Handle same tensor appearing multiple times in the cat input

backends/cadence/aot/memory_constraints.py

@@ -417,6 +417,10 @@ def is_slice_view(self, node: torch.fx.Node) -> bool:
             return not self.constraint.is_alias_of(source_info.source, node)
         return False
 
+    def has_relative_placement_constraint(self, node: torch.fx.Node) -> bool:
+        """Return if `node` already has any relative placement constraint."""
+        return self.constraint.get_relative_placement_source(node) is not None
+
     # Return true if the cat node performs concatenation along outermost dimension
     def is_cat_along_outermost_dim(
         self, graph_module: torch.fx.GraphModule, cat_node: torch.fx.Node
@@ -481,6 +485,17 @@ def is_removable_cat_op(
         if any(self.is_slice_view(arg) for arg in cat_tensors):
             return False
 
+        # If any of the tensors already has a relative placement constraint,
+        # we cannot add a new constraint for this cat without conflicting.
+        # This can happen when a tensor is used in multiple cat operations.
+        if any(self.has_relative_placement_constraint(arg) for arg in cat_tensors):
+            return False
+
+        # If the same tensor appears multiple times in the cat inputs,
+        # we cannot place it at multiple different offsets relative to the output.
+        if len(cat_tensors) != len(set(cat_tensors)):
+            return False
+
         # Many ops in HiFi require the input to be aligned to 8-byte boundary.
         # If the cat is not the graph's output, then ensure that the relative
         # offset of any concatenated non-placeholder tensor is a multiple of

backends/cadence/aot/tests/test_memory_passes.py

@@ -947,6 +947,110 @@ def test_cat_then_cat(self) -> None:
         self.assertEqual(count_node(graph_module, torch.ops.aten.cat.out), 0)
         self.verify_nop_memory_alloc(graph_module)
 
+    def test_cat_with_duplicate_input_tensor(self) -> None:
+        """
+        Test that cat is NOT optimized when the same tensor appears multiple
+        times in the cat input list. This is because we cannot place the same
+        tensor at multiple different offsets relative to the output.
+        """
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.ones(3, 6, dtype=torch.float32))
+        to_add_to_x = builder.call_operator(
+            op=exir_ops.edge.aten.full.default,
+            args=([3, 6], 123.0),
+            kwargs={"dtype": torch.float32},
+        )
+        add_x = builder.call_operator(
+            op=exir_ops.edge.aten.add.Tensor,
+            args=(x, to_add_to_x),
+        )
+        pre_created_output = builder.call_operator(
+            op=exir_ops.edge.aten.full.default,
+            args=([6, 6], 0.0),
+            kwargs={"dtype": torch.float32},
+        )
+        # Same tensor (add_x) appears twice in the cat inputs
+        cat = builder.call_operator(
+            op=torch.ops.aten.cat.out,
+            args=([add_x, add_x],),
+            kwargs={"dim": 0, "out": pre_created_output},
+        )
+        builder.output([cat])
+        original = builder.get_graph_module()
+        graph_module = self.run_memory_planning(original)
+        graph_module.graph.eliminate_dead_code()
+
+        # Assert that cat op is NOT optimized away since the same tensor
+        # appears multiple times in the input list
+        self.assertEqual(count_node(graph_module, torch.ops.aten.cat.out), 1)
+        self.assertEqual(count_node(graph_module, torch.ops.aten._cat_nop.out), 0)
+        self.verify_nop_memory_alloc(graph_module)
+
+    def test_cat_with_tensor_having_existing_constraint(self) -> None:
+        """
+        Test that the second cat is NOT optimized when a tensor already has a
+        relative placement constraint from a previous cat operation.
+        """
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.ones(8, 8, dtype=torch.float32))
+        to_add = builder.call_operator(
+            op=exir_ops.edge.aten.full.default,
+            args=([8, 8], 1.0),
+            kwargs={"dtype": torch.float32},
+        )
+        x1 = builder.call_operator(
+            op=exir_ops.edge.aten.add.Tensor,
+            args=(x, to_add),
+        )
+        x2 = builder.call_operator(
+            op=exir_ops.edge.aten.add.Tensor,
+            args=(x1, to_add),
+        )
+        x3 = builder.call_operator(
+            op=exir_ops.edge.aten.add.Tensor,
+            args=(x2, to_add),
+        )
+        # First cat: cat(x1, x2) - this will give x1 and x2 relative placement constraints
+        pre_created_output1 = builder.call_operator(
+            op=exir_ops.edge.aten.full.default,
+            args=([16, 8], 0.0),
+            kwargs={"dtype": torch.float32},
+        )
+        cat1 = builder.call_operator(
+            op=torch.ops.aten.cat.out,
+            args=([x1, x2],),
+            kwargs={"dim": 0, "out": pre_created_output1},
+        )
+        # Second cat: cat(x2, x3) - x2 already has a constraint from cat1,
+        # so this cat cannot be optimized
+        pre_created_output2 = builder.call_operator(
+            op=exir_ops.edge.aten.full.default,
+            args=([16, 8], 0.0),
+            kwargs={"dtype": torch.float32},
+        )
+        cat2 = builder.call_operator(
+            op=torch.ops.aten.cat.out,
+            args=([x2, x3],),
+            kwargs={"dim": 0, "out": pre_created_output2},
+        )
+        # Use both cat results to keep them alive
+        graph_output = builder.call_operator(
+            op=exir_ops.edge.aten.add.Tensor,
+            args=(cat1, cat2),
+        )
+        builder.output([graph_output])
+        original = builder.get_graph_module()
+        graph_module = self.run_memory_planning(
+            original, opt_level=3, alloc_graph_input=False
+        )
+        graph_module.graph.eliminate_dead_code()
+
+        # The first cat should be optimized to _cat_nop, but the second cat
+        # cannot be optimized because x2 already has a relative placement constraint
+        self.assertEqual(count_node(graph_module, torch.ops.aten._cat_nop.out), 1)
+        self.assertEqual(count_node(graph_module, torch.ops.aten.cat.out), 1)
+        self.verify_nop_memory_alloc(graph_module)
+
     def test_view_for_unallocated_output(self) -> None:
         builder = GraphBuilder()
         x = builder.placeholder("x", torch.ones(3, 5, dtype=torch.float32))