sparse matrix ops request floating point data types

README.md

@@ -163,17 +163,17 @@ from torch_sparse import spmm
 
 index = torch.tensor([[0, 0, 1, 2, 2],
                       [0, 2, 1, 0, 1]])
-value = torch.tensor([1, 2, 4, 1, 3])
-matrix = torch.tensor([[1, 4], [2, 5], [3, 6]])
+value = torch.tensor([1, 2, 4, 1, 3], dtype=torch.float)
+matrix = torch.tensor([[1, 4], [2, 5], [3, 6]], dtype=torch.float)
 
 out = spmm(index, value, 3, matrix)
 ```
 
 ```
 print(out)
-tensor([[7, 16],
-        [8, 20],
-        [7, 19]])
+tensor([[7.0, 16.0],
+        [8.0, 20.0],
+        [7.0, 19.0]])
 ```
 
 ## Sparse Sparse Matrix Multiplication
@@ -206,10 +206,10 @@ Both input sparse matrices need to be **coalesced**.
 from torch_sparse import spspmm
 
 indexA = torch.tensor([[0, 0, 1, 2, 2], [1, 2, 0, 0, 1]])
-valueA = torch.tensor([1, 2, 3, 4, 5])
+valueA = torch.tensor([1, 2, 3, 4, 5], dtype=torch.float)
 
 indexB = torch.tensor([[0, 2], [1, 0]])
-valueB = torch.tensor([2, 4])
+valueB = torch.tensor([2, 4], dtype=torch.float)
 
 indexC, valueC = spspmm(indexA, valueA, indexB, valueB, 3, 3, 2)
 ```
@@ -219,7 +219,7 @@ print(index)
 tensor([[0, 1, 2],
         [0, 1, 1]])
 print(value)
-tensor([8, 6, 8])
+tensor([8.0, 6.0, 8.0])
 ```
 
 ## Running tests