diff --git a/monai/transforms/spatial/array.py b/monai/transforms/spatial/array.py
index 8b1fb854f2..df3d3eb093 100644
--- a/monai/transforms/spatial/array.py
+++ b/monai/transforms/spatial/array.py
@@ -1065,7 +1065,7 @@ def __call__(
         """
         if grid is None:
             if spatial_size is not None:
-                grid = create_grid(spatial_size)
+                grid = create_grid(spatial_size, dtype=float)
             else:
                 raise ValueError("Incompatible values: grid=None and spatial_size=None.")
 
@@ -1084,9 +1084,7 @@ def __call__(
         else:
             affine = self.affine
 
-        if self.device not in (None, torch.device("cpu"), "cpu"):
-            grid, *_ = convert_data_type(grid, torch.Tensor, device=self.device)
-        grid, *_ = convert_data_type(grid, dtype=float)
+        grid, *_ = convert_data_type(grid, torch.Tensor, device=self.device, dtype=float)
         affine, *_ = convert_to_dst_type(affine, grid)
 
         grid = (affine @ grid.reshape((grid.shape[0], -1))).reshape([-1] + list(grid.shape[1:]))
diff --git a/monai/transforms/spatial/dictionary.py b/monai/transforms/spatial/dictionary.py
index d794e51e80..487225cb60 100644
--- a/monai/transforms/spatial/dictionary.py
+++ b/monai/transforms/spatial/dictionary.py
@@ -795,10 +795,8 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, N
         sp_size = fall_back_tuple(self.rand_affine.spatial_size, data[self.keys[0]].shape[1:])
         # change image size or do random transform
         do_resampling = self._do_transform or (sp_size != ensure_tuple(data[self.keys[0]].shape[1:]))
-
-        affine: NdarrayOrTensor = np.eye(len(sp_size) + 1, dtype=np.float64)
-        if device not in (None, torch.device("cpu"), "cpu"):
-            affine, *_ = convert_data_type(affine, torch.Tensor, device=device)
+        affine: torch.Tensor = torch.eye(len(sp_size) + 1, dtype=torch.float64, device=device)
+        # converting affine to tensor because the resampler currently only support torch backend
         grid = None
         if do_resampling:  # need to prepare grid
             grid = self.rand_affine.get_identity_grid(sp_size)
diff --git a/tests/test_rand_affined.py b/tests/test_rand_affined.py
index bec9602d62..0109175b16 100644
--- a/tests/test_rand_affined.py
+++ b/tests/test_rand_affined.py
@@ -211,6 +211,11 @@ def test_rand_affined(self, input_param, input_data, expected_val):
             expected = expected_val[key] if isinstance(expected_val, dict) else expected_val
             assert_allclose(result, expected, rtol=1e-4, atol=1e-4)
 
+        g.set_random_state(4)
+        res = g(input_data)
+        # affine should be tensor because the resampler only supports pytorch backend
+        self.assertTrue(isinstance(res["img_transforms"][0]["extra_info"]["affine"], torch.Tensor))
+
     def test_ill_cache(self):
         with self.assertWarns(UserWarning):
             # spatial size is None