SpectralMixtureKernel initialize_from_data CUDA bug

🐛 Bug

When running initialize_from_data with CUDA tensors, in this line zero tensor is initialized without input device consideration, so that code fails later.

To reproduce

import torch, gpytorch
train_x = torch.tensor([1.0,2.0]).cuda()
train_y = torch.ones(2).cuda()
kernel = gpytorch.kernels.SpectralMixtureKernel(num_mixtures=4).cuda()
kernel.initialize_from_data(train_x, train_y)

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-1-419d92435b05> in <module>
      3 train_y = torch.ones(2).cuda()
      4 kernel = gpytorch.kernels.SpectralMixtureKernel(num_mixtures=4).cuda()
----> 5 kernel.initialize_from_data(train_x, train_y)

~/storage/miniconda2/envs/water/lib/python3.6/site-packages/gpytorch/kernels/spectral_mixture_kernel.py in initialize_from_data(self, train_x, train_y, **kwargs)
    166         )
    167         # Draw means from Unif(0, 0.5 / minimum distance between two points)
--> 168         self.raw_mixture_means.data.uniform_().mul_(0.5).div_(min_dist)
    169         self.raw_mixture_means.data = self.raw_mixture_means_constraint.inverse_transform(self.raw_mixture_means.data)
    170         # Mixture weights should be roughly the stdv of the y values divided by the number of mixtures

RuntimeError: expected backend CUDA and dtype Float but got backend CPU and dtype Float

Expected Behavior

I expected it to get device info from training data.

System information

Please complete the following information:

• GPyTorch 0.3.2
• PyTorch 1.1.0
• MacOS 10.14

[jacobrgardner]

@tzoiker @adam-rysanek Sorry this took an embarrassingly long time to fix for how simple it was.

85a000b should resolve the issue internally.

[gpleiss]

looks like this should’ve been closed with 85a000b