diff --git a/imperative/python/src/tensor.cpp b/imperative/python/src/tensor.cpp
index 40852d7ae847e1fa59e011925fa18cace9452fb1..83b6b40ca56e25264b353e30c025e7153a3102ce 100644
--- a/imperative/python/src/tensor.cpp
+++ b/imperative/python/src/tensor.cpp
@@ -1017,10 +1017,14 @@ void init_tensor(py::module m) {
                 using namespace std::placeholders;
                 self.compiled = std::make_shared<CompiledTransformation>(
                         *self.trace_result, self.record_input_shapes);
-                self.compiled->set_value_comparator(
-                        std::bind(&Trace::compare_value, this, _1, _2));
-                self.options_visitor(py::cast(&self.compiled->options()));
-                self.compiled->compile();
+                    self.compiled->set_value_comparator(
+                            std::bind(&Trace::compare_value, this, _1, _2));
+                    self.options_visitor(py::cast(&self.compiled->options()));
+                try {
+                    self.compiled->compile();
+                } catch (const std::exception& e) {
+                    mgb_log_error(e.what());
+                }
             }
             // register transformations
             if (self.compiled) {
diff --git a/imperative/python/test/unit/core/test_formatted_tensor.py b/imperative/python/test/unit/core/test_formatted_tensor.py
index 6c71fe0a962ffc1b0192ae2c2ae6a2942c0eba18..f6d9bdc411070574de6fc31485ce456d583e3e39 100644
--- a/imperative/python/test/unit/core/test_formatted_tensor.py
+++ b/imperative/python/test/unit/core/test_formatted_tensor.py
@@ -5,6 +5,7 @@ import megengine as mge
 import megengine.functional as F
 from megengine import tensor
 from megengine.autodiff import GradManager
+from megengine.jit import trace
 
 
 def test_basic():
@@ -30,26 +31,29 @@ def test_basic():
     assert b.format == "nchw"
 
 
-def _compare_nchw_nhwc(data, func):
+def _compare_nchw_nhwc(data, func, is_symbolic=None):
     x1 = tensor(data, format="nchw")
     x2 = tensor(data.transpose(0, 2, 3, 1), format="nhwc")
+    if is_symbolic is not None:
+        func = trace(func, symbolic=is_symbolic)
     out1 = func(x1)
-    with mge.config._override(auto_format_convert=True):
-        out2 = func(x2)
+    out2 = func(x2)
     np.testing.assert_almost_equal(out1, out2, decimal=5)
 
 
-def test_dimshuffle():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_dimshuffle(is_symbolic):
     def func(x):
         out = F.transpose(x, [2, 3, 0, 1])
         assert out.format == "default"
         return out.numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
 
-def test_reshape():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_reshape(is_symbolic):
     # maintain NHWC format
     def func(x):
         out = F.reshape(x, (1, 2, 6, 2))
@@ -58,7 +62,7 @@ def test_reshape():
         return out.numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
     # not maintain NHWC format
     def func2(x):
@@ -66,18 +70,20 @@ def test_reshape():
         assert out.format == "default"
         return out.numpy()
 
-    _compare_nchw_nhwc(data, func2)
+    _compare_nchw_nhwc(data, func2, is_symbolic)
 
 
-def test_flatten():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_flatten(is_symbolic):
     def func(x):
         return F.flatten(x).numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
 
-def test_broadcast():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_broadcast(is_symbolic):
     # maintain NHWC format
     def func(x):
         out = F.broadcast_to(x, (4, 3, 2, 3))
@@ -86,7 +92,7 @@ def test_broadcast():
         return out.numpy()
 
     data = np.arange(0, 24).reshape((4, 3, 2, 1))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
     # not maintain NHWC format
     def func2(x):
@@ -94,30 +100,32 @@ def test_broadcast():
         assert out.format == "default"
         return out.numpy()
 
-    _compare_nchw_nhwc(data, func2)
+    _compare_nchw_nhwc(data, func2, is_symbolic)
 
 
 @pytest.mark.skip("repeat cannot maintain format yet")
-def test_repeat():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_repeat(is_symbolic):
     def func(x):
         rst = F.repeat(x, 3, axis=1)
         assert rst.format == x.format
         return rst.numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
 
-def test_getshape():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_getshape(is_symbolic):
     def func(x):
         return x.shape
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
 
 @pytest.mark.skip("symbolic shape is not supported yet")
-def test_get_symbolic_shape():
+def test_get_symbolic_shape(is_symbolic):
     from megengine.core._trace_option import set_symbolic_shape
 
     origin_opt = set_symbolic_shape(True)
@@ -126,77 +134,84 @@ def test_get_symbolic_shape():
         return x.shape.numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
     set_symbolic_shape(origin_opt)
 
 
-def test_getvalue():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_getvalue(is_symbolic):
     def func(x):
         return x.numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
 
-def test_get_set_subtensor():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_get_set_subtensor(is_symbolic):
     def get_subtensor(x):
         return x[:, :1, :2, :3].numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, get_subtensor)
+    _compare_nchw_nhwc(data, get_subtensor, is_symbolic)
 
     def set_subtensor(x):
         x[:, :1, :2, :3] = 0
         return x.numpy()
 
-    _compare_nchw_nhwc(data, set_subtensor)
+    _compare_nchw_nhwc(data, set_subtensor, is_symbolic)
 
 
-def test_get_set_advanced_indexing():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_get_set_advanced_indexing(is_symbolic):
     def get_advanced_indexing(x):
         x = x[:, : mge.tensor(2), : mge.tensor(2), [1, 2]].numpy()
         return x
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, get_advanced_indexing)
+    _compare_nchw_nhwc(data, get_advanced_indexing, is_symbolic)
 
     def set_advanced_indexing(x):
         x[:, : mge.tensor(2), : mge.tensor([2]), [1,]] = 0
         return x.numpy()
 
-    _compare_nchw_nhwc(data, set_advanced_indexing)
+    _compare_nchw_nhwc(data, set_advanced_indexing, is_symbolic)
 
 
-def test_typecvt():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_typecvt(is_symbolic):
     def typecvt(x):
         return x.astype("float16").numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, typecvt)
+    _compare_nchw_nhwc(data, typecvt, is_symbolic)
 
 
-def test_elemwise():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_elemwise(is_symbolic):
     def elemwise(x):
         return (x * 2 + x / 2).numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, elemwise)
+    _compare_nchw_nhwc(data, elemwise, is_symbolic)
 
 
-def test_concat():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_concat(is_symbolic):
     def func(x):
         rst = F.concat([x / 2, x * 2], axis=1)
         assert rst.format == x.format
         return rst.numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
 
 @pytest.mark.parametrize(
     "mode", ["bilinear", "nearest"],
 )
-def test_interpolate(mode):
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_interpolate(mode, is_symbolic):
     def func(x):
         if x.format == "nhwc":
             with mge.config._override(conv_format="NHWC"):
@@ -208,10 +223,11 @@ def test_interpolate(mode):
 
     # NHWC interpolate only suppoted channel is 1 or 3
     data = np.arange(0, 48).reshape((1, 3, 4, 4)).astype("float32")
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
 
-def test_conv2d():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_conv2d(is_symbolic):
     def conv2d(x):
         if x.format == "nhwc":
             with mge.config._override(conv_format="NHWC"):
@@ -226,10 +242,11 @@ def test_conv2d():
             return F.conv2d(x, F.ones((3, 2, 1, 1)), F.ones((1, 3, 1, 1))).numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, conv2d)
+    _compare_nchw_nhwc(data, conv2d, is_symbolic)
 
 
-def test_group_conv2d():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_group_conv2d(is_symbolic):
     def conv2d(x):
         if x.format == "nhwc":
             with mge.config._override(conv_format="NHWC"):
@@ -247,10 +264,11 @@ def test_group_conv2d():
             ).numpy()
 
     data = np.arange(0, 48).reshape((1, 4, 3, 4))
-    _compare_nchw_nhwc(data, conv2d)
+    _compare_nchw_nhwc(data, conv2d, is_symbolic)
 
 
-def test_bn():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_bn(is_symbolic):
     def func(x):
         if x.format == "nhwc":
             with mge.config._override(bn_format="dim_111c"):
@@ -279,14 +297,15 @@ def test_bn():
             )[0].numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
 
 @pytest.mark.parametrize(
     "pooling",
     [F.max_pool2d, F.avg_pool2d, F.adaptive_avg_pool2d, F.adaptive_max_pool2d],
 )
-def test_pooling2d(pooling):
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_pooling2d(pooling, is_symbolic):
     def func(x):
         if x.format == "nhwc":
             with mge.config._override(conv_format="NHWC"):
@@ -297,18 +316,25 @@ def test_pooling2d(pooling):
             return pooling(x.astype("float32"), 2).numpy()
 
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
-    _compare_nchw_nhwc(data, func)
+    _compare_nchw_nhwc(data, func, is_symbolic)
 
