Add generate kwargs to Seq2SeqTrainingArguments (#13339)

* Add generate kwargs to Seq2SeqTrainingArguments

* typo

* Address review comments + doc

* Style

examples/pytorch/summarization/run_summarization.py

@@ -556,12 +556,15 @@ def main():
 
     # Evaluation
     results = {}
+    max_length = (
+        training_args.generation_max_length
+        if training_args.generation_max_length is not None
+        else data_args.val_max_target_length
+    )
+    num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
-
-        metrics = trainer.evaluate(
-            max_length=data_args.val_max_target_length, num_beams=data_args.num_beams, metric_key_prefix="eval"
-        )
+        metrics = trainer.evaluate(max_length=max_length, num_beams=num_beams, metric_key_prefix="eval")
         max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
         metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
 
@@ -572,10 +575,7 @@ def main():
         logger.info("*** Predict ***")
 
         predict_results = trainer.predict(
-            predict_dataset,
-            metric_key_prefix="predict",
-            max_length=data_args.val_max_target_length,
-            num_beams=data_args.num_beams,
+            predict_dataset, metric_key_prefix="predict", max_length=max_length, num_beams=num_beams
         )
         metrics = predict_results.metrics
         max_predict_samples = (

examples/pytorch/translation/run_translation.py

@@ -549,12 +549,16 @@ def main():
 
     # Evaluation
     results = {}
+    max_length = (
+        training_args.generation_max_length
+        if training_args.generation_max_length is not None
+        else data_args.val_max_target_length
+    )
+    num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
-        metrics = trainer.evaluate(
-            max_length=data_args.val_max_target_length, num_beams=data_args.num_beams, metric_key_prefix="eval"
-        )
+        metrics = trainer.evaluate(max_length=max_length, num_beams=num_beams, metric_key_prefix="eval")
         max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
         metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
 
@@ -565,10 +569,7 @@ def main():
         logger.info("*** Predict ***")
 
         predict_results = trainer.predict(
-            predict_dataset,
-            metric_key_prefix="predict",
-            max_length=data_args.val_max_target_length,
-            num_beams=data_args.num_beams,
+            predict_dataset, metric_key_prefix="predict", max_length=max_length, num_beams=num_beams
         )
         metrics = predict_results.metrics
         max_predict_samples = (