NXP backend: Replace move relu before concat optimization (#15394)

### Summary
This PR replaces optimization in `move_relu_before_concat.py` by
`MoveActivationBeforeConcat` aten pass. The pass moves selected
activations that are supported for fusion on Neutron (Relu, Relu6,
Sigmoid, Tanh) before the `concat` node if concat input nodes are either
Conv 2D or Linear 2D. The whole node Logic is determined by target
specs, now supporting Neutron-C. Tests updated.

### Test plan
Unit tests provided (test_move_activation_before_concatenation.py).

cc @robert-kalmar

Author: roman-janik-nxp

backends/nxp/aten_passes/move_activation_before_concat.py

@@ -0,0 +1,102 @@
+# Copyright 2025 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.neutron_target_spec import NeutronTargetSpec
+
+from torch.fx import GraphModule, Node
+from torch.fx.passes.infra.pass_base import PassBase, PassResult
+
+
+class MoveActivationBeforeConcat(PassBase):
+    """Move some operators around in the following pattern.
+    This is a common pattern that emerges from the conversion of separable convolutions.
+    This optimization works together with joint quantization of compute nodes and activations. Without it,
+    it is not beneficial.
+
+             │                    │                               │                     │
+      ┌──────▼──────┐      ┌──────▼──────┐                 ┌──────▼──────┐       ┌──────▼──────┐
+      │ aten.conv2d │  ... │ aten.conv2d │                 │ aten.conv2d │  ...  │ aten.conv2d │
+      └──────┬──────┘      └──────┬──────┘                 └──────┬──────┘       └──────┬──────┘
+             └───────┐     ┌──────┘                               │                     │
+                  ┌──▼─────▼─┐           replace with       ┌─────▼─────┐         ┌─────▼─────┐
+                  │ aten.cat │          ──────────────►     │ aten.relu │   ...   │ aten.relu │
+                  └────┬─────┘                              └─────┬─────┘         └─────┬─────┘
+                       │                                          └───────┐     ┌───────┘
+                 ┌─────▼─────┐                                         ┌──▼─────▼─┐
+                 │ aten.relu │                                         │ aten.cat │
+                 └─────┬─────┘                                         └────┬─────┘
+                       │                                                    │
+    """
+
+    def __init__(self, neutron_target_spec: NeutronTargetSpec):
+        self.neutron_target_spec = neutron_target_spec
+
+    def call(self, module: GraphModule) -> bool:
+        def _is_concat(node_: Node) -> bool:
+            return (
+                node_.op == "call_function"
+                and node_.target == torch.ops.aten.cat.default
+            )
+
+        made_changes = False
+
+        for node in module.graph.nodes:
+            if not _is_concat(node):
+                continue  # Not cat node.
+
+            cat_node = node
+            activation = next(iter(cat_node.users))
+
+            # Check if all cat inputs nodes are conv 2D or linear 2D type and their only user is cat.
+            if not all(
+                self.neutron_target_spec.neutron_target_info.is_fusable_conv_or_linear__aten(
+                    input_node
+                )
+                and len(input_node.users) == 1
+                for input_node in cat_node.all_input_nodes
+            ):
+                continue
+
+            # Check if following activation is supported on Neutron as fused activation.
+            if not (
+                len(cat_node.users) == 1
+                and self.neutron_target_spec.neutron_target_info.is_supported_fused_activation__aten(
+                    activation
+                )
+            ):
+                continue
+
+            # Loop all Cat input nodes and insert new activation after node.
+            for input_node in cat_node.all_input_nodes:
+                with module.graph.inserting_after(input_node):
+                    new_activation = module.graph.call_function(
+                        activation.target,
+                        args=(),
+                        kwargs=activation.kwargs,
+                    )
+
+                    new_activation.meta["source_fn_stack"] = [
+                        (
+                            new_activation.name,
+                            activation.meta["source_fn_stack"][-1][-1],
+                        )
+                    ]
+                    new_activation.meta["val"] = input_node.meta["val"]
+
+                    # Replace the uses of the input node with the new activation node.
+                    input_node.replace_all_uses_with(new_activation)
+                    new_activation.args = (input_node, *activation.args[1:])
+
+            # Replace the uses of the activation node with the cat node.
+            activation.replace_all_uses_with(cat_node)
+
+            module.graph.erase_node(activation)
+
+            made_changes = True
+
+        return PassResult(module, made_changes)

