Note that in your first example both output tensors predict class1 only, so it’s expected to get the same loss value.
You can check it via:
torch.argmax(torch.tensor([[-1.,-1],[-1,1]]), 1)
> tensor([1, 1])

torch.argmax(torch.tensor([[1.,1],[-1,1]]), 1)
> tensor([1, 1])

I haven’t looked into the second experiment as the formatting is unclear, but you could rerun it with another reduction (e.g. reduction='sum'), since the default reduction='mean' would normalize the loss with the applied weights.