diff --git a/src/gopt/impl/tensor_reformat.cpp b/src/gopt/impl/tensor_reformat.cpp
index 87e4d4f3e8df986fab39cadcb4d9f0388389086a..3a0bc60189a521eaa8f44807576e8876c02cbd8c 100644
--- a/src/gopt/impl/tensor_reformat.cpp
+++ b/src/gopt/impl/tensor_reformat.cpp
@@ -1753,6 +1753,24 @@ void EnableNchwxxPass::fill_opr_convert_fun(size_t pack_c_size) {
         }
     };
 
+    auto replace_reduce_opr = [=](OperatorNodeBase* opr,
+                                  const VarNodeArray& new_inp) {
+        mgb_assert(opr->input().size() == new_inp.size());
+        auto& reduce_opr = opr->cast_final_safe<opr::Reduce>();
+
+        VarNodeArray temp_inp = new_inp;
+        if (!opr->input(0)->shape().eq_shape(new_inp[0]->shape())) {
+            mgb_assert(opr->input(0)->shape().ndim == 4);
+            mgb_assert(new_inp[0]->shape().ndim == 5);
+            if (reduce_opr.param().axis != 2 && reduce_opr.param().axis != 3) {
+                auto new_var =
+                        RelayoutPlaceholder::make(new_inp[0], src_to_nchw_mode);
+                temp_inp[0] = new_var.node();
+            }
+        }
+        return serialization::copy_opr_shallow(*opr, temp_inp, opr->config());
+    };
+
     //! When input change and all input can convert to nchwxx, this opr will run
     //! in nchwxx mode, else it will run in nchw mode, for example concat and
     //! elemwise opr
@@ -1829,13 +1847,13 @@ void EnableNchwxxPass::fill_opr_convert_fun(size_t pack_c_size) {
     replace_func[opr::TypeCvt::typeinfo()] = replace_multi_inp_opr;
     replace_func[opr::ElemwiseMultiType::typeinfo()] = replace_multi_inp_opr;
     replace_func[opr::PowC::typeinfo()] = replace_multi_inp_opr;
+    replace_func[opr::Reduce::typeinfo()] = replace_reduce_opr;
     //! not support yet
     replace_func[opr::ConvolutionBackwardData::typeinfo()] =
             relayout_inp_to_nchw;
     replace_func[opr::Subtensor::typeinfo()] = relayout_inp_to_nchw;
     replace_func[opr::GetVarShape::typeinfo()] = relayout_inp_to_nchw;
     replace_func[opr::Dimshuffle::typeinfo()] = relayout_inp_to_nchw;
-    replace_func[opr::Reduce::typeinfo()] = relayout_inp_to_nchw;
     replace_func[opr::AssertEqual::typeinfo()] = relayout_inp_to_nchw;
     replace_func[opr::IncrSubtensor::typeinfo()] = relayout_inp_to_nchw;
     replace_func[opr::WarpPerspectiveForward::typeinfo()] =
diff --git a/src/gopt/test/inference.cpp b/src/gopt/test/inference.cpp
index 3b8f59cf07139de670ebf10be98ad90cc8f50c9b..b16c0df91ab5c284fe50cdc01349ee24e9adf9cb 100644
--- a/src/gopt/test/inference.cpp
+++ b/src/gopt/test/inference.cpp
@@ -3476,14 +3476,24 @@ TEST(TestGoptInference, ConvertFormatNCHW88) {
     //! group
     auto w3 = mkcvar("w3", {1, 8, 8, 3, 3}), b3 = mkcvar("b3", {1, 8, 1, 1}),
          conv3 = opr::ConvBias::make(conv2, w3, b3, param_conv_bias);
-
-    auto shape_of = opr::GetVarShape::make(conv3);
+    //! reduce
+    opr::Reduce::Param param_reduce1;
+    param_reduce1.axis = 2;
+    param_reduce1.mode = opr::Reduce::Mode::SUM;
+    opr::Reduce::Param param_reduce2;
+    param_reduce2.axis = 0;
+    param_reduce2.mode = opr::Reduce::Mode::MAX;
+    auto reduce1 = conv3 + opr::Reduce::make(conv3, param_reduce1) +
+                   opr::Reduce::make(conv3, param_reduce2);
+
+    auto shape_of = opr::GetVarShape::make(reduce1);
     auto subtensor = opr::Subtensor::make(
             shape_of, {opr::Subtensor::AxisIndexer::make_interval(
                               0, x.make_scalar(2), None, x.make_scalar(1))});
     opr::Resize::Param param_resize;
     param_resize.format = opr::Resize::Param::Format::NCHW;
-    auto resize = opr::ResizeForward::make(conv3, subtensor * 2, param_resize);
+    auto resize =
+            opr::ResizeForward::make(reduce1, subtensor * 2, param_resize);
     auto mat = mkcvar("mat", {2, 3, 3}),
          warp = opr::WarpPerspectiveForward::make(
                  resize, mat, nullptr, cg::var_from_tensor_shape(x, {4, 4}));
@@ -3586,14 +3596,24 @@ TEST(TestGoptInference, ConvertFormatNCHW44) {
     //! group
     auto w3 = mkcvar("w3", {2, 4, 4, 3, 3}), b3 = mkcvar("b3", {1, 8, 1, 1}),
          conv3 = opr::ConvBias::make(conv2, w3, b3, param_conv_bias);
-
-    auto shape_of = opr::GetVarShape::make(conv3);
+    //! reduce
+    opr::Reduce::Param param_reduce1;
+    param_reduce1.axis = 1;
+    param_reduce1.mode = opr::Reduce::Mode::MIN;
+    opr::Reduce::Param param_reduce2;
+    param_reduce2.axis = 3;
+    param_reduce2.mode = opr::Reduce::Mode::SUM_SQR;
+    auto reduce1 = conv3 + opr::Reduce::make(conv3, param_reduce1) +
+                   opr::Reduce::make(conv3, param_reduce2);
+
+    auto shape_of = opr::GetVarShape::make(reduce1);
     auto subtensor = opr::Subtensor::make(
             shape_of, {opr::Subtensor::AxisIndexer::make_interval(
                               0, x.make_scalar(2), None, x.make_scalar(1))});
     opr::Resize::Param param_resize;
     param_resize.format = opr::Resize::Param::Format::NCHW;
-    auto resize = opr::ResizeForward::make(conv3, subtensor * 2, param_resize);
+    auto resize =
+            opr::ResizeForward::make(reduce1, subtensor * 2, param_resize);
     auto mat = mkcvar("mat", {2, 3, 3}),
          warp = opr::WarpPerspectiveForward::make(
                  resize, mat, nullptr, cg::var_from_tensor_shape(x, {4, 4}));