Upload tokenizer

added_tokens.json

@@ -0,0 +1,16 @@
+{
+ "<eop>": 151334,
+ "<sop>": 151333,
+ "<|assistant|>": 151337,
+ "<|begin_of_image|>": 151339,
+ "<|begin_of_video|>": 151341,
+ "<|end_of_image|>": 151340,
+ "<|end_of_video|>": 151342,
+ "<|endoftext|>": 151329,
+ "<|observation|>": 151338,
+ "<|system|>": 151335,
+ "<|user|>": 151336,
+ "[MASK]": 151330,
+ "[gMASK]": 151331,
+ "[sMASK]": 151332
+}

special_tokens_map.json

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+{
+ "additional_special_tokens": [
+ "<|endoftext|>",
+ "[MASK]",
+ "[gMASK]",
+ "[sMASK]",
+ "<sop>",
+ "<eop>",
+ "<|system|>",
+ "<|user|>",
+ "<|assistant|>",
+ "<|observation|>",
+ "<|begin_of_image|>",
+ "<|end_of_image|>",
+ "<|begin_of_video|>",
+ "<|end_of_video|>"
+ ],
+ "eos_token": {
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}

tokenization_chatglm.py

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+import regex as re
+import base64
+import os
+import json
+import tiktoken
+from typing import List, Optional, Union, Dict, Any
+from transformers import PreTrainedTokenizer
+from transformers.utils import logging, PaddingStrategy, TensorType
+from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
+
+
+class ChatGLM4Tokenizer(PreTrainedTokenizer):
+ vocab_files_names = {"vocab_file": "tokenizer.model"}
+ model_input_names = ["input_ids", "attention_mask", "position_ids"]
+
+ def __init__(
+ self,
+ vocab_file,
+ padding_side="left",
+ clean_up_tokenization_spaces=False,
+ encode_special_tokens=False,
+ **kwargs
+ ):
+ self.name = "GLM4Tokenizer"
+ self.vocab_file = vocab_file
+ pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+ self.pat_str = re.compile(pat_str)
+ self.encode_special_tokens = encode_special_tokens
+
+ mergeable_ranks = {}
+ with open(vocab_file) as f:
+ for line in f:
+ token, rank = line.strip().split()
+ rank = int(rank)
+ token = base64.b64decode(token)
+ mergeable_ranks[token] = rank
+
+ self.mergeable_ranks = mergeable_ranks
+
+ self.tokenizer = tiktoken.Encoding(
+ name="my_tokenizer",
+ pat_str=pat_str,
+ mergeable_ranks=mergeable_ranks,
+ special_tokens={}
+ )
+ self.decoder = {rank: token for token, rank in mergeable_ranks.items()}
+ self.n_words = len(self.decoder)
+
+ super().__init__(
+ padding_side=padding_side,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ **kwargs
+ )
+
+ @property
+ def vocab_size(self):
+ return self.n_words
+
+ def get_vocab(self):
+ """ Returns vocab as a dict """
+ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def convert_tokens_to_string(self, tokens: List[Union[bytes, str, int]]) -> str:
+ """
+ Converts a sequence of tokens in a single string.
+ """
+ text = ""
+ temp = b""
+ for t in tokens:
+ if isinstance(t, int):
+ t = chr(t)
+ if isinstance(t, str):
+ if temp:
+ text += temp.decode("utf-8", errors="replace")
+ elif isinstance(t, bytes):
+ temp += t
+ else:
+ raise TypeError("token should only be of type int, bytes or str")
+ if temp:
+ text += temp.decode("utf-8", errors="replace")
+ return text
+
+ def _tokenize(self, text, **kwargs):
+ tokens = []
+ ids = self.tokenizer.encode(text)
+ for t in ids:
+ tokens.append(self.decoder[t])
+ return tokens
+
+ def _convert_token_to_id(self, token):
+ """ Converts a token (str) in an id using the vocab. """
+ return self.mergeable_ranks[token]
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.decoder.get(index, "")
+
+ def save_vocabulary(self, save_directory, filename_prefix=None):
+ """
+ Save the vocabulary and special tokens file to a directory.
+
+ Args:
+ save_directory (`str`):
+ The directory in which to save the vocabulary.
+ filename_prefix (`str`, *optional*):
+ An optional prefix to add to the named of the saved files.
+
+ Returns:
+ `Tuple(str)`: Paths to the files saved.
+ """
+ if os.path.isdir(save_directory):
+ vocab_file = os.path.join(
+ save_directory, self.vocab_files_names["vocab_file"]
+ )
+ else:
+ vocab_file = save_directory
+
+ with open(self.vocab_file, 'rb') as fin:
+ proto_str = fin.read()
+
+ with open(vocab_file, "wb") as writer:
+ writer.write(proto_str)
+
+ return (vocab_file,)
+
+ def get_prefix_tokens(self):
+ prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")]
+ return prefix_tokens
+
+ def build_single_message(self, role, metadata, message, tokenize=True):
+ assert role in ["system", "user", "assistant", "observation"], role
+ if tokenize:
+ role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n",
+ disallowed_special=())
+ message_tokens = self.tokenizer.encode(message, disallowed_special=())
+ tokens = role_tokens + message_tokens
+ return tokens
+ else:
+ return str(f"<|{role}|>{metadata}\n{message}")
+
+ # Use Jinja Template in tokenizer_config.json
+ # def apply_chat_template(
+ # self,
+ # conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], "Conversation"],
+ # add_generation_prompt: bool = False,
+ # tokenize: bool = True,
+ # padding: bool = False,
+ # truncation: bool = False,
+ # max_length: Optional[int] = None,
+ # return_tensors: Optional[Union[str, TensorType]] = None,
+ # return_dict: bool = False,
+ # tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+ # add_special_tokens: bool = True,
+ # **kwargs,
+ # ) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
+ #
+ # if return_dict and not tokenize:
+ # raise ValueError(
+ # "`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
+ # "of tokenizer outputs to return."
+ # )
+ #
+ # def handle_single_conversation(conversation):
+ # input_ids = self.get_prefix_tokens() if add_special_tokens else []
+ # input_message = "[gMASK]<sop>" if add_special_tokens else ""
+ # for item in conversation:
+ # if item.get("tools"):
+ # tools = item["tools"]
+ # content = "你是一个名为 GhatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。"
+ # content += "\n\n# 可用工具"
+ # for tool in tools:
+ # if tool["type"] == "function":
+ # function = tool["function"]
+ # content += f"\n\n## {function['name']}\n\n{json.dumps(function, ensure_ascii=False, indent=4)}"
+ # content += "\n在调用上述函数时，请使用 Json 格式表示调用的参数。"
+ # elif tool["type"] == "python":
+ # content += "\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。"
+ # elif tool["type"] == "simple_browser":
+ # content += "\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。"
+ # elif tool["type"] == "cogview":
+ # content += "\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。"
+ # else:
+ # raise NotImplementedError(f"Unknown tool type {tool['type']}")
+ # input = self.build_single_message("system", "", content, tokenize=tokenize)
+ # if tokenize:
+ # input_ids.extend(input)
+ # else:
+ # input_message += input
+ # if item["content"]:
+ # input = self.build_single_message(
+ # item["role"],
+ # item.get("metadata", ""),
+ # item["content"],
+ # tokenize=tokenize
+ # )
+ # if tokenize:
+ # input_ids.extend(input)
+ # else:
+ # input_message += input
+ # if add_generation_prompt:
+ # if tokenize:
+ # input_ids.extend([self.convert_tokens_to_ids("<|assistant|>")])
+ # else:
+ # input_message += "<|assistant|>"
+ # return input_ids if tokenize else input_message
+ #
+ # # Main logic to handle different conversation formats
+ # if isinstance(conversation, list) and all(isinstance(i, dict) for i in conversation):
+ # result = handle_single_conversation(conversation)
+ # elif isinstance(conversation, list) and all(isinstance(i, list) for i in conversation):
+ # result = [handle_single_conversation(c) for c in conversation]
+ # elif hasattr(conversation, "messages"):
+ # result = handle_single_conversation(conversation.messages)
+ # else:
+ # raise ValueError("Invalid conversation format")
+ #
+ # if tokenize:
+ # output = self.batch_encode_plus(
+ # [result] if isinstance(result[0], int) else result,
+ # padding=padding,
+ # truncation=truncation,
+ # max_length=max_length,
+ # return_tensors=return_tensors,
+ # is_split_into_words=True,
+ # add_special_tokens=False
+ # )
+ # if return_dict:
+ # return output
+ # else:
+ # return output["input_ids"]
+ # else:
+ # return result
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+ ) -> List[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A BERT sequence has the following format:
+
+ - single sequence: `[CLS] X [SEP]`
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ prefix_tokens = self.get_prefix_tokens()
+ token_ids_0 = prefix_tokens + token_ids_0
+ if token_ids_1 is not None:
+ token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]
+ return token_ids_0
+
+ def _pad(
+ self,
+ encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+ Args:
+ encoded_inputs:
+ Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ padding_strategy: PaddingStrategy to use for padding.
+
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ # Load from model defaults
+ assert self.padding_side == "left"
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+ seq_length = len(required_input)
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(required_input)
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+ # Initialize attention mask if not present.
+ if "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * seq_length
+
+ if "position_ids" not in encoded_inputs:
+ encoded_inputs["position_ids"] = list(range(seq_length))
+
+ if needs_to_be_padded:
+ difference = max_length - len(required_input)
+
+ if "attention_mask" in encoded_inputs:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if "position_ids" in encoded_inputs:
+ encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+
+ return encoded_inputs

