NXP backend: Replace CustomDelegationOptions with NeutronTargetSpec in new Neutron C flow references

Author: StrycekSimon

backends/nxp/backend/custom_delegation_options.py

@@ -22,7 +22,3 @@ class CustomDelegationOptions:
     #  not create any NeutronGraph that can be called. This is done by the partitioner itself, and is not handled by
     #  the individual node converters.
     allow_no_op_partitions: bool = False
-
-    # The new neutron converter flow has different constraints for supported operators. These need to be addressed when
-    # deciding is operator is delegated or not in _is_supported_on_target().
-    use_new_flow_neutron_c: bool = False

backends/nxp/backend/ir/converter/node_converters/ops_converters/abs_converter.py

@@ -5,7 +5,6 @@
 
 
 import torch
-
 from executorch.backends.nxp.backend.ir.converter.node_converter import (
     CustomDelegationOptions,
     NeutronTargetSpec,
@@ -36,7 +35,7 @@ def _is_supported_on_target(
         custom_delegation_options: CustomDelegationOptions,
     ) -> bool:
 
-        if custom_delegation_options.use_new_flow_neutron_c:
+        if neutron_target_spec.use_new_flow_neutron_c:
             # Requirements specified by the new Neutron flow documentation.
 
             supported_types = [torch.int8, torch.uint8]

backends/nxp/backend/ir/converter/node_converters/ops_converters/avg_pool_2d_converter.py

@@ -5,7 +5,6 @@
 
 import numpy as np
 import torch
-
 from executorch.backends.nxp.backend.ir.converter.conversion import (
     aten_translator,
     common,
@@ -22,7 +21,6 @@
 from executorch.backends.nxp.backend.ir.tflite_generator.builtin_options import (
     average_pool_2d_options,
 )
-
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
 from torch.fx import Node
 from torch.nn import Parameter
@@ -66,7 +64,7 @@ def _is_supported_on_target(
         kernel = node.args[1]
         stride = node.args[2]
 
-        if custom_delegation_options.use_new_flow_neutron_c:
+        if neutron_target_spec.use_new_flow_neutron_c:
             # Requirements specified by the new Neutron flow documentation.
 
             supported_types = [torch.int8, torch.uint8]

backends/nxp/backend/ir/converter/node_converters/ops_converters/max_pool2d_with_indices_converter.py

@@ -7,7 +7,6 @@
 
 import numpy as np
 import torch
-
 from executorch.backends.nxp.backend.edge_helper import try_get_arg
 from executorch.backends.nxp.backend.ir.converter.conversion import (
     aten_translator,
@@ -74,7 +73,7 @@ def _is_supported_on_target(
             MaxPool2DWithIndicesConverter._get_node_args(node)
         )
 
-        if custom_delegation_options.use_new_flow_neutron_c:
+        if neutron_target_spec.use_new_flow_neutron_c:
             # Requirements specified by the new Neutron flow documentation.
 
             supported_types = [torch.int8, torch.uint8]

backends/nxp/backend/neutron_target_spec.py

@@ -102,6 +102,9 @@ def __init__(self, target: str, use_new_flow_neutron_c: bool = False):
         converter_manager.verify_target(target)
         neutron_converter = converter_manager.get_converter()
         self.neutron_target = neutron_converter.getNeutronTarget(target)
+
+        # The new neutron converter flow has different constraints for supported operators. These need to be addressed when
+        # deciding is operator is delegated or not in _is_supported_on_target().
         self.use_new_flow_neutron_c = use_new_flow_neutron_c
 
         if self.is_subsystem():

backends/nxp/nxp_backend.py

@@ -231,11 +231,11 @@ def preprocess(  # noqa C901
             )
             tflite_model, io_formats = EdgeProgramToIRConverter().convert_program(
                 edge_program,
-                neutron_target_spec=NeutronTargetSpec(target),
-                conversion_config=conversion_config,
-                custom_delegation_options=CustomDelegationOptions(
-                    use_new_flow_neutron_c=use_new_flow_neutron_c
+                neutron_target_spec=NeutronTargetSpec(
+                    target, use_new_flow_neutron_c=use_new_flow_neutron_c
                 ),
+                conversion_config=conversion_config,
+                custom_delegation_options=CustomDelegationOptions(),
             )
 
             neutron_model = NeutronConverterManager(dump_kernel_selection_code).convert(

backends/nxp/tests/executorch_pipeline.py

@@ -195,7 +195,6 @@ def to_quantized_edge_program(
     _neutron_target_spec = NeutronTargetSpec(
         target, use_new_flow_neutron_c=use_new_flow_neutron_c
     )
-    custom_delegation_options.use_new_flow_neutron_c = use_new_flow_neutron_c
     if get_quantizer_fn is None:
         get_quantizer_fn = partial(
             _get_default_quantizer, _neutron_target_spec, use_qat

examples/nxp/aot_neutron_compile.py

@@ -12,7 +12,6 @@
 
 import executorch.extension.pybindings.portable_lib
 import executorch.kernels.quantized  # noqa F401
-
 import torch
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
 from executorch.backends.nxp.edge_passes.neutron_edge_pass_manager import (
@@ -253,7 +252,9 @@ def get_model_and_inputs_from_name(model_name: str, use_random_dataset: bool):
     if args.debug:
         logging.basicConfig(level=logging.DEBUG, format=FORMAT, force=True)
 
-    neutron_target_spec = NeutronTargetSpec(target=args.target)
+    neutron_target_spec = NeutronTargetSpec(
+        target=args.target, use_new_flow_neutron_c=args.use_new_flow_neutron_c
+    )
 
     # 1. pick model from one of the supported lists
     model, example_inputs, calibration_inputs = get_model_and_inputs_from_name(