The same code with and without the utilization of a sparse tensor gives an error:

The following is a working example of the code without sparse tensor:

a = torch.randn(2, 3, 3).requires_grad_(True)
print(a)

b = torch.randn(3, 1, requires_grad=True)
print(b)

y = torch.matmul(a, b)
print(y)

y.sum().backward()
print(a.grad)

This is the same example code with a sparse tensor utilization:

a = torch.randn(2, 3, 3).to_sparse().requires_grad_(True)
print(a)

b = torch.randn(3, 1, requires_grad=True)
print(b)

y = torch.matmul(a, b)
print(y)

y.sum().backward()
print(a.grad)

The code with the sparse variant gives the following error:

NotImplementedError                       Traceback (most recent call last)
 Cella 22 in <cell line: 7>()
      [4] b = torch.randn(3, 1, requires_grad=True)
      [5] print(b)
----> [7] y = torch.matmul(a, b)
      [8] print(y)
     [10] y.sum().backward()

NotImplementedError: Tensors of type SparseTensorImpl do not have is_contiguous

I'm not very confident that I am not wrong, but I think that it is better if matmul for dense and sparse tensor follows the same behaviour.
If I do not understand something, how can I replicate the same behaviour in the sparse case?

Thank you!

Versions

Collecting environment information...
PyTorch version: 1.11.0+cu113
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Python version: 3.9.12 (tags/v3.9.12:b28265d, Mar 23 2022, 23:52:46) [MSC v.1929 64 bit (AMD64)] (64-bit runtime)
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cc @nikitaved @pearu @cpuhrsch @amjames @bhosmer