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Respect documentation on passive log level (#21700)

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* Respect documentation on passive log level

* Fix test and set log level in examples

* Add doc
This commit is contained in:
Sylvain Gugger
2023-02-22 09:39:18 +01:00
committed by GitHub
parent ee6e71e29c
commit b19d64d852
20 changed files with 85 additions and 9 deletions
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4
examples/pytorch/audio-classification/run_audio_classification.py
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@@ -209,6 +209,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
transformers.utils.logging.set_verbosity(log_level)

4
examples/pytorch/contrastive-image-text/run_clip.py
View File

@@ -246,6 +246,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
transformers.utils.logging.set_verbosity(log_level)

4
examples/pytorch/image-classification/run_image_classification.py
View File

@@ -187,6 +187,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
transformers.utils.logging.set_verbosity(log_level)

4
examples/pytorch/image-pretraining/run_mae.py
View File

@@ -186,6 +186,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
transformers.utils.logging.set_verbosity(log_level)

4
examples/pytorch/image-pretraining/run_mim.py
View File

@@ -250,6 +250,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
transformers.utils.logging.set_verbosity(log_level)

4
examples/pytorch/language-modeling/run_clm.py
View File

@@ -240,6 +240,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

4
examples/pytorch/language-modeling/run_mlm.py
View File

@@ -240,6 +240,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

4
examples/pytorch/language-modeling/run_plm.py
View File

@@ -231,6 +231,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

5
examples/pytorch/multiple-choice/run_swag.py
View File

@@ -235,6 +235,11 @@ def main():
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

4
examples/pytorch/question-answering/run_qa.py
View File

@@ -238,6 +238,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

5
examples/pytorch/question-answering/run_qa_beam_search.py
View File

@@ -236,6 +236,11 @@ def main():
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

4
examples/pytorch/question-answering/run_seq2seq_qa.py
View File

@@ -283,6 +283,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

4
examples/pytorch/semantic-segmentation/run_semantic_segmentation.py
View File

@@ -276,6 +276,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
transformers.utils.logging.set_verbosity(log_level)

5
examples/pytorch/summarization/run_summarization.py
View File

@@ -314,6 +314,11 @@ def main():
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

4
examples/pytorch/text-classification/run_glue.py
View File

@@ -227,6 +227,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

4
examples/pytorch/text-classification/run_xnli.py
View File

@@ -186,6 +186,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

4
examples/pytorch/token-classification/run_ner.py
View File

@@ -228,6 +228,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

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

@@ -272,6 +272,10 @@ def main():
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)

15
src/transformers/training_args.py
View File

@@ -231,9 +231,9 @@ class TrainingArguments:
Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
log_level (`str`, *optional*, defaults to `passive`):
Logger log level to use on the main process. Possible choices are the log levels as strings: 'debug',
'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the
application set the level.
log_level_replica (`str`, *optional*, defaults to `passive`):
'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and keeps the
current log level for the Transformers library (which will be `"warning"` by default).
log_level_replica (`str`, *optional*, defaults to `"warning"`):
Logger log level to use on replicas. Same choices as `log_level`"
log_on_each_node (`bool`, *optional*, defaults to `True`):
In multinode distributed training, whether to log using `log_level` once per node, or only on the main
@@ -690,7 +690,7 @@ class TrainingArguments:
},
)
log_level_replica: Optional[str] = field(
default="passive",
default="warning",
metadata={
"help": "Logger log level to use on replica nodes. Same choices and defaults as ``log_level``",
"choices": trainer_log_levels.keys(),
@@ -1774,7 +1774,8 @@ class TrainingArguments:
Returns the log level to be used depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process.
For the main process the log level defaults to `logging.INFO` unless overridden by `log_level` argument.
For the main process the log level defaults to the logging level set (`logging.WARNING` if you didn't do
anything) unless overridden by `log_level` argument.
For the replica processes the log level defaults to `logging.WARNING` unless overridden by `log_level_replica`
argument.
@@ -1786,8 +1787,8 @@ class TrainingArguments:
log_level = trainer_log_levels[self.log_level]
log_level_replica = trainer_log_levels[self.log_level_replica]
log_level_main_node = logging.INFO if log_level == -1 else log_level
log_level_replica_node = logging.WARNING if log_level_replica == -1 else log_level_replica
log_level_main_node = logging.get_verbosity() if log_level == -1 else log_level
log_level_replica_node = logging.get_verbosity() if log_level_replica == -1 else log_level_replica
return log_level_main_node if self.should_log else log_level_replica_node
@property

4
tests/trainer/test_trainer.py
View File

@@ -1098,11 +1098,11 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
logger = logging.get_logger()
log_info_string = "Running training"
# test with the default log_level - should be info and thus log on the main process
# test with the default log_level - should be warning and thus not log on the main process
with CaptureLogger(logger) as cl:
trainer = get_regression_trainer()
trainer.train()
self.assertIn(log_info_string, cl.out)
self.assertNotIn(log_info_string, cl.out)
# test with low log_level - lower than info
with CaptureLogger(logger) as cl: