diff --git a/src/transformers/models/bert/modeling_bert.py b/src/transformers/models/bert/modeling_bert.py
index 850e93ca59fb..93d6d469b512 100755
--- a/src/transformers/models/bert/modeling_bert.py
+++ b/src/transformers/models/bert/modeling_bert.py
@@ -908,7 +908,7 @@ class BertForPreTrainingOutput(ModelOutput):
             [`PreTrainedTokenizer.__call__`] for details.
 
             [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+        attention_mask (`torch.FloatTensor` of shape `({0})`or `(batch_size, sequence_length, target_length)`, *optional*):
             Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
 
             - 1 for tokens that are **not masked**,
@@ -1023,7 +1023,7 @@ def forward(
         encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
             Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
             the model is configured as a decoder.
-        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, target_length)`, *optional*):
             Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
             the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
 
@@ -1093,7 +1093,7 @@ def forward(
         )
 
         # Expand the attention mask
-        if use_sdpa_attention_masks:
+        if use_sdpa_attention_masks and attention_mask.dim() == 2:
             # Expand the attention mask for SDPA.
             # [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
             if self.config.is_decoder:
@@ -1120,7 +1120,7 @@ def forward(
             if encoder_attention_mask is None:
                 encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
 
-            if use_sdpa_attention_masks:
+            if use_sdpa_attention_masks and encoder_attention_mask.dim() == 2:
                 # Expand the attention mask for SDPA.
                 # [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
                 encoder_extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
diff --git a/tests/models/bert/test_modeling_bert.py b/tests/models/bert/test_modeling_bert.py
index 6ae9f6c279de..ac83011eeefe 100644
--- a/tests/models/bert/test_modeling_bert.py
+++ b/tests/models/bert/test_modeling_bert.py
@@ -498,6 +498,14 @@ def test_model_various_embeddings(self):
             config_and_inputs[0].position_embedding_type = type
             self.model_tester.create_and_check_model(*config_and_inputs)
 
+    def test_model_3d_mask_shapes(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        # manipulate input_mask
+        config_and_inputs = list(config_and_inputs)
+        batch_size, seq_length = config_and_inputs[3].shape
+        config_and_inputs[3] = random_attention_mask([batch_size, seq_length, seq_length])
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
     def test_model_as_decoder(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
@@ -530,6 +538,36 @@ def test_model_as_decoder_with_default_input_mask(self):
             encoder_attention_mask,
         )
 
+    def test_model_as_decoder_with_3d_input_mask(self):
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        ) = self.model_tester.prepare_config_and_inputs_for_decoder()
+
+        batch_size, seq_length = input_mask.shape
+        input_mask = random_attention_mask([batch_size, seq_length, seq_length])
+        batch_size, seq_length = encoder_attention_mask.shape
+        encoder_attention_mask = random_attention_mask([batch_size, seq_length, seq_length])
+
+        self.model_tester.create_and_check_model_as_decoder(
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
     def test_for_causal_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)