Most torch functions throw a user-friendly exception when they encounter tensors on incompatible devices; however torch.bmm segfaults instead.

To Reproduce

In an interactive shell:

> x = torch.randn(3, 3).cpu()
> x.mm(x.cuda())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
  RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'mat2'

x[None].bmm(x[None].cuda())
> Segmentation fault

Expected behavior

torch.bmm should throw an exception like torch.mm rather than segfault

Environment

PyTorch version: 1.0.0.dev20181005
Is debug build: No
CUDA used to build PyTorch: 9.0.176

OS: Ubuntu 16.04.4 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
CMake version: version 3.5.1

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 9.0.176
GPU models and configuration:
GPU 0: Quadro GP100
GPU 1: Quadro GP100

Nvidia driver version: 396.26
cuDNN version: Could not collect

Versions of relevant libraries:
[pip] numpy (1.15.2)
[pip] torch (1.0.0.dev20181005)
[conda] pytorch-nightly 1.0.0.dev20181005 py3.7_cuda9.0.176_cudnn7.1.2_0 pytorch