NXP backend: Add support for aten.permute_copy.default.

backends/nxp/TARGETS

@@ -32,6 +32,18 @@ runtime.python_library(
     ],
 )
 
+runtime.python_library(
+    name = "_passes",
+    srcs = glob([
+        "_passes/*.py",
+    ]),
+    deps = [
+        "//caffe2:torch",
+        "//executorch/exir:lib",
+        "//executorch/exir:pass_manager",
+    ],
+)
+
 runtime.python_library(
     name = "quantizer",
     srcs = [
@@ -65,6 +77,7 @@ runtime.python_library(
     deps = [
         ":neutron_sdk",
         ":aten_passes",
+        ":_passes",
         ":quantizer",
         "fbsource//third-party/pypi/flatbuffers:flatbuffers",
         "fbsource//third-party/pypi/ml-dtypes:ml-dtypes",

backends/nxp/_passes/remove_getitem_pass.py

@@ -0,0 +1,103 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# Copyright 2025 NXP
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+from executorch.backends.nxp.backend.node_format_inference import (
+    NodeFormat,
+    NXP_NODE_FORMAT,
+)
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass, PassResult
+
+
+class RemoveGetItemPass(ExportPass):
+    """
+    This remove item is used to remove getitem operator for max_pool2d_with_indices.default operator, and replace it with a single operator,
+    that extracts the first output. More specifically, we are only getting the first output from aten::maxpool2d operator.
+    Before Pass:
+        MaxPool2d ---> GetItem[max_values, max_indexes]
+    After Pass:
+        MaxPool2d -> max_values
+    """
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        module = graph_module
+        for node in module.graph.nodes:
+            if node.op == "call_function":
+                if (
+                    node.target.__name__ == "aten.max_pool2d_with_indices.default"
+                    or node.target.__name__ == "aten.max.dim"
+                ):
+                    users = list(node.users.keys())
+
+                    if len(users) != 1:
+                        if len(users) == 2 and node.target.__name__ == "aten.max.dim":
+                            # Two users is allowed for max.dim. For that case,
+                            # rather than removing the getitem node in this
+                            # pass, we handle the getitem nodes in the op's
+                            # visitor when serializing
+                            continue
+                        else:
+                            raise AssertionError(
+                                f"Invalid number of users for {node.target.__name__}: {len(users)}"
+                            )
+
+                    getitem_node = list(node.users.keys())[0]
+
+                    if getitem_node.target.__name__ != "getitem":
+                        raise AssertionError(
+                            f"Expected max node's user to be getitem, got {getitem_node.target.__name__}"
+                        )
+
+                    getitem_index = getitem_node.args[1]
+
+                    with module.graph.inserting_before(node):
+                        if (
+                            node.target.__name__
+                            == "aten.max_pool2d_with_indices.default"
+                        ):
+                            if getitem_index != 0:
+                                raise AssertionError(
+                                    f"Expected second argument of getitem node for {node.target.__name__} to be 0, got {getitem_index}. XNNPACK delegate currently only supports getting just the max values from the op but not getting the corresponding indices."
+                                )
+                            new_max_wd = module.graph.create_node(
+                                "call_function",
+                                exir_ops.edge.aten.max_pool2d.default,
+                                args=node.args,
+                                kwargs=node.kwargs,
+                            )
+
+                        else:
+                            if getitem_index != 0:
+                                raise AssertionError(
+                                    f"Expected second argument of getitem node for {node.target.__name__} to be 0, got {getitem_index}. XNNPACK delegate currently only supports getting just the max values or getting both the max values and their corresponding indices from the op, but not getting the indices alone."
+                                )
+                            new_max_wd = module.graph.create_node(
+                                "call_function",
+                                exir_ops.edge.aten.amax.default,
+                                args=node.args,
+                                kwargs=node.kwargs,
+                            )
+
+                    # MODIFIED PART START
+                    # Make sure to preserve the inferred node format.
+                    new_max_wd.meta[NXP_NODE_FORMAT] = node.meta.get(
+                        NXP_NODE_FORMAT, NodeFormat.NONE
+                    )
+                    # MODIFIED PART END
+
+                    getitem_node.replace_all_uses_with(new_max_wd)
+
+                    module.graph.erase_node(getitem_node)
+                    module.graph.erase_node(node)
+
+        graph_module.recompile()
+        # Propagate metadata and retrace module
+        graph_module = super().call(graph_module).graph_module
+
+        return PassResult(graph_module, True)

backends/nxp/backend/edge_program_converter.py

@@ -19,10 +19,7 @@
 from torch.nn.parameter import Parameter
 from executorch.backends.nxp.backend.ir.converter.node_converters.ops_converters import *  # noqa F403
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
-from executorch.backends.nxp.backend.node_format_inference import (
-    NodeFormat,
-    NodeFormatInference,
-)
+from executorch.backends.nxp.backend.node_format import NodeFormat, NXP_NODE_FORMAT
 from executorch.exir.dialects._ops import ops as exir_ops
 
 # noinspection PyProtectedMember
@@ -66,7 +63,7 @@ def convert_program(
         conversion_config: ConversionConfig = _default_conversion_config,
         neutron_target_spec: NeutronTargetSpec = _default_target_spec,
         custom_delegation_options: CustomDelegationOptions = _default_delegation_options,
-    ) -> (bytes, dict):
+    ) -> (bytes, dict[str, NodeFormat]):
         """
         Convert ExportedProgram in Edge dialect to IR (TFLite flatbuffers) as bytes.
 
@@ -76,12 +73,10 @@ def convert_program(
         :param custom_delegation_options: Custom user options which affect node delegation.
         :return: TFLite flatbuffers as bytes.
         """
-        node_formats = NodeFormatInference(edge_program).identify_node_formats()
         parameters_mapping = self.map_inputs_to_parameters(edge_program)
 
         cc = self.build_conversion_context(
             parameters_mapping,
-            node_formats,
             neutron_target_spec,
             conversion_config,
             custom_delegation_options,
@@ -92,13 +87,16 @@ def convert_program(
         self._convert_qdq_cluster_q_dq_nodes(edge_program.graph.nodes, cc)
         self._process_nodes(edge_program.graph.nodes, cc)
 
-        # Assign output
-        io_formats = cc.tflite_builder.assign_model_io_to_subgraph_and_get_io_formats(
-            edge_program.graph_signature
-        )
+        # Assign the model its inputs and outputs.
+        cc.tflite_builder.assign_model_io_to_subgraph(edge_program.graph_signature)
 
-        # TFLite model generation
+        # Apply optimizations and finalize the model.
         internal_tflite_model = cc.tflite_builder.finish()
+
+        # Extract the formats of the model's inputs and outputs.
+        io_formats = cc.tflite_builder.get_io_formats(edge_program.graph_signature)
+
+        # TFLite model generation
         flatbuffers_builder = flatbuffers.Builder()
         internal_tflite_model.gen_tflite(flatbuffers_builder)
 
@@ -108,7 +106,7 @@ def convert_program(
     def append_placeholders_and_tensors(nodes: list[Node], context: ConversionContext):
         for node in nodes:
             if node.op == "placeholder":
-                node_format = context.node_formats[node]
+                node_format = node.meta[NXP_NODE_FORMAT]
 
                 if node.name in context.parameters_mapping:
                     # Node is placeholder and has data -> append as static tensor with data
@@ -121,7 +119,7 @@ def append_placeholders_and_tensors(nodes: list[Node], context: ConversionContex
                     context.tflite_builder.append_as_fake_tensor(node, node_format)
             elif node.op == "call_function":
                 # Node is call function -> append only output as a tensor
-                node_format = context.node_formats[node]
+                node_format = node.meta[NXP_NODE_FORMAT]
                 context.tflite_builder.append_as_fake_tensor(node, node_format)
             elif node.op == "output":
                 # Nothing to do
@@ -179,7 +177,6 @@ def map_inputs_to_parameters(edge_program: ExportedProgram) -> dict[str, Paramet
     @staticmethod
     def build_conversion_context(
         parameters_mapping: dict,
-        node_formats: dict[Node, NodeFormat],
         neutron_target_spec: NeutronTargetSpec,
         conversion_config: ConversionConfig = _default_conversion_config,
         custom_delegation_options: CustomDelegationOptions = _default_delegation_options,
@@ -195,7 +192,6 @@ def build_conversion_context(
             tflite_builder,
             conversion_config,
             parameters_mapping,
-            node_formats,
             custom_delegation_options,
         )
 

backends/nxp/backend/ir/conversion_config.py

@@ -13,7 +13,7 @@ def __init__(self, args: dict | None = None):
 
         :param args: Optional dictionary with conversion arguments. Unknown arguments are ignored.
         """
-        self.keep_io_format: bool = False
+        self.use_neutron_for_format_conversion: bool = False
         self.allow_inputs_stripping: bool = True
         self.qdq_aware_conversion: bool = True
         self.symbolic_dimensions_mapping: dict[str, int] | None = None

backends/nxp/backend/ir/conversion_context.py

@@ -10,24 +10,20 @@
 from executorch.backends.nxp.backend.ir.converter.builder.aten_model_builder_director import (
     AtenModelBuilderDirector,
 )
-from executorch.backends.nxp.backend.node_format_inference import NodeFormat
-from torch import Node
 from torch.nn import Parameter
 
 
 class ConversionContext:
     tflite_builder: AtenModelBuilderDirector
     conversion_config: ConversionConfig
     parameters_mapping: dict[str, Parameter]
-    node_formats: dict[Node, NodeFormat]
     custom_delegation_options: CustomDelegationOptions
 
     def __init__(
         self,
         tflite_builder: AtenModelBuilderDirector,
         conversion_config: ConversionConfig,
         parameters_mapping: dict,
-        node_formats: dict[Node, NodeFormat],
         custom_delegation_options: CustomDelegationOptions,
     ):
         """
@@ -39,5 +35,4 @@ def __init__(
         self.tflite_builder = tflite_builder
         self.conversion_config = conversion_config
         self.parameters_mapping = parameters_mapping
-        self.node_formats = node_formats
         self.custom_delegation_options = custom_delegation_options

backends/nxp/backend/ir/converter/builder/aten_model_builder_director.py

@@ -9,7 +9,7 @@
 from executorch.backends.nxp.backend.ir.converter.conversion import translator
 from executorch.backends.nxp.backend.ir.tensor_formatting import TensorFormat
 from executorch.backends.nxp.backend.ir.tflite_generator import tflite_model
-from executorch.backends.nxp.backend.node_format_inference import NodeFormat
+from executorch.backends.nxp.backend.node_format import NodeFormat
 from torch.fx import Node
 from torch.nn import Parameter
 
@@ -88,19 +88,40 @@ def append_operators(self, ops_to_add: list[tflite_model.Operator]):
 
             self.check_and_append_operator(op)
 
-    def assign_model_io_to_subgraph_and_get_io_formats(
-        self, graph_signature
-    ) -> dict[str, dict]:
-        """
-        Assign model's inputs/outputs to SubGraph.
+    def get_io_formats(self, graph_signature) -> dict[str, dict[str, TensorFormat]]:
+        """Get a mapping from tensor names to their formats.
 
-        :param graph_signature: Instance of GraphSignature.
+        :param graph_signature:  Instance of GraphSignature.
         :returns: Mapping between IO tensors' names and their formats.
         """
         io_formats = {
             "inputs": {},
             "outputs": {},
         }
+        for input_name in graph_signature.user_inputs:
+            tensor = self.tensor_for_name(input_name)
+            assert input_name == tensor.name, (
+                "Program's input name doesn't match with tensor name in TFLite. "
+                "Input was probably redirected."
+            )
+            io_formats["inputs"][tensor.name] = tensor.tensor_format
+
+        for output_name in graph_signature.user_outputs:
+            tensor = self.tensor_for_name(output_name)
+            assert output_name == tensor.name, (
+                "Program's output name doesn't match with tensor name in TFLite. "
+                "Output was probably redirected."
+            )
+            io_formats["outputs"][tensor.name] = tensor.tensor_format
+
+        return io_formats
+
+    def assign_model_io_to_subgraph(self, graph_signature):
+        """
+        Assign model's inputs/outputs to SubGraph.
+
+        :param graph_signature: Instance of GraphSignature.
+        """
 
         self.get_sub_graph().inputs = tflite_model.SubGraphInputs()
         for input_name in graph_signature.user_inputs:
@@ -110,7 +131,6 @@ def assign_model_io_to_subgraph_and_get_io_formats(
                 "Input was probably redirected."
             )
             self.get_sub_graph().inputs.tmp_inputs.append(tensor)
-            io_formats["inputs"][tensor.name] = tensor.tensor_format
 
         self.get_sub_graph().outputs = tflite_model.SubGraphOutputs()
         for output_name in graph_signature.user_outputs:
@@ -120,7 +140,3 @@ def assign_model_io_to_subgraph_and_get_io_formats(
                 "Output was probably redirected."
             )
             self.get_sub_graph().outputs.tmp_outputs.append(tensor)
-
-            io_formats["outputs"][tensor.name] = tensor.tensor_format
-
-        return io_formats