-3B | |
| ] | |
| assert len(set([config.name_display for config in _all_tokenizer_config])) == len(_all_tokenizer_config) | |
| assert len(set([config.name_or_path for config in _all_tokenizer_config])) == len(_all_tokenizer_config) | |
| assert len(set([config.name_or_path.split("/")[-1] for config in _all_tokenizer_config])) == len(_all_tokenizer_config) | |
| class TokenizerFactory: | |
| def __init__(self): | |
| # self.all_tokenizer_configs = sorted(_all_tokenizer_config, key=lambda k: k.name_or_path) | |
| self.all_tokenizer_configs = sorted(_all_tokenizer_config, key=lambda k: k.name_display) | |
| self.all_tokenizer_names = [config.name_or_path for config in self.all_tokenizer_configs] | |
| self.name_to_config_list = [ | |
| {config.name_or_path: config for config in self.all_tokenizer_configs}, | |
| {config.name_display: config for config in self.all_tokenizer_configs}, | |
| {config.name_display.split("/")[-1]: config for config in self.all_tokenizer_configs}, | |
| ] | |
| self.tokenizer_cache = {} | |
| def get_tokenizer_config(self, tokenizer_name: str) -> TokenizerConfig: | |
| for name_to_config in self.name_to_config_list: | |
| if tokenizer_name in name_to_config: | |
| return name_to_config[tokenizer_name] | |
| return None | |
| def get_tokenizer(self, tokenizer_name: str): | |
| """ | |
| :param tokenizer_name: | |
| :return: | |
| """ | |
| tokenizer_config = self.get_tokenizer_config(tokenizer_name) | |
| # 1. load from cache | |
| if tokenizer_config in self.tokenizer_cache: | |
| return self.tokenizer_cache[tokenizer_config] | |
| # 2. load tokenizer | |
| tokenizer = self.load_tokenizer(tokenizer_config) | |
| self.tokenizer_cache[tokenizer_config] = tokenizer | |
| return tokenizer | |
| def get_name_with_hyperlink(self, tokenizer_name: str) -> str: | |
| def model_hyperlink(link, model_name): | |
| model_name = model_name | |
| return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>' | |
| tokenizer_config = self.get_tokenizer_config(tokenizer_name) | |
| return model_hyperlink(tokenizer_config.link, tokenizer_config.name_display.split("/")[-1]) | |
| def load_tokenizer(self, tokenizer_config): | |
| if tokenizer_config == None: | |
| print("dd") | |
| logger.info(f"loading tokenizer {tokenizer_config.name_or_path}") | |
| if tokenizer_config.impl == TokenizerImpl.TikToken and "openai" in tokenizer_config.name_or_path: | |
| tokenizer = tiktoken.encoding_for_model(tokenizer_config.name_or_path.replace("openai/", "")) | |
| else: | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| tokenizer_config.name_or_path, | |
| trust_remote_code=True, | |
| **tokenizer_config.init_kwargs | |
| ) | |
| return tokenizer | |
| def add_config(self, ): | |
| pass | |
| def add_tokenizer(self, tokenizer_name): | |
| pass | |
| tokenizer_factory = TokenizerFactory() | |
| def add_tokenizer(tokenizer_name: str): | |
| """ | |
| :param tokenizer_name: | |
| :return: | |
| """ | |
| if tokenizer_name in []: | |
| logger.info(f"{tokenizer_name} already exits") | |
| else: | |
| # add to config | |
| tokenizer_config = TokenizerConfig(tokenizer_name, org="-") | |
| # add to tokenizer | |
| tokenizer = tokenizer_factory.load_tokenizer(tokenizer_config) | |
| # refresh cache | |
| try: | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| tokenizer_name, | |
| trust_remote_code=True, | |
| **tokenizer_config.init_kwargs | |
| ) | |
| tokenizer_factory.all_tokenizer_configs.append( | |
| "", | |
| ) | |
| tokenizer_factory | |
| except Exception as e: | |
| logger.error(e) | |
| pass | |
| # class TokenizerType(Enum): | |
| # | |
| # # BERTTokenizer | |
| # # 依赖一个txt文件 | |
| # | |
| # | |
| # # https://github.com/EleutherAI/gpt-neox/blob/v2.0/megatron/tokenizer/tokenizer.py#L231 | |
| # # 依赖一个json文件，Tokenizer.from_file(vocab_file) | |
| # # 案例：gpt-neox-20B | |
| # HFTokenizer = auto() | |
| # | |
| # # 依赖: model_file, sentencepiece.SentencePieceProcessor(model_file) | |
| # # 案例： | |
| # SentencePieceTokenizer = auto() | |
| # | |
| # | |
| # # 依赖: 3个json文件：vocab.json, merges.txt, special_tokens.txt | |
| # # 源码: | |
| # # - https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/tokenizer/gpt2_tokenization.py#L92 | |
| # # Byte-level BPE | |
| # GPT2BPETokenizer = auto() | |
| if __name__ == "__main__": | |
| for tokenizer_config in tokenizer_factory.all_tokenizer_configs: | |
| if True: | |
| # if "t5" in tokenizer_config.name_or_path: | |
| tokenizer1 = tokenizer_factory.get_tokenizer(tokenizer_config.name_or_path) | |
| tokenizer2 = tokenizer_factory.get_tokenizer(tokenizer_config.name_display) | |
| tokenizer3 = tokenizer_factory.get_tokenizer(tokenizer_config.name_display.split("/")[-1]) | |
| assert tokenizer1 == tokenizer2 == tokenizer3 | |
| print(tokenizer_config.name_or_path, len(tokenizer1)) | |