diff --git a/imperative/python/megengine/jit/tracing.py b/imperative/python/megengine/jit/tracing.py
index 412ad2707ebb0efd14a54bd2953e1fda7437cb2a..a2d141b4a3041280add51736305c2d2a1611ca2b 100644
--- a/imperative/python/megengine/jit/tracing.py
+++ b/imperative/python/megengine/jit/tracing.py
@@ -394,6 +394,7 @@ class trace:
 
     def _apply_graph_options(self, graph):
 
+        graph.options.no_force_inplace = True
         graph.options.seq_opt.enable_seq_comp_node_opt = False
         # graph opt level
         if self._graph_opt_level is not None:
@@ -417,7 +418,6 @@ class trace:
 
     def _compile(self):
         graph = self._graph = G.Graph()
-        graph.options.no_force_inplace = True
         graph.options.async_exec_level = 0b100
         self._apply_graph_options(graph)
         # graph.options.graph_opt_level = 0
diff --git a/imperative/python/test/integration/test_bn.py b/imperative/python/test/integration/test_bn.py
index 8767af309a48fb33f8f0015e229ec4a86190a797..89e61dd6556a3cab8851d203aa2e6f9e3d2a8f0e 100644
--- a/imperative/python/test/integration/test_bn.py
+++ b/imperative/python/test/integration/test_bn.py
@@ -13,7 +13,8 @@ import megengine
 import megengine.autodiff as ad
 import megengine.optimizer as optimizer
 from megengine import Parameter, tensor
-from megengine.module import BatchNorm2d
+from megengine.jit import trace
+from megengine.module import BatchNorm2d, Module
 
 
 def test_frozen_bn():
@@ -89,3 +90,25 @@ def test_bn_no_track_stat3():
     data = np.random.random((6, nchannel, 2, 2)).astype("float32")
     with pytest.raises(Exception):
         m(data)
+
+
+def test_trace_bn_forward_twice():
+    class Simple(Module):
+        def __init__(self):
+            super().__init__()
+            self.bn = BatchNorm2d(1)
+
+        def forward(self, inp):
+            x = self.bn(inp)
+            x = self.bn(x)
+            return x
+
+    @trace(symbolic=True)
+    def train_bn(inp, net=None):
+        net.train()
+        pred = net(inp)
+        return pred
+
+    x = np.ones((1, 1, 32, 32), dtype=np.float32)
+    y = train_bn(x, net=Simple())
+    np.testing.assert_equal(y.numpy(), 0)