Updates torch.tensor, torch.as_tensor, and sparse ctors to use the device of inputs tensors they're given, by default (#41984)
Summary:
**BC-Breaking Note**
This PR changes the behavior of the torch.tensor, torch.as_tensor, and sparse constructors. When given a tensor as input and a device is not explicitly specified, these constructors now always infer their device from the tensor. Historically, if the optional dtype kwarg was provided then these constructors would not infer their device from tensor inputs. Additionally, for the sparse ctor a runtime error is now thrown if the indices and values tensors are on different devices and the device kwarg is not specified.
**PR Summary**
This PR's functional change is a single line:
```
auto device = device_opt.has_value() ? *device_opt : (type_inference ? var.device() : at::Device(computeDeviceType(dispatch_key)));
```
=>
```
auto device = device_opt.has_value() ? *device_opt : var.device();
```
in `internal_new_from_data`. This line entangled whether the function was performing type inference with whether it inferred its device from an input tensor, and in practice meant that
```
t = torch.tensor((1, 2, 3), device='cuda')
torch.tensor(t, dtype=torch.float64)
```
would return a tensor on the CPU, not the default CUDA device, while
```
t = torch.tensor((1, 2, 3), device='cuda')
torch.tensor(t)
```
would return a tensor on the device of `t`!
This behavior is niche and odd, but came up while aocsa was fixing https://github.com/pytorch/pytorch/issues/40648.
An additional side affect of this change is that the indices and values tensors given to a sparse constructor must be on the same device, or the sparse ctor must specify the dtype kwarg. The tests in test_sparse.py have been updated to reflect this behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41984
Reviewed By: ngimel
Differential Revision: D22721426
Pulled By: mruberry
fbshipit-source-id: 909645124837fcdf3d339d7db539367209eccd48
