add detach API for Variable in dygraph mode, test=develop (#19477)

* add to and detach for Variable in dygraph, test=develop

* add detach for Variable in dygraph, test=develop

* add detach for Variable in dygraph, test=develop

* add detach for Variable in dygraph, test=develop

* add detach for Variable in dygraph, test=develop

* add detach for Variable in dygraph, test=develop

* add exception check, test=develop

python/paddle/fluid/framework.py

@@ -543,6 +543,40 @@ class Variable(object):
             self._stop_gradient = stop_gradient
             self.is_data = is_data
 
+    def detach(self):
+        """
+        Returns a new Variable, detached from the current graph.
+        
+        Returns:
+            Variable: The detached Variable.
+
+        Examples:
+            .. code-block:: python
+
+                import paddle.fluid as fluid
+                from paddle.fluid.dygraph.base import to_variable
+                from paddle.fluid.dygraph import FC
+                import numpy as np
+
+                data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
+                with fluid.dygraph.guard():
+                    fc = FC("fc", 64, num_flatten_dims=2)
+                    data = to_variable(data)
+                    x = fc(data)
+                    y = x.detach()
+
+        """
+        if in_dygraph_mode():
+            new_var = self._cloneVar()
+            self.block.append_op(
+                type="assign",
+                inputs={'X': [self]},
+                outputs={'Out': [new_var]},
+                stop_gradient=True)
+            return new_var
+        else:
+            raise AttributeError("static graph model DO NOT supprt detach")
+
     def numpy(self):
         new_ivar = self._ivar._copy_to(core.CPUPlace(), True)
         return np.array(new_ivar.value().get_tensor())

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

@@ -0,0 +1,163 @@
+# Copyright (c) 2019 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.
+
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid as fluid
+
+from paddle.fluid import FC
+from paddle.fluid.dygraph import FC
+from paddle.fluid.dygraph.base import to_variable
+
+import unittest
+
+
+class Test_Detach(unittest.TestCase):
+    def generate_Data(self):
+        data = np.array(
+            [[1, 8, 3, 9], [7, 20, 9, 6], [4, 6, 8, 10]]).astype('float32')
+        return data
+
+    def no_detach_multi(self):
+        data = self.generate_Data()
+        with fluid.dygraph.guard():
+            fc_w_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(5.0))
+            fc_b_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(6.0))
+            fc = FC("fc",
+                    10,
+                    num_flatten_dims=1,
+                    param_attr=fc_w_param_attrs,
+                    bias_attr=fc_b_param_attrs)
+            fc1_w_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(7.0))
+            fc1_b_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(8.0))
+            fc1 = FC("fc",
+                     1,
+                     num_flatten_dims=1,
+                     param_attr=fc1_w_param_attrs,
+                     bias_attr=fc1_b_param_attrs)
+            fc2_w_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(9.0))
+            fc2_b_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(10.0))
+            fc2 = FC("fc",
+                     1,
+                     num_flatten_dims=1,
+                     param_attr=fc2_w_param_attrs,
+                     bias_attr=fc2_b_param_attrs)
+            data = to_variable(data)
+            x = fc(data)
+            x1 = fc1(x)
+            x2 = fc2(x)
+            loss = x1 + x2
+            # print(loss, loss.shape)
+            loss.backward()
+            return x.gradient()
+
+    def no_detach_single(self):
+        data = self.generate_Data()
+        with fluid.dygraph.guard():
+            fc_w_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(5.0))
+            fc_b_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(6.0))
+            fc = FC("fc",
+                    10,
+                    num_flatten_dims=1,
+                    param_attr=fc_w_param_attrs,
+                    bias_attr=fc_b_param_attrs)
+            fc1_w_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(7.0))
+            fc1_b_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(8.0))
+            fc1 = FC("fc",
+                     1,
+                     num_flatten_dims=1,
+                     param_attr=fc1_w_param_attrs,
+                     bias_attr=fc1_b_param_attrs)
+            data = to_variable(data)
+            x = fc(data)
+            x1 = fc1(x)
+            loss = x1
+            # print(loss, loss.shape)
+            loss.backward()
+            return x.gradient()
+
+    def detach_multi(self):
+        data = self.generate_Data()
+        with fluid.dygraph.guard():
+            fc_w_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(5.0))
+            fc_b_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(6.0))
+            fc = FC("fc",
+                    10,
+                    num_flatten_dims=1,
+                    param_attr=fc_w_param_attrs,
+                    bias_attr=fc_b_param_attrs)
+            fc1_w_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(7.0))
+            fc1_b_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(8.0))
+            fc1 = FC("fc",
+                     1,
+                     num_flatten_dims=1,
+                     param_attr=fc1_w_param_attrs,
+                     bias_attr=fc1_b_param_attrs)
+            fc2_w_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(9.0))
+            fc2_b_param_attrs = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(10.0))
+            fc2 = FC("fc",
+                     1,
+                     num_flatten_dims=1,
+                     param_attr=fc2_w_param_attrs,
+                     bias_attr=fc2_b_param_attrs)
+            data = to_variable(data)
+            x = fc(data)
+            x_detach = x.detach()
+            x1 = fc1(x)
+            x2 = fc2(x_detach)
+            loss = x1 + x2
+            # print(loss, loss.shape)
+            loss.backward()
+            return x.gradient()
+
+    def test_NoDetachMulti_DetachMulti(self):
+        array_no_detach_multi = self.no_detach_multi()
+        array_detach_multi = self.detach_multi()
+
+        assert not np.array_equal(array_no_detach_multi, array_detach_multi)
+
+    def test_NoDetachSingle_DetachMulti(self):
+        array_no_detach_single = self.no_detach_single()
+        array_detach_multi = self.detach_multi()
+        assert np.array_equal(array_no_detach_single, array_detach_multi)
+
+    def test_detach_exception(self):
+        x = fluid.layers.data(name="a", shape=[3, 4], dtype='float32')
+        y = fluid.layers.fc(input=x, size=10, bias_attr=True)
+        try:
+            y_detach = y.detach()
+        except Exception as e:
+            assert type(e) == AttributeError
+            assert str(e) == 'static graph model DO NOT supprt detach'
+
+
+if __name__ == '__main__':
+    unittest.main()