“https://github.com/THUDM/ChatGLM-6B”中的默认程序在我运行 api.py 时运行。 但最近当我尝试再次运行它时，它突然出现错误：“AttributeError：'ChatGLMTokenizer'对象没有属性'sp_tokenizer'”。 我试图自己解决这个问题，但什么也没发生。我重新安装了 Transformers==4.27.1，更新了 https://huggingface.co/THUDM/chatglm-6b/blob/main/tokenization_chatglm.py 上的 tokenization_chatglm.py 等。 我无法理解 tokenization_chatglm.py 或 api.py 如何工作，因为它们对我来说太复杂了。 我想知道是否还有什么我没有做的事情来解决我的问题，以及是否还有其他人遇到同样的问题。请有人帮助我。
默认的 api.py 位于此处
from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModel
import uvicorn, json, datetime
import torch
DEVICE = "cuda"
DEVICE_ID = "0"
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
def torch_gc():
if torch.cuda.is_available():
with torch.cuda.device(CUDA_DEVICE):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
app = FastAPI()
@app.post("/")
async def create_item(request: Request):
global model, tokenizer
json_post_raw = await request.json()
json_post = json.dumps(json_post_raw)
json_post_list = json.loads(json_post)
prompt = json_post_list.get('prompt')
history = json_post_list.get('history')
max_length = json_post_list.get('max_length')
top_p = json_post_list.get('top_p')
temperature = json_post_list.get('temperature')
response, history = model.chat(tokenizer,
prompt,
history=history,
max_length=max_length if max_length else 2048,
top_p=top_p if top_p else 0.7,
temperature=temperature if temperature else 0.95)
now = datetime.datetime.now()
time = now.strftime("%Y-%m-%d %H:%M:%S")
answer = {
"response": response,
"history": history,
"status": 200,
"time": time
}
log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
print(log)
torch_gc()
return answer
if __name__ == '__main__':
model_path = r"E:\huggingface\hub\models_THUDM_chatglm_6b"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, resume_download=True).quantize(4).half().cuda()
model.eval()
uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
最新的 tokenization_chatglm.py 在这里
"""Tokenization classes for ChatGLM."""
from typing import List, Optional, Union
import os
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging, PaddingStrategy
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
from typing import Dict
import sentencepiece as spm
import numpy as np
logger = logging.get_logger(__name__)
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"THUDM/chatglm-6b": 2048,
}
class TextTokenizer:
def __init__(self, model_path):
self.sp = spm.SentencePieceProcessor()
self.sp.Load(model_path)
self.num_tokens = self.sp.vocab_size()
def encode(self, text):
return self.sp.EncodeAsIds(text)
def decode(self, ids: List[int]):
return self.sp.DecodeIds(ids)
def tokenize(self, text):
return self.sp.EncodeAsPieces(text)
def convert_tokens_to_string(self, tokens):
return self.sp.DecodePieces(tokens)
def convert_tokens_to_ids(self, tokens):
return [self.sp.PieceToId(token) for token in tokens]
def convert_token_to_id(self, token):
return self.sp.PieceToId(token)
def convert_id_to_token(self, idx):
return self.sp.IdToPiece(idx)
def __len__(self):
return self.num_tokens
class SPTokenizer:
def __init__(
self,
vocab_file,
num_image_tokens=20000,
max_blank_length=80,
byte_fallback=True,
):
assert vocab_file is not None
self.vocab_file = vocab_file
self.num_image_tokens = num_image_tokens
self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
self.max_blank_length = max_blank_length
self.byte_fallback = byte_fallback
self.text_tokenizer = TextTokenizer(vocab_file)
def _get_text_tokenizer(self):
return self.text_tokenizer
@staticmethod
def get_blank_token(length: int):
assert length >= 2
return f"<|blank_{length}|>"
@staticmethod
def get_tab_token():
return f"<|tab|>"
@property
def num_text_tokens(self):
return self.text_tokenizer.num_tokens
@property
def num_tokens(self):
return self.num_image_tokens + self.num_text_tokens
@staticmethod
def _encode_whitespaces(text: str, max_len: int = 80):
text = text.replace("\t", SPTokenizer.get_tab_token())
for i in range(max_len, 1, -1):
text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
return text
def _preprocess(self, text: str, linebreak=True, whitespaces=True):
if linebreak:
text = text.replace("\n", "<n>")
if whitespaces:
text = self._encode_whitespaces(text, max_len=self.max_blank_length)
return text
def encode(
self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
) -> List[int]:
"""
@param text: Text to encode.
@param linebreak: Whether to encode newline (\n) in text.
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
"""
text = self._preprocess(text, linebreak, whitespaces)
if not add_dummy_prefix:
text = "<n>" + text
tmp = self._get_text_tokenizer().encode(text)
tokens = [x + self.num_image_tokens for x in tmp]
return tokens if add_dummy_prefix else tokens[2:]
def postprocess(self, text):
text = text.replace("<n>", "\n")
text = text.replace(SPTokenizer.get_tab_token(), "\t")
for i in range(2, self.max_blank_length + 1):
text = text.replace(self.get_blank_token(i), " " * i)
return text
def decode(self, text_ids: List[int]) -> str:
ids = [int(_id) - self.num_image_tokens for _id in text_ids]
ids = [_id for _id in ids if _id >= 0]
text = self._get_text_tokenizer().decode(ids)
text = self.postprocess(text)
return text
def decode_tokens(self, tokens: List[str]) -> str:
text = self._get_text_tokenizer().convert_tokens_to_string(tokens)
text = self.postprocess(text)
return text
def tokenize(
self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
) -> List[str]:
"""
@param text: Text to encode.
