Fix skip first batch being perminant

docs/source/usage_guides/checkpoint.mdx

@@ -77,5 +77,17 @@ train_dataloader = accelerator.prepare(train_dataloader)
 accelerator.load_state("my_state")
 
 # Assume the checkpoint was saved 100 steps into the epoch
-accelerator.skip_first_batches(train_dataloader, 100)
+skipped_dataloader = accelerator.skip_first_batches(train_dataloader, 100)
+
+# After the first iteration, go back to `train_dataloader`
+
+# First epoch
+for batch in skipped_dataloader:
+    # Do something
+    pass
+
+# Second epoch
+for batch in train_dataloader:
+    # Do something
+    pass
 ```

examples/by_feature/checkpointing.py

@@ -219,9 +219,12 @@ def training_function(config, args):
         # New Code #
         if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
             # We need to skip steps until we reach the resumed step
-            train_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
+            active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
             overall_step += resume_step
-        for step, batch in enumerate(train_dataloader):
+        else:
+            # After the first iteration though, we need to go back to the original dataloader
+            active_dataloader = train_dataloader
+        for step, batch in enumerate(active_dataloader):
             # We could avoid this line since we set the accelerator with `device_placement=True`.
             batch.to(accelerator.device)
             outputs = model(**batch)

examples/complete_cv_example.py

@@ -205,9 +205,12 @@ def training_function(config, args):
             total_loss = 0
         if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
             # We need to skip steps until we reach the resumed step
-            train_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
+            active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
             overall_step += resume_step
-        for batch in train_dataloader:
+        else:
+            # After the first iteration though, we need to go back to the original dataloader
+            active_dataloader = train_dataloader
+        for batch in active_dataloader:
             # We could avoid this line since we set the accelerator with `device_placement=True`.
             batch = {k: v.to(accelerator.device) for k, v in batch.items()}
             inputs = (batch["image"] - mean) / std

examples/complete_nlp_example.py

@@ -195,9 +195,12 @@ def collate_fn(examples):
             total_loss = 0
         if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
             # We need to skip steps until we reach the resumed step
-            train_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
+            active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
             overall_step += resume_step
-        for step, batch in enumerate(train_dataloader):
+        else:
+            # After the first iteration though, we need to go back to the original dataloader
+            active_dataloader = train_dataloader
+        for step, batch in enumerate(active_dataloader):
             # We could avoid this line since we set the accelerator with `device_placement=True`.
             batch.to(accelerator.device)
             outputs = model(**batch)

src/accelerate/accelerator.py

@@ -2750,13 +2750,19 @@ def skip_first_batches(self, dataloader, num_batches: int = 0):
 
         >>> accelerator = Accelerator()
         >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)
-
-        >>> for input, target in accelerator.skip_first_batches(dataloader, num_batches=2):
+        >>> skipped_dataloader = accelerator.skip_first_batches(dataloader, num_batches=2)
+        >>> # for the first epoch only
+        >>> for input, target in skipped_dataloader:
         ...     optimizer.zero_grad()
         ...     output = model(input)
         ...     loss = loss_func(output, target)
         ...     accelerator.backward(loss)
         ...     optimizer.step()
+
+        >>> # subsequent epochs
+        >>> for input, target in dataloader:
+        ...     optimizer.zero_grad()
+        ...     ...
         ```
         """
         return skip_first_batches(dataloader, num_batches=num_batches)

tests/test_examples.py

@@ -118,6 +118,7 @@ def test_cv_examples(self):
             " " * 16 + "},\n\n",
             " " * 16 + "step=epoch,\n",
             " " * 12,
+            " " * 8 + "for step, batch in enumerate(active_dataloader):\n",
         ]
         self.one_complete_example("complete_cv_example.py", True, cv_path, special_strings)
         self.one_complete_example("complete_cv_example.py", False, cv_path, special_strings)