Pytorch doesn’t have a sparse softmax function. However, you could work with the sparse matrix’s indices and values to do this. Let tensor be your sparse matrix. There are two main things you want to do:

Get each row of the (sparse) tensor
Apply softmax to each row

For 1), you can pick out the elements of tensor._indices() that have the same row by looking.
For 2), after you collect the relevant values of  tensor._values() for each row, you can just call softmax on it.