diff --git a/fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_inference.py b/fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_inference.py
index 9c6d1b5b19..2cf6301150 100644
--- a/fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_inference.py
+++ b/fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_inference.py
@@ -1137,78 +1137,6 @@ def reset_cache_states(self) -> None:
         self.lxu_state.fill_(0)
         self.timestep_counter.reset()
 
-    @torch.jit.export
-    def split_embedding_weights(
-        self,
-        split_scale_shifts: bool = True
-        # When true, return list of two tensors, the first with weights and
-        # the second with scale_bias.
-        # This should've been named as split_scale_bias.
-        # Keep as is for backward compatibility.
-    ) -> List[Tuple[Tensor, Optional[Tensor]]]:
-        """
-        Returns a list of weights, split by table
-        """
-        assert self.weight_initialized
-        splits: List[Tuple[Tensor, Optional[Tensor]]] = []
-        for t, (_, rows, dim, weight_ty, _) in enumerate(self.embedding_specs):
-            placement = self.weights_physical_placements[t]
-            if placement == EmbeddingLocation.DEVICE.value:
-                weights = self.weights_dev
-            elif placement == EmbeddingLocation.HOST.value:
-                weights = self.weights_host
-            else:
-                weights = self.weights_uvm
-            offset = self.weights_physical_offsets[t]
-            weights_shifts = weights.detach()[
-                offset : offset
-                + rows
-                * rounded_row_size_in_bytes(
-                    dim, weight_ty, self.row_alignment, self.scale_bias_size_in_bytes
-                )
-            ].view(
-                rows,
-                rounded_row_size_in_bytes(
-                    dim, weight_ty, self.row_alignment, self.scale_bias_size_in_bytes
-                ),
-            )
-
-            if split_scale_shifts:
-                # remove the padding at the end of each row.
-                weights_shifts = weights_shifts[
-                    :,
-                    : unpadded_row_size_in_bytes(
-                        dim, weight_ty, self.scale_bias_size_in_bytes
-                    ),
-                ]
-                if (
-                    weight_ty == SparseType.INT8
-                    or weight_ty == SparseType.INT4
-                    or weight_ty == SparseType.INT2
-                ):
-                    splits.append(
-                        (
-                            weights_shifts[:, self.scale_bias_size_in_bytes :],
-                            weights_shifts[:, : self.scale_bias_size_in_bytes],
-                        )
-                    )
-                else:
-                    assert (
-                        weight_ty == SparseType.FP8
-                        or weight_ty == SparseType.FP16
-                        or weight_ty == SparseType.FP32
-                    )
-                    splits.append(
-                        (
-                            weights_shifts,
-                            None,
-                        )
-                    )
-            else:
-                splits.append((weights_shifts, None))
-
-        return splits
-
     @torch.jit.export
     def split_embedding_weights_with_scale_bias(
         self, split_scale_bias_mode: int = 1
@@ -1300,6 +1228,28 @@ def split_embedding_weights_with_scale_bias(
 
         return splits
 
+    @torch.jit.export
+    def split_embedding_weights(
+        self,
+        split_scale_shifts: bool = True
+        # When true, return list of two tensors, the first with weights and
+        # the second with scale_bias.
+        # This should've been named as split_scale_bias.
+        # Keep as is for backward compatibility.
+    ) -> List[Tuple[Tensor, Optional[Tensor]]]:
+        """
+        Returns a list of weights, split by table
+        """
+        splits: List[
+            Tuple[Tensor, Optional[Tensor], Optional[Tensor]]
+        ] = self.split_embedding_weights_with_scale_bias(
+            split_scale_bias_mode=(1 if split_scale_shifts else 0)
+        )
+        return [
+            (split_weight_scale_bias[0], split_weight_scale_bias[1])
+            for split_weight_scale_bias in splits
+        ]
+
     @torch.jit.export
     def initialize_weights(self) -> None:
         if not self.weight_initialized: