test eye

test/test_eye.py

 from itertools import product
 
 import pytest
+import torch
 from torch_sparse.tensor import SparseTensor
 
 from .utils import dtypes, devices
@@ -8,38 +9,44 @@ from .utils import dtypes, devices
 
 @pytest.mark.parametrize('dtype,device', product(dtypes, devices))
 def test_eye(dtype, device):
-    mat = SparseTensor.eye(3, dtype=dtype, device=device)
-    assert mat.storage.row.tolist() == [0, 1, 2]
-    assert mat.storage.rowptr.tolist() == [0, 1, 2, 3]
-    assert mat.storage.col.tolist() == [0, 1, 2]
-    assert mat.storage.value.tolist() == [1, 1, 1]
-    assert len(mat.cached_keys()) == 0
-
-    mat = SparseTensor.eye(3, dtype=dtype, device=device, has_value=False)
-    assert mat.storage.row.tolist() == [0, 1, 2]
-    assert mat.storage.rowptr.tolist() == [0, 1, 2, 3]
-    assert mat.storage.col.tolist() == [0, 1, 2]
-    assert mat.storage.value is None
-    assert len(mat.cached_keys()) == 0
-
-    mat = SparseTensor.eye(3, 4, dtype=dtype, device=device, fill_cache=True)
-    assert mat.storage.row.tolist() == [0, 1, 2]
-    assert mat.storage.rowptr.tolist() == [0, 1, 2, 3]
-    assert mat.storage.col.tolist() == [0, 1, 2]
-    assert len(mat.cached_keys()) == 5
-    assert mat.storage.rowcount.tolist() == [1, 1, 1]
-    assert mat.storage.colptr.tolist() == [0, 1, 2, 3, 3]
-    assert mat.storage.colcount.tolist() == [1, 1, 1, 0]
-    assert mat.storage.csr2csc.tolist() == [0, 1, 2]
-    assert mat.storage.csc2csr.tolist() == [0, 1, 2]
-
-    mat = SparseTensor.eye(4, 3, dtype=dtype, device=device, fill_cache=True)
-    assert mat.storage.row.tolist() == [0, 1, 2]
-    assert mat.storage.rowptr.tolist() == [0, 1, 2, 3, 3]
-    assert mat.storage.col.tolist() == [0, 1, 2]
-    assert len(mat.cached_keys()) == 5
-    assert mat.storage.rowcount.tolist() == [1, 1, 1, 0]
-    assert mat.storage.colptr.tolist() == [0, 1, 2, 3]
-    assert mat.storage.colcount.tolist() == [1, 1, 1]
-    assert mat.storage.csr2csc.tolist() == [0, 1, 2]
-    assert mat.storage.csc2csr.tolist() == [0, 1, 2]
+    options = torch.tensor(0, dtype=dtype, device=device)
+
+    mat = SparseTensor.eye(3, options=options)
+    assert mat.storage.sparse_sizes() == (3, 3)
+    assert mat.storage.row().tolist() == [0, 1, 2]
+    assert mat.storage.rowptr().tolist() == [0, 1, 2, 3]
+    assert mat.storage.col().tolist() == [0, 1, 2]
+    assert mat.storage.value().tolist() == [1, 1, 1]
+    assert mat.storage.num_cached_keys() == 0
+
+    mat = SparseTensor.eye(3, options=options, has_value=False)
+    assert mat.storage.sparse_sizes() == (3, 3)
+    assert mat.storage.row().tolist() == [0, 1, 2]
+    assert mat.storage.rowptr().tolist() == [0, 1, 2, 3]
+    assert mat.storage.col().tolist() == [0, 1, 2]
+    assert mat.storage.value() is None
+    assert mat.storage.num_cached_keys() == 0
+
+    mat = SparseTensor.eye(3, 4, options=options, fill_cache=True)
+    assert mat.storage.sparse_sizes() == (3, 4)
+    assert mat.storage.row().tolist() == [0, 1, 2]
+    assert mat.storage.rowptr().tolist() == [0, 1, 2, 3]
+    assert mat.storage.col().tolist() == [0, 1, 2]
+    assert mat.storage.num_cached_keys() == 5
+    assert mat.storage.rowcount().tolist() == [1, 1, 1]
+    assert mat.storage.colptr().tolist() == [0, 1, 2, 3, 3]
+    assert mat.storage.colcount().tolist() == [1, 1, 1, 0]
+    assert mat.storage.csr2csc().tolist() == [0, 1, 2]
+    assert mat.storage.csc2csr().tolist() == [0, 1, 2]
+
+    mat = SparseTensor.eye(4, 3, options=options, fill_cache=True)
+    assert mat.storage.sparse_sizes() == (4, 3)
+    assert mat.storage.row().tolist() == [0, 1, 2]
+    assert mat.storage.rowptr().tolist() == [0, 1, 2, 3, 3]
+    assert mat.storage.col().tolist() == [0, 1, 2]
+    assert mat.storage.num_cached_keys() == 5
+    assert mat.storage.rowcount().tolist() == [1, 1, 1, 0]
+    assert mat.storage.colptr().tolist() == [0, 1, 2, 3]
+    assert mat.storage.colcount().tolist() == [1, 1, 1]
+    assert mat.storage.csr2csc().tolist() == [0, 1, 2]
+    assert mat.storage.csc2csr().tolist() == [0, 1, 2]

torch_sparse/tensor.py

@@ -64,7 +64,7 @@ class SparseTensor(object):
 
         rowptr = torch.arange(M + 1, dtype=torch.long, device=row.device)
         if M > N:
-            rowptr[N + 1:] = M
+            rowptr[N + 1:] = N
 
         value: Optional[torch.Tensor] = None
         if has_value: