Fix incorrect rank value in data splitting

src/datasets/iterable_dataset.py

@@ -3013,8 +3013,8 @@ def _split_by_node_iterable_dataset(dataset: IterableDataset, rank: int, world_s
         [`IterableDataset`]: The iterable dataset to be used on the node at rank `rank`.
     """
     if dataset._distributed:
-        world_size = world_size * dataset._distributed.world_size
         rank = world_size * dataset._distributed.rank + rank
+        world_size = world_size * dataset._distributed.world_size
     distributed = DistributedConfig(rank=rank, world_size=world_size)
     return IterableDataset(
         ex_iterable=dataset._ex_iterable,

tests/test_distributed.py

@@ -55,6 +55,26 @@ def gen(shards):
     assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size
 
 
+def test_split_dataset_by_node_iterable_distributed():
+    def gen():
+        return ({"i": i} for i in range(100))
+
+    world_size = 3
+    num_workers = 3
+    full_ds = IterableDataset.from_generator(gen)
+    full_size = len(list(full_ds))
+    datasets_per_rank = [
+        split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size)
+    ]
+    datasets_per_rank_per_worker = [
+        split_dataset_by_node(ds, rank=worker, world_size=num_workers)
+        for ds in datasets_per_rank
+        for worker in range(num_workers)
+    ]
+    assert sum(len(list(ds)) for ds in datasets_per_rank_per_worker) == full_size
+    assert len({tuple(x.values()) for ds in datasets_per_rank_per_worker for x in ds}) == full_size
+
+
 def test_distributed_shuffle_iterable():
     def gen():
         return ({"i": i} for i in range(17))