diff --git a/imperative/python/test/unit/core/test_indexing_op.py b/imperative/python/test/unit/core/test_indexing_op.py
index dad883c1070d0ea48a6dcd1324e6def9a109dc47..6f483eb6934d2573bc20511cb197a7ffa35f5c79 100644
--- a/imperative/python/test/unit/core/test_indexing_op.py
+++ b/imperative/python/test/unit/core/test_indexing_op.py
@@ -5,6 +5,7 @@ from tempfile import NamedTemporaryFile
 
 import numpy as np
 import pytest
+import torch
 from utils import make_tensor
 
 import megengine
@@ -13,6 +14,7 @@ import megengine.functional as F
 import megengine.jit as jit
 import megengine.random as rand
 import megengine.utils.comp_graph_tools as cgtools
+from megengine.autodiff import GradManager
 from megengine.core._imperative_rt.core2 import apply
 from megengine.core._trace_option import use_symbolic_shape
 from megengine.core.ops import builtin
@@ -622,6 +624,25 @@ def test_advance_indexing_with_bool(test_varnode):
     )
 
 
+def test_advance_indexing_autodiff():
+    x = Tensor([2, 2, 3, 4, 5, 6, 7, 8, 2], dtype="float32")
+    gm = GradManager()
+    gm.attach(x)
+    with gm:
+        a = x + 1
+        a[x > 3] = 0.3
+        b = a + 1
+        gm.backward(b.sum())
+    torch_x = torch.tensor(
+        [2, 2, 3, 4, 5, 6, 7, 8, 2], dtype=torch.float32, requires_grad=True
+    )
+    a = torch_x + 1
+    a[torch_x > 3] = 0.3
+    b = a + 1
+    (b.sum()).backward()
+    np.testing.assert_equal(x.grad.numpy(), torch_x.grad.numpy())
+
+
 @pytest.mark.parametrize("symbolic", [True, False, None])
 def test_subtensor_on_empty_tensor(symbolic):
     np_x = np.array([], dtype=np.float32).reshape(10, 0, 10)
diff --git a/imperative/src/impl/ops/tensor_manip.cpp b/imperative/src/impl/ops/tensor_manip.cpp
index 67adb0c6e08e79494995bafb3530800cad9b91bc..4b486f8d530d30844b12c9874f1894e69c014383 100644
--- a/imperative/src/impl/ops/tensor_manip.cpp
+++ b/imperative/src/impl/ops/tensor_manip.cpp
@@ -306,7 +306,7 @@ cg::OperatorNodeBase* apply_on_var_node(const OpDef& def, const VarNodeArray& in
 SmallVector<VarNode::LayoutConstraintCallback> get_input_layout_constraint(
         const OpDef& def, const SmallVector<TensorPtr>& inputs) {
     SmallVector<VarNode::LayoutConstraintCallback> layout_checker(inputs.size());
-    layout_checker[0] = [](const TensorLayout& layout) {
+    layout_checker[1] = [](const TensorLayout& layout) {
         return layout.is_contiguous();
     };
     return layout_checker;