[docs] DataLoaderConfiguration docstring (#3103)

src/accelerate/utils/dataclasses.py

@@ -733,6 +733,34 @@ class TensorInformation:
 class DataLoaderConfiguration:
     """
     Configuration for dataloader-related items when calling `accelerator.prepare`.
+
+    Args:
+        split_batches (`bool`, defaults to `False`):
+            Whether or not the accelerator should split the batches yielded by the dataloaders across the devices. If
+            `True`, the actual batch size used will be the same on any kind of distributed processes, but it must be a
+            round multiple of `num_processes` you are using. If `False`, actual batch size used will be the one set in
+            your script multiplied by the number of processes.
+        dispatch_batches (`bool`, defaults to `None`):
+            If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process
+            and then the batches are split and broadcast to each process. Will default to `True` for `DataLoader` whose
+            underlying dataset is an `IterableDataset`, `False` otherwise.
+        even_batches (`bool`, defaults to `True`):
+            If set to `True`, in cases where the total batch size across all processes does not exactly divide the
+            dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among
+            all workers.
+        use_seedable_sampler (`bool`, defaults to `False`):
+            Whether or not use a fully seedable random sampler ([`data_loader.SeedableRandomSampler`]). Ensures
+            training results are fully reproducable using a different sampling technique. While seed-to-seed results
+            may differ, on average the differences are neglible when using multiple different seeds to compare. Should
+            also be ran with [`~utils.set_seed`] for the best results.
+        non_blocking (`bool`, defaults to `False`):
+            If set to `True`, the dataloader prepared by the Accelerator will utilize non-blocking host-to-device
+            transfers, allowing for better overlap between dataloader communication and computation. Recommended that
+            the prepared dataloader has `pin_memory` set to `True` to work properly.
+        use_stateful_dataloader (`bool`, defaults to `False`):
+            If set to `True`, the dataloader prepared by the Accelerator will be backed by
+            [torchdata.StatefulDataLoader](https://github.com/pytorch/data/tree/main/torchdata/stateful_dataloader).
+            This requires `torchdata` version 0.8.0 or higher that supports StatefulDataLoader to be installed.
     """
 
     split_batches: bool = field(