fix(arm): validate partitions for dependency cycles after Q/DQ de-tagging

`_detag_boundary_nodes` removes Q/DQ nodes from partition boundaries
after `CapabilityBasedPartitioner` has produced cycle-free partitions.
However, this de-tagging can introduce dependency cycles for models
with complex attention blocks (e.g. MobileViT, where CNN and
Transformer ops are grouped into a single large partition).

The cycle occurs because removing Q/DQ bridge nodes creates paths
that exit the partition and re-enter it through the now-unpartitioned
nodes, making it impossible to extract the partition as a valid
subgraph.

This change adds cycle validation after `_detag_boundary_nodes`. When
a cycle is detected, the partition is split into connected components
of the surviving (still-tagged) nodes. Each component becomes a
separate partition that is individually cycle-free after de-tagging.

- Add `_validate_partition()`: BFS-based cycle detection (same
  algorithm as `torch.fx.passes.utils.fuser_utils.validate_partition`)
- Add `_find_connected_components()`: undirected graph traversal to
  split surviving nodes into disjoint sub-partitions
- Guard the nocompute-partition `tags.remove()` against already-removed
  tags from the cycle-split path

Tested with MobileViT-S on Ethos-U85: previously failed with
`AssertionError: Invalid partition, found dependency cycles`, now
successfully produces a .pte file (5.7 MB). Nine attention-block
partitions are each split into 3 sub-partitions. All sub-partitions
remain on NPU (no CPU fallback). Existing CNN-only models (ResNet,
MobileNetV2, EfficientNet) are unaffected as their partitions have
no cycles after de-tagging.

Author: beomwookang

backends/arm/tosa/partitioner.py

@@ -14,6 +14,7 @@
 """
 
 import logging
+from collections import deque
 from itertools import count
 from typing import Callable, List, Optional, Sequence, Tuple
 
@@ -131,6 +132,77 @@ def reject_partition(
             )
 
 
+def _validate_partition(nodes: set[torch.fx.Node]) -> bool:
+    """Check whether a set of nodes can be extracted as a subgraph without
+    cycles.
+
+    Perform a BFS from the external users of partition nodes. If any node
+    reached by BFS is itself inside the partition, then extracting the
+    partition would create a dependency cycle in the remaining graph.
+
+    Args:
+        nodes: The set of FX nodes that form the partition.
+
+    Returns:
+        True if the partition is valid (no cycles), False otherwise.
+
+    """
+    outputs: list[torch.fx.Node] = []
+    for node in nodes:
+        for user in node.users:
+            if user not in nodes:
+                outputs.append(user)
+
+    visited: set[torch.fx.Node] = set()
+    queue = deque(outputs)
+    while queue:
+        current = queue.popleft()
+        if current in visited:
+            continue
+        visited.add(current)
+        if current in nodes:
+            return False
+        for user in current.users:
+            if user not in visited:
+                queue.append(user)
+    return True
+
+
+def _find_connected_components(nodes: set[torch.fx.Node]) -> list[set[torch.fx.Node]]:
+    """Find connected components in a set of nodes treating edges as undirected.
+
+    Two nodes are connected if one is an input or user of the other and both
+    are in ``nodes``.
+
+    Args:
+        nodes: The node set to partition into components.
+
+    Returns:
+        A list of disjoint node sets, one per connected component.
+
+    """
+    remaining = set(nodes)
+    components: list[set[torch.fx.Node]] = []
+    while remaining:
+        seed = next(iter(remaining))
+        component: set[torch.fx.Node] = set()
+        queue = deque([seed])
+        while queue:
+            node = queue.popleft()
+            if node in component or node not in remaining:
+                continue
+            component.add(node)
+            for inp in node.all_input_nodes:
+                if inp in remaining and inp not in component:
+                    queue.append(inp)
+            for user in node.users:
+                if user in remaining and user not in component:
+                    queue.append(user)
+        remaining -= component
+        components.append(component)
+    return components
+
+
 class TOSAPartitioner(Partitioner):
     """Partition an exported program into TOSA-delegable subgraphs.
 
@@ -285,6 +357,30 @@ def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
                     reporter,
                 )
 
+                # After de-tagging, the remaining tagged nodes may form
+                # dependency cycles.  This happens when models contain complex
+                # attention blocks (e.g. MobileViT) where Q/DQ nodes act as
+                # bridges between partition segments.  Detect such cycles and
+                # split the partition into valid connected components.
+                surviving = {n for n in partition.nodes if is_partitioned(n, tag)}
+                if surviving and not _validate_partition(surviving):
+                    components = _find_connected_components(surviving)
+                    logger.info(
+                        f"Partition {tag} has dependency cycle after Q/DQ "
+                        f"de-tagging. Splitting into {len(components)} "
+                        f"sub-partition(s)."
+                    )
+                    # Remove the original tag from all nodes
+                    for node in surviving:
+                        del node.meta["delegation_tag"]
+                    tags.remove(tag)
+                    # Re-tag each connected component as a new partition
+                    for component in components:
+                        new_tag = f"tag{next(tag_iterator)}"
+                        tags.add(new_tag)
+                        for node in component:
+                            node.meta["delegation_tag"] = new_tag
+
             # Check whether the partition contains only no-op or non-computational ops. Such partitions don't make sense to delegate, and in the worst case may be optimized away during lowering, which can break compilation."
             is_nocompute_partition = all(
                 _is_noop_clone(node)
@@ -303,7 +399,8 @@ def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
                     partition,
                     reporter,
                 )
-                tags.remove(tag)
+                if tag in tags:
+                    tags.remove(tag)
         return tags
 
     def partition(self, exported_program: ExportedProgram) -> PartitionResult: