NXP backend: Update partitioning to avoid delegating no-op partitions.

backends/nxp/backend/custom_delegation_options.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.
@@ -17,3 +17,8 @@ class CustomDelegationOptions:
     #  of `num_macs`. The `force_delegate_cat` allows the user to turn off the defensive check if from the model design
     #  it is known this constraint will be satisfied.
     force_delegate_cat: bool = False
+
+    # Proposed partitions which only contain Neutron no-ops are normally not delegated, as the NeutronConverter would
+    #  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

backends/nxp/backend/edge_helper.py

@@ -1,8 +1,10 @@
-# 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 logging
+
 import torch
 
 from executorch.exir.dialects._ops import ops as exir_ops
@@ -19,6 +21,14 @@
     exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
 ]
 
+# A set of operators which could possibly be no-ops in certain conditions. The operators in this set will be proclaimed
+#  as no-ops (and potentially not delegated), if when run with random data their output matches their input.
+operators_which_may_be_no_ops = {
+    exir_ops.edge.aten.add.Tensor,
+    exir_ops.edge.aten.mul.Tensor,
+    exir_ops.edge.aten.sub.Tensor,
+}
+
 
 def input_tensor(node: Node, input_index: int) -> torch.Tensor:
     if len(node.all_input_nodes) <= input_index:
@@ -220,3 +230,124 @@ def get_non_qdq_parent(node: Node, input_index: int = 0) -> Node | None:
         return None
 
     return quant_node.args[0]
+
+
+def try_get_dequantized_data(
+    dequantize_node: Node, parameters_mapping: dict[str, Parameter]
+) -> Parameter | None:
+    """Get the dequantized data from the following pattern. The dequantization formula is `r = (q - Z) * S`, where `q`
+        represents the static quantized data.
+
+         ┌─────────────────────────┐
+         │ <static_quantized_data> │
+         └────────────┬────────────┘
+                      │
+                ┌─────▼──────┐
+                │ Dequantize │
+                └─────┬──────┘
+                      ▼
+
+
+    :param dequantize_node: The Dequantize node from the pattern, which dequantizes the static quantized data.
+    :param parameters_mapping: Dict mapping tensor names to their static data. Should be inferred from the
+                                `state_dict` attribute of an edge program.
+    :return: The dequantized static parameter, or `None` if the data is not available.
+    """
+    if not _is_dequantize(dequantize_node):
+        return None
+
+    if not node_is_static_tensor(param := dequantize_node.args[0], parameters_mapping):
+        return None
+
+    # The pattern is correct. Dequantize the static data and return it.
+    scale, zp = get_quantization_parameters_for(dequantize_node)
+    quantized_data = parameters_mapping[param.name]
+
+    dequantized_data = (quantized_data - zp) * scale
+    return dequantized_data
+
+
+def is_no_op_on_neutron(node: Node, parameters_mapping: dict[str, Parameter]) -> bool:
+    """Check if a node is a no-op operation from the perspective of Neutron."""
+    if node.op != "call_function":
+        raise ValueError(
+            f"is_no_op_on_neutron(): Expected call_function node, got {node.op}."
+        )
+
+    if node.target in [
+        exir_ops.edge.aten.view_copy.default,
+        exir_ops.edge.dim_order_ops._clone_dim_order.default,
+        exir_ops.edge.aten.clone.default,
+    ]:
+        # Known operators which are always no-ops on Neutron.
+        return True
+
+    if node.target == exir_ops.edge.aten.cat.default and len(node.args[0]) == 1:
+        # Concatenation with 1 input is a no-op.
+        return True
+
+    # For any other operators, run them with random data ad see if the output is identical to the input.
+    torch.manual_seed(42)
+    # noinspection PyBroadException
+    try:
+        input_data = None
+        args_with_random_data = []
+        for arg in node.args:
+            match arg:
+                case Node():
+                    # `arg` is either another operator, a model input, or a static parameter.
+
+                    if (
+                        data := try_get_dequantized_data(arg, parameters_mapping)
+                    ) is not None:
+                        # The `arg` is a static parameter. Use it's actual static data during the no-op test.
+                        args_with_random_data.append(data)
+
+                    else:
+                        # The `arg` is a compute node or a model input. Replace it with random data for the no-op test.
+                        if input_data is not None:
+                            # Some random input data for `node` has already been stored, which means that the node has
+                            #  more than 1 dynamic input node. Therefore, it cannot be a no-op.
+                            return False
+
+                        # Generate the random data. Use the range [-5, 5) to avoid proclaiming operations like Relu as
+                        #  no-ops.
+                        val = arg.meta["val"]
+                        input_data = torch.rand(val.shape, dtype=val.dtype) * 10 - 5
+                        args_with_random_data.append(input_data)
+
+                case list():
+                    # Multiple input nodes are not supported. `aten.cat` is explicitly supported above.
+                    return False
+
+                case _:
+                    # Generic argument (value). Not an input from a previous node. Store it in the arguments for the
+                    #  no-op test.
+                    args_with_random_data.append(arg)
+
+        # Run the operator with the random data. If the input equals the output, the node is considered a no-op.
+        output_data = node.target(*args_with_random_data)
+
+        val = node.meta["val"]
+        if (
+            output_data.dtype == val.dtype
+            and output_data.shape == val.shape
+            and torch.all(input_data == output_data)
+        ):
+            # The operator preserves the shape, data type, and data. Therefore, it is a no-op from the perspective of
+            #  Neutron.
+            if node.target in operators_which_may_be_no_ops:
+                return True
+            else:
+                logging.info(
+                    f"Found the operator `{node.target}`, which appears to be a no-op, but is not in the "
+                    "known no-op list. Please report this issue."
+                )
+
+        else:
+            # Type, shape, or data doesn't match.
+            return False
+
+    except Exception:
+        # If execution fails, assume it's not a no-op.
+        return False

backends/nxp/backend/edge_program_converter.py

@@ -13,13 +13,15 @@
 from executorch.backends.nxp.backend.ir.converter.node_converter import (
     CustomDelegationOptions,
 )
+from torch._subclasses import FakeTensor
 from torch.export import ExportedProgram
 from torch.export.graph_signature import InputKind
 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.ir.tensor_formatting import TensorFormat
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
-from executorch.backends.nxp.backend.node_format import NodeFormat, NXP_NODE_FORMAT
+from executorch.backends.nxp.backend.node_format import NXP_NODE_FORMAT
 from executorch.exir.dialects._ops import ops as exir_ops
 
 # noinspection PyProtectedMember
@@ -65,7 +67,7 @@ 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, dict[str, TensorFormat]]]:
         """
         Convert ExportedProgram in Edge dialect to IR (TFLite flatbuffers) as bytes.
 
@@ -161,20 +163,49 @@ def _process_nodes(self, nodes: list[Node], conversion_context: ConversionContex
                     )
 
     @staticmethod
-    def map_inputs_to_parameters(edge_program: ExportedProgram) -> dict[str, Parameter]:
+    def map_inputs_to_parameters(
+        edge_program: ExportedProgram,
+        post_quantization_state_dict: dict[str, Parameter] | None = None,
+    ) -> dict[str, Parameter]:
         """
         Create mapping between program parameters (input nodes & static data nodes) and their names.
 
         :param edge_program: EdgeProgram instance.
+        :param post_quantization_state_dict: State-dict of the model right after quantization. During partitioning, the
+                                              `edge_program` only contains fake tensors without any data. In this case,
+                                              this state dict is used instead (if provided). Notice: It may potentially
+                                              contain outdated data,
         :return: Mapping from parameter name to parameter instance.
         """
         result_map = {}
 
         for input_spec in edge_program.graph_signature.input_specs:
             if input_spec.kind in [InputKind.PARAMETER, InputKind.BUFFER]:
-                result_map[input_spec.arg.name] = edge_program.state_dict[
-                    input_spec.target
-                ]
+
+                # First, try to load the static data from the model.
+                param = edge_program.state_dict[input_spec.target]
+
+                if not isinstance(param, FakeTensor):
+                    # Use the data from the model.
+                    result_map[input_spec.arg.name] = param
+
+                else:
+                    # It is the partitioning stage, which uses a FakeModel with FakeTensors (without the actual data).
+                    # Try to load the data from the post-quantization state dict.
+                    if (
+                        post_quantization_state_dict is not None
+                        and (
+                            param := post_quantization_state_dict.get(
+                                input_spec.target, None
+                            )
+                        )
+                        is not None
+                    ):
+                        result_map[input_spec.arg.name] = param
+
+                    else:
+                        # There is no data available.
+                        continue
 
         return result_map
 

backends/nxp/backend/ir/converter/node_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.
@@ -75,7 +75,10 @@ def _is_supported_in_IR(
             Classes which implement conversion for individual operators must overwrite this method.
 
         :param node: torch.Node to check.
-        :param parameters_mapping: Dictionary mapping tensor names to their static data (if they have it).
+        :param parameters_mapping: Dictionary mapping static parameter names to Parameter objects containing their data
+                                    (if they have any). During partitioning, this data is extracted from the model right
+                                    after quantization and before edge dialect passes. Therefore, it could potentially
+                                    be outdated.
         :param custom_delegation_options: Custom options which affect delegation.
         """
         pass
@@ -93,7 +96,10 @@ def _is_supported_on_target(
 
         :param node: The node (edge operator) to check.
         :param neutron_target_spec: Object for querying the target platform to retrieve its properties.
-        :param parameters_mapping: Dictionary mapping tensor names to their static data (if they have it).
+        :param parameters_mapping: Dictionary mapping static parameter names to Parameter objects containing their data
+                                    (if they have any). During partitioning, this data is extracted from the model right
+                                    after quantization and before edge dialect passes. Therefore, it could potentially
+                                    be outdated.
         :param custom_delegation_options: Custom options which affect delegation.
         """
         return True
@@ -110,7 +116,10 @@ def is_supported(
 
         :param node: torch.Node to check.
         :param neutron_target_spec: Object for querying the target platform to retrieve its properties.
-        :param parameters_mapping: Dict mapping tensor names to their data.
+        :param parameters_mapping: Dictionary mapping static parameter names to Parameter objects containing their data
+                                    (if they have any). During partitioning, this data is extracted from the model right
+                                    after quantization and before edge dialect passes. Therefore, it could potentially
+                                    be outdated.
         :param custom_delegation_options: Custom user options which affect node delegation.
         """
         return cls._is_supported_in_IR(
@@ -136,7 +145,10 @@ def supports_partitioning_result(
         :param partition_list: List of proposed partitions.
         :param custom_delegation_options: Custom user options which affect node delegation.
         :param neutron_target_spec: NeutronTargetSpec instance.
-        :param parameters_mapping: Dictionary mapping tensor names to their static data.
+        :param parameters_mapping: Dictionary mapping static parameter names to Parameter objects containing their data
+                                    (if they have any). During partitioning, this data is extracted from the model right
+                                    after quantization and before edge dialect passes. Therefore, it could potentially
+                                    be outdated.
         :return: Boolean indicating whether the node supports the current partitioning.
         """
         return True

backends/nxp/backend/ir/converter/node_converters/ops_converters/view_copy_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.
@@ -20,7 +20,6 @@
 )
 from executorch.backends.nxp.backend.ir.converter.node_converter import (
     CustomDelegationOptions,
-    is_not_qdq_node,
     NodeConverter,
 )
 from executorch.backends.nxp.backend.ir.converter.node_converters.shared.reshape_transposition import (
@@ -59,24 +58,6 @@ def _is_supported_in_IR(
 
         return True
 
-    @classmethod
-    def _partition_contains_compute_nodes(cls, view_copy_partition: Partition) -> bool:
-        non_q_dq_partition_nodes = list(
-            filter(is_not_qdq_node, view_copy_partition.nodes)
-        )
-
-        if len(non_q_dq_partition_nodes) == 1:
-            # The `view_copy` cannot be the only node in a partition.
-            return False
-
-        # It is common for a `clone` node to come before the `view_copy`. Make sure these are not the only two nodes
-        #  in the partition.
-        if any("clone" in n.name for n in non_q_dq_partition_nodes):
-            if len(non_q_dq_partition_nodes) <= 2:
-                return False
-
-        return True
-
     @classmethod
     def supports_partitioning_result(
         cls,
@@ -91,9 +72,6 @@ def supports_partitioning_result(
         ]
         assert len(view_copy_partitions) == 1
 
-        if not cls._partition_contains_compute_nodes(view_copy_partitions[0]):
-            return False
-
         input_format = node.args[0].meta[NXP_NODE_FORMAT]
         output_format = node.meta[NXP_NODE_FORMAT]
         input_shape = list(node.args[0].meta["val"].shape)