 
-def test_backward():
+@pytest.mark.parametrize("is_symbolic", [None])
+def test_backward(is_symbolic):
     data = np.arange(0, 24).reshape((1, 2, 3, 4))
     x = tensor(data.transpose(0, 2, 3, 1), format="nhwc")
     w = mge.tensor(np.ones((3, 1, 1, 2)), format="nhwc")
     b = mge.tensor(np.ones((1, 1, 1, 3)), format="nhwc")
     gm = GradManager().attach([w, b])
+
+    def func(x, w, b):
+        return F.conv2d(x, w, b)
+
     with gm:
         with mge.config._override(auto_format_convert=True, conv_format="NHWC"):
-            x = F.conv2d(x, w, b)
+            if is_symbolic is not None:
+                func = trace(func, symbolic=is_symbolic)
+            x = func(x, w, b)
             # TODO: fix manually convert to NHWC, usually used in detection head
             # x = x.transpose(0, 2, 3, 1).reshape(1, 18, 2)
             gm.backward(x)
diff --git a/imperative/src/impl/transformations/format.cpp b/imperative/src/impl/transformations/format.cpp
index fa431665ef101c2a7782994cfe115fdafc0d46ab..bdb4a77aea8f510ce137fed2b7c08963e355efc6 100644
--- a/imperative/src/impl/transformations/format.cpp
+++ b/imperative/src/impl/transformations/format.cpp
@@ -7,53 +7,62 @@ namespace imperative {
 
 using FT = Format::Type;
 
-TypedValueRef<FormattedTensorValue> FormattedTensorValue::as(const FT& target) const {
-    return FormattedTensorValue::make(m_value, target);
+TypedValueRef<FormattedTensorValue> FormatTransformation::as(
+        const FormattedTensorValue& tensor, const FT& target) const {
+    return m_value_type.make(tensor.value(), target);
 }
 
-TypedValueRef<FormattedTensorValue> FormattedTensorValue::to(
-        const FT& target, const std::string& scope) const {
+TypedValueRef<FormattedTensorValue> FormatTransformation::to(
+        const FormattedTensorValue& tensor, const FT& target,
+        const std::string& scope) const {
     std::vector<int32_t> pattern;
-    if (m_format == FT::NHWC && target == FT::NCHW) {
+    if (tensor.format() == FT::NHWC && target == FT::NCHW) {
         pattern = {0, 3, 1, 2};
-    } else if (m_format == FT::NCHW && target == FT::NHWC) {
+    } else if (tensor.format() == FT::NCHW && target == FT::NHWC) {
         pattern = {0, 2, 3, 1};
     } else {
         mgb_throw(
                 MegBrainError, "Unsupport format conversion from %s to %s",
-                m_format.to_string().c_str(), Format(target).to_string().c_str());
+                tensor.format().to_string().c_str(),
+                Format(target).to_string().c_str());
     }
     auto output = imperative::apply(
-            *Dimshuffle::make(pattern, scope), std::vector<ValueRef>{m_value})[0];
-    return FormattedTensorValue::make(output, target);
+            *Dimshuffle::make(pattern, scope),
+            SmallVector<ValueRef>{tensor.value()})[0];
+    return m_value_type.make(output, target);
 }
 
-namespace {
-
-ValueRef unwrap_input(const ValueRef& input) {
-    if (auto format_input = input.as_ref<FormattedTensorValue>()) {
+inline ValueRef FormatTransformation::unwrap_input(const ValueRef& input) const {
+    if (auto format_input = input.as_ref(m_value_type)) {
         return format_input->value();
     } else {
         return input;
     }
 }
 
-std::vector<ValueRef> unwrap_inputs(const Span<ValueRef>& inputs) {
-    std::vector<ValueRef> unwrapped_inputs;
-    for (auto&& input : inputs) {
-        unwrapped_inputs.push_back(unwrap_input(input));
+inline ValueRefList FormatTransformation::unwrap_inputs(
+        const Span<ValueRef>& inputs) const {
+    ValueRefList unwrapped_inputs(inputs.size());
+    for (size_t i = 0; i < inputs.size(); ++i) {
+        unwrapped_inputs[i] = unwrap_input(inputs[i]);
     }
     return unwrapped_inputs;
 }
 
-std::vector<ValueRef> wrap_outputs(
-        const std::vector<ValueRef>& outputs, FT type = FT::DEFAULT) {
-    std::vector<ValueRef> wrapped_outputs;
-    for (auto&& output : outputs) {
-        wrapped_outputs.push_back(FormattedTensorValue::make(output, type));
+inline ValueRef FormatTransformation::wrap_output(
+        const ValueRef& output, FT type) const {
+    return m_value_type.make(output, type);
+}
+
+inline ValueRefList FormatTransformation::wrap_outputs(
+        const ValueRefList& outputs, FT type) const {
+    ValueRefList wrapped_outputs(outputs.size());
+    for (size_t i = 0; i < outputs.size(); ++i) {
+        wrapped_outputs[i] = wrap_output(outputs[i], type);
     }
     return wrapped_outputs;
 }
+namespace {
 
 ValueShape convert_nhwc2nchw_shape(const ValueShape& shape) {
     mgb_assert(shape.ndim == 4);
@@ -64,20 +73,21 @@ ValueShape convert_nhwc2nchw_shape(const ValueShape& shape) {
     return out;
 }
 
-using FormatRule = std::function<std::vector<ValueRef>(
-        const OpDef&, Span<ValueRef>&, const bool&)>;
+using FormatRule = std::function<ValueRefList(
+        const OpDef&, Span<ValueRef>&, const bool&, const FormatTransformation&)>;
 static std::unordered_map<Typeinfo*, FormatRule> format_rules;
 
 template <typename T>
-void register_format_rule(
-        std::vector<ValueRef> (*rule)(const T&, Span<ValueRef>&, const bool&)) {
+void register_format_rule(ValueRefList (*rule)(
+        const T&, Span<ValueRef>&, const bool&, const FormatTransformation&)) {
     format_rules[T::typeinfo()] = [rule](const OpDef& def, Span<ValueRef>& inputs,
-                                         const bool& auto_convert) {
-        return (*rule)(def.cast_final_safe<T>(), inputs, auto_convert);
+                                         const bool& auto_convert,
+                                         const FormatTransformation& t) {
+        return (*rule)(def.cast_final_safe<T>(), inputs, auto_convert, t);
     };
 }
 
-auto convert_nchw2nhwc_pattern(const std::vector<int32_t>& pattern) {
+inline auto convert_nchw2nhwc_pattern(const std::vector<int32_t>& pattern) {
     mgb_assert(pattern.size() == 4);
     auto nhwc_pattern = pattern;
     for (size_t idx = 0; idx < 4; ++idx) {
@@ -93,19 +103,20 @@ auto convert_nchw2nhwc_pattern(const std::vector<int32_t>& pattern) {
     return nhwc_pattern;
 }
 
-std::vector<ValueRef> dimshuffle_rule(
-        const Dimshuffle& op, Span<ValueRef>& inputs, const bool& auto_convert) {
+ValueRefList dimshuffle_rule(
+        const Dimshuffle& op, Span<ValueRef>& inputs, const bool& auto_convert,
+        const FormatTransformation& t) {
     mgb_assert(inputs.size() == 1);
-    auto& src = inputs[0].cast<FormattedTensorValue>();
+    auto& src = inputs[0].cast(t.value_type());
     // Only support converting pattern from NCHW to NHWC currently.
     if (auto_convert && src.format() == FT::NHWC) {
         auto pattern = convert_nchw2nhwc_pattern(op.pattern);
         // dimshuffle will not maintain NHWC Format
-        return wrap_outputs(imperative::apply(
+        return t.wrap_outputs(imperative::apply(
                 *Dimshuffle::make(std::move(pattern), op.scope()),
-                unwrap_inputs(inputs)));
+                t.unwrap_inputs(inputs)));
     }
-    return wrap_outputs(imperative::apply(op, unwrap_inputs(inputs)));
+    return t.wrap_outputs(imperative::apply(op, t.unwrap_inputs(inputs)));
 }
 
 ValueRef convert_nchw2nhwc_tensornd(const HostTensorND& shape) {
@@ -125,53 +136,55 @@ ValueRef convert_nchw2nhwc_tensornd(const HostTensorND& shape) {
     return nhwc_shape_input;
 }
 
-std::vector<ValueRef> reshape_rule(
-        const Reshape& op, Span<ValueRef>& inputs, const bool& auto_convert) {
+ValueRefList reshape_rule(
+        const Reshape& op, Span<ValueRef>& inputs, const bool& auto_convert,
+        const FormatTransformation& t) {
     mgb_assert(inputs.size() == 2);
-    auto& src = inputs[0].cast<FormattedTensorValue>();
+    auto& src = inputs[0].cast(t.value_type());
     if (auto_convert && src.format() == FT::NHWC) {
-        auto shape = unwrap_input(inputs[1]).numpy().cast<HostValue>().as_nd();
+        auto shape = t.unwrap_input(inputs[1]).numpy()->as_nd();
         if (shape.layout().total_nr_elems() == 4) {
             // output is still NHWC format
             auto nhwc_shape = convert_nchw2nhwc_tensornd(shape);
             auto outputs = imperative::apply(
-                    op, std::vector<ValueRef>{unwrap_input(inputs[0]), nhwc_shape});
-            return wrap_outputs(outputs, FT::NHWC);
+                    op, SmallVector<ValueRef>{t.unwrap_input(inputs[0]), nhwc_shape});
+            return t.wrap_outputs(outputs, FT::NHWC);
         } else {
             // will not maintain src's format
-            auto nchw_src = src.to(FT::NCHW, op.scope())->value();
+            auto nchw_src = t.to(src, FT::NCHW, op.scope())->value();
             auto outputs = imperative::apply(
-                    op, std::vector<ValueRef>{nchw_src, unwrap_input(inputs[1])});
-            return wrap_outputs(outputs);
+                    op, SmallVector<ValueRef>{nchw_src, t.unwrap_input(inputs[1])});
+            return t.wrap_outputs(outputs);
         }
     }
-    return wrap_outputs(imperative::apply(op, unwrap_inputs(inputs)));
+    return t.wrap_outputs(imperative::apply(op, t.unwrap_inputs(inputs)));
 }
 
