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

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 = []