Flows demo fails because of missing .to(ptu.device)

[xavisuau]

The AutoregressiveFlow and RealNVP cells fails with the error message copied at the end of the message. I ran all the cells sequentially from the beginning.

If I add real_nvp = real_nvp.to(ptu.device), all works fine:

real_nvp = RealNVP([AffineTransform("left", n_hidden=2, hidden_size=64),
                    AffineTransform("right", n_hidden=2, hidden_size=64),
                    AffineTransform("left", n_hidden=2, hidden_size=64),
                    AffineTransform("right", n_hidden=2, hidden_size=64)],
                   train_loader.dataset, 'moons', train_labels)
real_nvp = real_nvp.to(ptu.device) # <-- ADDED THIS LINE 
train_losses, test_losses = train_epochs(real_nvp, train_loader, test_loader, dict(epochs=250, lr=5e-3, epochs_to_plot=[0, 3, 6, 10, 25, 249]))

---

Error messages:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-54-8b25cbd7d1cc> in <module>
      1 ar_flow = AutoregressiveFlow(train_loader.dataset, 'moons', train_labels)
----> 2 train_losses, test_losses = train_epochs(ar_flow, train_loader, test_loader, dict(epochs=100, lr=5e-3, epochs_to_plot=[0, 1, 3, 6, 10, 99]))

<ipython-input-41-5d16b72566e2> in train_epochs(model, train_loader, test_loader, train_args)
     37     for epoch in tqdm_notebook(range(epochs), desc='Epoch', leave=False):
     38         model.train()
---> 39         train(model, train_loader, optimizer)
     40         train_loss = eval_loss(model, train_loader)
     41         train_losses.append(train_loss)

<ipython-input-41-5d16b72566e2> in train(model, train_loader, optimizer)
      4     for x in train_loader:
      5         x = x.to(ptu.device).float()
----> 6         loss = model.nll(x)
      7         optimizer.zero_grad()
      8         loss.backward()

<ipython-input-52-b941007c468c> in nll(self, x)
    100 
    101     def nll(self, x):
--> 102         return - self.log_prob(x).mean()
    103 
    104     def plot(self, title):

<ipython-input-52-b941007c468c> in log_prob(self, x)
     96 
     97     def log_prob(self, x):
---> 98         z, log_det = self.flow(x)
     99         return (self.base_dist.log_prob(z) + log_det).sum(dim=1) # shape: [batch_size, dim]
    100 

<ipython-input-52-b941007c468c> in flow(self, x)
     92         x1, x2 = torch.chunk(x, 2, dim=1)
     93         z1, log_det1 = self.dim1_flow.flow(x1.squeeze())
---> 94         z2, log_det2 = self.dim2_flow.flow(x2, cond=x1)
     95         return torch.cat([z1.unsqueeze(1), z2.unsqueeze(1)], dim=1), torch.cat([log_det1.unsqueeze(1), log_det2.unsqueeze(1)], dim=1)
     96 

<ipython-input-52-b941007c468c> in flow(self, x, cond)
     33     def flow(self, x, cond):
     34         # parameters of flow on x depend on what it's conditioned on
---> 35         loc, log_scale, weight_logits = torch.chunk(self.mlp(cond), 3, dim=1)
     36         weights = F.softmax(weight_logits)
     37 

~/.virtualenvs/deepul/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    487             result = self._slow_forward(*input, **kwargs)
    488         else:
--> 489             result = self.forward(*input, **kwargs)
    490         for hook in self._forward_hooks.values():
    491             hook_result = hook(self, input, result)

<ipython-input-52-b941007c468c> in forward(self, x)
     12 
     13     def forward(self, x):
---> 14         return self.layers(x)
     15 
     16 # same CDF flow as in Demo 1, but conditioned on an auxillary variable

~/.virtualenvs/deepul/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    487             result = self._slow_forward(*input, **kwargs)
    488         else:
--> 489             result = self.forward(*input, **kwargs)
    490         for hook in self._forward_hooks.values():
    491             hook_result = hook(self, input, result)

~/.virtualenvs/deepul/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
     90     def forward(self, input):
     91         for module in self._modules.values():
---> 92             input = module(input)
     93         return input
     94 

~/.virtualenvs/deepul/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    487             result = self._slow_forward(*input, **kwargs)
    488         else:
--> 489             result = self.forward(*input, **kwargs)
    490         for hook in self._forward_hooks.values():
    491             hook_result = hook(self, input, result)

~/.virtualenvs/deepul/lib/python3.6/site-packages/torch/nn/modules/linear.py in forward(self, input)
     65     @weak_script_method
     66     def forward(self, input):
---> 67         return F.linear(input, self.weight, self.bias)
     68 
     69     def extra_repr(self):

~/.virtualenvs/deepul/lib/python3.6/site-packages/torch/nn/functional.py in linear(input, weight, bias)
   1350     if input.dim() == 2 and bias is not None:
   1351         # fused op is marginally faster
-> 1352         ret = torch.addmm(torch.jit._unwrap_optional(bias), input, weight.t())
   1353     else:
   1354         output = input.matmul(weight.t())

RuntimeError: Expected object of backend CPU but got backend CUDA for argument #4 'mat1'

[xavisuau]

Awesome notebook BTW! 👏👏

[hemildesai]

I've encountered this. You can set the ptu.device using ptu.set_gpu_mode('gpu', 0) # 0 for the first cuda device