back to simple load, but leave space for future dispatching

Author: rjzamora

distributed/shuffle/_core.py

@@ -302,13 +302,14 @@ def _sync_output_partition(self, partition_id: _T_partition_id, key: Key) -> Non
         self.raise_if_closed()
         sync(self._loop, self._ensure_output_worker, partition_id, key)
         if not self.transferred:
-            raise RuntimeError("`get_output_partition` called before barrier task")
+            raise RuntimeError("`_sync_output_partition` called before barrier task")
         sync(self._loop, self.flush_receive)
 
     def get_output_partition(
         self, partition_id: _T_partition_id, key: Key, **kwargs: Any
     ) -> _T_partition_type:
-        self._sync_output_partition(partition_id, key)
+        if kwargs.pop("sync", True):
+            self._sync_output_partition(partition_id, key)
         return self._get_output_partition(partition_id, key, **kwargs)
 
     @abc.abstractmethod
@@ -504,24 +505,39 @@ def __init__(
         self.key = key
         self.kwargs = kwargs
 
+    def pre_serialize(self) -> Any:
+        """Make the unloaded partition serializable"""
+        # TODO: Add mechanism to dispatch on meta.
+        # Right now, serializing an UnloadedPartition object
+        # will convert it to `type(self.shuffle_run.meta)`.
+        # However, it may be beneficial to futher delay the
+        # use of GPU memory for cudf/cupy-based data.
+        return self.load()
+
     def load(self) -> Any:
+        """Load the shuffle output partition into memory"""
         with handle_unpack_errors(self.shuffle_run.id):
-            return self.shuffle_run._get_output_partition(
-                self.partition_id, self.key, **self.kwargs
+            return self.shuffle_run.get_output_partition(
+                self.partition_id,
+                self.key,
+                # We need sync=False, because `_sync_output_partition`
+                # was already called for the current shuffle run
+                sync=False,
+                **self.kwargs,
             )
 
 
 @dask_serialize.register(UnloadedPartition)
-def _serialize_unloaded(obj: UnloadedPartition) -> tuple[None, list[bytes]]:
+def _serialize_unloaded(obj):
     # Convert to LoadedPartition before serializing. Note that
     # we don't convert all the way to DataFrame, because this
     # adds unnecessary overhead and memory pressure for the
     # cudf backend (and minor overhead for pandas)
-    return None, [pickle.dumps(obj.load())]
+    return None, [pickle.dumps(obj.pre_serialize())]
 
 
 @dask_deserialize.register(UnloadedPartition)
-def _deserialize_unloaded(header: None, frames: list[bytes]) -> Any:
+def _deserialize_unloaded(header, frames):
     return pickle.loads(frames[0])
 
 
@@ -534,4 +550,5 @@ def load_output_partition(
     assert barrier_key
     if isinstance(data, UnloadedPartition):
         data = data.load()
+        assert not isinstance(data, UnloadedPartition)
     return data

distributed/shuffle/_shuffle.py

@@ -91,7 +91,7 @@ def shuffle_unpack(
         )
 
 
-def shuffle_unpack_unloaded(
+def delayed_shuffle_unpack(
     id: ShuffleId, output_partition: int, barrier_run_id: int
 ) -> pd.DataFrame:
     with handle_unpack_errors(id):
@@ -305,7 +305,7 @@ def _construct_graph(self) -> _T_LowLevelGraph:
         name = self.name
         for part_out in self.parts_out:
             dsk[(name, part_out)] = (
-                shuffle_unpack_unloaded,
+                delayed_shuffle_unpack,
                 token,
                 part_out,
                 _barrier_key,