Comments (4)
No that's not possible. I suggest do stack your matrices diagonal and perform classic two-dimensional spspmm
.
from pytorch_sparse.
Thank you for your prompt response.
Could you please provide further elaboration on the details of your recommendations?
from pytorch_sparse.
If you stack the matrices diagonally, they have shape [B*M, B*N] @ [B*N, B*F]
, which results in an output of shape [B*M, B*F]
, which you can then "reshape" back to [B, M, F]
. Implementation-wise, this would be
from torch_sparse import cat
mats1 = [SparseTensor(...), SparseTensor(...), ...]
mats2 = [SparseTensor(...), SparseTensor(...), ...]
mat1 = cat(mats1, dim=(0, 1))
mat2 = cat(mats2, dim=(0, 1))
out = mat1 @ mat2
from pytorch_sparse.
Thanks
from pytorch_sparse.
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from pytorch_sparse.