Add VBE support for PositionWeightedModuleCollection

Summary:
As titled, we have seen wins from position encoding in modeling and would like to leverage PositionWeightedModuleCollection to reduce the cost https://fb.workplace.com/groups/204375858345877/permalink/884618276988295/

I have  a stack locally that show NE equivalence between PositionWeightedModuleCollection and position encoding in modeling
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Given IG has adopted VBE, I am adding necessary plumbing for VBE in PositionWeightedModuleCollection

**Diffs will land after code freeze but publish first to get the review underway**

Differential Revision: D67526005

torchrec/modules/feature_processor_.py

@@ -10,7 +10,7 @@
 #!/usr/bin/env python3
 
 import abc
-from typing import Dict, Optional
+from typing import Dict, List, Optional
 
 import torch
 
@@ -150,6 +150,13 @@ def get_weights_list(
     return torch.cat(weights_list) if weights_list else features.weights_or_none()
 
 
+@torch.fx.wrap
+def get_stride_per_key_per_rank(kjt: KeyedJaggedTensor) -> Optional[List[List[int]]]:
+    if not kjt.variable_stride_per_key():
+        return None
+    return kjt.stride_per_key_per_rank()
+
+
 class PositionWeightedModuleCollection(FeatureProcessorsCollection, CopyMixIn):
     def __init__(
         self, max_feature_lengths: Dict[str, int], device: Optional[torch.device] = None
@@ -193,6 +200,7 @@ def forward(self, features: KeyedJaggedTensor) -> KeyedJaggedTensor:
             offsets=features.offsets(),
             stride=features.stride(),
             length_per_key=features.length_per_key(),
+            stride_per_key_per_rank=get_stride_per_key_per_rank(features),
         )
 
     def copy(self, device: torch.device) -> nn.Module: