NXP backend: Resolve limitations of uncertain tensor formats. (#14576)

### Summary
This PR resolves format related issues by inferring the format
(NCHW/NHWC) for all nodes before partitioning. These formats are then
used by the NeutronPartitioner to accurately determine which nodes are
supported on Neutron.

### Test plan
Unit tests provided, and correct function is tested by nearly every test
in the nxp backend.


cc @kimishpatel

Author: MartinPavella

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 +1,108 @@
+# 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 "
+                                    f"{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 "
+                                    f"{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 NXP_NODE_FORMAT
 from executorch.exir.dialects._ops import ops as exir_ops
 
 # noinspection PyProtectedMember
@@ -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,
@@ -108,7 +103,7 @@ 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 +116,7 @@ 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 +174,6 @@ 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 +189,6 @@ def build_conversion_context(
             tflite_builder,
             conversion_config,
             parameters_mapping,
-            node_formats,
             custom_delegation_options,
         )
 

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
 

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

@@ -9,6 +9,9 @@
     CustomDelegationOptions,
 )
 from executorch.backends.nxp.backend.ir.converter.conversion import translator
+from executorch.backends.nxp.backend.ir.converter.conversion.translator import (
+    create_channels_first_to_channels_last_permutation,
+)
 from executorch.backends.nxp.backend.ir.converter.node_converter import (
     _is_dequant_node,
     _is_quant_node,
@@ -18,6 +21,7 @@
     Concatenation,
 )
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
+from executorch.backends.nxp.backend.node_format import NXP_NODE_FORMAT
 from torch.fx import Node
 from torch.nn import Parameter
 
@@ -85,39 +89,48 @@ def _is_supported_on_target(
         if dim == 0:
             return False
 
-        # If all input shapes are equal, the neutron is able to pad the last dimension of inputs and outputs.
-        input_shapes = [_get_shape(input_) for input_ in node.all_input_nodes]
-        if input_shapes.count(input_shapes[0]) == len(input_shapes):
-            if dim == len(input_shapes[0]) - 1:
-                return True
+        # Neutron requires the channels to be a multiple of `num_macs`. The channels could either be the second or the
+        #  last dimension, depending on the formats of the node.
+        if node.meta[NXP_NODE_FORMAT].is_channels_first():
+            # During conversion to IR, the shape will be permuted to channels last, and the dimension on index
+            #  `1` will end up being the channels (last dim in NHWC).
+            channels_index = 1
+            to_nhwc_perm = create_channels_first_to_channels_last_permutation(
+                len(node.meta["val"].shape), True
+            )
+            dim = to_nhwc_perm.index(
+                dim
+            )  # Make sure the dim points to the NHWC dimension.
+        else:
+            # The shape will not be permuted during conversion, so the channels will remain the last dimension.
+            channels_index = -1
 
-        # Neutron requires the channels to be a multiple of numMacs. The channels could either be the second or the
-        #  last dimension, depending on the formats of the node. The format, however, cannot be determined
-        #  during conversion, as it depends on what other nodes are delegated.
         input_channels = [
-            # The second dimension is the channels in PyTorch. If the inputs/output are not channels first, it
-            #  will still be the channels in the IR.
-            _get_shape(input_)[1]
-            for input_ in node.all_input_nodes
-        ] + [
-            # If the inputs/outputs are channels first, the last dimension will be the channels.
-            _get_shape(input_)[-1]
-            for input_ in node.all_input_nodes
+            _get_shape(input_)[channels_index] for input_ in node.all_input_nodes
         ]
-        if any(
-            (input_channel % neutron_target_spec.get_num_macs()) != 0
-            for input_channel in input_channels
-        ):
+        output_channels = _get_shape(node)[channels_index]
+
+        num_macs = neutron_target_spec.get_num_macs()
+        input_shapes = [_get_shape(input_) for input_ in node.all_input_nodes]
+        if any((input_channel % num_macs) != 0 for input_channel in input_channels):
             # neutron-library/src/utils/NeutronLibraryInterrogation.cpp#1492
-            return False
 
-        output_channels = [_get_shape(node)[1], _get_shape(node)[-1]]
-        # neutron-library/src/utils/NeutronLibraryInterrogation.cpp#1493
-        if any(
-            (out_c % neutron_target_spec.get_num_macs()) != 0
-            for out_c in output_channels
-        ):
-            return False
+            # If all input shapes are equal, the neutron is able to pad the last dimension of the inputs.
+            if not (
+                input_shapes.count(input_shapes[0]) == len(input_shapes)
+                and dim == len(input_shapes[0]) - 1
+            ):
+                return False
+
+        if (output_channels % num_macs) != 0:
+            # neutron-library/src/utils/NeutronLibraryInterrogation.cpp#1493
+
+            # If all input shapes are equal, the neutron is able to pad the last dimension of the output.
+            if not (
+                input_shapes.count(input_shapes[0]) == len(input_shapes)
+                and dim == len(input_shapes[0]) - 1
+            ):
+                return False
 
         if len(node.all_input_nodes) < 2:  # Not supported on Neutron
             # TODO Try to skip the operator if this case is realistic.

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

@@ -27,6 +27,8 @@
     pad_v2_options,
 )
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
+
+from executorch.backends.nxp.backend.node_format import NXP_NODE_FORMAT
 from torch.fx import Node
 from torch.nn import Parameter
 
@@ -40,9 +42,16 @@ def _is_supported_on_target(
         custom_delegation_options: CustomDelegationOptions,
     ) -> bool:
         paddings = node.args[1]
-        if len(paddings) > 4 and paddings[4:6] != [0, 0]:
-            # Attempt to Pad channels dimension, which is not supported on Neutron.
-            return False
+        if node.meta[NXP_NODE_FORMAT].is_channels_first():
+            # Dim `1` will end up being the channels. It is padded by paddings[4:6].
+            if len(paddings) > 4 and paddings[4:6] != [0, 0]:
+                # Attempt to Pad channels dimension -> currently not supported
+                return False
+        else:
+            # Dim `-1` will end up being the channels. It is padded by paddings[:2].
+            if len(paddings) > 0 and paddings[:2] != [0, 0]:
+                # Attempt to Pad channels dimension -> currently not supported
+                return False
 
         return True
 
@@ -65,10 +74,6 @@ def _is_supported_in_IR(
         if not NodeConverter._has_shared_q_params_if_quantized(node):
             return False
 
-        if len(paddings) > 4 and paddings[4:6] != [0, 0]:
-            # Attempt to Pad channels dimension -> currently not supported
-            return False
-
         return True
 
     # noinspection PyMethodMayBeStatic

backends/nxp/backend/ir/tensor_formatting.py

@@ -6,7 +6,7 @@
 #
 from enum import Enum
 
-from executorch.backends.nxp.backend.node_format_inference import NodeFormat
+from executorch.backends.nxp.backend.node_format import NodeFormat
 
 
 class TensorFormat(Enum):