-std::vector<ValueRef> broadcast_rule(
-        const Broadcast& op, Span<ValueRef>& inputs, const bool& auto_convert) {
+ValueRefList broadcast_rule(
+        const Broadcast& op, Span<ValueRef>& inputs, const bool& auto_convert,
+        const FormatTransformation& t) {
     mgb_assert(inputs.size() == 2);
-    auto& src = inputs[0].cast<FormattedTensorValue>();
+    auto& src = inputs[0].cast(t.value_type());
     if (auto_convert && src.format() == FT::NHWC) {
-        auto shape = unwrap_input(inputs[1]).numpy().cast<HostValue>().as_nd();
+        auto shape = t.unwrap_input(inputs[1]).numpy()->as_nd();
         if (shape.layout().total_nr_elems() == 4) {
             // output is still NHWC format
             auto nhwc_shape = convert_nchw2nhwc_tensornd(shape);
             auto outputs = imperative::apply(
-                    op, std::vector<ValueRef>{unwrap_input(inputs[0]), nhwc_shape});
-            return wrap_outputs(outputs, FT::NHWC);
+                    op, SmallVector<ValueRef>{t.unwrap_input(inputs[0]), nhwc_shape});
+            return t.wrap_outputs(outputs, FT::NHWC);
         } else {
             // will not maintain src's format
-            auto nchw_src = src.to(FT::NCHW, op.scope())->value();
+            auto nchw_src = t.to(src, FT::NCHW, op.scope())->value();
             auto outputs = imperative::apply(
-                    op, std::vector<ValueRef>{nchw_src, unwrap_input(inputs[1])});
-            return wrap_outputs(outputs);
+                    op, SmallVector<ValueRef>{nchw_src, t.unwrap_input(inputs[1])});
+            return t.wrap_outputs(outputs);
         }
     }
-    return wrap_outputs(imperative::apply(op, unwrap_inputs(inputs)));
+    return t.wrap_outputs(imperative::apply(op, t.unwrap_inputs(inputs)));
 }
 
-bool is_reduce_ndim_idx_items(
+inline bool is_reduce_ndim_idx_items(
         const std::vector<std::tuple<int8_t, bool, bool, bool, bool>>& items,
         const Span<ValueRef>& inputs) {
     for (auto i = 0; i < items.size(); ++i) {
@@ -184,7 +197,7 @@ bool is_reduce_ndim_idx_items(
     return false;
 }
 
-auto convert_nchw2nhwc_idx_items(
+inline auto convert_nchw2nhwc_idx_items(
         const std::vector<std::tuple<int8_t, bool, bool, bool, bool>>& items) {
     auto nhwc_items = items;
     for (auto i = 0; i < nhwc_items.size(); ++i) {
@@ -199,51 +212,55 @@ auto convert_nchw2nhwc_idx_items(
 }
 
 template <typename T>
-std::vector<ValueRef> subtensor_rule(
-        const T& op, Span<ValueRef>& inputs, const bool& auto_convert) {
+ValueRefList subtensor_rule(
+        const T& op, Span<ValueRef>& inputs, const bool& auto_convert,
+        const FormatTransformation& t) {
     mgb_assert(inputs.size() >= 1);
-    auto& src = inputs[0].cast<FormattedTensorValue>();
+    auto& src = inputs[0].cast(t.value_type());
     bool is_reduce_ndim = is_reduce_ndim_idx_items(
             op.items, {&inputs[1], &inputs[inputs.size() - 1]});
     if (!is_reduce_ndim) {
         // only support NHWC2NCHW convert, otherwise maintain src's format
         if (!(auto_convert && src.format() == FT::NHWC)) {
-            return {FormattedTensorValue::make(
-                    imperative::apply(op, unwrap_inputs(inputs))[0], src.format())};
+            return {t.wrap_output(
+                    imperative::apply(op, t.unwrap_inputs(inputs))[0],
+                    src.format().type())};
         }
         auto nhwc_items = convert_nchw2nhwc_idx_items(op.items);
         auto outputs = imperative::apply(
-                *T::make(std::move(nhwc_items), op.scope()), unwrap_inputs(inputs));
-        return wrap_outputs(outputs, FT::NHWC);
+                *T::make(std::move(nhwc_items), op.scope()), t.unwrap_inputs(inputs));
+        return t.wrap_outputs(outputs, FT::NHWC);
     }
-    return wrap_outputs(imperative::apply(op, unwrap_inputs(inputs)));
+    return t.wrap_outputs(imperative::apply(op, t.unwrap_inputs(inputs)));
 }
 
 template <typename T>
-std::vector<ValueRef> setsubtensor_rule(
-        const T& op, Span<ValueRef>& inputs, const bool& auto_convert) {
+ValueRefList setsubtensor_rule(
+        const T& op, Span<ValueRef>& inputs, const bool& auto_convert,
+        const FormatTransformation& t) {
     mgb_assert(inputs.size() >= 2);
-    auto& src = inputs[0].cast<FormattedTensorValue>();
+    auto& src = inputs[0].cast(t.value_type());
     bool is_reduce_ndim = is_reduce_ndim_idx_items(
             op.items, {&inputs[2], &inputs[inputs.size() - 1]});
     if (!is_reduce_ndim) {
         // only support NHWC2NCHW convert, otherwise maintain src's format
         if (!(auto_convert && src.format() == FT::NHWC)) {
-            return {FormattedTensorValue::make(
-                    imperative::apply(op, unwrap_inputs(inputs))[0], src.format())};
+            return {t.wrap_output(
+                    imperative::apply(op, t.unwrap_inputs(inputs))[0],
+                    src.format().type())};
         }
         // value has been broadcasted to src's fake NCHW shape.
-        auto& value = inputs[1].cast<FormattedTensorValue>();
+        auto& value = inputs[1].cast(t.value_type());
         auto& format = value.format();
-        auto nhwc_inputs = std::vector<ValueRef>(inputs.size());
+        auto nhwc_inputs = ValueRefList(inputs.size());
         if (format == FT::DEFAULT || format == FT::NCHW) {
             // value for setsubtensor should transpose to match shape.
-            auto nhwc_value = value.as(FT::NCHW)->to(FT::NHWC);
+            auto nhwc_value = t.to(*(t.as(value, FT::NCHW)), FT::NHWC);
             // make new inputs for setsubtensor
             nhwc_inputs[0] = src.value();
             nhwc_inputs[1] = nhwc_value->value();
             for (auto i = 2; i < inputs.size(); ++i) {
-                nhwc_inputs[i] = inputs[i].as_ref<FormattedTensorValue>()->value();
+                nhwc_inputs[i] = t.unwrap_input(inputs[i]);
             }
         } else if (format != FT::NHWC) {
             mgb_throw(
@@ -253,15 +270,15 @@ std::vector<ValueRef> setsubtensor_rule(
         auto nhwc_items = convert_nchw2nhwc_idx_items(op.items);
         auto outputs = imperative::apply(
                 *T::make(std::move(nhwc_items), op.scope()), nhwc_inputs);
-        return wrap_outputs(outputs, FT::NHWC);
+        return t.wrap_outputs(outputs, FT::NHWC);
     }
-    return wrap_outputs(imperative::apply(op, unwrap_inputs(inputs)));
+    return t.wrap_outputs(imperative::apply(op, t.unwrap_inputs(inputs)));
 }
 
-FT get_inputs_format(Span<ValueRef>& inputs) {
+inline FT get_inputs_format(Span<ValueRef>& inputs, const FormatTransformation& t) {
     FT format(FT::DEFAULT);
     for (auto& inp : inputs) {
-        auto& inp_format = inp.cast<FormattedTensorValue>().format();
+        auto& inp_format = inp.cast(t.value_type()).format();
         if (inp_format != FT::DEFAULT) {
             mgb_assert(format == FT::DEFAULT || inp_format == format);
             format = inp_format.type();
@@ -270,11 +287,12 @@ FT get_inputs_format(Span<ValueRef>& inputs) {
     return format;
 }
 
-std::vector<ValueRef> concat_rule(
-        const Concat& op, Span<ValueRef>& inputs, const bool& auto_convert) {
-    FT format = get_inputs_format(inputs);
+ValueRefList concat_rule(
+        const Concat& op, Span<ValueRef>& inputs, const bool& auto_convert,
+        const FormatTransformation& t) {
+    FT format = get_inputs_format(inputs, t);
     if (!(format == FT::NHWC && auto_convert)) {
-        return wrap_outputs(imperative::apply(op, unwrap_inputs(inputs)), format);
+        return t.wrap_outputs(imperative::apply(op, t.unwrap_inputs(inputs)), format);
     }
     // TODO: handle 5D NHWC Tensor from group conv
     auto axis = op.axis;
@@ -283,25 +301,26 @@ std::vector<ValueRef> concat_rule(
     } else if (axis == 1) {
         axis = 3;
     }
-    return wrap_outputs(
+    return t.wrap_outputs(
             imperative::apply(
                     *Concat::make(axis, op.comp_node, op.scope()),
-                    unwrap_inputs(inputs)),
+                    t.unwrap_inputs(inputs)),
             format);
 }
 
-std::vector<ValueRef> elemwise_rule(
-        const Elemwise& op, Span<ValueRef>& inputs, const bool& auto_convert) {
-    FT format = get_inputs_format(inputs);
-    return wrap_outputs(imperative::apply(op, unwrap_inputs(inputs)), format);
+ValueRefList elemwise_rule(
+        const Elemwise& op, Span<ValueRef>& inputs, const bool& auto_convert,
+        const FormatTransformation& t) {
+    FT format = get_inputs_format(inputs, t);
+    return t.wrap_outputs(imperative::apply(op, t.unwrap_inputs(inputs)), format);
 }
 
