diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
index 53928ebed1b25fffe0cd3b16e260e87bfec2c4ac..f502a1dfc340dedeb94a519ba847a08be47e7d6f 100644
--- a/python/paddle/hapi/model.py
+++ b/python/paddle/hapi/model.py
@@ -792,14 +792,15 @@ class Model(object):
     switched by `paddle.disable_static()`. The usage is as follows.
     But note, the switching between dynamic and static should be before
     instantiating a Model. The input description, i.e, paddle.static.InputSpec,
-    must be required.
+    must be required for static graph.
 
     Args:
         network (paddle.nn.Layer): The network is an instance of
             paddle.nn.Layer.
         inputs (InputSpec|list|dict|None): `inputs`, entry points of network,
             could be a InputSpec instance, or lits of InputSpec instances,
-            or dict ({name: InputSpec}), and it couldn't be None.
+            or dict ({name: InputSpec}), and it couldn't be None in static
+            graph.
         labels (InputSpec|list|None): `labels`, entry points of network,
             could be a InputSpec instnace or lits of InputSpec instances,
             or None. For static graph, if labels is required in loss,
@@ -848,9 +849,10 @@ class Model(object):
         self._optimizer = None
         self._test_dataloader = None
 
-        if not isinstance(inputs, (list, dict, Input)):
-            raise TypeError(
-                "'inputs' must be list or dict, and couldn't be None.")
+        if not in_dygraph_mode():
+            if not isinstance(inputs, (list, dict, Input)):
+                raise TypeError(
+                    "'inputs' must be list or dict, and couldn't be None.")
         self._inputs = self._verify_spec(inputs, True)
         self._labels = self._verify_spec(labels)
 
@@ -1863,7 +1865,18 @@ class Model(object):
     def _verify_spec(self, specs, is_input=False):
         out_specs = []
 
-        if isinstance(specs, dict):
+        if specs is None:
+            # Note(Aurelius84): If not specific specs of `Input`, using argument names of `forward` function
+            # to generate `Input`. But how can we know the actual shape of each input tensor?
+            if is_input:
+                out_specs = [
+                    Input(
+                        name=n, shape=[None])
+                    for n in extract_args(self.network.forward) if n != 'self'
+                ]
+            else:
+                out_specs = to_list(specs)
+        elif isinstance(specs, dict):
             assert is_input == False
             out_specs = [specs[n] \
                 for n in extract_args(self.network.forward) if n != 'self']