Always use SeedableRandomSampler

src/accelerate/data_loader.py

@@ -476,7 +476,10 @@ def set_epoch(self, epoch: int):
         # In case it is manually passed in, the user can set it to what they like
         if self.iteration != epoch:
             self.iteration = epoch
-        if hasattr(self.batch_sampler, "sampler") and hasattr(self.batch_sampler.sampler, "set_epoch"):
+        if hasattr(self.batch_sampler, "set_epoch"):
+            # Case: `SkipBatchSampler`
+            self.batch_sampler.set_epoch(epoch)
+        elif hasattr(self.batch_sampler, "sampler") and hasattr(self.batch_sampler.sampler, "set_epoch"):
             self.batch_sampler.sampler.set_epoch(epoch)
         # We support if a custom `Dataset` implementation has `set_epoch`
         # or in general HF datasets `Datasets`
@@ -836,17 +839,19 @@ def prepare_data_loader(
         sampler = getattr(dataloader.sampler, "sampler", None)
     else:
         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
-        # samples in the same order if a seed is set. This requires a tweak
-        # to the `torch.utils.data.RandomSampler` class (if used).
-        sampler = SeedableRandomSampler(
-            data_source=sampler.data_source,
-            replacement=sampler.replacement,
-            num_samples=sampler._num_samples,
-            generator=getattr(sampler, "generator", torch.Generator()),
-        )
+    if isinstance(sampler, RandomSampler):
+        # CPU's specifically do not require this workaround
+        if not ((num_processes == 1) and (device.type == "cpu")):
+            # When iterating through the dataloader we want to ensure that
+            # on each process we are iterating through the same
+            # samples in the same order if a seed is set. This requires a tweak
+            # to the `torch.utils.data.RandomSampler` class (if used).
+            sampler = SeedableRandomSampler(
+                data_source=sampler.data_source,
+                replacement=sampler.replacement,
+                num_samples=sampler._num_samples,
+                generator=getattr(sampler, "generator", torch.Generator()),
+            )
 
     # No change if no multiprocess
     if (num_processes != 1 or state.distributed_type == DistributedType.MEGATRON_LM) and not dispatch_batches: