I am trying to build a next word prediction model with pytorch in google colab. As my vocabulary size is over 1.5 million, I am using AdaptiveLogSoftmaxWithLoss module of pytorch to reduce RAM consumption.
The simple BiLSTM model definition is as follows:
class BLSTM(nn.Module):
    def __init__(self, emb_size, hidden_size, num_layers, vocab_size, cutoffs):
        super(BLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.blstm = nn.LSTM(emb_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
        self.fc = nn.AdaptiveLogSoftmaxWithLoss(hidden_size*2, vocab_size, cutoffs)
        # self.fc = nn.Linear(hidden_size*2, vocab_size)
    
    def forward(self, x):
        # Set initial states
        h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)
        c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)

        # Forward propagate LSTM
        embed = nn.Embedding(vocab_size, emb_size)
        out, _ = self.blstm(embed(x), (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size*2)
        
        # Decode the hidden state of the last time step
        out = self.fc(out[:, -1, :])
        
        return out

The model and loss function are called as follows:
model = BLSTM(emb_size, hidden_size, num_layers, vocab_size, cutoffs)

# Loss and optimizer
criterion = nn.NLLLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

Inside the epoch the loss is calculated as follows:
        inputs = x[0].to(device)
        targets = x[1].to(device)

        # Forward pass
        outputs = model(inputs)
        outputs = outputs.to(device)
        loss = criterion(outputs, targets)
        print(loss.item())
        
        #Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

This is the complete error trace I am getting after running the epoch loop:
TypeError                                 Traceback (most recent call last)
<ipython-input-33-51e15380f8c7> in <module>()
      8 
      9         # Forward pass
---> 10         outputs = model(inputs)
     11         outputs = outputs.to(device)
     12         loss = criterion(outputs, targets)

2 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

TypeError: forward() missing 1 required positional argument: 'target'

I tried the same code with a simple nn.Linear() layer and the code runs fine. But when I replace the Linear layer with AdaptiveLogSoftmaxWithLoss, I get the above mentioned error.