fix Q.to on multi-gpu gptq when proceeding fast and has many experts and gpus

[avtc]

This fixes Q.to on multi-gpu gptq when proceeding fast and has many experts and gpus (for example with mock_quantization=True on 8 gpus on GLM-4.5-Air)

The error originally thrown:
torch.AcceleratorError: CUDA error: invalid argument
For me it throws after first layer with experts finished processing.

Retry of Q.to fixes it.

Original code Q.type_as not only changes the type but also moves the tensor to weight.data.device
The logic of moving to device preserved.
And it seems that wq = wq.to(device=DEVICE_0, non_blocking=False) is redundant so was removed.

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torch_empty_cache is redundant as well, as retry works without it
I have tried torch.sync and torch.accelerator.synchronize but only retry with try catch works

[Qubitium]

@avtc Good catch! Looks like you found a torch/cuda sync bug ,or, gtqmode is not safely locking the cuda ops in our GIL=0 setup. I have a feeling it's the latter. Merge for now and log this for future TODO.