chore: import upstream snapshot with attribution

tests/python/pytorch/sparse/test_unary_op.py

@@ -0,0 +1,40 @@
+import sys
+
+import backend as F
+import torch
+
+from dgl.sparse import diag, spmatrix
+
+
+def test_neg():
+    ctx = F.ctx()
+    row = torch.tensor([1, 1, 3]).to(ctx)
+    col = torch.tensor([1, 2, 3]).to(ctx)
+    val = torch.tensor([1.0, 1.0, 2.0]).to(ctx)
+    A = spmatrix(torch.stack([row, col]), val)
+    neg_A = -A
+    assert A.shape == neg_A.shape
+    assert A.nnz == neg_A.nnz
+    assert torch.allclose(-A.val, neg_A.val)
+    assert torch.allclose(torch.stack(A.coo()), torch.stack(neg_A.coo()))
+    assert A.val.device == neg_A.val.device
+
+
+def test_diag_neg():
+    ctx = F.ctx()
+    val = torch.arange(3).float().to(ctx)
+    D = diag(val)
+    neg_D = -D
+    assert D.shape == neg_D.shape
+    assert torch.allclose(-D.val, neg_D.val)
+    assert D.val.device == neg_D.val.device
+
+
+def test_diag_inv():
+    ctx = F.ctx()
+    val = torch.arange(1, 4).float().to(ctx)
+    D = diag(val)
+    inv_D = D.inv()
+    assert D.shape == inv_D.shape
+    assert torch.allclose(1.0 / D.val, inv_D.val)
+    assert D.val.device == inv_D.val.device