Multi device bugfix

[asglover]

This is a PR to harden multi device behavior.

• It adds testing to make sure all tensors are on the same device
• it adds usage of "device guards". This is how PyTorch signals to apis which do not explicitly take device, which device to use.
• It also adjust how BLAS handles work. BLAS handles must differ across devices. PyTorch keeps a pool of BLAS handles. This PR switches OEQ to using PyTorch's BLAS handles. Just a note that this current design does not set the BLAS precision mode which means it inherits from pytorch settings. I think that's a reasonable design because people can control precision with the idiomatic pytorch methods. If you think this calculation should never be in TF32, then I can add a manual setting command.
• It adds stream tests to that should fail before the changes from the PR come in on multi device. And should be fixed later.

[asglover]

This is a draft PR for now because I'm making these changes based on pytorch documentation and recommendations for multi device. I need to get access to multi device machines in order to test properly. The lack of AMD machine availability for testing is very annoying. I don't have any access through work (currently). And AMD didn't respond to my application for developer grants for this project.

[asglover]

@vbharadwaj-bk I tested this on a multi device host and verified the problems the branch is trying to address exist in main and verified that using the device guard works as intended. There are a few aspects to this change.

• having the streams come from the correct stream for the device.
  - This happens at the C++ level by using the torch APIs defined for the purpose.
• using the pytorch cuBLAS handles.
  • BLAS handles are one per device, and the easiest way to handle this is to reuse the handles that pytorch hands out to other pytorch ops for calling blas
  • from rocBLAS to hipBLAS because torch uses hipBLAS
• It makes the workspace be a "buffer" associated with a module, this will make the buffer follow the module around as you send it from device to device. It also selects "persist" = false, so that the the workspace is not serialized during export. Which is nice because they are just zeros.
• I added an all on the same device check to the C++ level. It's different than the existing check tensor, I can unify the mechanisms or leave it separate.
• Adds a check that CPU pointers are on the right device.

I have not tested this on a AMD machine. I cannot get access. I need help testing this aspect of the change.