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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>
This commit is contained in:
Matt 2023-11-15 18:18:54 +00:00 committed by GitHub
parent e6522e49a7
commit 2e72bbab2c
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 53 additions and 55 deletions
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2
examples/flax/summarization/run_summarization_flax.py
View File

@ -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`, "

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

@ -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``, "

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

@ -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``, "

26
examples/tensorflow/multiple-choice/run_swag.py
View File

@ -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")

36
examples/tensorflow/summarization/run_summarization.py
View File

@ -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

36
examples/tensorflow/translation/run_translation.py
View File

@ -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

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
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 "