Fix usage of head masks by TF encoder-decoder models' generate() function (#11775)

* Fix Bart

* Fix Blenderbot{,_small}

* Fix LED

* Fix Marian

* Fix MBart

* Fix Pegasus

* Fix T5

* Add test for generation with head_mask

* Add a common TF test

* Override a test for the LED model as head masking is not yet properly implemented

* Remove all head_masks from input preparation for LED

* Drop masking for T5 as it needs a bit of refactor

src/transformers/models/bart/modeling_tf_bart.py

@@ -1452,6 +1452,8 @@ class TFBartForConditionalGeneration(TFBartPretrainedModel, TFCausalLanguageMode
         past,
         attention_mask,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         **kwargs,
     ) -> Dict:
@@ -1487,6 +1489,8 @@ class TFBartForConditionalGeneration(TFBartPretrainedModel, TFCausalLanguageMode
             "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_tf_blenderbot.py

@@ -1476,6 +1476,8 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausal
         past,
         attention_mask,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         **kwargs,
     ) -> Dict:
@@ -1511,6 +1513,8 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausal
             "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_tf_blenderbot_small.py

@@ -1451,6 +1451,8 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel
         past,
         attention_mask,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         **kwargs,
     ) -> Dict:
@@ -1486,6 +1488,8 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel
             "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_tf_led.py

@@ -2477,7 +2477,15 @@ class TFLEDForConditionalGeneration(TFLEDPreTrainedModel):
             encoder_global_attentions=enc_g_attns,
         )
 
-    def prepare_inputs_for_generation(self, decoder_input_ids, past, attention_mask, use_cache, **kwargs) -> Dict:
+    def prepare_inputs_for_generation(
+        self,
+        decoder_input_ids,
+        past,
+        attention_mask,
+        head_mask=None,
+        use_cache=None,
+        **kwargs,
+    ) -> Dict:
         assert past is not None and len(past) in {1, 2}, f"past has to be an iterable of length 1,2 got {past}"
         if len(past) == 1:
             assert isinstance(past[0], tf.Tensor), f"`past[0]` has to be of type `tf.Tensor`, but is {type(past[0])}"
@@ -2510,6 +2518,7 @@ class TFLEDForConditionalGeneration(TFLEDPreTrainedModel):
             "past_key_values": past_key_values,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
+            "head_mask": head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 

src/transformers/models/marian/modeling_tf_marian.py

@@ -1480,6 +1480,8 @@ class TFMarianMTModel(TFMarianPreTrainedModel, TFCausalLanguageModelingLoss):
         past,
         attention_mask,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         **kwargs,
     ) -> Dict:
@@ -1515,6 +1517,8 @@ class TFMarianMTModel(TFMarianPreTrainedModel, TFCausalLanguageModelingLoss):
             "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_tf_mbart.py

@@ -1464,6 +1464,8 @@ class TFMBartForConditionalGeneration(TFMBartPreTrainedModel, TFCausalLanguageMo
         past,
         attention_mask,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         **kwargs,
     ) -> Dict:
@@ -1499,6 +1501,8 @@ class TFMBartForConditionalGeneration(TFMBartPreTrainedModel, TFCausalLanguageMo
             "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_tf_pegasus.py

@@ -1489,6 +1489,8 @@ class TFPegasusForConditionalGeneration(TFPegasusPreTrainedModel, TFCausalLangua
         past,
         attention_mask,
         head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
         use_cache=None,
         **kwargs,
     ) -> Dict:
@@ -1524,6 +1526,8 @@ class TFPegasusForConditionalGeneration(TFPegasusPreTrainedModel, TFCausalLangua
             "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/t5/modeling_tf_t5.py

@@ -1464,7 +1464,14 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
             encoder_attentions=enc_attns,
         )
 
-    def prepare_inputs_for_generation(self, inputs, past, attention_mask, use_cache, **kwargs):
+    def prepare_inputs_for_generation(
+        self,
+        inputs,
+        past,
+        attention_mask,
+        use_cache=None,
+        **kwargs,
+    ):
         assert past is not None, "past has to be defined for encoder_outputs"
 
         # first step

tests/test_modeling_tf_common.py

@@ -1195,6 +1195,40 @@ class TFModelTesterMixin:
 
                 self.assertEqual(loss.shape, [loss_size])
 
+    def test_generate_with_headmasking(self):
+        attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_generative_model_classes:
+            model = model_class(config)
+
+            # We want to test only encoder-decoder models
+            if not config.is_encoder_decoder:
+                continue
+
+            head_masking = {
+                "head_mask": tf.zeros((config.encoder_layers, config.encoder_attention_heads)),
+                "decoder_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)),
+                "cross_attn_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)),
+            }
+
+            signature = inspect.signature(model.call)
+            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(
+                    inputs_dict["input_ids"],
+                    num_beams=1,
+                    max_length=inputs_dict["input_ids"] + 5,
+                    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([tf.reduce_sum(w).numpy() for w in attn_weights]), 0.0)
+
     def _generate_random_bad_tokens(self, num_bad_tokens, model):
         # special tokens cannot be bad tokens
         special_tokens = []

tests/test_modeling_tf_led.py

@@ -370,6 +370,10 @@ class TFLEDModelTest(TFModelTesterMixin, unittest.TestCase):
         # This test is too long (>30sec) and makes fail the CI
         pass
 
+    def test_generate_with_headmasking(self):
+        # TODO: Head-masking not yet implement
+        pass
+
 
 def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
     """If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""

tests/test_modeling_tf_t5.py

@@ -310,6 +310,10 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
         model = TFT5Model.from_pretrained("t5-small")
         self.assertIsNotNone(model)
 
+    def test_generate_with_headmasking(self):
+        # TODO: Fix head-masking according to PyTorch T5 model
+        pass
+
 
 class TFT5EncoderOnlyModelTester:
     def __init__(