Delete tokenization_chatglm.py

tokenization_chatglm.py

@@ -1,323 +0,0 @@
-import regex as re
-import base64
-import os
-import json
-import tiktoken
-from torch import TensorType
-from typing import List, Optional, Union, Dict, Any
-from transformers import PreTrainedTokenizer
-from transformers.utils import logging, PaddingStrategy
-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}")
-
-    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