streamlining 'checkpointing_steps' parsing (#18755)

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -406,12 +406,9 @@ def main():
     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
-    if hasattr(args.checkpointing_steps, "isdigit"):
-        checkpointing_steps = args.checkpointing_steps
-        if args.checkpointing_steps.isdigit():
-            checkpointing_steps = int(args.checkpointing_steps)
-    else:
-        checkpointing_steps = None
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -508,12 +508,9 @@ def main():
     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
-    if hasattr(args.checkpointing_steps, "isdigit"):
-        checkpointing_steps = args.checkpointing_steps
-        if args.checkpointing_steps.isdigit():
-            checkpointing_steps = int(args.checkpointing_steps)
-    else:
-        checkpointing_steps = None
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -552,12 +552,9 @@ def main():
     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
-    if hasattr(args.checkpointing_steps, "isdigit"):
-        checkpointing_steps = args.checkpointing_steps
-        if args.checkpointing_steps.isdigit():
-            checkpointing_steps = int(args.checkpointing_steps)
-    else:
-        checkpointing_steps = None
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -505,12 +505,9 @@ def main():
     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
-    if hasattr(args.checkpointing_steps, "isdigit"):
-        checkpointing_steps = args.checkpointing_steps
-        if args.checkpointing_steps.isdigit():
-            checkpointing_steps = int(args.checkpointing_steps)
-    else:
-        checkpointing_steps = None
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -764,12 +764,9 @@ def main():
     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
-    if hasattr(args.checkpointing_steps, "isdigit"):
-        checkpointing_steps = args.checkpointing_steps
-        if args.checkpointing_steps.isdigit():
-            checkpointing_steps = int(args.checkpointing_steps)
-    else:
-        checkpointing_steps = None
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
 
     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -779,12 +779,9 @@ def main():
     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
-    if hasattr(args.checkpointing_steps, "isdigit"):
-        checkpointing_steps = args.checkpointing_steps
-        if args.checkpointing_steps.isdigit():
-            checkpointing_steps = int(args.checkpointing_steps)
-    else:
-        checkpointing_steps = None
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

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

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -451,12 +451,9 @@ def main():
     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
-    if hasattr(args.checkpointing_steps, "isdigit"):
-        checkpointing_steps = args.checkpointing_steps
-        if args.checkpointing_steps.isdigit():
-            checkpointing_steps = int(args.checkpointing_steps)
-    else:
-        checkpointing_steps = None
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -566,12 +566,9 @@ def main():
     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
-    if hasattr(args.checkpointing_steps, "isdigit"):
-        checkpointing_steps = args.checkpointing_steps
-        if args.checkpointing_steps.isdigit():
-            checkpointing_steps = int(args.checkpointing_steps)
-    else:
-        checkpointing_steps = None
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.

examples/pytorch/translation/run_translation_no_trainer.py

@@ -549,12 +549,9 @@ def main():
     # Afterwards we recalculate our number of training epochs
     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
-    if hasattr(args.checkpointing_steps, "isdigit"):
-        checkpointing_steps = args.checkpointing_steps
-        if args.checkpointing_steps.isdigit():
-            checkpointing_steps = int(args.checkpointing_steps)
-    else:
-        checkpointing_steps = None
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # We initialize the trackers only on main process because `accelerator.log`