Does spspmm operation support autograd?

[changym3]

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.

[rusty1s]

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.

[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!

[LuciusMos]

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!

Thank you so much for your question raising! It really troubles me for almost a week!

[rusty1s]

Sorry for the inconveniences. I have plans to add backward support for spspmm back in ASAP, see https://github.com/rusty1s/pytorch_geometric/issues/1465.

[jlevy44]

Do you have any updates on autograd support?

[jlevy44]

I'm parameterizing the weights of a sparse matrix to treat it as a locally connected network for a sparsely connected MLP implementation. Could I still run a backward pass to update these weights after calling matmul between this sparse matrix and a dense input?

[jlevy44]

Nevermind, already seeing some nice implementations out there! https://pypi.org/project/sparselinear/ https://stackoverflow.com/questions/63893602/neural-network-layer-without-all-connections

[JRD971000]

Does spspmm still lack autograd support?

[github-actions[bot]]

This issue had no activity for 6 months. It will be closed in 2 weeks unless there is some new activity. Is this issue already resolved?

[jaynanavati-az]

Does spspmm still lack autograd support? @rusty1s .. it seems to use SparseTensor, which is supposed to be fully supported by autograd?

[rusty1s]

Sadly yes :(

[jaynanavati-az]

Is there an alternative? It is difficult to get earlier versions of torch sparse that have this to work on newer cuda versions.. :(

[rusty1s]

There isn‘t a workaround except for installing an earlier version. If you are interested, we can try to bring it back with your help. WDYT?

[jaynanavati-az]

@rusty1s sounds good, why don't we start with putting back your existing implementation? is it not better than having nothing?

[rusty1s]

Here's the roadmap in order to achieve this:

• [ ] Add the CPU spspmm_bw code from https://github.com/rusty1s/pytorch_sparse/blob/0.4.4/cpu/spspmm.cpp#L19-L63
• [ ] Add the CUDA spspmm_bw code from https://github.com/rusty1s/pytorch_sparse/blob/0.4.4/cuda/spspmm_kernel.cu#L115-L166
• [ ] Register the autograd function as in https://github.com/rusty1s/pytorch_sparse/blob/master/csrc/spmm.cpp#L55-L86
• [ ] Add a basic backward test as in https://github.com/rusty1s/pytorch_sparse/blob/0.4.4/test/test_spspmm.py#L29-L35

[jaynanavati-az]

Is there an alternative? It is difficult to get earlier versions of torch sparse that have this to work on newer cuda versions.. :(

---

From: Matthias Fey @.> Sent: 14 April 2022 22:05 To: rusty1s/pytorch_sparse @.> Cc: Nanavati, Jay @.>; Comment @.> Subject: Re: [rusty1s/pytorch_sparse] Does spspmm operation support autograd? (#45)

Sadly yes :(

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[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 :)