RuntimeError：张量 a (4000) 的大小必须与非单维 1 的张量 b (512) 的大小相匹配

Question

我正在尝试建立一个文档分类模型。我正在使用BERT 和PyTorch。

我得到了下面代码的bert模型。

bert = AutoModel.from_pretrained('bert-base-uncased')

这是训练的代码。

for epoch in range(epochs):
 
    print('\n Epoch {:} / {:}'.format(epoch + 1, epochs))

    #train model
    train_loss, _ = modhelper.train(proc.train_dataloader)

    #evaluate model
    valid_loss, _ = modhelper.evaluate()

    #save the best model
    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(modhelper.model.state_dict(), 'saved_weights.pt')

    # append training and validation loss
    train_losses.append(train_loss)
    valid_losses.append(valid_loss)

    print(f'\nTraining Loss: {train_loss:.3f}')
    print(f'Validation Loss: {valid_loss:.3f}')

这是我的 train 方法，可通过对象 modhelper 访问。

def train(self, train_dataloader):
    self.model.train()
    total_loss, total_accuracy = 0, 0
    
    # empty list to save model predictions
    total_preds=[]
    
        # iterate over batches
    for step, batch in enumerate(train_dataloader):
        
        # progress update after every 50 batches.
        if step % 50 == 0 and not step == 0:
            print('  Batch {:>5,}  of  {:>5,}.'.format(step, len(train_dataloader)))
        
        # push the batch to gpu
        #batch = [r.to(device) for r in batch]
        
        sent_id, mask, labels = batch
        
        # clear previously calculated gradients 
        self.model.zero_grad()        

        print(sent_id.size(), mask.size())
        # get model predictions for the current batch
        preds = self.model(sent_id, mask) #This line throws the error
        
        # compute the loss between actual and predicted values
        self.loss = self.cross_entropy(preds, labels)
        
        # add on to the total loss
        total_loss = total_loss + self.loss.item()
        
        # backward pass to calculate the gradients
        self.loss.backward()
        
        # clip the the gradients to 1.0. It helps in preventing the exploding gradient problem
        torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
        
        # update parameters
        self.optimizer.step()
        
        # model predictions are stored on GPU. So, push it to CPU
        #preds=preds.detach().cpu().numpy()
        
        # append the model predictions
        total_preds.append(preds)
      
    # compute the training loss of the epoch
    avg_loss = total_loss / len(train_dataloader)
    
    # predictions are in the form of (no. of batches, size of batch, no. of classes).
    # reshape the predictions in form of (number of samples, no. of classes)
    total_preds  = np.concatenate(total_preds, axis=0)
      
    #returns the loss and predictions
    return avg_loss, total_preds

preds = self.model(sent_id, mask)这一行抛出以下错误（包括完整的回溯）。

 Epoch 1 / 1
torch.Size([32, 4000]) torch.Size([32, 4000])
Traceback (most recent call last):

File "<ipython-input-39-17211d5a107c>", line 8, in <module>
train_loss, _ = modhelper.train(proc.train_dataloader)

File "E:\BertTorch\model.py", line 71, in train
preds = self.model(sent_id, mask)

File "E:\BertTorch\venv\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)

File "E:\BertTorch\model.py", line 181, in forward
#pass the inputs to the model

File "E:\BertTorch\venv\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)

File "E:\BertTorch\venv\lib\site-packages\transformers\modeling_bert.py", line 837, in forward
embedding_output = self.embeddings(

File "E:\BertTorch\venv\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)

File "E:\BertTorch\venv\lib\site-packages\transformers\modeling_bert.py", line 201, in forward
embeddings = inputs_embeds + position_embeddings + token_type_embeddings

RuntimeError: The size of tensor a (4000) must match the size of tensor b (512) at non-singleton dimension 1

如果您发现我在代码中打印了手电筒尺寸。 print(sent_id.size(), mask.size())

那行代码的输出是torch.Size([32, 4000]) torch.Size([32, 4000])。

我们可以看到大小是相同的，但它会引发错误。请提出你的想法。真的很感激。

如果您需要更多信息，请发表评论。我会尽快添加所需的内容。

Answer 1

问题在于 BERT 对字数的限制。我已将字数设为 4000，其中支持的最大值为 512（必须在字符串的开头和结尾再放弃 2 个 '[cls]' 和 '[Sep]'，所以它只有 510） .减少字数或为您的问题使用其他模型。 @cronoik 在上面的 cmets 中建议的 Longformers 之类的东西。

谢谢。