feat: added checks for support of activations on target

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

@@ -4,6 +4,7 @@
 # LICENSE file in the root directory of this source tree.
 
 from abc import ABC, abstractmethod
+from typing import Callable
 
 import torch
 
@@ -181,6 +182,33 @@ def _has_shared_q_params_if_quantized(node: Node) -> bool:
         # Node not quantized
         return True
 
+    @staticmethod
+    def is_node_alone_in_partition(
+        node: Node, partition_list: list[Partition], filter_fn: Callable
+    ):
+        """Return True if `node` is the only node in its partition for which `filter_fn`
+        returns True.
+
+        The function finds the unique partition containing `node` and applies
+        `filter_fn` to all nodes in that partition. If only one node passes the
+        predicate — and that node is `node` — the function returns True.
+
+        :param node: The torch.Node to check.
+        :param partition_list: List of proposed partitions.
+        :param filter_fn: Predicate applied to nodes in the partition.
+                        `node` is considered alone if it is the only node
+                        for which this predicate returns True.
+        """
+        partitions = [p for p in partition_list if node in p.nodes]
+        if len(partitions) != 1:
+            return False  # Should never happen
+
+        partition = partitions[0]
+        filtered_partition_nodes = list(filter(filter_fn, partition.nodes))
+        return (
+            len(filtered_partition_nodes) == 1 and filtered_partition_nodes[0] == node
+        )
+
     def assert_convertible(self, node):
         """Assert that the call `is_supported()` returns `True`. Otherwise, raise an exception and print an
         error message.

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

@@ -12,6 +12,9 @@
 from executorch.backends.nxp.backend.ir.lib.tflite.BuiltinOperator import (
     BuiltinOperator,
 )
+from executorch.backends.nxp.backend.neutron_operator_support import (
+    activation_supported_on_target,
+)
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
 from torch.fx import Node
 from torch.fx.passes.infra.partitioner import Partition
@@ -77,18 +80,11 @@ def supports_partitioning_result(
         bounds = cls._get_clamp_bounds(node)
 
         if bounds in [cls.SUPPORTED_BOUNDS["Relu"], cls.SUPPORTED_BOUNDS["Relu6"]]:
-            # If this is the only operator in the partition, NeutronConverter will not create a NeutronNode for some
-            #  reason.
-            clamp_partitions = [p for p in partition_list if node in p.nodes]
-            if len(clamp_partitions) != 1:
-                return False  # Should never happen
-
-            clamp_partition = clamp_partitions[0]
-            non_q_dq_partition_nodes = list(
-                filter(is_not_qdq_node, clamp_partition.nodes)
+            is_alone_in_partition = cls.is_node_alone_in_partition(
+                node, partition_list, filter_fn=is_not_qdq_node
             )
-            if len(non_q_dq_partition_nodes) <= 1:
-                return False  # This would be the only node in the partition, which would cause a crash later on.
+            if is_alone_in_partition:
+                return activation_supported_on_target(node, neutron_target_spec)
 
         return True
 

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

@@ -1,47 +1,108 @@
-# 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.
 
 from executorch.backends.nxp.backend.ir.converter.node_converter import (
     CustomDelegationOptions,
+    is_not_qdq_node,
     NodeConverter,
+    Partition,
 )
 from executorch.backends.nxp.backend.ir.lib.tflite.BuiltinOperator import (
     BuiltinOperator,
 )
+from executorch.backends.nxp.backend.neutron_operator_support import (
+    activation_supported_on_target,
+)
+from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
 from torch.fx import Node
 from torch.nn import Parameter
 
 
 class HardTanhConverter(NodeConverter):
 
     # Maps possible input parameters of HardTanh to equivalent ReLU-based operators supported by TFLite.
-    supported_modes_map = {
+    SUPPORTED_MODES_MAP = {
         (0.0, 6.0): BuiltinOperator.RELU6,
         (-1.0, 1.0): BuiltinOperator.RELU_N1_TO_1,
         (0.0, 1.0): BuiltinOperator.RELU_0_TO_1,
         (0.0, float("inf")): BuiltinOperator.RELU,
     }
 
+    # Maps possible modes of HardTanh to equivalent ReLU bounds.
+    SUPPORTED_BOUNDS_MAP = {
+        "ReluN1To1": (-1.0, 1.0),
+        "Relu0To1": (0.0, 1.0),
+        "Relu6": (0.0, 6.0),
+        "Relu": (0.0, float("inf")),
+    }
+
+    @staticmethod
+    def _get_hardtanh_bounds(node: Node) -> tuple[int, int]:
+        args = node.args
+
+        match len(args):
+            case 1:
+                min_val = -1
+                max_val = 1
+
+            case 2:
+                min_val = args[1]
+                max_val = 1
+
+            case 3:
+                min_val = args[1]
+                max_val = args[2]
+
+            case _:
+                # should not occur
+                min_val = 0
+                max_val = 1
+
+        return min_val, max_val
+
     @staticmethod
     def _is_supported_in_IR(
         node: Node,
         parameters_mapping: dict[str, Parameter],
         custom_delegation_options: CustomDelegationOptions,
     ) -> bool:
-        _, min_value, max_value = node.args
-        return (min_value, max_value) in HardTanhConverter.supported_modes_map.keys()
+        bounds = HardTanhConverter._get_hardtanh_bounds(node)
+        return bounds in HardTanhConverter.SUPPORTED_MODES_MAP.keys()
+
+    @classmethod
+    def supports_partitioning_result(
+        cls,
+        node: Node,
+        partition_list: list[Partition],
+        custom_delegation_options: CustomDelegationOptions,
+        neutron_target_spec: NeutronTargetSpec,
+        parameters_mapping: dict[str, Parameter],
+    ) -> bool:
+        bounds = HardTanhConverter._get_hardtanh_bounds(node)
+
+        if bounds in [
+            cls.SUPPORTED_BOUNDS_MAP["Relu"],
+            cls.SUPPORTED_BOUNDS_MAP["Relu6"],
+        ]:
+            is_alone_in_partition = cls.is_node_alone_in_partition(
+                node, partition_list, filter_fn=is_not_qdq_node
+            )
+            if is_alone_in_partition:
+                return activation_supported_on_target(node, neutron_target_spec)
+
+        return True
 
     def convert(self, node: Node):
         """Convert 'aten::hardtanh' to it's supported ReLU equivalent."""
         self.assert_convertible(node)
 
         t_op = self._create_tflite_op_with_io_tensors(node)
 
-        _, min_value, max_value = node.args
+        bounds = HardTanhConverter._get_hardtanh_bounds(node)
 
-        op = self.supported_modes_map[(min_value, max_value)]
+        op = self.SUPPORTED_MODES_MAP[bounds]
         t_op.opcode_index = self.builder.op_code_index_for_op_type(op)
 
         self.builder.append_operators([t_op])

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

@@ -1,15 +1,21 @@
-# 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.
 
 from executorch.backends.nxp.backend.ir.converter.node_converter import (
     CustomDelegationOptions,
+    is_not_qdq_node,
     NodeConverter,
+    Partition,
 )
 from executorch.backends.nxp.backend.ir.lib.tflite.BuiltinOperator import (
     BuiltinOperator,
 )
+from executorch.backends.nxp.backend.neutron_operator_support import (
+    activation_supported_on_target,
+    NeutronTargetSpec,
+)
 from torch.fx import Node
 from torch.nn import Parameter
 
@@ -24,6 +30,23 @@ def _is_supported_in_IR(
     ) -> bool:
         return True
 
+    @classmethod
+    def supports_partitioning_result(
+        cls,
+        node: Node,
+        partition_list: list[Partition],
+        custom_delegation_options: CustomDelegationOptions,
+        neutron_target_spec: NeutronTargetSpec,
+        parameters_mapping: dict[str, Parameter],
+    ) -> bool:
+        is_alone_in_partition = cls.is_node_alone_in_partition(
+            node, partition_list, filter_fn=is_not_qdq_node
+        )
+        if is_alone_in_partition:
+            return activation_supported_on_target(node, neutron_target_spec)
+
+        return True
+
     def convert(self, node: Node):
         self.assert_convertible(node)
 

backends/nxp/backend/neutron_operator_support.py

@@ -1,9 +1,15 @@
-# 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.
 
+from executorch.backends.nxp.backend.data_format import NXP_NODE_FORMAT
+from executorch.backends.nxp.backend.edge_helper import input_tensor
+from executorch.backends.nxp.backend.ir.converter.conversion.translator import (
+    dims_to_channels_last,
+)
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
+from torch.fx import Node
 
 
 def is_tensor_invariant_permutation(
@@ -77,3 +83,23 @@ def transposition_is_supported_on_neutron(
         return True
 
     return False
+
+
+def activation_supported_on_target(
+    node: Node, neutron_target_spec: NeutronTargetSpec
+) -> bool:
+    """This function determines if the current NeutronSoftware properly supports an activation operator represented by the given node.
+
+    :param node: The node representing the activation operator.
+    :param neutron_target_spec: Object for querying the target platform to retrieve its properties.
+    """
+    input_shape = list(input_tensor(node, 0).shape)
+    if node.args[0].meta[NXP_NODE_FORMAT].is_channels_first():
+        input_shape = dims_to_channels_last(input_shape)
+
+    c = input_shape[-1]
+    num_macs = neutron_target_spec.get_num_macs()
+
+    # activations in Neutron are delegable only
+    # if `num_channels` % `num_macs` == 0
+    return c % num_macs == 0

backends/nxp/tests/ir/converter/node_converter/test_clamp_converter.py

@@ -100,21 +100,28 @@ def test_convert_clamp__supported(mocker, min, max):
 
 # noinspection PyShadowingBuiltins
 @pytest.mark.parametrize(
-    "min, max",
+    "input_shape, min, max",
     [
-        pytest.param(0, 6, id="min = 0, max = 6 (Relu6)"),
-        pytest.param(0, None, id="min = 0, max = None (Relu)"),
+        pytest.param(
+            (1, 7, 9, 11),
+            0,
+            6,
+            id="min = 0, max = 6 (Relu6), num_channels not divisible by NUM_MACS, alone in partition",
+        ),
+        pytest.param(
+            (1, 7, 9, 11),
+            0,
+            None,
+            id="min = 0, max = None (Relu), num_channels not divisible by NUM_MACS, alone in partition",
+        ),
     ],
 )
-def test_convert_clamp__single_op__not_delegated_variants(min, max):
-    # Test that Clamp representable as Relu6 or Relu is NOT delegated, because it is a single op model which is not
-    #  supported by Neutron.
-    input_shape = (23,)
+def test_convert_clamp__unsupported_shape(input_shape, min, max):
     model = ClampModule(min, max)
 
     delegated_ep = to_quantized_edge_program(model, input_shape).exported_program()
 
-    # Make sure the `clamp` was NOT delegated (single op model).
+    # Make sure the `clamp` was NOT delegated.
     assert not graph_contains_any_of_ops(delegated_ep.graph, [ExecutorchDelegateCall])
     assert graph_contains_any_of_ops(delegated_ep.graph, [Clamp])