[examples/summarization] deal with max_length and num_beams (hugg…

…ingface#21740)

* Override the decoding parameters of Seq2SeqTrainer

* Fix quality

* Fix max_length parameter

* Fix quality

* Remove redundant parameter max_length

* Separate the preprocess of train and validation to use different max_target_length

examples/pytorch/summarization/run_summarization.py

@@ -639,6 +639,16 @@ def compute_metrics(eval_preds):
         result["gen_len"] = np.mean(prediction_lens)
         return result
 
+    # Override the decoding parameters of Seq2SeqTrainer
+    training_args.generation_max_length = (
+        training_args.generation_max_length
+        if training_args.generation_max_length is not None
+        else data_args.val_max_target_length
+    )
+    training_args.generation_num_beams = (
+        data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
+    )
+
     # Initialize our Trainer
     trainer = Seq2SeqTrainer(
         model=model,
@@ -672,15 +682,9 @@ def compute_metrics(eval_preds):
 
     # 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=max_length, num_beams=num_beams, metric_key_prefix="eval")
+        metrics = trainer.evaluate(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))
 
@@ -690,9 +694,7 @@ def compute_metrics(eval_preds):
     if training_args.do_predict:
         logger.info("*** Predict ***")
 
-        predict_results = trainer.predict(
-            predict_dataset, metric_key_prefix="predict", max_length=max_length, num_beams=num_beams
-        )
+        predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict")
         metrics = predict_results.metrics
         max_predict_samples = (
             data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -161,15 +161,6 @@ def parse_args():
             "param of ``model.generate``, which is used during ``evaluate`` and ``predict``."
         ),
     )
-    parser.add_argument(
-        "--max_length",
-        type=int,
-        default=128,
-        help=(
-            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
-            " sequences shorter will be padded if `--pad_to_max_lengh` is passed."
-        ),
-    )
     parser.add_argument(
         "--num_beams",
         type=int,
@@ -473,6 +464,9 @@ def main():
                 f"--summary_column' value '{args.summary_column}' needs to be one of: {', '.join(column_names)}"
             )
 
+    if args.val_max_target_length is None:
+        args.val_max_target_length = args.max_target_length
+
     # Temporarily set max_target_length for training.
     max_target_length = args.max_target_length
     padding = "max_length" if args.pad_to_max_length else False
@@ -497,7 +491,7 @@ def preprocess_function(examples):
         return model_inputs
 
     with accelerator.main_process_first():
-        processed_datasets = raw_datasets.map(
+        train_dataset = raw_datasets["train"].map(
             preprocess_function,
             batched=True,
             num_proc=args.preprocessing_num_workers,
@@ -506,8 +500,16 @@ def preprocess_function(examples):
             desc="Running tokenizer on dataset",
         )
 
-    train_dataset = processed_datasets["train"]
-    eval_dataset = processed_datasets["validation"]
+        # Temporarily set max_target_length for validation.
+        max_target_length = args.val_max_target_length
+        eval_dataset = raw_datasets["validation"].map(
+            preprocess_function,
+            batched=True,
+            num_proc=args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not args.overwrite_cache,
+            desc="Running tokenizer on dataset",
+        )
 
     # Log a few random samples from the training set:
     for index in random.sample(range(len(train_dataset)), 1):
@@ -667,11 +669,9 @@ def postprocess_text(preds, labels):
                 break
 
         model.eval()
-        if args.val_max_target_length is None:
-            args.val_max_target_length = args.max_target_length
 
         gen_kwargs = {
-            "max_length": args.val_max_target_length if args is not None else config.max_length,
+            "max_length": args.val_max_target_length,
             "num_beams": args.num_beams,
         }
         for step, batch in enumerate(eval_dataloader):