Prevent union-finding cycles for shared qspecs

test/quantization/pt2e/test_quantize_pt2e.py

@@ -1,5 +1,6 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
+# Copyright 2025 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD 3-Clause license found in the
 # LICENSE file in the root directory of this source tree.
@@ -1215,6 +1216,108 @@ def validate(self, model: torch.fx.GraphModule) -> None:
         self.assertIsNot(observers[0], observers[2])
         self.assertIsNot(observers[1], observers[2])
 
+    def test_allow_implicit_sharing_with_shared_input_edge(self):
+        """This tests implicit sharing when an input edge x is shared between
+        two ops in the following manner:
+
+          /-----------------> eq(a, x) -> b
+         /                   /
+        x -> clone(x) -> a -/
+
+        Clone is annotated such that (x, clone) uses a QuantizationSpec and
+        its output (clone) a SharedQuantizationSpec pointing to its input
+        (x, clone).
+
+        Eq is annotated such that (clone, eq) uses a QuantizationSpec and
+        (x, eq) uses a SharedQuantizationSpec to the former.
+        The output (eq) is not quantized (bool output).
+
+        Verify that the input to clone and its output share the same observer;
+        inputs to eq should also share that same observer due to implicit
+        sharing.
+
+        Context: This test used to trigger a cyclic recursion bug in the
+        following manner:
+        1) Processing edge (x, clone): implicit sharing sees that eq is
+           another user of x with an identical qspec, so (x, clone) starts
+           sharing with (x, eq) by pointing to it.
+        2) Processing edge (clone, eq): implicit sharing tries to share this
+           input edge with the producer output clone. But clone's output
+           uses SharedQuantizationSpec((x, clone)), and from step (1),
+           (x, clone) already points to (x, eq). Therefore unwrapping leads to
+           (x, eq) and (clone, eq) is set to share with (x, eq) by pointing to
+           it.
+        3) Processing edge (x, eq): when resolving its qspec, the algorithm
+           follows the shared reference to (clone, eq), which immediately
+           points back to (x, eq) from step (2). This created a cycle and the
+           unwrap logic recursed endlessly.
+        """
+
+        class BackendAQuantizer(Quantizer):
+            def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+                for node in model.graph.nodes:
+                    if node.target in [
+                        torch.ops.aten.clone.default,
+                        torch.ops.aten.eq.Tensor,
+                    ]:
+                        input_qspec_map = {}
+                        qspec = QuantizationSpec(
+                            dtype=torch.uint8,
+                            quant_min=0,
+                            quant_max=255,
+                            qscheme=torch.per_tensor_affine,
+                            is_dynamic=False,
+                            observer_or_fake_quant_ctr=observer.default_observer,
+                        )
+                        shared_qspec = SharedQuantizationSpec((node.args[0], node))
+
+                        if node.target is torch.ops.aten.clone.default:
+                            input_qspec_map[node.args[0]] = qspec
+                            output_qspec = shared_qspec
+                        elif node.target is torch.ops.aten.eq.Tensor:
+                            input_qspec_map[node.args[0]] = qspec
+                            input_qspec_map[node.args[1]] = shared_qspec
+                            # Output is bool, quantization not applicable
+                            output_qspec = None
+                        else:
+                            assert False
+
+                        node.meta["quantization_annotation"] = QuantizationAnnotation(
+                            input_qspec_map=input_qspec_map,
+                            output_qspec=output_qspec,
+                            allow_implicit_sharing=True,
+                            _annotated=True,
+                        )
+
+            def validate(self, model: torch.fx.GraphModule) -> None:
+                pass
+
+        class M(torch.nn.Module):
+            def forward(self, x):
+                a = x.clone()
+                b = torch.eq(a, x)
+                return b
+
+        m = M().eval()
+        example_inputs = (torch.randn(1, 5),)
+        m = torch.export.export(m, example_inputs, strict=True).module()
+        prepare_pt2e(m, BackendAQuantizer())
+        m(*example_inputs)
+        observers = []
+        for n in m.graph.nodes:
+            if n.target == torch.ops.aten.clone.default:
+                input_obs1 = getattr(m, n.args[0].target)
+                output_obs = getattr(m, next(iter(n.users)).target)
+                self.assertIs(input_obs1, output_obs)
+                observers.append(input_obs1)
+            if n.target == torch.ops.aten.eq.Tensor:
+                input_obs1 = getattr(m, n.args[0].target)
+                input_obs2 = getattr(m, n.args[1].target)
+                self.assertIs(input_obs1, input_obs2)
+                observers.append(input_obs1)
+        assert len(observers) == 2
+        self.assertIs(observers[0], observers[1])
+
     @parametrize("dtype", (torch.float32, torch.bfloat16))
     @parametrize("quant_dtype", (torch.int16, torch.float8_e5m2, torch.float8_e4m3fn))
     def test_quantization_dtype(self, dtype, quant_dtype):

torchao/quantization/pt2e/prepare.py

@@ -1,5 +1,6 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
+# Copyright 2025 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD 3-Clause license found in the
 # LICENSE file in the root directory of this source tree.
@@ -146,18 +147,31 @@ def _union(
     parent: EdgeOrNode,
     child: EdgeOrNode,
     shared_with_map: dict[EdgeOrNode, EdgeOrNode],
+    edge_or_node_to_qspec: dict[EdgeOrNode, QuantizationSpecBase],
 ) -> None:
     """Merge the subtree for `child` with `parent`, the order is important here"""
     root_parent = _find_root_edge_or_node(parent, shared_with_map)
     root_child = _find_root_edge_or_node(child, shared_with_map)
-    # union the two trees by pointing the root of child to root of parent
-    shared_with_map[root_child] = root_parent
+
+    parent_qspec = edge_or_node_to_qspec[root_parent]
+    if (
+        isinstance(parent_qspec, SharedQuantizationSpec)
+        and parent_qspec.edge_or_node == root_child
+    ):
+        # Parent already references child with a shared qspec. We would create
+        # a cycle if we formed an edge from the child to the parent. Therefore,
+        # we reverse the edge in this particular case.
+        shared_with_map[root_parent] = root_child
+    else:
+        # union the two trees by pointing the root of child to root of parent
+        shared_with_map[root_child] = root_parent
 
 
 def _update_shared_with(
     child: EdgeOrNode,
     qspec: QuantizationSpecBase,
     shared_with_map: dict[EdgeOrNode, EdgeOrNode],
+    edge_or_node_to_qspec: dict[EdgeOrNode, QuantizationSpecBase],
 ):
     """Update the `shared_with_map` based on the qspec, this applies the `SharedQuantizationSpec`
     configuration and established the relationship between `edge_or_node` with the edge/node that it
@@ -167,7 +181,7 @@ def _update_shared_with(
         parent = qspec.edge_or_node
         # we point from edge_or_node to the node that it is sharing_with, e.g.
         # qspec for a = SharedQuantizationSpec(b) means `a` points to `b`
-        _union(parent, child, shared_with_map)
+        _union(parent, child, shared_with_map, edge_or_node_to_qspec)
 
 
 def _unwrap_shared_qspec(
@@ -249,7 +263,7 @@ def _union_input_edge_with(
         # since dtype is the same (we may want to extend this to be a more strict check
         # in the future)
         # so we point from `input_edge` to `arg` (output of the argument)
-        _union(edge_or_node, input_edge, shared_with_map)
+        _union(edge_or_node, input_edge, shared_with_map, edge_or_node_to_qspec)
 
 
 def _get_edge_or_node_to_group_id(
@@ -311,7 +325,9 @@ def _get_edge_or_node_to_group_id(
     for edge_or_node, qspec in edge_or_node_to_qspec.items():
         if isinstance(edge_or_node, torch.fx.Node):
             output_node = edge_or_node
-            _update_shared_with(output_node, qspec, shared_with_map)
+            _update_shared_with(
+                output_node, qspec, shared_with_map, edge_or_node_to_qspec
+            )
         else:
             input_edge = edge_or_node
             input_edge_root_qspec = _unwrap_shared_qspec(
@@ -332,9 +348,6 @@ def _get_edge_or_node_to_group_id(
                 # because we will point the root of (node1, node2) (in this case node1) to the root of (node1, node3)
                 # Step 3. and when we process (node1, node3), it can try to point to node1 as well, then we'll
                 # have a circular dependency
-                # the following order works around this issue, but this does not allow arbitrary configuration
-                # of sharing so it might break in a different case in the future, when it breaks
-                # quantizer writer can check the notes here to debug the issue
 
                 # sharing with other users of the producer node
                 # (arg, user)
@@ -363,7 +376,9 @@ def _get_edge_or_node_to_group_id(
                     shared_with_map,
                 )
 
-            _update_shared_with(input_edge, qspec, shared_with_map)
+            _update_shared_with(
+                input_edge, qspec, shared_with_map, edge_or_node_to_qspec
+            )
 
     # now that we get the sharing relations between all edges and nodes, we can assingn group ids
     cur_group_id = 0