Torch: Investigate if `set_default_device/with_device` is a deal-breaker

[ricardoV94]

Description

The easiest way to give PyTensor users global (not fine-grained) control over CPU/GPU for the PyTorch backend would be the set_default_device/with_device. However, this may bee too slow, according to: https://github.com/pytorch/pytorch/issues/92701

We should benchmark to see if it is a problem. If yes, we may want to use a PyTensor config flag to get the same control without the PyTorch overhead.

[aseyboldt]

It does seem to have more overhead than I'd like, but I don't think this sounds terrible for real functions.

This microbenchmark slows down from 8ms to 11ms on my machine:

%%timeit
# Explicit device
a = torch.zeros(3, device="cuda")
for _ in range(1000):
    a = a + 1
out = a.cpu().numpy()

%%timeit
# Using default device
with torch.device("cuda"):
    a = torch.zeros(3)
    for _ in range(1000):
        a = a + 1
    out = a.cpu().numpy()