I have a fairly simple network:
class Network(nn.Module):
    def __init__(self, input_dim, lstm_layers=2, hidden_size=20, dropout=0, bidirectional=False):
        super(Network, self).__init__()
        self.lstm_layers = nn.LSTM(input_dim, hidden_size, num_layers=lstm_layers, batch_first=True, dropout=dropout, bidirectional=bidirectional)
        
        self.output_lstm_layer = nn.LSTM(hidden_size, 1, num_layers=1, batch_first=True)
        
    def forward(self, x):
        x, _ = self.lstm_layers(x)
        x, _ = self.output_lstm_layer(x)
        return x

However, when I try and train it:
for i, (data, labels) in enumerate(train_dataloader):
    data = data.cuda()
    data[0].requires_grad_()
    labels = labels.cuda()
    labels[0].requires_grad_()

    optim.zero_grad()

    out, lengths = net(data)
    
    print(out)
    l = loss(out.view(-1), labels[0])
    print(l)
    l.backward() # <- Error Here: RuntimeError: CUDNN_STATUS_BAD_PARAM
    optimizer.step()
    print(l)

data, and labels out of train_dataloader are both PackedSequences.  They are initialized with “requires_grad = False”, which is why I use the requires_grad_() method on them.  (with more thought, I’m not sure that this is needed).
Unfortunately, while a loss is computed, l.backward() fails:
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-55-c606886d4e12> in <module>()
     11     l = loss(out.data.view(-1), labels.data)
     12     print(l)
---> 13     l.backward()
     14     optimizer.step()
     15     print(l)

~/.virtualenvs/truu_walking_not_walking_lstm-JqLRVldl/lib/python3.6/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)
     91                 products. Defaults to ``False``.
     92         """
---> 93         torch.autograd.backward(self, gradient, retain_graph, create_graph)
     94 
     95     def register_hook(self, hook):

~/.virtualenvs/truu_walking_not_walking_lstm-JqLRVldl/lib/python3.6/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
     87     Variable._execution_engine.run_backward(
     88         tensors, grad_tensors, retain_graph, create_graph,
---> 89         allow_unreachable=True)  # allow_unreachable flag
     90 
     91 

RuntimeError: CUDNN_STATUS_BAD_PARAM

And I have no idea why.
Anyone have an idea?