diff --git a/paddle/fluid/imperative/tracer.cc b/paddle/fluid/imperative/tracer.cc
index 78e95f672232e82332fefd2a9eef30756fa0cae6..a01225ccee4a82f77ec2a23df75d1cf7b719bdb7 100644
--- a/paddle/fluid/imperative/tracer.cc
+++ b/paddle/fluid/imperative/tracer.cc
@@ -133,11 +133,11 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
           grad_in_vars.push_back(fwd_var_it->second->var_);
         } else {
           VarBase* var = vars[var_it->second];
-          if (!var->grads_->IsInitialized()) {
-            InitVar(var->var_, var->grads_);
+          if (!var->grads_->var_->IsInitialized()) {
+            InitVar(var->var_, var->grads_->var_);
           }
           // Douts.
-          grad_in_vars.push_back(var->grads_);
+          grad_in_vars.push_back(var->grads_->var_);
         }
       }
     }
@@ -149,10 +149,10 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
         auto var_it = grad_to_var->find(grad_outvar);
         PADDLE_ENFORCE(var_it != grad_to_var->end());
         VarBase* var = vars[var_it->second];
-        if (!var->grads_->IsInitialized()) {
-          InitVar(var->var_, var->grads_);
+        if (!var->grads_->var_->IsInitialized()) {
+          InitVar(var->var_, var->grads_->var_);
         }
-        grad_out_vars.push_back(var->grads_);
+        grad_out_vars.push_back(var->grads_->var_);
       }
     }
   }
@@ -194,13 +194,13 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
       grad_input_vars.push_back(out->var_);
     }
     for (VarBase* out : outputs) {
-      grad_input_vars.push_back(out->grads_);
+      grad_input_vars.push_back(out->grads_->var_);
       if (!grad_input_vars.back()->IsInitialized()) {
         InitVar(out->var_, grad_input_vars.back());
       }
     }
     for (const VarBase* inp : inputs) {
-      grad_output_vars.push_back(inp->grads_);
+      grad_output_vars.push_back(inp->grads_->var_);
       if (!grad_output_vars.back()->IsInitialized()) {
         InitVar(inp->var_, grad_output_vars.back());
       }
diff --git a/python/paddle/fluid/imperative/layers.py b/python/paddle/fluid/imperative/layers.py
index 8027d9ba3bcf4d37f3573bc928faf574dcde1038..6d3987c9d5437463960910834a2202be9fb32cfe 100644
--- a/python/paddle/fluid/imperative/layers.py
+++ b/python/paddle/fluid/imperative/layers.py
@@ -55,18 +55,18 @@ class PyLayer(core.PyLayer):
         super(PyLayer, self).__init__()
 
     @staticmethod
-    def forward(inputs):
+    def forward(*inputs):
         raise NotImplementedError
 
     @staticmethod
-    def backward(douts):
+    def backward(*douts):
         raise NotImplementedError
 
     @classmethod
-    def __call__(cls, inputs):
+    def __call__(cls, *inputs):
         tracer = framework._imperative_tracer()
         block = framework.default_main_program().current_block()
-        inputs = [x._ivar for x in inputs]
+        ivar_inputs = [x._ivar for x in inputs]
 
         if not hasattr(cls, 'forward_id'):
             cls.forward_id = core.PyLayer.num_funcs() + 1
@@ -78,11 +78,11 @@ class PyLayer(core.PyLayer):
         iop.forward_id = cls.forward_id
         iop.backward_id = cls.backward_id
         block.ops.append(iop)
-        ivars = tracer.py_trace(iop, inputs, False)
+        ivars = tracer.py_trace(iop, ivar_inputs, False)
         # ivars = core.PyLayer.apply(cls.forward, inputs)
         ret = []
         for ivar in ivars:
-            tensor = ivar.value.get_tensor()
+            tensor = ivar.value().get_tensor()
             py_var = framework.Variable(
                 block,
                 type=core.VarDesc.VarType.LOD_TENSOR,
diff --git a/python/paddle/fluid/tests/unittests/test_imperative.py b/python/paddle/fluid/tests/unittests/test_imperative.py
index e3e1ce7ca3127969e9c4430649a18b08e0e71889..86baff3c589d7b8a14938886b3e2104b0beb1cc9 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative.py
@@ -97,35 +97,35 @@ class TestImperative(unittest.TestCase):
                     super(PyLayer1, self).__init__()
 
                 @staticmethod
-                def forward(inputs):
-                    return inputs
+                def forward(input):
+                    return input
 
                 @staticmethod
-                def backward(inputs):
-                    return inputs
+                def backward(input):
+                    return input
 
             class PyLayer2(fluid.imperative.PyLayer):
                 def __init__(self):
                     super(PyLayer2, self).__init__()
 
                 @staticmethod
-                def forward(inputs):
-                    return inputs
+                def forward(input):
+                    return input
 
                 @staticmethod
-                def backward(inputs):
-                    return inputs
+                def backward(input):
+                    return input
 
             py_layer_1 = PyLayer1()
             py_layer_2 = PyLayer2()
-            py_layer_1([fluid.imperative.base.to_variable(np.ones([2, 2]))])
-            py_layer_2([fluid.imperative.base.to_variable(np.ones([2, 2]))])
+            py_layer_1(fluid.imperative.base.to_variable(np.ones([2, 2])))
+            py_layer_2(fluid.imperative.base.to_variable(np.ones([2, 2])))
             id = py_layer_1.forward_id
             self.assertGreater(id, 0)
             self.assertEqual(py_layer_1.backward_id, id + 1)
             self.assertEqual(py_layer_2.forward_id, id + 2)
             self.assertEqual(py_layer_2.backward_id, id + 3)
-            py_layer_1([fluid.imperative.base.to_variable(np.ones([2, 2]))])
+            py_layer_1(fluid.imperative.base.to_variable(np.ones([2, 2])))
             self.assertEqual(py_layer_1.forward_id, id)
 
     def test_pylayer(self):
@@ -133,7 +133,7 @@ class TestImperative(unittest.TestCase):
         with fluid.imperative.guard():
             my_py_layer = MyPyLayer()
             var_inp = fluid.imperative.base.to_variable(np_inp)
-            outs = my_py_layer([var_inp])
+            outs = my_py_layer(var_inp)
             dy_out = np.sum(outs[0]._numpy())
             outs[0]._backward()
             dy_grad = var_inp._gradient()