NXP backend: Unify ExecuTorch and Neutron data format enums.

backends/nxp/_passes/remove_getitem_pass.py

@@ -1,13 +1,13 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
-# Copyright 2025 NXP
+# Copyright 2025-2026 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 import NodeFormat, NXP_NODE_FORMAT
+from executorch.backends.nxp.backend.data_format import DataFormat, NXP_NODE_FORMAT
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass, PassResult
 
@@ -89,7 +89,7 @@ def call(self, graph_module: torch.fx.GraphModule):
                     # 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
+                        NXP_NODE_FORMAT, DataFormat.NONE
                     )
                     # MODIFIED PART END
 

backends/nxp/backend/data_format.py

@@ -0,0 +1,49 @@
+# Copyright 2023 Martin Pavella
+# Copyright 2023-2026 NXP
+#
+# License: MIT
+# See the LICENSE_MIT for more details.
+#
+
+from enum import Enum
+
+# Key into the `meta` attribute of nodes, which is mapped to their inferred node format.
+NXP_NODE_FORMAT = "nxp_node_format"
+
+
+class DataFormat(Enum):
+    CHANNELS_FIRST = 0
+
+    CHANNELS_LAST = 10
+
+    # The format of TFLite Conv3D weights tensor: [output_channels, input_channels, D, H, W]
+    CONV_3D_WEIGHT_FORMAT = 11
+
+    # Intermediate format between 'Transpose' and 'Reshape' ops when single dimension with value 1
+    # is added/removed via reshaping
+    RESHAPE_SINGLE_UNITARY_TRANSPOSITION = 12
+
+    # The format of TFLite TransposeConv 2D weights tensor: [M/group, kH, kW, C]
+    TRANSPOSE_CONV_2D_WEIGHT_FORMAT = 13
+
+    # No special format (matrices, vectors, shapes etc.). All tensors with the FORMATLESS format MUST have EXACTLY
+    #  the same shape and data in the NeutronIR model and in the ExecuTorch model.
+    FORMATLESS = 20
+
+    NONE = 30  # Format has not been identified
+
+    def is_channels_first(self) -> bool:
+        return self == DataFormat.CHANNELS_FIRST
+
+    def is_channels_last(self) -> bool:
+        return self == DataFormat.CHANNELS_LAST
+
+    @staticmethod
+    def convert_executorch_format_to_neutron(
+        executorch_format: "DataFormat",
+    ) -> "DataFormat":
+        if executorch_format == DataFormat.CHANNELS_FIRST:
+            return DataFormat.CHANNELS_LAST  # Format is converted.
+
+        else:
+            return executorch_format  # Other formats remain unchanged.

backends/nxp/backend/edge_program_converter.py

@@ -18,8 +18,8 @@
 from torch.fx import Node
 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.data_format import DataFormat, NXP_NODE_FORMAT
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
-from executorch.backends.nxp.backend.node_format import NodeFormat, NXP_NODE_FORMAT
 from executorch.exir.dialects._ops import ops as exir_ops
 
 # noinspection PyProtectedMember
@@ -65,7 +65,7 @@ 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[str, NodeFormat]):
+    ) -> tuple[bytes, dict[str, DataFormat]]:
         """
         Convert ExportedProgram in Edge dialect to IR (TFLite flatbuffers) as bytes.
 

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

@@ -1,15 +1,14 @@
-# Copyright 2024 NXP
+# Copyright 2024,2026 NXP
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+from executorch.backends.nxp.backend.data_format import DataFormat
 from executorch.backends.nxp.backend.ir.converter.builder.model_builder import (
     ModelBuilder,
 )
 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 import NodeFormat
 from torch.fx import Node
 from torch.nn import Parameter
 
@@ -20,13 +19,13 @@ class AtenModelBuilderDirector(ModelBuilder):
     contains methods related to Edge program nodes conversion.
     """
 
-    def append_as_fake_tensor(self, node: Node, node_format: NodeFormat):
+    def append_as_fake_tensor(self, node: Node, node_format: DataFormat):
         """
         Append node into ModelBuilder as tensor without data (FakeTensor). Can be used
         for activations and output tensors.
 
         :param node: Node instance.
-        :param node_format: NodeFormat definition.
+        :param node_format: DataFormat definition.
         """
         if self.tensor_exists(node.name):
             return
@@ -41,17 +40,19 @@ def append_as_fake_tensor(self, node: Node, node_format: NodeFormat):
             shape = translator.dims_to_channels_last(shape)
 
         tensor = self.create_empty_tensor(node.name, _type, shape)
-        tensor.tensor_format = TensorFormat.from_node_format(node_format)
+        tensor.tensor_format = DataFormat.convert_executorch_format_to_neutron(
+            node_format
+        )
 
     def append_as_static_tensor(
-        self, node: Node, node_format: NodeFormat, tensor: Parameter
+        self, node: Node, node_format: DataFormat, tensor: Parameter
     ):
         """
         Append node into ModelBuilder as tensor with data (static). Can be used for weights,
         permutations etc.
 
         :param node: Node instance.
-        :param node_format: NodeFormat definition.
+        :param node_format: DataFormat definition.
         :param tensor: Torch Tensor (Parameter) that holds tensor data.
         """
         assert not self.tensor_exists(node.name), f"Tensor '{node.name}' already added!"
@@ -65,7 +66,9 @@ def append_as_static_tensor(
             data = translator.convert_data_to_channels_last(data)
 
         tensor = self.create_tensor_for_data(data, node.name)
-        tensor.tensor_format = TensorFormat.from_node_format(node_format)
+        tensor.tensor_format = DataFormat.convert_executorch_format_to_neutron(
+            node_format
+        )
 
     def append_operators(self, ops_to_add: list[tflite_model.Operator]):
         """
@@ -88,7 +91,7 @@ def append_operators(self, ops_to_add: list[tflite_model.Operator]):
 
             self.check_and_append_operator(op)
 
-    def get_io_formats(self, graph_signature) -> dict[str, dict[str, TensorFormat]]:
+    def get_io_formats(self, graph_signature) -> dict[str, dict[str, DataFormat]]:
         """Get a mapping from tensor names to their formats.
 
         :param graph_signature:  Instance of GraphSignature.

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

@@ -7,13 +7,13 @@
 #
 
 from copy import deepcopy
-from itertools import chain
 from typing import List, Optional, Union
 
 import executorch.backends.nxp.backend.ir.converter.conversion.translator as translator
 import executorch.backends.nxp.backend.ir.logger as logger
 import executorch.backends.nxp.backend.ir.tflite_generator.tflite_model as tflite_model
 import numpy as np
+from executorch.backends.nxp.backend.data_format import DataFormat
 from executorch.backends.nxp.backend.edge_helper import is_channels_last_dim_order
 from executorch.backends.nxp.backend.ir.conversion_config import ConversionConfig
 from executorch.backends.nxp.backend.ir.converter.builder import (
@@ -35,7 +35,6 @@
 )
 from executorch.backends.nxp.backend.ir.lib.tflite.TensorType import TensorType
 from executorch.backends.nxp.backend.ir.neutron_ir_post_processing import optimizer
-from executorch.backends.nxp.backend.ir.tensor_formatting import TensorFormat
 from executorch.backends.nxp.backend.ir.tflite_generator.builtin_options import (
     cast_options,
     dequantize_options,
@@ -227,7 +226,7 @@ def channels_first_version_of(self, t_tensor: tflite_model.Tensor):
         new_tensor.shape = translator.channels_last_shape_to_channels_first(
             t_tensor.shape
         )
-        new_tensor.tensor_format = TensorFormat.CHANNELS_FIRST
+        new_tensor.tensor_format = DataFormat.CHANNELS_FIRST
 
         perm = translator.create_channels_last_to_channels_first_permutation(
             t_tensor.rank
@@ -398,7 +397,7 @@ def _make_inputs_channels_first(self):
                     input_tensor, input_tensor.name + "_channels_first"
                 )
                 new_input.shape = new_input_shape
-                new_input.tensor_format = TensorFormat.CHANNELS_FIRST
+                new_input.tensor_format = DataFormat.CHANNELS_FIRST
 
                 transpose = self._create_transpose_operator(
                     new_input, input_tensor, perm
@@ -484,14 +483,6 @@ def _keep_one_empty_buffer(self):
             # It's safe to replace the buffer.
             t.tmp_buffer = empty_buffer
 
-    def replace_io_tensor_format_with_node_format(self):
-        for t in chain(
-            self.get_sub_graph().inputs.tmp_inputs,
-            self.get_sub_graph().outputs.tmp_outputs,
-        ):
-            if isinstance(t.tensor_format, TensorFormat):
-                t.tensor_format = t.tensor_format.to_equal_node_format()
-
     def finish(self) -> tflite_model.Model:
         """Finalize and optimize the converted TFLite model. Then return it.
 
@@ -514,8 +505,6 @@ def finish(self) -> tflite_model.Model:
 
         self._keep_one_empty_buffer()
 
-        self.replace_io_tensor_format_with_node_format()
-
         # Remove outputs, which are not produced by any node. Otherwise, there would be errors after inference.
         operator_outputs = []
         for op in self.get_operators().vector:
@@ -1584,7 +1573,7 @@ def prepare_dynamic_tensor_for_correct_broadcasting_with_channels_first_tensors(
         transpose_output.shape = tflite_model.Shape(
             translator.apply_permutation_to(transpose_output.shape.vector, perm)
         )
-        transpose_output.tensor_format = TensorFormat.CHANNELS_LAST
+        transpose_output.tensor_format = DataFormat.CHANNELS_LAST
 
         transpose = self._create_transpose_operator(
             transpose_input, transpose_output, perm

backends/nxp/backend/ir/converter/node_converter.py

@@ -173,13 +173,22 @@ def assert_convertible(self, node):
         """Assert that the call `_is_supported_in_IR()` returns `True`. Otherwise, raise an exception and print an
         error message.
         """
-        assert self._is_supported_in_IR(
+        supported_in_ir = self._is_supported_in_IR(
             node,
             self.context.parameters_mapping,
             self.context.custom_delegation_options,
-        ), (
-            f"Node `{node}` is not convertible to the intermediate representation. "
-            "There is an error in the partitioner."
+        )
+
+        supported_on_target = self._is_supported_on_target(
+            node,
+            self.neutron_target_spec,
+            self.context.parameters_mapping,
+            self.context.custom_delegation_options,
+        )
+
+        assert supported_in_ir and supported_on_target, (
+            f"Node `{node}` was selected for delegation to Neutron, but it is not convertible to the intermediate "
+            "representation. There is an error in the NXP partitioner,]. Please report this."
         )
 
     @property

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

@@ -1,4 +1,4 @@
-# Copyright 2025 NXP
+# Copyright 2025-2026 NXP
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -8,6 +8,7 @@
 from executorch.backends.nxp.backend.custom_delegation_options import (
     CustomDelegationOptions,
 )
+from executorch.backends.nxp.backend.data_format import NXP_NODE_FORMAT
 from executorch.backends.nxp.backend.edge_helper import previous_non_qdq_node
 from executorch.backends.nxp.backend.ir.converter.conversion import translator
 from executorch.backends.nxp.backend.ir.converter.conversion.translator import (
@@ -23,7 +24,6 @@
     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.fx.passes.infra.partitioner import Partition
 from torch.nn import Parameter

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

@@ -1,4 +1,4 @@
-# Copyright 2024-2025 NXP
+# Copyright 2024-2026 NXP
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -8,6 +8,8 @@
 
 import numpy as np
 
+from executorch.backends.nxp.backend.data_format import NXP_NODE_FORMAT
+
 from executorch.backends.nxp.backend.edge_helper import input_rank
 from executorch.backends.nxp.backend.ir.converter.conversion.translator import (
     apply_permutation_to,
@@ -27,8 +29,6 @@
     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
 

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

@@ -1,10 +1,11 @@
-# Copyright 2024-2025 NXP
+# Copyright 2024-2026 NXP
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
 import numpy as np
 import torch
+from executorch.backends.nxp.backend.data_format import DataFormat
 
 from executorch.backends.nxp.backend.edge_helper import (
     input_tensor,
@@ -37,7 +38,6 @@
 )
 from executorch.backends.nxp.backend.ir.converter.tensor_utils import tensor_has_data
 from executorch.backends.nxp.backend.ir.lib.tflite.TensorType import TensorType
-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.ir.tflite_generator.builtin_options import (
     conv_2d_options,
@@ -386,7 +386,7 @@ def _convert_transpose_conv(
                 w.quantization.quantized_dimension = 0
         else:
             raise NotImplementedError("Dynamic Transpose Conv weights.")
-        w.tensor_format = TensorFormat.TRANSPOSE_CONV_2D_WEIGHT_FORMAT
+        w.tensor_format = DataFormat.TRANSPOSE_CONV_2D_WEIGHT_FORMAT
 
         output_shape_tensor_data = np.asarray(y.shape.vector, dtype=np.int32)
         o = self.builder.create_tensor_for_data(

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

@@ -1,9 +1,10 @@
-# Copyright 2025 NXP
+# Copyright 2025-2026 NXP
 #
 # 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.data_format import NXP_NODE_FORMAT
 
 from executorch.backends.nxp.backend.ir.converter.conversion.translator import (
     create_channels_last_to_channels_first_permutation,
@@ -19,7 +20,6 @@
     mean_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