make the split_by_worker and slpit_by_rank optional

squirrel/iterstream/base.py

@@ -324,11 +324,23 @@ def split_by_rank_pytorch(self, torch_dist_group: t.Optional[str] = None) -> Com
 
         return self.compose(SplitByRank, torch_dist_group)
 
-    def to_torch_iterable(self) -> Composable:
-        """Convert the stream to a torch iterable."""
+    def to_torch_iterable(self, enforce_rank_check: bool = True, enforce_worker_check: bool = True) -> Composable:
+        """
+        Convert the stream to a torch iterable.
+
+        Args:
+            enforce_rank_check: if set to true, checks that the method `split_by_rank_pytorch` has been called prior to
+                calling `to_torch_iterable`. This is important to avoid loading the same sample more than once in the
+                multi-rank pytorch environment.
+            enforce_worker_check: if set to true, checks that the method `split_by_worker_pytorch` has been called
+                prior to calling `to_torch_iterable`. This is important to avoid loading the same sample more than
+                once in the multi-worker pytorch environment.
+        """
         from squirrel.iterstream.torch_composables import TorchIterable
 
-        return self.compose(TorchIterable)
+        return self.compose(
+            partial(TorchIterable, enforce_rank_check=enforce_rank_check, enforce_worker_check=enforce_worker_check)
+        )
 
 
 class _Iterable(Composable):

squirrel/iterstream/torch_composables.py

@@ -61,19 +61,21 @@ def __iter__(self) -> Iterator:
 class TorchIterable(Composable, IterableDataset):
     """Mixin-Composable to have squirrel pipeline inherit from PyTorch IterableDataset"""
 
-    def __init__(self) -> None:
+    def __init__(self, enforce_rank_check: bool = True, enforce_worker_check: bool = True) -> None:
         """Init"""
         super().__init__()
+        self.enforce_rank_check = enforce_rank_check
+        self.enforce_worker_check = enforce_worker_check
 
     def __iter__(self) -> Iterator:
         """Method to iterate over the source"""
-        if _in_multi_rank_env():
+        if self.enforce_rank_check and _in_multi_rank_env():
             if not self._contains_rank_split(self.source):
                 raise PyTorchSplittingException(
                     "Composable was not split by rank. This will lead to unexpected iteration behaviour."
                     "Add a 'split_by_rank_pytorch' call to your composable to avoid this error. "
                 )
-        if _in_multi_worker_env():
+        if self.enforce_worker_check and _in_multi_worker_env():
             if not self._contains_worker_split(self.source):
                 raise PyTorchSplittingException(
                     "Composable was not split by worker. This will lead to unexpected iteration behaviour."

test/test_iterstream/test_torch_composables.py

@@ -212,6 +212,9 @@ def test_error_when_not_splitting_in_mp(mock_get_worker_info: Any, samples: List
         it = IterableSource(samples).to_torch_iterable()
         next(iter(it))
 
+    res = IterableSource(samples).to_torch_iterable(enforce_worker_check=False, enforce_rank_check=False).collect()
+    assert res == samples
+
     # Split by rank and worker, this should work
 
     # ADD SIMPLE MAP FN