Fix placeholder lifetime bug in memory planning

exir/memory_planning.py

@@ -268,7 +268,9 @@ def _is_inplace_node(node: torch.fx.Node) -> bool:
     )
 
 
-def update_tensor_lifetime(spec: TensorSpec, node_idx: int) -> None:
+def update_tensor_lifetime(
+    node: torch.fx.Node, spec: TensorSpec, node_idx: int
+) -> None:
     r"""
     Update the lifetime of the tensor to cover node_idx. A tensor's lifetime
     are represented by the index of the first and last node referring
@@ -279,7 +281,10 @@ def update_tensor_lifetime(spec: TensorSpec, node_idx: int) -> None:
         node_idx: extend the tensor's lifetime to cover node_idx
     """
     start, end = spec.lifetime
-    start = node_idx if start is None or start > node_idx else start
+    if node.op == "placeholder":
+        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
     spec.lifetime = [start, end]
 
@@ -444,7 +449,7 @@ def update_all_tensors_lifetime(
             do_assertion=False,
             ignore_dynamic_unbound_tensor=False,
         ):
-            update_tensor_lifetime(spec, node_idx)
+            update_tensor_lifetime(node, spec, node_idx)
             specs.add(spec)
     return specs
 

exir/tests/test_memory_planning.py

@@ -14,14 +14,15 @@
 
 import torch
 from executorch.exir import ExecutorchBackendConfig, to_edge
+from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.memory_planning import (
     filter_nodes,
     get_node_tensor_specs,
     greedy,
     naive,
     Verifier,
 )
-from executorch.exir.pass_base import PassResult
+from executorch.exir.pass_base import ExportPass, PassResult
 from executorch.exir.pass_manager import PassManager
 from executorch.exir.passes import (  # noqa
     MemoryPlanningPass,
@@ -593,3 +594,65 @@ def count_planned_inputs(
             num_placeholders,
             5,
         )
+
+    def test_placeholder_lifetime(self) -> None:
+        class TestModel(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+
+            def forward(self, a, b, x):
+                a = a + b
+                b = a + b
+                y = self.linear(x)
+                return a, b, y
+
+        model = TestModel()
+        example_inputs = (torch.rand(1, 6, 2), torch.rand(1, 6, 2), torch.randn(5, 5))
+        exported_model = torch.export.export(model, example_inputs)
+        edge = to_edge(exported_model)
+
+        class TestPass(ExportPass):
+            def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+                permute_dims = [1, 0, 2]
+                for node in graph_module.graph.nodes:
+                    if node.op == "placeholder" and str(node) == "a":
+                        inverse_dims = [
+                            permute_dims.index(x) for x in range(len(permute_dims))
+                        ]
+
+                        with graph_module.graph.inserting_after(node):
+                            permute = graph_module.graph.call_function(
+                                exir_ops.edge.aten.permute_copy.default,
+                                args=(node, inverse_dims),
+                            )
+                            permute.meta = node.meta.copy()
+                            node.meta["val"] = node.meta["val"].permute(permute_dims)
+                            node.replace_all_uses_with(
+                                permute, lambda x, permute=permute: x is not permute
+                            )
+                            break
+                return PassResult(graph_module, True)
+
+        edge = edge.transform([TestPass()])
+        et = edge.to_executorch()
+        et_program = et.executorch_program
+        inputs = et_program.execution_plan[0].inputs
+        self.assertNotEqual(
+            et_program.execution_plan[0]  # pyre-ignore
+            .values[inputs[0]]
+            .val.allocation_info.memory_offset_low,
+            et_program.execution_plan[0]  # pyre-ignore
+            .values[inputs[1]]
+            .val.allocation_info.memory_offset_low,
+        )
+
+        constants = 0
+        for node in et.exported_program().graph_module.graph.nodes:
+            if node.op == "placeholder" and node.meta.get("spec"):
+                meta_spec = node.meta["spec"]
+                if meta_spec.const is True:
+                    constants += 1
+                    self.assertIsNone(node.meta["spec"].mem_offset)
+                    self.assertIsNone(node.meta["spec"].mem_id)
+        self.assertEqual(constants, 2)