backends/nxp/aten_passes/neutron_aten_pass_manager.py

@@ -16,6 +16,9 @@
 from executorch.backends.nxp.aten_passes.fuse_linear_and_add_pass import (
     FuseLinearAndAddPass,
 )
+from executorch.backends.nxp.aten_passes.move_activation_before_concat import (
+    MoveActivationBeforeConcat,
+)
 from executorch.backends.nxp.aten_passes.remove_nodes_with_known_outputs import (
     RemoveNodesWithKnownOutputs,
 )
@@ -25,6 +28,7 @@
 from executorch.backends.nxp.aten_passes.split_gru_based_on_num_layers import (
     SplitGRUBasedOnNumLayers,
 )
+from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
 from executorch.exir.pass_manager import PassManager
 from torch import nn
 from torch.fx.passes.infra.pass_base import PassResult
@@ -34,14 +38,17 @@
 
 class NeutronAtenPassManager(PassManager):
 
-    def __init__(self, passes: list[PassType] = None):
+    def __init__(
+        self, neutron_target_spec: NeutronTargetSpec, passes: list[PassType] = None
+    ):
         passes: list[PassType] = passes or [
             FuseBatchNormWithConvPass(),
             FuseBatchNormWithLinearPass(),
             SplitGroupConvolution(),
             SplitGRUBasedOnNumLayers(),
             RemoveNodesWithKnownOutputs(),
             FuseLinearAndAddPass(),
+            MoveActivationBeforeConcat(neutron_target_spec),
         ]
 
         super().__init__(passes)

backends/nxp/backend/ir/tflite_optimizer/optimizations/move_relu_before_concat.py

@@ -11,9 +11,6 @@
 
 from executorch.backends.nxp.backend.ir import logger
 from executorch.backends.nxp.backend.ir.conversion_config import ConversionConfig
-from executorch.backends.nxp.backend.ir.tflite_optimizer.optimizations.move_relu_before_concat import (
-    MoveActivationBeforeConcatenation,
-)
 from executorch.backends.nxp.backend.ir.tflite_optimizer.optimizations.permute_fully_connected_weights_after_reshape import (
     PermuteFullyConnectedWeightsAfterReshape,
 )
@@ -29,8 +26,6 @@ class Optimization(Enum):
 
     PERMUTE_FULLY_CONNECTED_WEIGHTS_AFTER_RESHAPE = 12
 
-    MOVE_ACTIVATION_BEFORE_CONCAT = 15
-
 
 class Optimizer:
     """
@@ -68,9 +63,6 @@ def __init__(
             Optimization.PERMUTE_FULLY_CONNECTED_WEIGHTS_AFTER_RESHAPE: PermuteFullyConnectedWeightsAfterReshape(
                 builder, conversion_config
             ),
-            Optimization.MOVE_ACTIVATION_BEFORE_CONCAT: MoveActivationBeforeConcatenation(
-                builder, conversion_config
-            ),
         }
 
     def optimize(

backends/nxp/backend/ir/tflite_optimizer/optimizer.py

@@ -12,6 +12,7 @@
 from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
 from executorch.backends.nxp.quantizer.patterns import (
     AbsPattern,
+    ActivationsConcatClusterPattern,
     AdaptiveAvgPoolPattern,
     AddmmPattern,
     AddTensorPattern,
@@ -225,13 +226,16 @@ def __init__(self, neutron_target_spec: NeutronTargetSpec):
         self.op_to_applied_quantizer = {
             pt: False for q in self.quantizers for pt in q.pattern.partition_types()
         }
+        self.cluster_quantizers = [
+            NeutronAtenQuantizer(ActivationsConcatClusterPattern(self), static_qconfig)
+        ]
 
     def transform_for_annotation(
         self, model: torch.fx.GraphModule
     ) -> torch.fx.GraphModule:
         model.graph.eliminate_dead_code()  # Remove dead code to simplify the graph for the passes.
 
-        model = NeutronAtenPassManager()(model).graph_module
+        model = NeutronAtenPassManager(self.neutron_target_spec)(model).graph_module
 
         model.graph.eliminate_dead_code()  # Remove dead code again, in case it was created by the passes.
 
@@ -240,6 +244,10 @@ def transform_for_annotation(
     def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
         self._annotate_inputs(model)
 
+        # Annotate node clusters in model
+        for cluster_quantizer in self.cluster_quantizers:
+            cluster_quantizer.annotate(model)
+
         nodes = list(model.graph.nodes)
         for node in nodes:
             if (