Arm backend: Allow --quantize in non delegated using aot_arm_compiler

examples/arm/aot_arm_compiler.py

@@ -620,12 +620,28 @@ def save_bpte_program(exec_prog, original_model: torch.nn.Module, output_name: s
     save_bundled_program(exec_prog, method_test_suites, output_name)
 
 
+def quantize_model(
+    exported_program, args, model: torch.nn.Module, example_inputs, compile_spec
+):
+    model_int8 = quantize(
+        model,
+        args.model_name,
+        compile_spec,
+        example_inputs,
+        args.evaluate,
+        args.evaluate_config,
+    )
+    # Wrap quantized model back into an exported_program
+    exported_program = torch.export.export_for_training(
+        model_int8, example_inputs, strict=True
+    )
+
+    return model_int8, exported_program
+
+
 def to_edge_TOSA_delegate(
-    exported_program,
-    args,
-    model: torch.nn.Module,
+    exported_program, args, model: torch.nn.Module, example_inputs
 ):
-    model_int8 = None
     # As we can target multiple output encodings, one must
     # be specified.
     compile_spec = get_compile_spec(
@@ -634,23 +650,13 @@ def to_edge_TOSA_delegate(
         args.system_config,
         args.memory_mode,
     )
+
+    model_int8 = None
     if args.quantize:
-        model = quantize(
-            model,
-            args.model_name,
-            compile_spec,
-            example_inputs,
-            args.evaluate,
-            args.evaluate_config,
+        model_int8, exported_program = quantize_model(
+            exported_program, args, model, example_inputs, compile_spec
         )
-        model_int8 = model
-        # Wrap quantized model back into an exported_program
-        exported_program = torch.export.export_for_training(
-            model, example_inputs, strict=True
-        )
-
-        if args.intermediates:
-            os.makedirs(args.intermediates, exist_ok=True)
+        model = model_int8
 
     if is_ethosu(compile_spec):
         partitioner = EthosUPartitioner(compile_spec)
@@ -669,6 +675,31 @@ def to_edge_TOSA_delegate(
     return model_int8, edge
 
 
+def to_edge_no_delegate(exported_program, args, model: torch.nn.Module, example_inputs):
+    model_int8 = None
+    if args.quantize:
+        # As we can target multiple output encodings, one must
+        # be specified.
+        compile_spec = get_compile_spec(
+            args.target,
+            args.intermediates,
+            args.system_config,
+            args.memory_mode,
+        )
+        model, exported_program = quantize_model(
+            exported_program, args, model, example_inputs, compile_spec
+        )
+        model_int8 = model
+
+    edge = to_edge_transform_and_lower(
+        exported_program,
+        compile_config=EdgeCompileConfig(
+            _check_ir_validity=False,
+        ),
+    )
+    return model_int8, edge
+
+
 if __name__ == "__main__":  # noqa: C901
     args = get_args()
 
@@ -686,16 +717,18 @@ def to_edge_TOSA_delegate(
     model = exported_program.module()
     model_fp32 = model
 
+    if args.intermediates:
+        os.makedirs(args.intermediates, exist_ok=True)
+
     # Quantize if required
     model_int8 = None
     if args.delegate:
-        model_int8, edge = to_edge_TOSA_delegate(exported_program, args, model)
+        model_int8, edge = to_edge_TOSA_delegate(
+            exported_program, args, model, example_inputs
+        )
     else:
-        edge = to_edge_transform_and_lower(
-            exported_program,
-            compile_config=EdgeCompileConfig(
-                _check_ir_validity=False,
-            ),
+        model_int8, edge = to_edge_no_delegate(
+            exported_program, args, model, example_inputs
         )
 
     dump_delegation_info(edge, args.intermediates)