Add deduce_jagged_tensor_with_graph_analysis flag for batch dim distinguish

Summary:
For vdd, it seems that the jagged tensor batch dim is identical to dense tensor batch dim, which caused issue in bmm kernel, that it cannot handle batch size as large as 2^16.

This fix adds a flag `deduce_jagged_tensor_with_graph_analysis` so that when it is turnt on, we depend on graph analysis, i.e. `try_getting_jagged_tensor_map`, to deduce batch dim for jagged tensor. This can be more reliable than deducing based on value.

Differential Revision: D49262422

Author: tissue3

fx2ait/fx2ait/tensor_spec.py

@@ -306,8 +306,9 @@ def _get_max_seq_lens_from_offsets(
 
         return max_seq_lens
 
-    @staticmethod
-    def _try_getting_jagged_tensor_map(
+    @classmethod
+    def try_getting_jagged_tensor_map(
+        cls,
         inputs: List[torch.Tensor],
         jagged_tensor_batch_dims: Set[int],
         fx_inputs: Optional[List[torch.fx.Node]] = None,
@@ -371,6 +372,7 @@ def from_input_list_with_batch_size_jagged_tensor(
         additional_inputs: List[torch.Tensor] = None,
         infer_max_seq_lens_from_offsets: bool = False,
         fx_inputs: List[torch.fx.Node] = None,
+        jagged_tensor_map: Optional[Dict[int, int]] = None,
     ) -> List["TensorSpec"]:
         """
         Most of the recommendation models will work fine using this function.
@@ -385,21 +387,6 @@ def from_input_list_with_batch_size_jagged_tensor(
                 jagged_offsets_batch_dims=jagged_offsets_batch_dims,
             )
 
-        jagged_tensor_map = cls._try_getting_jagged_tensor_map(
-            inputs=inputs,
-            jagged_tensor_batch_dims=jagged_tensor_batch_dims,
-            fx_inputs=fx_inputs,
-        )
-        if jagged_tensor_map:
-            logger.info("Successfully detected a jagged_tensor_map:")
-            for input_id, jagged_tensor_id in jagged_tensor_map.items():
-                logger.info(f"{input_id=}, {jagged_tensor_id=}")
-        else:
-            logger.info(
-                "Unable to detect a jagged_tensor_map: falling back "
-                "to the batch dim-based jagged tensor detection."
-            )
-
         result: List = []
         result_unsorted: List = []
         left_inputs: List = []