test_sparse_tensor_spspmm

test/test_spspmm.py

@@ -2,7 +2,7 @@ from itertools import product
 
 import pytest
 import torch
-from torch_sparse import spspmm
+from torch_sparse import spspmm, SparseTensor
 
 from .utils import grad_dtypes, devices, tensor
 
@@ -17,3 +17,32 @@ def test_spspmm(dtype, device):
     indexC, valueC = spspmm(indexA, valueA, indexB, valueB, 3, 3, 2)
     assert indexC.tolist() == [[0, 1, 2], [0, 1, 1]]
     assert valueC.tolist() == [8, 6, 8]
+
+
+@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
+def test_sparse_tensor_spspmm(dtype, device):
+    x = SparseTensor(
+        row=torch.tensor(
+            [0, 1, 1, 1, 2, 3, 4, 5, 5, 6, 6, 7, 7, 7, 8, 8, 9, 9],
+            device=device
+        ),
+        col=torch.tensor(
+            [0, 5, 10, 15, 1, 2, 3, 7, 13, 6, 9, 5, 10, 15, 11, 14, 5, 15],
+            device=device
+        ),
+        value=torch.tensor(
+            [1, 3**-0.5, 3**-0.5, 3**-0.5, 1, 1, 1, -2**-0.5, -2**-0.5,
+             -2**-0.5, -2**-0.5, 6**-0.5, -6**0.5 / 3, 6**-0.5, -2**-0.5,
+             -2**-0.5, 2**-0.5, -2**-0.5],
+            dtype=dtype, device=device
+        ),
+    )
+
+    i0 = torch.eye(10, dtype=dtype, device=device)
+
+    i1 = x @ x.to_dense().t()
+    assert torch.allclose(i0, i1)
+
+    i1 = x @ x.t()
+    i1 = i1.to_dense()
+    assert torch.allclose(i0, i1)