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streamlining 'checkpointing_steps' parsing (#18755)

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This commit is contained in:
Rahul A R
2022-08-25 11:00:38 -04:00
committed by GitHub
parent fbf382c84d
commit e9442440fc
10 changed files with 30 additions and 60 deletions
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9
examples/pytorch/image-classification/run_image_classification_no_trainer.py
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@@ -406,12 +406,9 @@ def main():
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration. # We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process. # The trackers initializes automatically on the main process.

9
examples/pytorch/language-modeling/run_clm_no_trainer.py
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@@ -508,12 +508,9 @@ def main():
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration. # We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process. # The trackers initializes automatically on the main process.

9
examples/pytorch/language-modeling/run_mlm_no_trainer.py
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@@ -552,12 +552,9 @@ def main():
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration. # We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process. # The trackers initializes automatically on the main process.

9
examples/pytorch/multiple-choice/run_swag_no_trainer.py
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@@ -505,12 +505,9 @@ def main():
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration. # We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process. # The trackers initializes automatically on the main process.

9
examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
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@@ -764,12 +764,9 @@ def main():
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration # We need to initialize the trackers we use, and also store our configuration
if args.with_tracking: if args.with_tracking:

9
examples/pytorch/question-answering/run_qa_no_trainer.py
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@@ -779,12 +779,9 @@ def main():
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration. # We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process. # The trackers initializes automatically on the main process.

9
examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
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@@ -475,12 +475,9 @@ def main():
) )
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# Scheduler and math around the number of training steps. # Scheduler and math around the number of training steps.
overrode_max_train_steps = False overrode_max_train_steps = False

9
examples/pytorch/text-classification/run_glue_no_trainer.py
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@@ -451,12 +451,9 @@ def main():
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration. # We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process. # The trackers initializes automatically on the main process.

9
examples/pytorch/token-classification/run_ner_no_trainer.py
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@@ -566,12 +566,9 @@ def main():
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration. # We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process. # The trackers initializes automatically on the main process.

9
examples/pytorch/translation/run_translation_no_trainer.py
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@@ -549,12 +549,9 @@ def main():
# Afterwards we recalculate our number of training epochs # Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states # Figure out how many steps we should save the Accelerator states
if hasattr(args.checkpointing_steps, "isdigit"): checkpointing_steps = args.checkpointing_steps
checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit():
if args.checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps)
checkpointing_steps = int(args.checkpointing_steps)
else:
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration. # We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log` # We initialize the trackers only on main process because `accelerator.log`