How to fix "RuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.FloatTensor but got torch.LongTensor"
I encountered this bug when I am running the machine translation code.


  RuntimeError                              Traceback (most recent call
  last)  in 
        5 decoder = Decoder(len(out_vocab), embed_size, num_hiddens, num_layers,
        6                   attention_size, drop_prob)
  ----> 7 train(encoder, decoder, dataset, lr, batch_size, num_epochs)
  
   in train(encoder, decoder, dataset,
  lr, batch_size, num_epochs)
       13             dec_optimizer.zero_grad()
       14             l = batch_loss(encoder, decoder, X, Y, loss)
  ---> 15             l.backward()
       16             enc_optimizer.step()
       17             dec_optimizer.step()
  
  /usr/lib64/python3.6/site-packages/torch/tensor.py in backward(self,
  gradient, retain_graph, create_graph)
      105                 products. Defaults to False.
      106         """
  --> 107         torch.autograd.backward(self, gradient, retain_graph, create_graph)
      108 
      109     def register_hook(self, hook):
  
  /usr/lib64/python3.6/site-packages/torch/autograd/init.py in
  backward(tensors, grad_tensors, retain_graph, create_graph,
  grad_variables)
       91     Variable._execution_engine.run_backward(
       92         tensors, grad_tensors, retain_graph, create_graph,
  ---> 93         allow_unreachable=True)  # allow_unreachable flag
       94 
       95 
  
  RuntimeError: Function AddBackward0 returned an invalid gradient at
  index 1 - expected type torch.FloatTensor but got torch.LongTensor


I think the bug is in the batch_loss function. But I don't know why and can't fix it.

def batch_loss(encoder, decoder, X, Y, loss):
    batch_size = X.shape[0]
    enc_state = None
    enc_outputs, enc_state = encoder(X, enc_state)
    # 初始化解码器的隐藏状态
    dec_state = decoder.begin_state(enc_state)
    # 解码器在最初时间步的输入是BOS
    dec_input = torch.tensor([out_vocab.stoi[BOS]] * batch_size)
    # 我们将使用掩码变量mask来忽略掉标签为填充项PAD的损失
    mask, num_not_pad_tokens = torch.ones(batch_size), 0
    l = torch.tensor([0])
    for y in Y.t():
        dec_output, dec_state = decoder(dec_input, dec_state, enc_outputs)
        l = l + (mask * loss(dec_output, y)).sum()
        dec_input = y  # 使用强制教学
        num_not_pad_tokens += mask.sum().item()
        # 当遇到EOS时，序列后面的词将均为PAD，相应位置的掩码设成0
        mask = mask * (y != out_vocab.stoi[EOS]).float()
    return l / num_not_pad_tokens

def train(encoder, decoder, dataset, lr, batch_size, num_epochs):
    d2lt.params_init(encoder, init=nn.init.xavier_uniform_)
    d2lt.params_init(decoder, init=nn.init.xavier_uniform_)

    enc_optimizer = optim.Adam(encoder.parameters(), lr=lr)
    dec_optimizer = optim.Adam(decoder.parameters(), lr=lr)
    loss = nn.CrossEntropyLoss(reduction='none')
    data_iter = tdata.DataLoader(dataset, batch_size, shuffle=True)
    for epoch in range(num_epochs):
        l_sum = 0.0
        for X, Y in data_iter:
            enc_optimizer.zero_grad()
            dec_optimizer.zero_grad()
            l = batch_loss(encoder, decoder, X, Y, loss)
            l.backward()
            enc_optimizer.step()
            dec_optimizer.step()
            l_sum += l.item()
        if (epoch + 1) % 10 == 0:
            print("epoch %d, loss %.3f" % (epoch + 1, l_sum / len(data_iter)))


Looking forward to a positive reply.

Thank Proyag. Just replace l = torch.tensor([0]) with l = torch.tensor([0], dtype=torch.float) solves my problem.