-std::vector<ValueRef> identity_rule_helper(
-        const OpDef& op, const Span<ValueRef>& inputs) {
+ValueRefList identity_rule_helper(
+        const OpDef& op, const Span<ValueRef>& inputs, const FormatTransformation& t) {
     // mgb_assert(inputs.size() == 1);
-    auto& src = inputs[0].cast<FormattedTensorValue>();
-    return wrap_outputs(
-            imperative::apply(op, unwrap_inputs(inputs)), src.format().type());
+    auto& src = inputs[0].cast(t.value_type());
+    return t.wrap_outputs(
+            imperative::apply(op, t.unwrap_inputs(inputs)), src.format().type());
 }
 
 // clang-format off
@@ -318,10 +337,11 @@ std::vector<ValueRef> identity_rule_helper(
     cb(Identity)
 // clang-format on
 
-#define CREATE_IDENTITY_OP_RULE(op)                                            \
-    std::vector<ValueRef> op##_rule(                                           \
-            const op& _op, Span<ValueRef>& inputs, const bool& auto_convert) { \
-        return identity_rule_helper(_op, inputs);                              \
+#define CREATE_IDENTITY_OP_RULE(op)                                          \
+    ValueRefList op##_rule(                                                  \
+            const op& _op, Span<ValueRef>& inputs, const bool& auto_convert, \
+            const FormatTransformation& t) {                                 \
+        return identity_rule_helper(_op, inputs, t);                         \
     }
 FOREACH_IDENTITY_OP(CREATE_IDENTITY_OP_RULE)
 #undef CREATE_IDENTITY_OP_RULE
@@ -344,22 +364,26 @@ struct FormatRuleRegistry {
 #undef REGISTER_IDENTITY_OP_RULE
 }  // namespace
 
-std::vector<ValueRef> FormatTransformation::apply_transformation(
+ValueRefList FormatTransformation::apply_transformation(
         const Operator& op, Span<ValueRef> inputs) {
     if (auto* apply_op = op.as<ApplyOp>()) {
         // all inputs should be FormattedTensorValue
         auto iter = format_rules.find(apply_op->op().dyn_typeinfo());
         if (iter != format_rules.end()) {
-            return iter->second(apply_op->op(), inputs, m_auto_convert);
+            return iter->second(apply_op->op(), inputs, m_auto_convert, *this);
         } else {
             return wrap_outputs(imperative::apply(op, unwrap_inputs(inputs)));
         }
     } else if (auto* create_tensor = op.as<CreateTensor>()) {
         auto format = create_tensor->format();
-        return {FormattedTensorValue::make(imperative::apply(op, inputs)[0], format)};
+        return {wrap_output(imperative::apply(op, inputs)[0], format.type())};
     } else if (auto* get_attr = op.as<GetAttr>()) {
-        auto* src = inputs.as_array<1>()[0].as<FormattedTensorValue>();
-        if (!m_auto_convert || !src || src->format() != FT::NHWC) {
+        auto&& input = inputs.item();
+        if (!input.is(m_value_type)) {
+            return imperative::apply(op, input);
+        }
+        auto& src = input.cast(m_value_type);
+        if (!(m_auto_convert && src.format() == FT::NHWC)) {
             return imperative::apply(op, unwrap_inputs(inputs));
         }
         switch (get_attr->attr()) {
@@ -369,16 +393,16 @@ std::vector<ValueRef> FormatTransformation::apply_transformation(
                 return {ShapeValue::make(shape)};
             }
             case GetAttr::Value: {
-                auto nchw_src = unwrap_input(src->to(FT::NCHW, ""));
-                return imperative::apply(op, std::vector<ValueRef>{nchw_src});
+                auto nchw_src = unwrap_input(to(src, FT::NCHW, ""));
+                return imperative::apply(op, SmallVector<ValueRef>{nchw_src});
             }
             default:
                 return imperative::apply(op, unwrap_inputs(inputs));
         }
     } else if (op.is<GetFormat>()) {
-        bool is_formatted_tensor = inputs.as_array<1>()[0].is<FormattedTensorValue>();
+        bool is_formatted_tensor = inputs.item().is(m_value_type);
         if (is_formatted_tensor) {
-            return {FormatValue::make(inputs[0].cast<FormattedTensorValue>().format())};
+            return {FormatValue::make(inputs[0].cast(m_value_type).format())};
         } else {
             mgb_log_warn(
                     "Not FormattedTensorValue input for GetFormat op: %s",
@@ -386,9 +410,9 @@ std::vector<ValueRef> FormatTransformation::apply_transformation(
             return {FormatValue::make(FT::DEFAULT)};
         }
     } else if (op.is<Operator::IdentityLike>()) {
-        bool is_formatted_tensor = inputs.as_array<1>()[0].is<FormattedTensorValue>();
+        bool is_formatted_tensor = inputs.item().is(m_value_type);
         if (is_formatted_tensor) {
-            auto& format = inputs[0].cast<FormattedTensorValue>().format();
+            auto&& format = inputs[0].cast(m_value_type).format();
             return wrap_outputs(
                     imperative::apply(op, unwrap_inputs(inputs)), format.type());
         } else {
diff --git a/imperative/src/include/megbrain/imperative/transformations/format.h b/imperative/src/include/megbrain/imperative/transformations/format.h
index 81fd83bfaee5c735f7ebdae988908a53326cd81b..0d1b5c5939495a035b710a35bf8270c188df9d8f 100644
--- a/imperative/src/include/megbrain/imperative/transformations/format.h
+++ b/imperative/src/include/megbrain/imperative/transformations/format.h
@@ -7,7 +7,7 @@
 
