Can I use pytorch .backward function without having created the input forward tensors first?
I have been trying to understand RNNs better and am creating an RNN from scratch myself using numpy.  I am at the point where I have calculated a Loss but it was suggested to me that rather than do the gradient descent and weight matrix updates myself, I use pytorch .backward function.  I started to read some of the documentation and posts here about how it works and it seems like it will calculate the gradients where a torch tensor has requires_grad=True in the function call.
So it seems that unless create a torch tensor, I am not able to use the .backward.  When I try to do this on the loss scalar, I get a 'numpy.float64' object has no attribute 'backward' error.  I just wanted to confirm.  Thank you!

Yes, this will only work on PyTorch Tensors.
If the tensors are on CPU, they are basically numpy arrays wrapped into PyTorch Tensors API (i.e., running .numpy() on such a tensor returns exactly the data, it can modified etc.)
