Formatted test_indexing (#2688)

* added tensor.clone() for more correct cloning behavior

* some work and randint issue

* formatted

* final cleanups

* oops, bug fix

test/test_ops.py

@@ -666,11 +666,13 @@ class TestOps(unittest.TestCase):
 
   def test_slice_errors(self):
     a = Tensor.ones(4, 3)
+    b = Tensor(2)
     with self.assertRaises(IndexError): a[1, 77, 77, 77] # IndexError: (finds too many indices before the out of bounds)
     with self.assertRaises(IndexError): a[1, 77] # IndexError: (out of bounds).
     with self.assertRaises(IndexError): a[1, -77]
     with self.assertRaises(IndexError): a[..., ...] # IndexError: only single ellipsis
     with self.assertRaises(ValueError): a[::0, 1] # no 0 strides
+    with self.assertRaises(IndexError): b[:] # slice cannot be applied to a 0-dim tensor
 
   def test_slice_ellipsis(self):
     helper_test_op([(3,3,3,3)], lambda x: x[..., 0], lambda x: x[..., 0])
@@ -1312,8 +1314,7 @@ class TestOps(unittest.TestCase):
     # TODO: currently we do not support tensor indexing for list of list tensor
     # ex: torch.tensor([1,2])[[[[torch.tensor(1)]]]] -> tensor([[2]])
     # currently we return ValueError: setting an array element with a sequence.
-    # E   TypeError: only integer tensors of a single element can be converted to an index
-
+    # TypeError: only integer tensors of a single element can be converted to an index
 
   def test_gather(self):
     # indices cannot have gradient

tinygrad/tensor.py

@@ -313,8 +313,8 @@ class Tensor:
     # 1. indices normalization and validation
     # treat internal tuples and lists as Tensors and standardize indices to list type
     if isinstance(indices, (tuple, list)):
-      if isinstance(indices, list) and all(isinstance(i, int) for i in indices): indices = [Tensor(indices)] # special case <indices: List[int]>, a lil ugly
-      else: indices = [Tensor(list(i)) if isinstance(i, (tuple, list)) else i for i in indices]
+      if isinstance(indices, list) and all(isinstance(i, int) for i in indices): indices = [Tensor(indices, dtype=dtypes.int32, requires_grad=False, device=self.device)] # special case <indices: List[int]>, a lil ugly
+      else: indices = [Tensor(list(i), dtype=dtypes.int32, requires_grad=False, device=self.device) if isinstance(i, (tuple, list)) else i for i in indices]
     else: indices = [indices]
 
     # filter ellipsis and fill with slice(None) or fill rest of indices with slice(None)
@@ -334,6 +334,7 @@ class Tensor:
     for dim,i in enumerate(indices_filtered): type_dim[type(i)].append(dim)
 
     # validation! raise Errors
+    if slice in type_dim and self.ndim == 0: raise IndexError("slice cannot be applied to a 0-dim tensor.")
     if len(ellipsis_idx) > 1: raise IndexError("an index can only have a single ellipsis ('...')")
     if float in type_dim: raise IndexError("float type is not valid index")
     if any(isinstance(i, slice) and i.step == 0 for i in indices): raise ValueError('slice step cannot be 0')