• codegeex2-6b-int4 部署

            codegeex2-6b-int4                模型文件

            CodeGeeX2                          仓库文件地址

            CodeGeeX2                          推理教程

            

    conda create -n codegeex2 python=3.10 -y

    conda activate codegeex2

    pip install -r requirements.txt -i https://pypi.mirrors.ustc.edu.cn/simple 

    启动 Gradio DEMO：

    python ./demo/run_demo.py

    启动FAST API:

    python ./demo/fastapicpu.py

    报错

    AttributeError: 'ChatGLMTokenizer' object has no attribute 'tokenizer'. Did you mean: 'tokenize'?

    解决方法：

    用以下代码替换掉tokenization_chatglm.py里面内容

    1. import os
    2. import torch
    3. from typing import List, Optional, Union, Dict
    4. from sentencepiece import SentencePieceProcessor
    5. from transformers import PreTrainedTokenizer
    6. from transformers.utils import logging, PaddingStrategy
    7. from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
    8.  
    9.  
    10. class SPTokenizer:
    11.     def __init__(self, model_path: str):
    12.         # reload tokenizer
    13.         assert os.path.isfile(model_path), model_path
    14.         self.sp_model = SentencePieceProcessor(model_file=model_path)
    15.  
    16.         # BOS / EOS token IDs
    17.         self.n_words: int = self.sp_model.vocab_size()
    18.         self.bos_id: int = self.sp_model.bos_id()
    19.         self.eos_id: int = self.sp_model.eos_id()
    20.         self.pad_id: int = self.sp_model.unk_id()
    21.         assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
    22.  
    23.         special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"]
    24.         self.special_tokens = {}
    25.         self.index_special_tokens = {}
    26.         for token in special_tokens:
    27.             self.special_tokens[token] = self.n_words
    28.             self.index_special_tokens[self.n_words] = token
    29.             self.n_words += 1
    30.  
    31.     def tokenize(self, s: str):
    32.         return self.sp_model.EncodeAsPieces(s)
    33.  
    34.     def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
    35.         assert type(s) is str
    36.         t = self.sp_model.encode(s)
    37.         if bos:
    38.             t = [self.bos_id] + t
    39.         if eos:
    40.             t = t + [self.eos_id]
    41.         return t
    42.  
    43.     def decode(self, t: List[int]) -> str:
    44.         return self.sp_model.decode(t)
    45.  
    46.     def decode_tokens(self, tokens: List[str]) -> str:
    47.         text = self.sp_model.DecodePieces(tokens)
    48.         return text
    49.  
    50.     def convert_token_to_id(self, token):
    51.         """ Converts a token (str) in an id using the vocab. """
    52.         if token in self.special_tokens:
    53.             return self.special_tokens[token]
    54.         return self.sp_model.PieceToId(token)
    55.  
    56.     def convert_id_to_token(self, index):
    57.         """Converts an index (integer) in a token (str) using the vocab."""
    58.         if index in self.index_special_tokens or index in [self.eos_id, self.bos_id, self.pad_id] or index < 0:
    59.             return ""
    60.         return self.sp_model.IdToPiece(index)
    61.  
    62.  
    63. class ChatGLMTokenizer(PreTrainedTokenizer):
    64.     vocab_files_names = {"vocab_file": "tokenizer.model"}
    65.  
    66.     model_input_names = ["input_ids", "attention_mask", "position_ids"]
    67.  
    68.     def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, **kwargs):
    69.         self.name = "GLMTokenizer"
    70.  
    71.         self.vocab_file = vocab_file
    72.         self.tokenizer = SPTokenizer(vocab_file)
    73.         self.special_tokens = {
    74.             "": self.tokenizer.bos_id,
    75.             "": self.tokenizer.eos_id,
    76.             "": self.tokenizer.pad_id
    77.         }
    78.         super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)
    79.  
    80.     def get_command(self, token):
    81.         if token in self.special_tokens:
    82.             return self.special_tokens[token]
    83.         assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
    84.         return self.tokenizer.special_tokens[token]
    85.  
    86.     @property
    87.     def unk_token(self) -> str:
    88.         return ""
    89.  
    90.     @property
    91.     def pad_token(self) -> str:
    92.         return ""
    93.  
    94.     @property
    95.     def pad_token_id(self):
    96.         return self.get_command("")
    97.  
    98.     @property
    99.     def eos_token(self) -> str:
    100.         return ""
    101.  
    102.     @property
    103.     def eos_token_id(self):
    104.         return self.get_command("")
    105.  
    106.     @property
    107.     def vocab_size(self):
    108.         return self.tokenizer.n_words
    109.  
    110.     def get_vocab(self):
    111.         """ Returns vocab as a dict """
    112.         vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
    113.         vocab.update(self.added_tokens_encoder)
    114.         return vocab
    115.  
    116.     def _tokenize(self, text, **kwargs):
    117.         return self.tokenizer.tokenize(text)
    118.  
    119.     def _convert_token_to_id(self, token):
    120.         """ Converts a token (str) in an id using the vocab. """
    121.         return self.tokenizer.convert_token_to_id(token)
    122.  
    123.     def _convert_id_to_token(self, index):
    124.         """Converts an index (integer) in a token (str) using the vocab."""
    125.         return self.tokenizer.convert_id_to_token(index)
    126.  
    127.     def convert_tokens_to_string(self, tokens: List[str]) -> str:
    128.         return self.tokenizer.decode_tokens(tokens)
    129.  
    130.     def save_vocabulary(self, save_directory, filename_prefix=None):
    131.         """
    132.         Save the vocabulary and special tokens file to a directory.
    133.         Args:
    134.             save_directory (`str`):
    135.                 The directory in which to save the vocabulary.
    136.             filename_prefix (`str`, *optional*):
    137.                 An optional prefix to add to the named of the saved files.
    138.         Returns:
    139.             `Tuple(str)`: Paths to the files saved.
    140.         """
    141.         if os.path.isdir(save_directory):
    142.             vocab_file = os.path.join(
    143.                 save_directory, self.vocab_files_names["vocab_file"]
    144.             )
    145.         else:
    146.             vocab_file = save_directory
    147.  
    148.         with open(self.vocab_file, 'rb') as fin:
    149.             proto_str = fin.read()
    150.  
    151.         with open(vocab_file, "wb") as writer:
    152.             writer.write(proto_str)
    153.  
    154.         return (vocab_file,)
    155.  
    156.     def get_prefix_tokens(self):
    157.         prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")]
    158.         return prefix_tokens
    159.  
    160.     def build_prompt(self, query, history=None):
    161.         if history is None:
    162.             history = []
    163.         prompt = ""
    164.         for i, (old_query, response) in enumerate(history):
    165.             prompt += "[Round {}]\n\n问：{}\n\n答：{}\n\n".format(i + 1, old_query, response)
    166.         prompt += "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query)
    167.         return prompt
    168.  
    169.     def build_inputs_with_special_tokens(
    170.             self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    171.     ) -> List[int]:
    172.         """
    173.         Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
    174.         adding special tokens. A BERT sequence has the following format:
    175.         - single sequence: `[CLS] X [SEP]`
    176.         - pair of sequences: `[CLS] A [SEP] B [SEP]`
    177.         Args:
    178.             token_ids_0 (`List[int]`):
    179.                 List of IDs to which the special tokens will be added.
    180.             token_ids_1 (`List[int]`, *optional*):
    181.                 Optional second list of IDs for sequence pairs.
    182.         Returns:
    183.             `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
    184.         """
    185.         prefix_tokens = self.get_prefix_tokens()
    186.         token_ids_0 = prefix_tokens + token_ids_0
    187.         if token_ids_1 is not None:
    188.             token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("")]
    189.         return token_ids_0
    190.  
    191.     def _pad(
    192.             self,
    193.             encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
    194.             max_length: Optional[int] = None,
    195.             padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
    196.             pad_to_multiple_of: Optional[int] = None,
    197.             return_attention_mask: Optional[bool] = None,
    198.     ) -> dict:
    199.         """
    200.         Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
    201.         Args:
    202.             encoded_inputs:
    203.                 Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
    204.             max_length: maximum length of the returned list and optionally padding length (see below).
    205.                 Will truncate by taking into account the special tokens.
    206.             padding_strategy: PaddingStrategy to use for padding.
    207.                 - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
    208.                 - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
    209.                 - PaddingStrategy.DO_NOT_PAD: Do not pad
    210.                 The tokenizer padding sides are defined in self.padding_side:
    211.                     - 'left': pads on the left of the sequences
    212.                     - 'right': pads on the right of the sequences
    213.             pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
    214.                 This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
    215.                 `>= 7.5` (Volta).
    216.             return_attention_mask:
    217.                 (optional) Set to False to avoid returning attention mask (default: set to model specifics)
    218.         """
    219.         # Load from model defaults
    220.         assert self.padding_side == "left"
    221.  
    222.         required_input = encoded_inputs[self.model_input_names[0]]
    223.         seq_length = len(required_input)
    224.  
    225.         if padding_strategy == PaddingStrategy.LONGEST:
    226.             max_length = len(required_input)
    227.  
    228.         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
    229.             max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
    230.  
    231.         needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
    232.  
    233.         # Initialize attention mask if not present.
    234.         if "attention_mask" not in encoded_inputs:
    235.             encoded_inputs["attention_mask"] = [1] * seq_length
    236.  
    237.         if "position_ids" not in encoded_inputs:
    238.             encoded_inputs["position_ids"] = list(range(seq_length))
    239.  
    240.         if needs_to_be_padded:
    241.             difference = max_length - len(required_input)
    242.  
    243.             if "attention_mask" in encoded_inputs:
    244.                 encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
    245.             if "position_ids" in encoded_inputs:
    246.                 encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
    247.             encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
    248.  
    249.         return encoded_inputs
    250.

    参考：【已解决】AttributeError: ‘ChatGLMTokenizer‘ object has no attribute ‘tokenizer‘_attributeerror: 'chatglmtokenizer' object has no a-CSDN博客

    ***

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  • 原文地址：https://blog.csdn.net/m0_60657960/article/details/139668935