diff --git a/imperative/python/megengine/experimental/__init__.py b/imperative/python/megengine/experimental/__init__.py
index 0253e7dc..19b1fc6b 100644
--- a/imperative/python/megengine/experimental/__init__.py
+++ b/imperative/python/megengine/experimental/__init__.py
@@ -6,3 +6,4 @@
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+from .weight_scaler import get_scaled_model
diff --git a/imperative/python/megengine/experimental/weight_scaler.py b/imperative/python/megengine/experimental/weight_scaler.py
new file mode 100644
index 00000000..0908257b
--- /dev/null
+++ b/imperative/python/megengine/experimental/weight_scaler.py
@@ -0,0 +1,100 @@
+import types
+from functools import partial
+
+import megengine.functional as F
+import megengine.module as M
+from megengine.functional.tensor import zeros
+from megengine.utils.module_utils import set_module_mode_safe
+
+
+def get_norm_mod_value(weight, norm_value):
+    weight = weight.reshape(-1)
+    norm = F.norm(weight)
+    scale = norm_value / norm
+    round_log = F.floor(F.log(scale) / F.log(2))
+    rounded_scale = 2 ** round_log
+    return rounded_scale.detach()
+
+
+def get_scaled_model(model, scale_submodel, input_shape=None):
+    submodule_list = None
+    scale_value = None
+    accumulated_scale = 1.0
+
+    def scale_calc(mod_calc_func):
+        def calcfun(self, inp, weight, bias):
+            scaled_weight = weight
+            scaled_bias = bias
+            if self.training:
+                scaled_weight = (
+                    weight * self.weight_scale if weight is not None else None
+                )
+                scaled_bias = bias * self.bias_scale if bias is not None else None
+            return mod_calc_func(inp, scaled_weight, scaled_bias)
+
+        return calcfun
+
+    def scale_module_structure(
+        scale_list: list = None, scale_value: tuple = None,
+    ):
+        nonlocal accumulated_scale
+        for i in range(len(scale_list)):
+            key, mod = scale_list[i]
+            w_scale_value = scale_value[1]
+            if scale_value[0] is not "CONST":
+                w_scale_value = get_norm_mod_value(mod.weight, scale_value[1])
+
+            accumulated_scale *= w_scale_value
+
+            mod.weight_scale = w_scale_value
+            mod.bias_scale = accumulated_scale
+
+            if isinstance(mod, M.conv.Conv2d):
+                mod.calc_conv = types.MethodType(scale_calc(mod.calc_conv), mod)
+            else:
+                mod._calc_linear = types.MethodType(scale_calc(mod._calc_linear), mod)
+
+    def forward_hook(submodel, inputs, outpus, modelname=""):
+        nonlocal submodule_list
+        nonlocal scale_value
+        nonlocal accumulated_scale
+        if modelname in scale_submodel:
+            scale_value = scale_submodel[modelname]
+            if isinstance(submodel, (M.conv.Conv2d, M.linear.Linear)):
+                scale_module_structure([(modelname, submodel)], scale_value)
+            else:
+                submodule_list = []
+
+        if isinstance(submodel, (M.conv.Conv2d, M.linear.Linear)) and (
+            submodule_list is not None
+        ):
+            submodule_list.append((modelname, submodel))
+
+        if isinstance(submodel, M.batchnorm.BatchNorm2d) and (
+            submodule_list is not None
+        ):
+            scale_module_structure(submodule_list, scale_value)
+            submodule_list = None
+            scale_value = None
+            accumulated_scale = 1.0
+
+    if input_shape is None:
+        raise ValueError("input_shape is required for calculating scale value")
+
+    input = zeros(input_shape)
+
+    hooks = []
+    for modelname, submodel in model.named_modules():
+        hooks.append(
+            submodel.register_forward_pre_hook(
+                partial(forward_hook, modelname=modelname, outpus=None)
+            )
+        )
+
+    with set_module_mode_safe(model, training=False) as model:
+        model(input)
+
+    for hook in hooks:
+        hook.remove()
+
+    return model