Connect stop gradient with trainbale (#25248)

* connect stop_gradient with trainable; test=develop

* add value error message; test=develop

python/paddle/fluid/framework.py

@@ -5111,7 +5111,8 @@ class ParamBase(core.VarBase):
                                         list(shape) if shape else [], name,
                                         core.VarDesc.VarType.LOD_TENSOR, True)
 
-        self.trainable = kwargs.get('trainable', True)
+        trainable = kwargs.get('trainable', True)
+        self.stop_gradient = not trainable
 
         self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})
 
@@ -5126,6 +5127,19 @@ class ParamBase(core.VarBase):
     def __str__(self):
         return self.to_string(True)
 
+    @property
+    def trainable(self):
+        return not self.stop_gradient
+
+    @trainable.setter
+    def trainable(self, trainable):
+        if isinstance(trainable, bool):
+            self.stop_gradient = not trainable
+        else:
+            raise ValueError(
+                "The type of trainable MUST be bool, but the type is ",
+                type(trainable))
+
     def to_string(self, throw_on_error, with_details=False):
         """
         To debug string.

python/paddle/fluid/tests/unittests/test_imperative_layer_trainable.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import paddle.fluid as fluid
+import numpy as np
+
+import paddle.fluid.dygraph as dygraph
+
+
+class TestImperativeLayerTrainable(unittest.TestCase):
+    def test_set_trainable(self):
+        with fluid.dygraph.guard():
+            label = np.random.uniform(-1, 1, [10, 10]).astype(np.float32)
+
+            label = dygraph.to_variable(label)
+
+            linear = dygraph.Linear(10, 10)
+            y = linear(label)
+            self.assertTrue(y.stop_gradient == False)
+
+            linear.weight.trainable = False
+            linear.bias.trainable = False
+
+            self.assertTrue(linear.weight.trainable == False)
+            self.assertTrue(linear.weight.stop_gradient == True)
+
+            y = linear(label)
+            self.assertTrue(y.stop_gradient == True)
+
+            with self.assertRaises(ValueError):
+                linear.weight.trainable = "1"
+
+
+if __name__ == '__main__':
+    unittest.main()