[Sparse] Fix SpMM and SDDMM test precision problem (#5060)

tests/pytorch/mock_sparse2/test_matmul.py

@@ -44,9 +44,10 @@ def test_spmm(create_func, shape, nnz, out_dim):
     if out_dim is None:
         torch_sparse_result = torch_sparse_result.view(-1)
     torch_sparse_result.backward(grad)
-    assert torch.allclose(sparse_result, torch_sparse_result)
-    assert torch.allclose(X.grad, XX.grad)
+    assert torch.allclose(sparse_result, torch_sparse_result, atol=1e-05)
+    assert torch.allclose(X.grad, XX.grad, atol=1e-05)
     assert torch.allclose(
         adj.grad.coalesce().to_dense(),
         sparse_matrix_to_dense(val_like(A, A.val.grad)),
+        atol=1e-05,
     )

tests/pytorch/mock_sparse2/test_sddmm.py

@@ -44,7 +44,7 @@ def test_sddmm(create_func, shape, nnz, hidden):
     dense_val = dense_result[row, col] * A_val_clone
     dense_val.backward(grad)
 
-    assert torch.allclose(dense_val, sparse_result.val)
-    assert torch.allclose(dense_C.grad, C.grad)
-    assert torch.allclose(dense_B.grad, B.grad)
-    assert torch.allclose(A_val_clone.grad, A.val.grad)
+    assert torch.allclose(dense_val, sparse_result.val, atol=1e-05)
+    assert torch.allclose(dense_C.grad, C.grad, atol=1e-05)
+    assert torch.allclose(dense_B.grad, B.grad, atol=1e-05)
+    assert torch.allclose(A_val_clone.grad, A.val.grad, atol=1e-05)