Fix the type mismatch issue during TGIF publish (#1796)

Summary:
Pull Request resolved: #1796

Annotate _embedding_names_per_rank_per_sharding to make sure it's type of List[List[List[str]]]. The annotation has to be in a separate function with input parameter so that it won't be dropped during symbolic trace.

Reviewed By: s4ayub

Differential Revision: D54442150

fbshipit-source-id: 8940a31c405eb6a5cb947c057ca4e77a08b6f245

torchrec/distributed/quant_embedding.py

@@ -134,6 +134,9 @@ def _construct_jagged_tensors_tw(
     for i in range(len(embeddings)):
         embeddings_i: torch.Tensor = embeddings[i]
         features_i: KeyedJaggedTensor = features[i]
+        if features_i.lengths().numel() == 0:
+            # No table on the rank, skip.
+            continue
 
         lengths = features_i.lengths().view(-1, features_i.stride())
         values = features_i.values()
@@ -287,6 +290,34 @@ def _construct_jagged_tensors(
         return _construct_jagged_tensors_tw(embeddings, features, need_indices)
 
 
+# Wrap the annotation in a separate function with input parameter so that it won't be dropped during symbolic trace.
+# Please note the input parameter is necessary, though is not used, otherwise this function will be optimized.
+@torch.fx.has_side_effect
+@torch.fx.wrap
+def annotate_embedding_names(
+    embedding_names: List[str],
+    dummy: List[List[torch.Tensor]],
+) -> List[str]:
+    return torch.jit.annotate(List[str], embedding_names)
+
+
+def format_embedding_names_per_rank_per_sharding(
+    embedding_names_per_rank_per_sharding: List[List[List[str]]],
+    dummy: List[List[torch.Tensor]],
+) -> List[List[List[str]]]:
+    annotated_embedding_names_per_rank_per_sharding: List[List[List[str]]] = []
+    for embedding_names_per_rank in embedding_names_per_rank_per_sharding:
+        annotated_embedding_names_per_rank: List[List[str]] = []
+        for embedding_names in embedding_names_per_rank:
+            annotated_embedding_names_per_rank.append(
+                annotate_embedding_names(embedding_names, dummy)
+            )
+        annotated_embedding_names_per_rank_per_sharding.append(
+            annotated_embedding_names_per_rank
+        )
+    return annotated_embedding_names_per_rank_per_sharding
+
+
 @torch.fx.wrap
 def output_jt_dict(
     sharding_types: List[str],
@@ -709,11 +740,14 @@ def output_dist(
                 # pyre-ignore
                 sharding_ctx.features_before_input_dist
             )
+
         return output_jt_dict(
             sharding_types=list(self._sharding_type_to_sharding.keys()),
             emb_per_sharding=emb_per_sharding,
             features_per_sharding=features_per_sharding,
-            embedding_names_per_rank_per_sharding=self._embedding_names_per_rank_per_sharding,
+            embedding_names_per_rank_per_sharding=format_embedding_names_per_rank_per_sharding(
+                self._embedding_names_per_rank_per_sharding, output
+            ),
             need_indices=self._need_indices,
             features_before_input_dist_per_sharding=features_before_input_dist_per_sharding,
             unbucketize_tensor_idxs_per_sharding=unbucketize_tensor_idxs_per_sharding,