I am using google colab to train a Bidirectional RNN model and I get the error:

```
RuntimeError Traceback (most recent call last)
<ipython-input-34-0029e71ae99b> in <module>()
20 inputs, labels = inputs.to(device), labels.to(device)
21
---> 22 output = model(inputs)
23 loss = criterion(output.squeeze(), labels.float())
24 optimizer.zero_grad()
5 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py in forward_impl(self, input, hx, batch_sizes, max_batch_size, sorted_indices)
524 if batch_sizes is None:
525 result = _VF.lstm(input, hx, self._get_flat_weights(), self.bias, self.num_layers,
--> 526 self.dropout, self.training, self.bidirectional, self.batch_first)
527 else:
528 result = _VF.lstm(input, batch_sizes, hx, self._get_flat_weights(), self.bias,
RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED
```

I tried this solution 1 and this 2 and still get the error.

Here’s my BiRnn Model code:

```
class BiRNN(nn.Module):
def __init__(self, n_vocab, n_embed, hidden_size, seq_len, num_layers, output_size, drop_prob):
super(BiRNN, self).__init__()
self.hidden_size = hidden_size
self.seq_len = seq_len
self.num_layers = num_layers
self.embedding = nn.Embedding(n_vocab, n_embed)
self.lstm = nn.LSTM(n_embed, hidden_size, num_layers, batch_first=True, bidirectional=True)
self.dropout = nn.Dropout(drop_prob)
self.fc = nn.Linear(hidden_size*2, output_size)
def forward(self, x):
# Set initial states
h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
x = self.embedding(x).to(device)
# Forward propagate LSTM
lstm_out, _ = self.lstm(x, (h0, c0))
lstm_out = lstm_out.contiguous().view(-1, self.seq_len, 2, self.hidden_size)
# get backward output in first node
lstm_out_bw = lstm_out[:, 0, 1, :]
# get forward output in last node
lstm_out_fw = lstm_out[:, -1, 0, :]
lstm_out = torch.cat((lstm_out_fw, lstm_out_bw), -1)
drop_out = self.dropout(lstm_out)
logits = self.fc(drop_out)
return logits
```