From 8fb2f176aac2d9c70adbee7eb9c1569361e32299 Mon Sep 17 00:00:00 2001
From: xiongkun <xiongkun03@baidu.com>
Date: Wed, 11 Oct 2023 11:33:28 +0000
Subject: [PATCH] fix stop-gradient not put in SIR problem

---
 .../executor/function_graph.py                | 47 ++++++++++---------
 1 file changed, 26 insertions(+), 21 deletions(-)

diff --git a/sot/opcode_translator/executor/function_graph.py b/sot/opcode_translator/executor/function_graph.py
index 1bbfa969..cda7302f 100644
--- a/sot/opcode_translator/executor/function_graph.py
+++ b/sot/opcode_translator/executor/function_graph.py
@@ -475,30 +475,35 @@ def symbolic_call(self, infer_meta_fn, compute_fn, func, *args, **kwargs):
                 FunctionGraph.get_opcode_executor_stack()
             ),
         )
-        if outputs is not None:
-            if is_inplace_api(func):
-                # if we want to use a non-inplace api (static api) to replace an inplace behavior (in simulation)
-                # just set it back in SIR, and return outputs to replace tensor meta (it might changes?)
-                # in this case, the output will not exactly be used
-                compute_fn(
-                    func,
-                    inputs_symbols,
-                    convert_to_symbol(args[0]),
-                    stmt_stacks,
-                )
-            else:
-                compute_fn(
-                    func,
-                    inputs_symbols,
-                    convert_to_symbol(outputs),
-                    stmt_stacks,
-                )  # symbolic only contain symbols.
-                self._put_inner(outputs)
+        if is_inplace_api(func):
+            # if we want to use a non-inplace api (static api) to replace an inplace behavior (in simulation)
+            # just set it back in SIR, and return outputs to replace tensor meta (it might changes?)
+            # in this case, the output will not exactly be used
+            compute_fn(
+                func,
+                inputs_symbols,
+                convert_to_symbol(args[0]),
+                stmt_stacks,
+            )
+        elif outputs is not None:
+            compute_fn(
+                func,
+                inputs_symbols,
+                convert_to_symbol(outputs),
+                stmt_stacks,
+            )  # symbolic only contain symbols.
+            self._put_inner(outputs)
             return VariableFactory.from_value(
                 outputs, self, DummyTracker(list(args) + list(kwargs.values()))
             )
-        else:
-            return None
+        elif outputs is None:
+            # tensor.stop_gradient=True
+            compute_fn(
+                func,
+                inputs_symbols,
+                None,
+                stmt_stacks,
+            )
 
     def _put_inner(self, vars: VariableBase):
         """