split to_cadence_edge_executorch API to to_cadence and to_executorch_gen_etrecord

Differential Revision: D65917897

Pull Request resolved: #6880

Author: zonglinpeng

backends/cadence/aot/compiler.py

@@ -196,7 +196,26 @@ def export_to_edge(
 # Export the model and lower it to an EdgeProgramManager (in edge IR), and
 # apply passes specific to Cadence DSP execution. Return both to print the
 # differences.
-def export_to_cadence_edge_executorch(
+def export_to_cadence(
+    model: torch.nn.Module,
+    inputs: tuple[object, ...],
+    dump_graphs: bool = False,
+    output_dir: Optional[str] = None,
+    opt_level: int = 1,
+) -> EdgeProgramManager:
+    edge_prog_manager = export_to_edge(model, inputs)
+    cadence_passes = get_cadence_passes(opt_level)
+
+    # Run a couple required passes for quant/dequant ops
+    cadence_prog_manager = edge_prog_manager.transform(
+        cast(
+            list[Callable[[torch.fx.GraphModule], Optional[PassResult]]], cadence_passes
+        )
+    )
+    return cadence_prog_manager
+
+
+def export_to_executorch_gen_etrecord(
     model: torch.nn.Module,
     inputs: tuple[object, ...],
     dump_graphs: bool = False,

backends/cadence/aot/export_example.py

@@ -16,7 +16,7 @@
 
 from executorch.backends.cadence.aot.compiler import (
     convert_pt2,
-    export_to_cadence_edge_executorch,
+    export_to_executorch_gen_etrecord,
     fuse_pt2,
 )
 
@@ -86,8 +86,8 @@ def export_model(
     quantized_model = fuse_pt2(converted_model, quantizer)
 
     # Get edge program after Cadence specific passes
-    exec_prog: ExecutorchProgramManager = export_to_cadence_edge_executorch(
-        quantized_model, example_inputs, working_dir
+    exec_prog: ExecutorchProgramManager = export_to_executorch_gen_etrecord(
+        quantized_model, example_inputs, output_dir=working_dir
     )
 
     logging.info("Final exported graph:\n")