Allow passing planner to _shard_modules (pytorch#2732)

Summary:

`_shard_modules` function is used in fx_traceability tests for SDD and SemiSync pipeline. It uses a default ShardingPlanner and topology that use hardcoded batch size (512) and HBM memory limit (32Gb), respectively. This change allows specifying the ShardingPlanner and Topology to more accurately reflect the machine capabilities. The change is intentionally limited to `_shard_modules` only and not public `shard_modules` to avoid changing the contract for the latter.

Reviewed By: sarckk

Differential Revision: D69163227

torchrec/distributed/shard.py

@@ -204,6 +204,7 @@ def _shard_modules(  # noqa: C901
     plan: Optional[ShardingPlan] = None,
     sharders: Optional[List[ModuleSharder[torch.nn.Module]]] = None,
     init_params: Optional[bool] = False,
+    planner: Optional[EmbeddingShardingPlanner] = None,
 ) -> nn.Module:
     """
     See shard_modules
@@ -238,13 +239,14 @@ def _shard_modules(  # noqa: C901
         assert isinstance(
             env, ShardingEnv
         ), "Currently hybrid sharding only support use manual sharding plan"
-        planner = EmbeddingShardingPlanner(
-            topology=Topology(
-                local_world_size=get_local_size(env.world_size),
-                world_size=env.world_size,
-                compute_device=device.type,
+        if planner is None:
+            planner = EmbeddingShardingPlanner(
+                topology=Topology(
+                    local_world_size=get_local_size(env.world_size),
+                    world_size=env.world_size,
+                    compute_device=device.type,
+                )
             )
-        )
         pg = env.process_group
         if pg is not None:
             plan = planner.collective_plan(module, sharders, pg)