Python：BERT Model 池错误 - mean() 收到了无效的 arguments 组合 - 得到（str，int）

Question

I am writing the code to train a bert model on my dataset. By when I run the code it throws an error in the average pool layer. I am unable to understand what causes this error.

Model

class BERTBaseUncased(nn.Module):
    def __init__(self, bert_path):
        super(BERTBaseUncased, self).__init__()
        self.bert_path = bert_path
        self.bert = transformers.BertModel.from_pretrained(self.bert_path)
        self.bert_drop = nn.Dropout(0.3)
        self.out = nn.Linear(768 * 2, 1)

    def forward(
            self,
            ids,
            mask,
            token_type_ids
    ):
        o1, _ = self.bert(
            ids,
            attention_mask=mask,
            token_type_ids=token_type_ids)
        
        apool = torch.mean(o1, 1)
        mpool, _ = torch.max(o1, 1)
        cat = torch.cat((apool, mpool), 1)

        bo = self.bert_drop(cat)
        p2 = self.out(bo)
        return p2

Error

Exception in device=TPU:0: mean() received an invalid combination of arguments - got (str, int), but expected one of:
 * (Tensor input, *, torch.dtype dtype)
 * (Tensor input, tuple of names dim, bool keepdim, *, torch.dtype dtype, Tensor out)
 * (Tensor input, tuple of ints dim, bool keepdim, *, torch.dtype dtype, Tensor out)

Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 228, in _start_fn
    fn(gindex, *args)
  File "<ipython-input-12-94e926c1f4df>", line 4, in _mp_fn
    a = _run()
  File "<ipython-input-5-ef9fa564682f>", line 146, in _run
    train_loop_fn(para_loader.per_device_loader(device), model, optimizer, device, scheduler=scheduler)
  File "<ipython-input-5-ef9fa564682f>", line 22, in train_loop_fn
    token_type_ids=token_type_ids
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 577, in __call__
    result = self.forward(*input, **kwargs)
  File "<ipython-input-11-9196e0d23668>", line 73, in forward
    apool = torch.mean(o1, 1)
TypeError: mean() received an invalid combination of arguments - got (str, int), but expected one of:
 * (Tensor input, *, torch.dtype dtype)
 * (Tensor input, tuple of names dim, bool keepdim, *, torch.dtype dtype, Tensor out)
 * (Tensor input, tuple of ints dim, bool keepdim, *, torch.dtype dtype, Tensor out)

I am trying to run this on a Kaggle TPU. How to fix this?

Answer 1

Since one of the 3.X updates, the models return now task-specific output objects (which are dictionaries) instead of plain tuples. You can either force the model to return a tuple by specifying return_dict=False :

o1, _ = self.bert(
            ids,
            attention_mask=mask,
            token_type_ids=token_type_ids,
            return_dict=False)

or by utilizing the basemodeloutputwithpoolingandcrossattentions object:

o = self.bert(
            ids,
            attention_mask=mask,
            token_type_ids=token_type_ids)
#you can view the other attributes with o.keys()
o1 = o.last_hidden_state