diff --git a/src/transformers/modeling_bart.py b/src/transformers/modeling_bart.py
index af32b954005e65..a029828763e438 100644
--- a/src/transformers/modeling_bart.py
+++ b/src/transformers/modeling_bart.py
@@ -304,7 +304,7 @@ def forward(self, input_ids, attention_mask=None, output_attentions=False):
             if self.training and (dropout_probability < self.layerdrop):  # skip the layer
                 attn = None
             else:
-                x, attn = encoder_layer(x, attention_mask)
+                x, attn = encoder_layer(x, attention_mask, output_attentions=output_attentions)
 
             if output_attentions:
                 all_attentions.append(attn)
@@ -830,6 +830,7 @@ def forward(
             decoder_padding_mask, causal_mask = None, None
 
         assert decoder_input_ids is not None
+
         if encoder_outputs is None:
             encoder_outputs = self.encoder(
                 input_ids=input_ids, attention_mask=attention_mask, output_attentions=output_attentions,
@@ -843,8 +844,10 @@ def forward(
             decoder_padding_mask,
             decoder_causal_mask=causal_mask,
             decoder_cached_states=decoder_cached_states,
+            output_attentions=output_attentions,
             use_cache=use_cache,
         )
+
         # Attention and hidden_states will be [] or None if they aren't needed
         decoder_outputs: Tuple = _filter_out_falsey_values(decoder_outputs)
         assert isinstance(decoder_outputs[0], torch.Tensor)
diff --git a/src/transformers/modeling_t5.py b/src/transformers/modeling_t5.py
index 1a8940b9c48e3c..b41cee48c38e21 100644
--- a/src/transformers/modeling_t5.py
+++ b/src/transformers/modeling_t5.py
@@ -539,6 +539,7 @@ def forward(
                 past_key_value_state=cross_attn_past_key_value_state,
                 query_length=query_length,
                 use_cache=use_cache,
+                output_attentions=output_attentions,
             )
             hidden_states = cross_attention_outputs[0]
             # Combine self attn and cross attn key value states
@@ -966,6 +967,7 @@ def forward(
             encoder_attention_mask=attention_mask,
             head_mask=head_mask,
             use_cache=use_cache,
+            output_attentions=output_attentions,
         )
 
         if use_cache is True:
@@ -1117,6 +1119,7 @@ def forward(
             encoder_attention_mask=attention_mask,
             head_mask=head_mask,
             use_cache=use_cache,
+            output_attentions=output_attentions,
         )
 
         # insert decoder past at right place
diff --git a/src/transformers/modeling_xlnet.py b/src/transformers/modeling_xlnet.py
index 839388510bec93..f472e886ab839c 100644
--- a/src/transformers/modeling_xlnet.py
+++ b/src/transformers/modeling_xlnet.py
@@ -374,7 +374,14 @@ def forward(
             if target_mapping is not None:
                 q_head_g = torch.einsum("mbnd,mlb->lbnd", q_head_g, target_mapping)
                 attn_vec_g = self.rel_attn_core(
-                    q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_mask=head_mask
+                    q_head_g,
+                    k_head_h,
+                    v_head_h,
+                    k_head_r,
+                    seg_mat=seg_mat,
+                    attn_mask=attn_mask_g,
+                    head_mask=head_mask,
+                    output_attentions=output_attentions,
                 )
 
                 if output_attentions:
diff --git a/tests/test_modeling_xlnet.py b/tests/test_modeling_xlnet.py
index 99696ca5323db7..1e533939169fcc 100644
--- a/tests/test_modeling_xlnet.py
+++ b/tests/test_modeling_xlnet.py
@@ -238,7 +238,7 @@ def create_and_check_xlnet_base_model_with_att_output(
             model.to(torch_device)
             model.eval()
 
-            _, _, attentions = model(input_ids_1, target_mapping=target_mapping)
+            _, _, attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)
 
             self.parent.assertEqual(len(attentions), config.n_layer)
             self.parent.assertIsInstance(attentions[0], tuple)