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

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* 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>
This commit is contained in:
Matt
2023-11-15 18:18:54 +00:00
committed by GitHub
parent e6522e49a7
commit 2e72bbab2c
7 changed files with 53 additions and 55 deletions
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2
examples/flax/summarization/run_summarization_flax.py
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@@ -312,7 +312,7 @@ class DataTrainingArguments:
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
) )
num_beams: Optional[int] = field( num_beams: Optional[int] = field(
default=None, default=1,
metadata={ metadata={
"help": ( "help": (
"Number of beams to use for evaluation. This argument will be passed to `model.generate`, " "Number of beams to use for evaluation. This argument will be passed to `model.generate`, "

2
examples/pytorch/summarization/run_summarization.py
View File

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

2
examples/pytorch/translation/run_translation.py
View File

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

2
examples/tensorflow/multiple-choice/run_swag.py
View File

@@ -415,7 +415,6 @@ def main():
if data_args.max_train_samples is not None: if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples) max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(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( train_dataset = train_dataset.map(
preprocess_function, preprocess_function,
batched=True, batched=True,
@@ -430,7 +429,6 @@ def main():
if data_args.max_eval_samples is not None: if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(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( eval_dataset = eval_dataset.map(
preprocess_function, preprocess_function,
batched=True, batched=True,

4
examples/tensorflow/summarization/run_summarization.py
View File

@@ -238,7 +238,7 @@ class DataTrainingArguments:
}, },
) )
num_beams: Optional[int] = field( num_beams: Optional[int] = field(
default=None, default=1,
metadata={ metadata={
"help": ( "help": (
"Number of beams to use for evaluation. This argument will be passed to ``model.generate``, " "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: if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples) max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(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( train_dataset = train_dataset.map(
preprocess_function, preprocess_function,
batched=True, batched=True,
@@ -508,7 +507,6 @@ def main():
if data_args.max_eval_samples is not None: if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(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( eval_dataset = eval_dataset.map(
preprocess_function, preprocess_function,
batched=True, batched=True,

4
examples/tensorflow/translation/run_translation.py
View File

@@ -226,7 +226,7 @@ class DataTrainingArguments:
}, },
) )
num_beams: Optional[int] = field( num_beams: Optional[int] = field(
default=None, default=1,
metadata={ metadata={
"help": ( "help": (
"Number of beams to use for evaluation. This argument will be passed to ``model.generate``, " "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: if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples) max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(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( train_dataset = train_dataset.map(
preprocess_function, preprocess_function,
batched=True, batched=True,
@@ -474,7 +473,6 @@ def main():
if data_args.max_eval_samples is not None: if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(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( eval_dataset = eval_dataset.map(
preprocess_function, preprocess_function,
batched=True, batched=True,

4
src/transformers/generation/configuration_utils.py
View File

@@ -409,6 +409,10 @@ class GenerationConfig(PushToHubMixin):
) )
# 2. detect beam-only parameterization when not in beam mode # 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: if self.num_beams == 1:
single_beam_wrong_parameter_msg = ( single_beam_wrong_parameter_msg = (
"`num_beams` is set to 1. However, `{flag_name}` is set to `{flag_value}` -- this flag is only used " "`num_beams` is set to 1. However, `{flag_name}` is set to `{flag_value}` -- this flag is only used "