Does spspmm operation support autograd?

Hi, you say autograd is supported for values tensors, but it seems it doesn’t work in spspmm.

Like this:

indexA = torch.tensor([[0, 0, 1, 2, 2], [1, 2, 0, 0, 1]])
valueA = torch.tensor([1, 2.0, 3, 4, 5], requires_grad=True)
indexB = torch.tensor([[0, 2], [1, 0]])
valueB = torch.tensor([2, 4.0], requires_grad=True)
indexC, valueC = torch_sparse.spspmm(indexA, valueA, indexB, valueB, 3, 3, 2)

print(valueC.requires_grad)
print(valueC.grad_fn)

And the answer is:

False
None

In my case, I want to parameterize the sparse adjacent matrix and feature matrix in GCN, so the inputs need to be both differentiable. I wonder if there’re some bugs or just the way it is.

Regards.

[changym3]

That’s the only function that does not have proper autograd support. Gradients for sparse-sparse matrix multiplication are quite difficult to obtain (since they are usually dense). I had a working, but slow implementation up to 0.4.4 release, but removed it since it wasn’t a really good implementation. If you desperately need it, feel free to try it out.

Hey! Thanks for your great work! I have installed the 0.4.4 release of torch_sparse and it totally works out in my experiments! Maybe you could add this information to the documentation. It takes me so long to figure out this no-autograd problem.

Thanks a lot again!

[rusty1s]

With PyTorch 1.12, I assume you can also try to use the sparse-matrix multiplication from PyTorch directly. PyTorch recently integrated better sparse matrix support into its library 😃