Incorrect setting for num_beams in translation and summarization examples (#27519)

* Remove the torch main_process_first context manager from TF examples

* Correctly set num_beams=1 in our examples, and add a guard in GenerationConfig.validate()

* Update src/transformers/generation/configuration_utils.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

examples/flax/summarization/run_summarization_flax.py

@@ -312,7 +312,7 @@ class DataTrainingArguments:
         default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
     )
     num_beams: Optional[int] = field(
-        default=None,
+        default=1,
         metadata={
             "help": (
                 "Number of beams to use for evaluation. This argument will be passed to `model.generate`, "

examples/pytorch/summarization/run_summarization.py

@@ -249,7 +249,7 @@ class DataTrainingArguments:
         },
     )
     num_beams: Optional[int] = field(
-        default=None,
+        default=1,
         metadata={
             "help": (
                 "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "

examples/pytorch/translation/run_translation.py

@@ -217,7 +217,7 @@ class DataTrainingArguments:
         },
     )
     num_beams: Optional[int] = field(
-        default=None,
+        default=1,
         metadata={
             "help": (
                 "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "

examples/tensorflow/multiple-choice/run_swag.py

@@ -415,13 +415,12 @@ def main():
         if data_args.max_train_samples is not None:
             max_train_samples = min(len(train_dataset), data_args.max_train_samples)
             train_dataset = train_dataset.select(range(max_train_samples))
-        with training_args.main_process_first(desc="train dataset map pre-processing"):
-            train_dataset = train_dataset.map(
-                preprocess_function,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                load_from_cache_file=not data_args.overwrite_cache,
-            )
+        train_dataset = train_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            load_from_cache_file=not data_args.overwrite_cache,
+        )
 
     if training_args.do_eval:
         if "validation" not in raw_datasets:
@@ -430,13 +429,12 @@ def main():
         if data_args.max_eval_samples is not None:
             max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
             eval_dataset = eval_dataset.select(range(max_eval_samples))
-        with training_args.main_process_first(desc="validation dataset map pre-processing"):
-            eval_dataset = eval_dataset.map(
-                preprocess_function,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                load_from_cache_file=not data_args.overwrite_cache,
-            )
+        eval_dataset = eval_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            load_from_cache_file=not data_args.overwrite_cache,
+        )
 
     if data_args.pad_to_max_length:
         data_collator = DefaultDataCollator(return_tensors="np")

examples/tensorflow/summarization/run_summarization.py

@@ -238,7 +238,7 @@ class DataTrainingArguments:
         },
     )
     num_beams: Optional[int] = field(
-        default=None,
+        default=1,
         metadata={
             "help": (
                 "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
@@ -488,15 +488,14 @@ def main():
         if data_args.max_train_samples is not None:
             max_train_samples = min(len(train_dataset), data_args.max_train_samples)
             train_dataset = train_dataset.select(range(max_train_samples))
-        with training_args.main_process_first(desc="train dataset map pre-processing"):
-            train_dataset = train_dataset.map(
-                preprocess_function,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on train dataset",
-            )
+        train_dataset = train_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on train dataset",
+        )
     else:
         train_dataset = None
 
@@ -508,15 +507,14 @@ def main():
         if data_args.max_eval_samples is not None:
             max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
             eval_dataset = eval_dataset.select(range(max_eval_samples))
-        with training_args.main_process_first(desc="validation dataset map pre-processing"):
-            eval_dataset = eval_dataset.map(
-                preprocess_function,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on validation dataset",
-            )
+        eval_dataset = eval_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on validation dataset",
+        )
     else:
         eval_dataset = None
     # endregion

examples/tensorflow/translation/run_translation.py

@@ -226,7 +226,7 @@ class DataTrainingArguments:
         },
     )
     num_beams: Optional[int] = field(
-        default=None,
+        default=1,
         metadata={
             "help": (
                 "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
@@ -454,15 +454,14 @@ def main():
         if data_args.max_train_samples is not None:
             max_train_samples = min(len(train_dataset), data_args.max_train_samples)
             train_dataset = train_dataset.select(range(max_train_samples))
-        with training_args.main_process_first(desc="train dataset map pre-processing"):
-            train_dataset = train_dataset.map(
-                preprocess_function,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on train dataset",
-            )
+        train_dataset = train_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on train dataset",
+        )
     else:
         train_dataset = None
 
@@ -474,15 +473,14 @@ def main():
         if data_args.max_eval_samples is not None:
             max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
             eval_dataset = eval_dataset.select(range(max_eval_samples))
-        with training_args.main_process_first(desc="validation dataset map pre-processing"):
-            eval_dataset = eval_dataset.map(
-                preprocess_function,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on validation dataset",
-            )
+        eval_dataset = eval_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on validation dataset",
+        )
     else:
         eval_dataset = None
     # endregion