报错：
RuntimeError: stack(): functions with out=… arguments don’t support automatic differentiation, but one of the arguments requires grad.
或者
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.
原因：
训练模型的时候，输入数据x，y不应该requires_grad，而bert模型输出的embeddings默认是requires_grad的，所以会报错。
解决方法：
提取完embeddings之后，使用 embeddings.detach() 解除绑定就行了。
最后的代码：
from transformers import BertTokenizer, BertModelclass BertFeatureExtractor:def __init__(self):self.tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')self.model = BertModel.from_pretrained('bert-base-chinese')def extract_features(self, text: str):inputs = self.tokenizer(text, return_tensors="pt")if len(inputs["input_ids"]) > 512:inputs["input_ids"] = inputs["input_ids"][:512]inputs["attention_mask"] = inputs["attention_mask"][:512]outputs = self.model(**inputs)return outputs.last_hidden_state[:,0,:]feat = feat.detach()
