Fix usage of head masks by PT encoder-decoder models' generate() function (#11621)

* Add missing head masking for generate() function

* Add head_mask, decoder_head_mask and cross_attn_head_mask
into prepare_inputs_for_generation for generate() function
for multiple encoder-decoder models.

* Add test_genereate_with_head_masking

* [WIP] Update the new test and handle special cases

* make style

* Omit ProphetNet test so far

* make fix-copies

src/transformers/generation_utils.py

@@ -409,7 +409,9 @@ class GenerationMixin:
             # retrieve encoder hidden states
             encoder = self.get_encoder()
             encoder_kwargs = {
-                argument: value for argument, value in model_kwargs.items() if not argument.startswith("decoder_")
+                argument: value
+                for argument, value in model_kwargs.items()
+                if not (argument.startswith("decoder_") or argument.startswith("cross_attn"))
             }
             model_kwargs["encoder_outputs"]: ModelOutput = encoder(input_ids, return_dict=True, **encoder_kwargs)
         return model_kwargs

src/transformers/models/bart/modeling_bart.py

@@ -1327,6 +1327,8 @@ class BartForConditionalGeneration(BartPretrainedModel):
         past=None,
         attention_mask=None,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         encoder_outputs=None,
         **kwargs
@@ -1342,6 +1344,8 @@ class BartForConditionalGeneration(BartPretrainedModel):
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -2530,6 +2530,8 @@ class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel):
         past=None,
         attention_mask=None,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         encoder_outputs=None,
         **kwargs
@@ -2545,6 +2547,8 @@ class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel):
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -1321,6 +1321,8 @@ class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel):
         past=None,
         attention_mask=None,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         encoder_outputs=None,
         **kwargs
@@ -1336,6 +1338,8 @@ class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel):
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -1296,6 +1296,8 @@ class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel):
         past=None,
         attention_mask=None,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         encoder_outputs=None,
         **kwargs
@@ -1311,6 +1313,8 @@ class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel):
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/fsmt/modeling_fsmt.py

@@ -1215,7 +1215,16 @@ class FSMTForConditionalGeneration(PretrainedFSMTModel):
         )
 
     def prepare_inputs_for_generation(
-        self, decoder_input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
+        self,
+        decoder_input_ids,
+        past=None,
+        attention_mask=None,
+        head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
+        use_cache=None,
+        encoder_outputs=None,
+        **kwargs
     ):
         return {
             "input_ids": None,  # encoder_outputs is defined. input_ids not needed
@@ -1223,6 +1232,9 @@ class FSMTForConditionalGeneration(PretrainedFSMTModel):
             "past_key_values": past,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
+            "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/led/modeling_led.py

@@ -2356,6 +2356,8 @@ class LEDForConditionalGeneration(LEDPreTrainedModel):
         past=None,
         attention_mask=None,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         encoder_outputs=None,
         **kwargs,
@@ -2371,6 +2373,8 @@ class LEDForConditionalGeneration(LEDPreTrainedModel):
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/m2m_100/modeling_m2m_100.py

@@ -1324,6 +1324,8 @@ class M2M100ForConditionalGeneration(M2M100PreTrainedModel):
         past=None,
         attention_mask=None,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         encoder_outputs=None,
         **kwargs,
@@ -1339,6 +1341,8 @@ class M2M100ForConditionalGeneration(M2M100PreTrainedModel):
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/marian/modeling_marian.py

@@ -1309,6 +1309,8 @@ class MarianMTModel(MarianPreTrainedModel):
         past=None,
         attention_mask=None,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         encoder_outputs=None,
         **kwargs
@@ -1324,6 +1326,8 @@ class MarianMTModel(MarianPreTrainedModel):
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/mbart/modeling_mbart.py

@@ -1327,7 +1327,16 @@ class MBartForConditionalGeneration(MBartPreTrainedModel):
         )
 
     def prepare_inputs_for_generation(
-        self, decoder_input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
+        self,
+        decoder_input_ids,
+        past=None,
+        attention_mask=None,
+        head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
+        use_cache=None,
+        encoder_outputs=None,
+        **kwargs
     ):
         # cut decoder_input_ids if past is used
         if past is not None:
@@ -1339,6 +1348,9 @@ class MBartForConditionalGeneration(MBartPreTrainedModel):
             "past_key_values": past,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
+            "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/pegasus/modeling_pegasus.py

