Fix batch sampler

src/accelerate/data_loader.py

@@ -833,9 +833,9 @@ def prepare_data_loader(
     synchronized_generator = None
     sampler_is_batch_sampler = isinstance(dataloader.sampler, BatchSampler)
     if sampler_is_batch_sampler:
-        sampler = dataloader.sampler.sampler
+        sampler = getattr(dataloader.sampler, "sampler", None)
     else:
-        sampler = dataloader.batch_sampler.sampler
+        sampler = getattr(dataloader.batch_sampler, "sampler", None)
     if isinstance(sampler, RandomSampler) and num_processes > 1:
         # When iterating through the dataloader during distributed processes
         # we want to ensure that on each process we are iterating through the same

src/accelerate/test_utils/scripts/test_script.py

@@ -21,8 +21,9 @@
 from copy import deepcopy
 from pathlib import Path
 
+import numpy as np
 import torch
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, Dataset
 
 from accelerate import Accelerator
 from accelerate.data_loader import SeedableRandomSampler, prepare_data_loader
@@ -288,6 +289,47 @@ def central_dl_preparation_check():
         print("Shuffled central dataloader passing.")
 
 
+def custom_sampler_check():
+    state = AcceleratorState()
+
+    class CustomDataset(Dataset):
+        def __init__(self, data):
+            self.data = data
+
+        def __len__(self):
+            return len(self.data)
+
+        def __getitem__(self, index):
+            return self.data[index]
+
+    class CustomBatchSampler:
+        def __init__(self, dataset_length: int, batch_size: int, shuffle: bool = True):
+            self.batch_size = batch_size
+            self.data_index = np.arange(dataset_length)
+            self.shuffle = shuffle
+
+        def __iter__(self):
+            num_batches = len(self)
+            if self.shuffle:
+                index = np.random.permutation(self.data_index)
+            else:
+                index = self.data_index
+            output = np.array_split(index, num_batches)
+            yield from output
+
+        def __len__(self):
+            return math.ceil(len(self.data_index) / self.batch_size)
+
+    dataset = CustomDataset(range(32 * state.num_processes))
+    sampler = CustomBatchSampler(len(dataset), batch_size=8)
+    dl = DataLoader(dataset, batch_sampler=sampler)
+    dl = prepare_data_loader(dl, state.device, state.num_processes, state.process_index, put_on_device=True)
+    # We need just ensure that `dl.batch_sampler` remains unchanged
+    assert isinstance(
+        dl.batch_sampler, CustomBatchSampler
+    ), "Custom sampler was changed after calling `prepare_data_loader`"
+
+
 def mock_training(length, batch_size, generator):
     set_seed(42)
     generator.manual_seed(42)
@@ -608,6 +650,7 @@ def main():
     dl_preparation_check()
     if state.distributed_type != DistributedType.TPU:
         central_dl_preparation_check()
+    custom_sampler_check()
 
     # Trainings are not exactly the same in DeepSpeed and CPU mode
     if state.distributed_type == DistributedType.DEEPSPEED: