Fix remaining mypy issues mostly ignoring them

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torchdata/dataloader2/reading_service.py

@@ -325,7 +325,7 @@ def initialize(self, datapipe: DataPipe) -> DataPipe:
         dispatching_dp = find_lca_round_robin_sharding_dp(graph)
         # TODO(ejguan): When the last DataPipe is round_robin_sharding, use InPrcoessReadingService
         if dispatching_dp is not None:
-            dummy_dp = _DummyIterDataPipe()
+            dummy_dp = _DummyIterDataPipe()  # type: ignore
             graph = replace_dp(graph, dispatching_dp, dummy_dp)  # type: ignore[arg-type]
             datapipe = list(graph.values())[0][0]
             # TODO(ejguan): Determine buffer_size at runtime or use unlimited buffer

torchdata/datapipes/iter/__init__.pyi.in

@@ -15,7 +15,7 @@ from torch.utils.data import DataChunk, IterableDataset, default_collate
 from torch.utils.data.datapipes._typing import _DataPipeMeta
 from torch.utils.data.datapipes.iter.sharding import SHARDING_PRIORITIES
 
-from typing import Any, Callable, Dict, List, Optional, Sequence, TypeVar, Union, Hashable
+from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, TypeVar, Union, Hashable
 
 try:
     import torcharrow
@@ -24,6 +24,7 @@ except ImportError:
 
 T = TypeVar("T")
 T_co = TypeVar("T_co", covariant=True)
+ForkIterDataPipeCopyOptions = Literal["shallow", "deep"]
 
 class IterDataPipe(IterableDataset[T_co], metaclass=_DataPipeMeta):
     functions: Dict[str, Callable] = ...

torchdata/datapipes/iter/util/combining.py

@@ -288,7 +288,7 @@ def __new__(cls, datapipe: IterDataPipe, num_instances: int, buffer_size: int =
             return [datapipe]
 
         datapipe = datapipe.enumerate()
-        container = _RoundRobinDemultiplexerIterDataPipe(datapipe, num_instances, buffer_size=buffer_size)
+        container = _RoundRobinDemultiplexerIterDataPipe(datapipe, num_instances, buffer_size=buffer_size)  # type: ignore
         return [_ChildDataPipe(container, i).map(_drop_index) for i in range(num_instances)]
 
 
@@ -357,7 +357,7 @@ def __new__(
             )
 
         # The implementation basically uses Forker but only yields a specific element within the sequence
-        container = _UnZipperIterDataPipe(source_datapipe, instance_ids, buffer_size)  # type: ignore[arg-type]
+        container = _UnZipperIterDataPipe(source_datapipe, instance_ids, buffer_size)  # type: ignore
         return [_ChildDataPipe(container, i) for i in range(len(instance_ids))]
 
 

torchdata/datapipes/iter/util/randomsplitter.py

@@ -72,7 +72,7 @@ def __new__(
                     "RandomSplitter needs `total_length`, but it is unable to infer it from "
                     f"the `source_datapipe`: {source_datapipe}."
                 )
-        container = _RandomSplitterIterDataPipe(source_datapipe, total_length, weights, seed)
+        container = _RandomSplitterIterDataPipe(source_datapipe, total_length, weights, seed)  # type: ignore
         if target is None:
             return [SplitterIterator(container, k) for k in list(weights.keys())]
         else:
@@ -101,7 +101,7 @@ def __init__(
         self._rng = random.Random(self._seed)
         self._lengths: List[int] = []
 
-    def draw(self) -> T:
+    def draw(self) -> T:  # type: ignore
         selected_key = self._rng.choices(self.keys, self.weights)[0]
         index = self.key_to_index[selected_key]
         self.weights[index] -= 1