Move user-defined passes after SpecPropPass

examples/models/llama/export_llama_lib.py

@@ -895,7 +895,6 @@ def _to_edge_and_lower_llama_xnnpack(
     if gen_tag_fn is not None:
         from executorch.exir.passes.external_constants_pass import (
             delegate_external_constants_pass_unlifted,
-            external_constants_pass,
         )
 
         assert (
@@ -906,18 +905,14 @@ def _to_edge_and_lower_llama_xnnpack(
             gen_tag_fn=gen_tag_fn,
         )
 
-        # Also add a pass for 'to_executorch' to tag weights that aren't delegated.
-        additional_passes.append(
-            partial(external_constants_pass, gen_tag_fn=gen_tag_fn)
-        )
-
     builder = builder.to_edge_transform_and_lower(partitioners)
     if verbose:
         print_delegation_info(builder.edge_manager.exported_program().graph_module)
 
-    # we need builder.export_program
-
-    return builder.to_executorch(passes=additional_passes)
+    # Add gen_tag_fn to tag non-delegated weights as well.
+    return builder.to_executorch(
+        passes=additional_passes, external_constants_tag=gen_tag_fn
+    )
 
 
 def _to_edge_and_lower_llama_openvino(

exir/program/_program.py

@@ -839,13 +839,11 @@ def edge_to_executorch_passes(
     Get the pre memory planning passes based on the method name, if the pass is not in the dict, use the default pass.
     """
     passes: List[PassType] = [
-        SpecPropPass(),
         # ExecuTorch backend ops are unable to handle unbacked symints. So after
         # this pass, passes cannot be Interpreter-based, because it will fail if
         # there exists an unbacked symint operation.
         *config.passes,
-        # config.passes may contain external_constants_pass. This pass has to
-        # run after SpecPropPass, which populates tensor names.
+        SpecPropPass(),
         EdgeToBackendOpsPass(),
         RemoveGraphAssertsPass(),
     ] + pre_memory_planning_passes(config, name)

extension/llm/export/builder.py

@@ -473,7 +473,11 @@ def to_edge_transform_and_lower(
         return self
 
     def to_executorch(
-        self, passes: Optional[List[ExportPass]] = None
+        self,
+        passes: Optional[List[ExportPass]] = None,
+        external_constants_tag: Optional[
+            Callable[[torch.fx.Node], Optional[str]]
+        ] = None,
     ) -> "LLMEdgeManager":
         """
         Lower the model to executorch and get an ExecutorchProgram.
@@ -506,6 +510,7 @@ def to_executorch(
                 do_quant_fusion_and_const_prop=True,
                 memory_planning_pass=MemoryPlanningPass(alloc_graph_input=False),
                 sym_shape_eval_pass=ConstraintBasedSymShapeEvalPass(),
+                external_constants=external_constants_tag,
             )
         )
         logging.info(