tokenization_chatglm.py

import base64
import json
import os
from typing import List, Optional, Union, Dict, Any

import regex as re
import tiktoken
from torch import TensorType
from transformers import PreTrainedTokenizer
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
from transformers.utils import PaddingStrategy


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={v.content: int(k) for k, v in kwargs['added_tokens_decoder'].items()}
            # 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

    @staticmethod
    def convert_tokens_to_string(tokens: List[Union[bytes, str]]) -> str:
        """
        Converts a sequence of tokens in a single string.
        """
        text = ""
        temp = b""
        for t in tokens:
            if isinstance(t, str):
                if temp:
                    text += temp.decode("utf-8", errors="replace")
                    temp = b""
                text += t
            elif isinstance(t, bytes):
                temp += t
            else:
                raise TypeError("token should only be of type types 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 apply_chat_template(
            self,
            conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],
            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(messages):
            content = "你是一位智能编程助手，你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题，并提供格式规范、可以执行、准确安全的代码，并在必要时提供详细的解释。"
            input_message = self.build_single_message("system", "", content)
            for item in messages:
                role = item.get("role", "")
                if not role:
                    raise ValueError("Invalid conversation format, 'role' must be given")
                # function call
                elif role == "tool":
                    content = self.build_function_sys_prompt(item["content"])
                    input_message = self.build_single_message("system", "", content)
                # chat
                elif role == "system":
                    input_message = self.build_single_message("system", item.get("metadata", ""), item["content"])
                else:
                    input_message += self.build_single_message(item["role"], item.get("metadata", ""), item["content"])

            if add_generation_prompt:
                input_message += "<|assistant|>\n"
            if tokenize:
                input_ids = self.get_prefix_tokens() if add_special_tokens else []
                input_ids += self.tokenizer.encode(input_message, allowed_special='all', disallowed_special=set())
                return input_ids
            else:
                return 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

    @staticmethod
    def build_single_message(role, metadata, message):
        assert role in ["system", "user", "assistant", "observation"], role
        return f"<|{role}|>{metadata}\n{message}"

    @staticmethod
    def build_function_sys_prompt(item: dict) -> str:
        prompt = """
你将接收到一个用户提出的问题，并请撰写清晰、简洁且准确的答案。

# Note
- 我将给你提供一些函数工具的接口信息，包括函数的定义、用途、名字、参数名和参数类型。
- 请根据这些信息，为用户的指令，从中选择最合适的函数，并给出调用时需要使用的参数。
- **返回类型为一个json格式的字符串，包含函数名和参数字典。**
    - name: 函数名
    - arguments: 参数字典，其中key为参数名，value为参数类型。
- **只需要生成答案即可，无需在你的回答之前或之后做出解释，也不要直接回答用户的问题。**
- 只用当提供的函数工具不足以完成任务时，请你用正常的语气告知用户并解释原因。

# Functions
以下是可使用的函数工具的接口信息。
""".lstrip()

        if isinstance(item['function'], dict):
            func = item['function']
            prompt += f"\n## Function 1\n"
            prompt += f"\n### Name\n{func['name']}\n"
            prompt += f"\n### Description\n{func['description']}\n"
            prompt += f"\n### Parameters\n```json\n{json.dumps(func['parameters'], ensure_ascii=False)}\n```\n"
            return prompt
        elif isinstance(item['function'], list):
            for idx, func in enumerate(item['function']):
                prompt += f"\n## Function {idx + 1}\n"
                prompt += f"\n### Name\n{func['name']}\n"
                prompt += f"\n### Description\n{func['description']}\n"
                prompt += f"\n### Parameters\n```json\n{json.dumps(func['parameters'], ensure_ascii=False)}\n```\n"
        return prompt

    def apply_infilling_template(
            self,
            message: dict,
            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,
            add_special_tokens: bool = True,
    ) -> 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."
            )

        if not isinstance(message, dict):
            raise ValueError("Invalid conversation format")
        content = self.build_infilling_prompt(message)
        input_message = self.build_single_message("user", "", content)
        if add_generation_prompt:
            input_message += "<|assistant|>\n"
        if not tokenize:
            return input_message

        input_ids = self.get_prefix_tokens() if add_special_tokens else []
        input_ids += self.tokenizer.encode(input_message, allowed_special='all', disallowed_special=set())
        output = self.batch_encode_plus(
            [input_ids] if isinstance(input_ids[0], int) else input_ids,
            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"]

    @staticmethod
    def build_infilling_prompt(item: dict) -> str:
        prompt = ""
        if "path" in item:
            prompt += f"###PATH:{item['path']}\n"
        if "language" in item:
            prompt += f"###LANGUAGE:{item['language']}\n"
        elif "lang" in item:
            prompt += f"###LANGUAGE:{item['lang']}\n"
        if "mode" in item and item['mode'].lower() == "line":
            prompt += "###MODE:LINE\n"
        else:
            prompt += "###MODE:BLOCK\n"
        prompt += f"<|code_suffix|>{item['suffix']}"
        prompt += f"<|code_prefix|>{item['prefix']}"
        prompt += "<|code_middle|>"
        return prompt