diff --git a/setup.py b/setup.py
index 70197c209f..bddf49e464 100644
--- a/setup.py
+++ b/setup.py
@@ -129,7 +129,7 @@ _deps = [
     "keras-nlp>=0.3.1",
     "librosa",
     "nltk",
-    "natten>=0.14.5",
+    "natten>=0.14.6",
     "numpy>=1.17",
     "onnxconverter-common",
     "onnxruntime-tools>=1.4.2",
diff --git a/src/transformers/dependency_versions_table.py b/src/transformers/dependency_versions_table.py
index aa23c1ad06..c0cacd7596 100644
--- a/src/transformers/dependency_versions_table.py
+++ b/src/transformers/dependency_versions_table.py
@@ -35,7 +35,7 @@ deps = {
     "keras-nlp": "keras-nlp>=0.3.1",
     "librosa": "librosa",
     "nltk": "nltk",
-    "natten": "natten>=0.14.5",
+    "natten": "natten>=0.14.6",
     "numpy": "numpy>=1.17",
     "onnxconverter-common": "onnxconverter-common",
     "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
diff --git a/src/transformers/models/dinat/modeling_dinat.py b/src/transformers/models/dinat/modeling_dinat.py
index efeb68846f..2cacab8ac9 100644
--- a/src/transformers/models/dinat/modeling_dinat.py
+++ b/src/transformers/models/dinat/modeling_dinat.py
@@ -356,7 +356,7 @@ class NeighborhoodAttention(nn.Module):
         # seem a bit unusual, but is taken from the original Transformer paper.
         attention_probs = self.dropout(attention_probs)
 
-        context_layer = natten2dav(attention_probs, value_layer, self.dilation)
+        context_layer = natten2dav(attention_probs, value_layer, self.kernel_size, self.dilation)
         context_layer = context_layer.permute(0, 2, 3, 1, 4).contiguous()
         new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
         context_layer = context_layer.view(new_context_layer_shape)
diff --git a/src/transformers/models/nat/modeling_nat.py b/src/transformers/models/nat/modeling_nat.py
index 3a93b81e4b..dfe801d199 100644
--- a/src/transformers/models/nat/modeling_nat.py
+++ b/src/transformers/models/nat/modeling_nat.py
@@ -348,7 +348,7 @@ class NeighborhoodAttention(nn.Module):
         # seem a bit unusual, but is taken from the original Transformer paper.
         attention_probs = self.dropout(attention_probs)
 
-        context_layer = natten2dav(attention_probs, value_layer, 1)
+        context_layer = natten2dav(attention_probs, value_layer, self.kernel_size, 1)
         context_layer = context_layer.permute(0, 2, 3, 1, 4).contiguous()
         new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
         context_layer = context_layer.view(new_context_layer_shape)