[T5] Add 3D attention mask to T5 model (2) (#9643) (#11197)

* Add 3D attention mask to T5 model (#9643)

Added code for 3D attention mask in T5 model. Similar to BERT model.

* Add test for 3D attention mask

Added test for 3D attention mask: test_decoder_model_past_with_3d_attn_mask()
3D attention mask of the shape [Batch_size, Seq_length, Seq_length] both for
attention mask and decoder attention mask. Test is passing.

src/transformers/models/t5/modeling_t5.py

@@ -914,7 +914,13 @@ class T5Stack(T5PreTrainedModel):
         # ourselves in which case we just need to make it broadcastable to all heads.
         extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, inputs_embeds.device)
 
-        if self.is_decoder and encoder_attention_mask is not None:
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.is_decoder and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
             encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
         else:
             encoder_extended_attention_mask = None

tests/test_modeling_t5.py

@@ -530,6 +530,34 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
 
+    def test_decoder_model_past_with_3d_attn_mask(self):
+        (
+            config,
+            input_ids,
+            decoder_input_ids,
+            attention_mask,
+            decoder_attention_mask,
+            lm_labels,
+        ) = self.model_tester.prepare_config_and_inputs()
+
+        attention_mask = ids_tensor(
+            [self.model_tester.batch_size, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length],
+            vocab_size=2,
+        )
+        decoder_attention_mask = ids_tensor(
+            [self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length],
+            vocab_size=2,
+        )
+
+        self.model_tester.create_and_check_decoder_model_attention_mask_past(
+            config,
+            input_ids,
+            decoder_input_ids,
+            attention_mask,
+            decoder_attention_mask,
+            lm_labels,
+        )
+
     def test_decoder_model_past_with_large_inputs(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)