diff --git a/test/utils/test_augmentation.py b/test/utils/test_augmentation.py
index 249a1c578ac4..3988c8431e34 100644
--- a/test/utils/test_augmentation.py
+++ b/test/utils/test_augmentation.py
@@ -54,20 +54,21 @@ def test_mask_feature():
 
     torch.manual_seed(7)
     out = mask_feature(x, mode='all')
-    assert out[0].tolist() == [[1.0, 0.0, 0.0, 4.0], [5.0, 6.0, 7.0, 0.0],
-                               [9.0, 10.0, 0.0, 12.0]]
+    assert out[0].tolist() == [[0.0, 2.0, 3.0, 0.0], [5.0, 0.0, 0.0, 8.0],
+                               [0.0, 0.0, 0.0, 0.0]]
 
-    assert out[1].tolist() == [[True, False, False, True],
-                               [True, True, True, False],
-                               [True, True, False, True]]
+    assert out[1].tolist() == [[False, True, True, False],
+                               [True, False, False, True],
+                               [False, False, False, False]]
 
     torch.manual_seed(7)
     out = mask_feature(x, mode='all', fill_value=-1)
-    assert out[0].tolist() == [[1.0, -1., -1., 4.0], [5.0, 6.0, 7.0, -1.],
-                               [9.0, 10.0, -1., 12.0]]
-    assert out[1].tolist() == [[True, False, False, True],
-                               [True, True, True, False],
-                               [True, True, False, True]]
+    assert out[0].tolist() == [[-1.0, 2.0, 3.0, -1.0], [5.0, -1.0, -1.0, 8.0],
+                               [-1.0, -1.0, -1.0, -1.0]]
+
+    assert out[1].tolist() == [[False, True, True, False],
+                               [True, False, False, True],
+                               [False, False, False, False]]
 
 
 def test_add_random_edge():
diff --git a/torch_geometric/utils/augmentation.py b/torch_geometric/utils/augmentation.py
index 16f0f79ce62e..f1df3028dcc3 100644
--- a/torch_geometric/utils/augmentation.py
+++ b/torch_geometric/utils/augmentation.py
@@ -143,7 +143,7 @@ def mask_feature(x: Tensor, p: float = 0.5, mode: str = 'col',
         mask = torch.rand(x.size(1), device=x.device) >= p
         mask = mask.view(1, -1)
     else:
-        mask = x.bernoulli(1 - p).to(torch.bool)
+        mask = torch.randn_like(x) >= p
 
     x = x.masked_fill(~mask, fill_value)
     return x, mask