Properly set mutable buffer lifespans

exir/memory_planning.py

@@ -301,7 +301,11 @@ def _is_inplace_node(node: torch.fx.Node) -> bool:
 
 
 def update_tensor_lifetime(
-    node: torch.fx.Node, spec: TensorSpec, node_idx: int
+    node: torch.fx.Node,
+    spec: TensorSpec,
+    node_idx: int,
+    max_node_idx: int,
+    gs: Optional[ExportGraphSignature] = None,
 ) -> None:
     r"""
     Update the lifetime of the tensor to cover node_idx. A tensor's lifetime
@@ -317,7 +321,12 @@ def update_tensor_lifetime(
         start = 0
     else:
         start = node_idx if start is None or start > node_idx else start
-    end = node_idx if end is None or end < node_idx else end
+
+    if node.op == "placeholder" and _is_mutable_buffer(node, gs):
+        # mutable buffers are never freed
+        end = max_node_idx
+    else:
+        end = node_idx if end is None or end < node_idx else end
     spec.lifetime = [start, end]
 
 
@@ -497,7 +506,7 @@ def update_all_tensors_lifetime(
     Set the lifetime for all the tensors encountered in the Fx graph.
     """
     specs = set()
-
+    max_node_idx = len(graph_module.graph.nodes) - 1
     for node_idx, node in enumerate(graph_module.graph.nodes):
         for spec in collect_specs_from_nodes(
             filter_nodes(itertools.chain([node], node.args, node.kwargs.values())),
@@ -509,7 +518,7 @@ def update_all_tensors_lifetime(
             do_assertion=False,
             ignore_dynamic_unbound_tensor=False,
         ):
-            update_tensor_lifetime(node, spec, node_idx)
+            update_tensor_lifetime(node, spec, node_idx, max_node_idx, graph_signature)
             specs.add(spec)
     return specs
 

exir/tests/test_memory_planning.py

@@ -664,6 +664,47 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             .val.allocation_info.memory_offset_high,
         )
 
+    def test_mutable_buffers_infinite_lifespan(self) -> None:
+        class Simple(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.register_buffer("state", torch.zeros(1))
+
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                self.state.index_put_(
+                    [
+                        torch.tensor([0]),
+                    ],
+                    x,
+                )
+                y = x + self.state
+                z = x * y
+                return z
+
+        model = Simple()
+        inputs = (torch.ones(1),)
+
+        et = to_edge(export(model, inputs, strict=True)).to_executorch(
+            ExecutorchBackendConfig(
+                emit_mutable_buffer_names=True, run_reinplace_pass=True
+            )
+        )
+
+        serialized_state = et.executorch_program.execution_plan[0].values[0].val
+        self.assertEqual(
+            serialized_state.extra_tensor_info.fully_qualified_name, "state"
+        )
+        memory_base = serialized_state.allocation_info.memory_offset_low
+        memory_size = memory_base + 4  # 4 bytes for a single float
+        for value in et.executorch_program.execution_plan[0].values[1:]:
+            val = value.val
+            if hasattr(val, "allocation_info") and val.allocation_info is not None:
+                not_overlapping = (
+                    val.allocation_info.memory_offset_low < memory_base
+                    or val.allocation_info.memory_offset_low >= memory_size
+                )
+                self.assertTrue(not_overlapping)
+
     def test_constants_not_memory_planned(self) -> None:
         class Simple(torch.nn.Module):
             def __init__(self) -> None: