Add logits_processor parameter, used by generate, to Seq2SeqTrainer methods evaluate and predict (#17805)

* Add logits_processor parameter, used by `generate`, to `Seq2SeqTrainer` methods `evaluate` and `predict`

* Add all generate parameters to `Seq2SeqTrainer`, and also to `QuestionAnsweringSeq2SeqTrainer` which overrides it

* Remove `self._num_beams` from trainer classes

* - Run fixup
- Fix "Constraint" not exposed
- Fix synced_gpus to actually read from param

* Use kwargs

* Copy kwargs before making changes to it

* Fix style issues unused imports

examples/pytorch/question-answering/trainer_seq2seq_qa.py

@@ -41,11 +41,16 @@ class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer):
         eval_examples=None,
         ignore_keys: Optional[List[str]] = None,
         metric_key_prefix: str = "eval",
-        max_length: Optional[int] = None,
-        num_beams: Optional[int] = None,
+        **gen_kwargs,
     ) -> Dict[str, float]:
-        self._max_length = max_length if max_length is not None else self.args.generation_max_length
-        self._num_beams = num_beams if num_beams is not None else self.args.generation_num_beams
+        gen_kwargs = gen_kwargs.copy()
+        gen_kwargs["max_length"] = (
+            gen_kwargs["max_length"] if gen_kwargs.get("max_length") is not None else self.args.generation_max_length
+        )
+        gen_kwargs["num_beams"] = (
+            gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams
+        )
+        self._gen_kwargs = gen_kwargs
 
         eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset
         eval_dataloader = self.get_eval_dataloader(eval_dataset)
@@ -87,7 +92,11 @@ class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer):
         self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics)
         return metrics
 
-    def predict(self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test"):
+    def predict(
+        self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test", **gen_kwargs
+    ):
+        self._gen_kwargs = gen_kwargs.copy()
+
         predict_dataloader = self.get_test_dataloader(predict_dataset)
 
         # Temporarily disable metric computation, we will do it in the loop here.