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

@@ -415,7 +415,6 @@ 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,
@@ -430,7 +429,6 @@ 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,

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``, "
@@ -488,7 +488,6 @@ 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,
@@ -508,7 +507,6 @@ 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,

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``, "
@@ -454,7 +454,6 @@ 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,
@@ -474,7 +473,6 @@ 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,

src/transformers/generation/configuration_utils.py

@@ -409,6 +409,10 @@ class GenerationConfig(PushToHubMixin):
                 )
 
         # 2. detect beam-only parameterization when not in beam mode
+        if self.num_beams is None:
+            logging.warning("`num_beams` is set to None - defaulting to 1.", UserWarning)
+            self.num_beams = 1
+
         if self.num_beams == 1:
             single_beam_wrong_parameter_msg = (
                 "`num_beams` is set to 1. However, `{flag_name}` is set to `{flag_value}` -- this flag is only used "