@param linebreak: Whether to encode newline (\n) in text.
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
"""
text = self._preprocess(text, linebreak, whitespaces)
if not add_dummy_prefix:
text = "<n>" + text
tokens = self._get_text_tokenizer().tokenize(text)
return tokens if add_dummy_prefix else tokens[2:]
def __getitem__(self, x: Union[int, str]):
if isinstance(x, int):
if x < self.num_image_tokens:
return "<image_{}>".format(x)
else:
return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
elif isinstance(x, str):
if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
return int(x[7:-1])
else:
return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
else:
raise ValueError("The key should be str or int.")
class ChatGLMTokenizer(PreTrainedTokenizer):
"""
Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = {"vocab_file": "ice_text.model"}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask", "position_ids"]
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=False,
bos_token='<sop>',
eos_token='<eop>',
end_token='</s>',
mask_token='[MASK]',
gmask_token='[gMASK]',
padding_side="left",
pad_token="<pad>",
unk_token="<unk>",
num_image_tokens=20000,
**kwargs
) -> None:
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
padding_side=padding_side,
bos_token=bos_token,
eos_token=eos_token,
end_token=end_token,
mask_token=mask_token,
gmask_token=gmask_token,
pad_token=pad_token,
unk_token=unk_token,
num_image_tokens=num_image_tokens,
**kwargs
)
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.vocab_file = vocab_file
self.bos_token = bos_token
self.eos_token = eos_token
self.end_token = end_token
self.mask_token = mask_token
self.gmask_token = gmask_token
self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens)
""" Initialisation """
@property
def gmask_token_id(self) -> Optional[int]:
if self.gmask_token is None:
return None
return self.convert_tokens_to_ids(self.gmask_token)
@property
def end_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the end of context token in the vocabulary. Returns `None` if the token has not been
set.
"""
if self.end_token is None:
return None
return self.convert_tokens_to_ids(self.end_token)
@property
def vocab_size(self):
""" Returns vocab size """
return self.sp_tokenizer.num_tokens
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 preprocess_text(self, inputs):
if self.remove_space:
outputs = " ".join(inputs.strip().split())
else:
outputs = inputs
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def _tokenize(self, text, **kwargs):
""" Returns a tokenized string. """
text = self.preprocess_text(text)
seq = self.sp_tokenizer.tokenize(text)
return seq
def convert_tokens_to_string(self, tokens: List[str]) -> str:
return self.sp_tokenizer.decode_tokens(tokens)
def _decode(
self,
token_ids: Union[int, List[int]],
**kwargs
) -> str:
if isinstance(token_ids, int):
token_ids = [token_ids]
if len(token_ids) == 0:
return ""
if self.pad_token_id in token_ids: # remove pad
token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
return super()._decode(token_ids, **kwargs)
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
return self.sp_tokenizer[token]
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_tokenizer[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 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.
"""
gmask_id = self.sp_tokenizer[self.gmask_token]
eos_id = self.sp_tokenizer[self.eos_token]
token_ids_0 = token_ids_0 + [gmask_id, self.sp_tokenizer[self.bos_token]]
if token_ids_1 is not None:
token_ids_0 = token_ids_0 + token_ids_1 + [eos_id]
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
bos_token_id = self.sp_tokenizer[self.bos_token]
mask_token_id = self.sp_tokenizer[self.mask_token]
gmask_token_id = self.sp_tokenizer[self.gmask_token]
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 max_length is not None:
if "attention_mask" not in encoded_inputs:
if bos_token_id in required_input:
context_length = required_input.index(bos_token_id)
else:
context_length = seq_length
attention_mask = np.ones((1, seq_length, seq_length))
attention_mask = np.tril(attention_mask)
attention_mask[:, :, :context_length] = 1
attention_mask = np.bool_(attention_mask < 0.5)
encoded_inputs["attention_mask"] = attention_mask
if "position_ids" not in encoded_inputs:
if bos_token_id in required_input:
context_length = required_input.index(bos_token_id)
else:
context_length = seq_length
position_ids = np.arange(seq_length, dtype=np.int64)
mask_token = mask_token_id if mask_token_id in required_input else gmask_token_id
if mask_token in required_input:
mask_position = required_input.index(mask_token)
position_ids[context_length:] = mask_position
block_position_ids = np.concatenate(
[np.zeros(context_length, dtype=np.int64),
np.arange(1, seq_length - context_length + 1, dtype=np.int64)])
encoded_inputs["position_ids"] = np.stack([position_ids, block_position_ids], axis=0)
if needs_to_be_padded:
difference = max_length - len(required_input)
if "attention_mask" in encoded_inputs:
encoded_inputs["attention_mask"] = np.pad(encoded_inputs["attention_mask"],
pad_width=[(0, 0), (difference, 0), (difference, 0)],
mode='constant', constant_values=True)
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
if "position_ids" in encoded_inputs:
encoded_inputs["position_ids"] = np.pad(encoded_inputs["position_ids"],
pad_width=[(0, 0), (difference, 0)])
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
return encoded_inputs
我重新安装了 Transformers==4.27.1，更新了 https://huggingface.co/THUDM/chatglm-6b/blob/main/tokenization_chatglm.py 上的 tokenization_chatglm.py 等。 希望 api.py 运行成功，这样我就可以离线使用本地 ChatGLM。
哦，现在我明白了。 我选择的变形金刚版本还是不合适。 安装了 Transformer-4.33.2 并解决了问题。 如果浪费了您的时间，我很抱歉，真诚地抱歉。
pip uninstall transformers
pip install transformers==4.33.2