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a493598071550244b2ee7f26118f3edec2150b9dfa967929a99052ac83fe716
+size 2623634

tokenizer_config.json

@@ -0,0 +1,148 @@
+{
+ "added_tokens_decoder": {
+ "151329": {
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151330": {
+ "content": "[MASK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151331": {
+ "content": "[gMASK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151332": {
+ "content": "[sMASK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151333": {
+ "content": "<sop>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151334": {
+ "content": "<eop>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151335": {
+ "content": "<|system|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151336": {
+ "content": "<|user|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151337": {
+ "content": "<|assistant|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151338": {
+ "content": "<|observation|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151339": {
+ "content": "<|begin_of_image|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151340": {
+ "content": "<|end_of_image|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151341": {
+ "content": "<|begin_of_video|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151342": {
+ "content": "<|end_of_video|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "additional_special_tokens": [
+ "<|endoftext|>",
+ "[MASK]",
+ "[gMASK]",
+ "[sMASK]",
+ "<sop>",
+ "<eop>",
+ "<|system|>",
+ "<|user|>",
+ "<|assistant|>",
+ "<|observation|>",
+ "<|begin_of_image|>",
+ "<|end_of_image|>",
+ "<|begin_of_video|>",
+ "<|end_of_video|>"
+ ],
+ "auto_map": {
+ "AutoTokenizer": [
+ "tokenization_chatglm.ChatGLM4Tokenizer",
+ null
+ ]
+ },
+ "chat_template": "{{ '[gMASK]<sop>' }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|system|>\n' + system_message }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|user|>\n' + content + '<|assistant|>' }}{% elif message['role'] == 'assistant' %}{{ '\n' + content }}{% endif %}{% endfor %}",
+ "clean_up_tokenization_spaces": false,
+ "do_lower_case": false,
+ "eos_token": "<|endoftext|>",
+ "model_max_length": 128000,
+ "pad_token": "<|endoftext|>",
+ "padding_side": "left",
+ "remove_space": false,
+ "split_special_tokens": false,
+ "tokenizer_class": "ChatGLM4Tokenizer"
+}