[DTensor] have split_strategy return OpStrategy instead of TupleStrategy (#158051)

**Summary**
`split_strategy` used `TupleStrategy` as return type because DTensor sharding
propagation's `OpStrategy` support on multi-returns only applies to `Tuple`.

However, `TupleStrategy`'s not a good fit for `split` op. `TupleStrategy` was
initially introduced to handle the sharding strategy of `foreach_*` ops where
the input args can be split into independent subsets regarding sharding decisions,
so are the outputs.

To address the misuse, this PR adds `OpStrategy` propagation for `List[Tensor]`
(note that this support is INCOMPLETE because it only checks the return type
to be `torch.ListType`). Nevertheless, the logic for `Tuple` returns also made
similar assumption so I think it's fine to unblock in such a way.

Besides adding `OpStrategy` support to ops having `List[Tensor]` return type,
this PR also changes `split_strategy`'s return from `TupleStrategy` to `OpStrategy`.

**Test**
`pytest test/distributed/tensor/test_tensor_ops.py -s -k test_split_on_partial`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/158051
Approved by: https://github.com/wconstab, https://github.com/zpcore

torch/distributed/tensor/_op_schema.py

@@ -345,6 +345,13 @@ class OpSchema:
             return_types[0].type, torch.TensorType
         )
 
+    def return_type_list_tensor_like(self) -> bool:
+        # returns True if the return type is a List
+        return_types = self.op._schema.returns
+        return len(return_types) == 1 and isinstance(
+            return_types[0].type, torch.ListType
+        )
+
     def return_type_tensor(self) -> bool:
         return_types = self.op._schema.returns
         # all dispatch ops only return Tensor or Tuple[Tensor] for tensor like

torch/distributed/tensor/_ops/_tensor_ops.py

@@ -1074,7 +1074,7 @@ def prop_index(op_schema: OpSchema) -> OutputSharding:
     ],
     RuntimeSchemaInfo(1),
 )
-def split_strategy(op_schema: OpSchema) -> TupleStrategy:
+def split_strategy(op_schema: OpSchema) -> OpStrategy:
     input_strategy = op_schema.args_schema[0]
     split_size_or_sections = op_schema.args_schema[1]
     assert isinstance(input_strategy, OpStrategy)
@@ -1097,11 +1097,7 @@ def split_strategy(op_schema: OpSchema) -> TupleStrategy:
     )
     assert isinstance(output_size_list, Sized)
 
-    split_strategies = []
-
-    for _ in range(len(output_size_list)):
-        op_strategy = OpStrategy([])
-
+    all_strategies = []
     for strategy in input_strategy.strategies:
         spec = strategy.output_spec
         placements = spec.placements
@@ -1109,11 +1105,19 @@ def split_strategy(op_schema: OpSchema) -> TupleStrategy:
             # if the input is sharded on the split dim, we need to unshard it
             placements = unshard_tensor_dim(spec.placements, dim=dim)
 
-            spec = DTensorSpec(spec.mesh, placements)
-
-            op_strategy.strategies.append(
-                OpSpec(output_specs=spec, input_specs=([spec]))
+        input_spec = DTensorSpec(spec.device_mesh, placements, spec.tensor_meta)
+        output_specs = tuple(
+            DTensorSpec(spec.device_mesh, placements)
+            for _ in range(len(output_size_list))
+        )
+        all_strategies.append(
+            OpSpec(
+                output_specs=output_specs,
+                input_specs=(input_spec,),
+                redistribute_cost=[
+                    generate_redistribute_costs(input_strategy, input_spec)
+                ],
+            )
         )
-        split_strategies.append(op_strategy)
 
-    return TupleStrategy(split_strategies)
+    return OpStrategy(all_strategies)

torch/distributed/tensor/_sharding_prop.py

@@ -353,7 +353,10 @@ class ShardingPropagator:
                                 for _ in range(len(op_schema.op._schema.returns))
                             ]
                         )
-                elif op_schema.return_type_tensor():
+                elif (
+                    op_schema.return_type_tensor()
+                    or op_schema.return_type_list_tensor_like()
+                ):
                     output_specs = output_strategy.output_specs
                 else:
                     output_specs = None