 namespace mgb::imperative {
 
-class FormattedTensorValue final : public ValueImpl<FormattedTensorValue> {
+class FormattedTensorValue final : public ObjectValue<FormattedTensorValue> {
 private:
     ValueRef m_value;
     Format m_format;
@@ -26,10 +26,6 @@ public:
 
     const Format& format() const { return m_format; }
 
-    TypedValueRef<FormattedTensorValue> as(const Format::Type& target) const;
-    TypedValueRef<FormattedTensorValue> to(
-            const Format::Type& target, const std::string& scope = "") const;
-
     void clear() override {
         m_value = {};
         m_format = {};
@@ -40,23 +36,18 @@ public:
     void on_unwatch() override { m_value.unwatch(); }
 };
 
-/**
- * \brief simulates scalar because megbrain graph system don't support scalar
- *
- * Assume that we has 'a = ScalarValue(b)', thus 'a.shape == []', 'b.shape == [1]'.
- * This transformation simulates scalars with a flag. If a value is ScalarValue, it is
- * scalar, vice versa. So there is not scalar down this layer.
- */
 class FormatTransformation final : public Transformation {
 private:
-    bool m_auto_convert = false;
+    // enable auto_convert by default to be easier to use.
+    bool m_auto_convert = true;
+    ObjectType<FormattedTensorValue> m_value_type{"FormattedTensorValue"};
 
 public:
-    std::vector<ValueRef> apply_transformation(
+    ValueRefList apply_transformation(
             const Operator& op, Span<ValueRef> inputs) override;
 
     ValueRef unwrap(ValueRef value) override {
-        mgb_assert(!value.is<FormattedTensorValue>());
+        mgb_assert(!value.is(m_value_type));
         return value;
     }
 
@@ -65,6 +56,22 @@ public:
     }
     void set_auto_convert(bool enabled) { m_auto_convert = enabled; }
     bool get_auto_convert() const { return m_auto_convert; }
+
+    const Type<FormattedTensorValue>& value_type() const { return m_value_type; }
+
+    inline ValueRef unwrap_input(const ValueRef& input) const;
+    inline ValueRefList unwrap_inputs(const Span<ValueRef>& inputs) const;
+    inline ValueRef wrap_output(
+            const ValueRef& output, Format::Type type = Format::Type::DEFAULT) const;
+    inline ValueRefList wrap_outputs(
+            const ValueRefList& outputs,
+            Format::Type type = Format::Type::DEFAULT) const;
+
+    TypedValueRef<FormattedTensorValue> as(
+            const FormattedTensorValue&, const Format::Type& target) const;
+    TypedValueRef<FormattedTensorValue> to(
+            const FormattedTensorValue&, const Format::Type& target,
+            const std::string& scope = "") const;
 };
 
 }  // namespace mgb::imperative
diff --git a/imperative/src/include/megbrain/imperative/value.h b/imperative/src/include/megbrain/imperative/value.h
index ecf63b48d654b3a11a77672ffc2c4b4e5b4f6223..9543aa49e3d5b0a1453f4e647cc103102b40ba8d 100644
--- a/imperative/src/include/megbrain/imperative/value.h
+++ b/imperative/src/include/megbrain/imperative/value.h
@@ -67,6 +67,7 @@ template <typename T>
 class Type : public IType {
 protected:
     Type(std::string name) : IType(std::move(name)) {}
+    Type(IType&& type) : IType(std::move(type)) {}
     // TODO: each type owns an allocator
 
 public:
@@ -104,6 +105,7 @@ template <typename T>
 class ObjectType : public Type<T> {
 public:
     ObjectType(std::string name) : Type<T>(name) {}
+    ObjectType(IType&& type) : Type<T>(std::move(type)) {}
 };
 
 /**