clean up test

test/test_spspmm.py

@@ -2,7 +2,7 @@ from itertools import product
 
 import pytest
 import torch
-from torch_sparse import spspmm, SparseTensor, transpose
+from torch_sparse import spspmm, SparseTensor
 
 from .utils import grad_dtypes, devices, tensor
 
@@ -19,62 +19,27 @@ def test_spspmm(dtype, device):
     assert valueC.tolist() == [8, 6, 8]
 
 
-@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
-def test_spspmm_2(dtype, device):
-    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
-    )
-    index = torch.stack([row, col])
-
-    m = value.new_zeros(10, 16)
-    m[index[0], index[1]] = value
-
-    index_t, value_t = transpose(index, value, 10, 16)
-
-    index, value = spspmm(index, value, index_t, value_t, 10, 16, 10)
-
-    mask = value.abs() > 1e-4
-    index, value = index[:, mask], value[mask]
-
-    assert index.tolist() == [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]
-    assert value.tolist() == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-
-
 @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
-        ),
+            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
-        ),
+            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)
+    expected = torch.eye(10, dtype=dtype, device=device)
 
-    i1 = x @ x.to_dense().t()
-    assert torch.allclose(i0, i1)
+    out = x @ x.to_dense().t()
+    assert torch.allclose(out, expected, atol=1e-7)
 
-    i1 = x @ x.t()
-    i1 = i1.to_dense()
-    assert torch.allclose(i0, i1)
+    out = x @ x.t()
+    out = out.to_dense()
+    assert torch.allclose(out, expected, atol=1e-7)