typo

torch_sparse/__init__.py

-from .matmul import sparse_sparse_matmul
+from .matmul import spspmm
 
 __all__ = [
-    'sparse_sparse_matmul',
+    'spspmm',
 ]

torch_sparse/matmul.py

@@ -3,11 +3,11 @@ from torch import from_numpy
 from scipy.sparse import coo_matrix
 
 
-class SparseSparseMatmul(torch.autograd.Function):
+class SpSpMM(torch.autograd.Function):
     @staticmethod
     def forward(ctx, matrix1, matrix2):
         ctx.save_for_backawrd(matrix1, matrix2)
-        return matmul(matrix1, matrix2)
+        return mm(matrix1, matrix2)
 
     @staticmethod
     def backward(ctx, grad_out):
@@ -15,25 +15,25 @@ class SparseSparseMatmul(torch.autograd.Function):
         grad_matrix1 = grad_matrix2 = None
 
         if ctx.needs_input_grad[0]:
-            grad_matrix1 = matmul(grad_out, matrix2.t())
+            grad_matrix1 = mm(grad_out, matrix2.t())
 
         if ctx.needs_input_grad[0]:
-            grad_matrix2 = matmul(matrix1.t(), grad_out)
+            grad_matrix2 = mm(matrix1.t(), grad_out)
 
         return grad_matrix1, grad_matrix2
 
 
-sparse_sparse_matmul = SparseSparseMatmul.apply
+spspmm = SpSpMM.apply
 
 
-def matmul(A, B):
+def mm(A, B):
     if A[0].is_cuda:
         pass
     else:
-        return matmul_cpu(A, B)
+        return mm_cpu(A, B)
 
 
-def matmul_cpu(A, B):
+def mm_cpu(A, B):
     A, B, = to_csr(A), to_csr(B)
     C = A.dot(B).tocoo()
     row, col, value = from_numpy(C.row), from_numpy(C.col), from_numpy(C.data)