@@ -1312,6 +1312,8 @@ class PegasusForConditionalGeneration(PegasusPreTrainedModel):
         past=None,
         attention_mask=None,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         encoder_outputs=None,
         **kwargs
@@ -1327,6 +1329,8 @@ class PegasusForConditionalGeneration(PegasusPreTrainedModel):
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/prophetnet/modeling_prophetnet.py

@@ -2020,6 +2020,8 @@ class ProphetNetForConditionalGeneration(ProphetNetPreTrainedModel):
         past=None,
         attention_mask=None,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         encoder_outputs=None,
         **kwargs,
@@ -2036,6 +2038,8 @@ class ProphetNetForConditionalGeneration(ProphetNetPreTrainedModel):
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,
         }
 

src/transformers/models/t5/modeling_t5.py

@@ -1655,7 +1655,16 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
         )
 
     def prepare_inputs_for_generation(
-        self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
+        self,
+        input_ids,
+        past=None,
+        attention_mask=None,
+        head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
+        use_cache=None,
+        encoder_outputs=None,
+        **kwargs
     ):
 
         # cut decoder_input_ids if past is used
@@ -1667,6 +1676,9 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
             "past_key_values": past,
             "encoder_outputs": encoder_outputs,
             "attention_mask": attention_mask,
+            "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,
         }
 

tests/test_generation_utils.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 
+import inspect
 import unittest
 
 from transformers import is_torch_available
@@ -1072,6 +1073,40 @@ class GenerationTesterMixin:
                     output, input_ids, model.config, num_return_sequences=num_return_sequences * beam_scorer.num_beams
                 )
 
+    def test_generate_with_head_masking(self):
+        """Test designed for encoder-decoder models to ensure the attention head masking is used."""
+        attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
+        for model_class in self.all_generative_model_classes:
+            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
+            model = model_class(config)
+            # We want to test only encoder-decoder models
+            if not config.is_encoder_decoder:
+                continue
+
+            head_masking = {
+                "head_mask": torch.zeros(config.encoder_layers, config.encoder_attention_heads),
+                "decoder_head_mask": torch.zeros(config.decoder_layers, config.decoder_attention_heads),
+                "cross_attn_head_mask": torch.zeros(config.decoder_layers, config.decoder_attention_heads),
+            }
+
+            signature = inspect.signature(model.forward)
+            # We want to test only models where encoder/decoder head masking is implemented
+            if set(head_masking.keys()) < set([*signature.parameters.keys()]):
+                continue
+
+            for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
+                out = model.generate(
+                    input_ids,
+                    num_beams=1,
+                    max_length=max_length,
+                    output_attentions=True,
+                    return_dict_in_generate=True,
+                    **{name: mask},
+                )
+                # We check the state of decoder_attentions and cross_attentions just from the last step
+                attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
+                self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0)
+
     def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
         batch_size, seq_length = input_ids.shape
         num_sequences_in_output = batch_size * num_return_sequences

tests/test_modeling_prophetnet.py

@@ -1088,6 +1088,10 @@ class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.Test
         self.assertIsNotNone(encoder_hidden_states.grad)
         self.assertIsNotNone(encoder_attentions.grad)
 
+    def test_generate_with_head_masking(self):
+        """Generating with head_masking has not been implemented for ProphetNet models yet."""
+        pass
+
 
 @require_torch
 class ProphetNetStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):

tests/test_modeling_t5.py

@@ -600,6 +600,37 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
                 input_names=["input_ids", "decoder_input_ids"],
             )
 
+    def test_generate_with_head_masking(self):
+        attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        config = config_and_inputs[0]
+        max_length = config_and_inputs[1].shape[-1] + 3
+        model = T5ForConditionalGeneration(config)
+
+        head_masking = {
+            "head_mask": torch.zeros(config.num_layers, config.num_heads),
+            "decoder_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads),
+            "cross_attn_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads),
+        }
+
+        for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
+            head_masks = {name: mask}
+            # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
+            if name == "head_mask":
+                head_masks["decoder_head_mask"] = torch.ones(config.num_decoder_layers, config.num_heads)
+
+            out = model.generate(
+                config_and_inputs[1],
+                num_beams=1,
+                max_length=max_length,
+                output_attentions=True,
+                return_dict_in_generate=True,
+                **head_masks,
+            )
+            # We check the state of decoder_attentions and cross_attentions just from the last step
+            attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
+            self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0)
+
 
 class T5EncoderOnlyModelTester:
     def __init__(