[Dy2Static]Add param_guard in ParameterList to support @to_static

python/paddle/fluid/dygraph/container.py

@@ -15,6 +15,7 @@
 from collections import OrderedDict
 from ..framework import Parameter
 from .layers import Layer
+from .base import param_guard
 
 __all__ = [
     'Sequential',
@@ -159,7 +160,8 @@ class ParameterList(Layer):
                 self.add_parameter(str(idx), param)
 
     def __getitem__(self, idx):
-        return self._parameters[str(idx)]
+        with param_guard(self._parameters):
+            return self._parameters[str(idx)]
 
     def __setitem__(self, idx, param):
         assert isinstance(param, Parameter)
@@ -169,7 +171,8 @@ class ParameterList(Layer):
         return len(self._parameters)
 
     def __iter__(self):
-        return iter(self._parameters.values())
+        with param_guard(self._parameters):
+            return iter(self._parameters.values())
 
     def append(self, parameter):
         """Appends a given parameter at the end of the list.

python/paddle/fluid/tests/unittests/dygraph_to_static/test_param_guard.py

+# Copyright (c) 2021 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 paddle
+import numpy as np
+import unittest
+
+from paddle.jit import to_static, ProgramTranslator
+
+
+class NetWithParameterList(paddle.nn.Layer):
+    def __init__(self, in_size, out_size):
+        super(NetWithParameterList, self).__init__()
+        weight = self.create_parameter([in_size, out_size])
+        bias = self.create_parameter([out_size], is_bias=True)
+        self.params = paddle.nn.ParameterList([weight, bias])
+
+    @to_static
+    def forward(self, x):
+        out = paddle.matmul(x, self.params[0])
+        out = paddle.add(out, self.params[1])
+        out = paddle.tanh(out)
+        return out
+
+
+class NetWithParameterListIter(NetWithParameterList):
+    def __init__(self, in_size, out_size):
+        super(NetWithParameterListIter, self).__init__(in_size, out_size)
+
+    @to_static
+    def forward(self, x):
+        # NOTE: manually trigger `__iter__` logic.
+        params = list(self.params.__iter__())
+        out = paddle.matmul(x, params[0])
+        out = paddle.add(out, params[1])
+        out = paddle.tanh(out)
+        return out
+
+
+class TestParameterList(unittest.TestCase):
+    def setUp(self):
+        self.seed = 2021
+        self.iter_num = 5
+        self.prog_trans = ProgramTranslator()
+
+    def train(self, is_iter, to_static):
+        paddle.seed(self.seed)
+        np.random.seed(self.seed)
+        self.prog_trans.enable(to_static)
+        if is_iter:
+            net = NetWithParameterList(10, 3)
+        else:
+            net = NetWithParameterListIter(10, 3)
+        sgd = paddle.optimizer.SGD(0.1, parameters=net.parameters())
+
+        for batch_id in range(self.iter_num):
+            x = paddle.rand([4, 10], dtype='float32')
+            out = net(x)
+            loss = paddle.mean(out)
+            loss.backward()
+            sgd.step()
+            sgd.clear_grad()
+
+        return loss
+
+    def test_parameter_list(self):
+        static_loss = self.train(False, to_static=True)
+        dygraph_loss = self.train(False, to_static=False)
+        self.assertTrue(
+            np.allclose(dygraph_loss, static_loss),
+            msg='dygraph result is {}\nstatic result is {}'.format(dygraph_loss,
+                                                                   static_loss))
+
+    def test_parameter_list_iter(self):
+        static_loss = self.train(True, to_static=True)
+        dygraph_loss = self.train(True, to_static=False)
+        self.assertTrue(
+            np.allclose(dygraph_loss, static_loss),
+            msg='dygraph result is {}\nstatic result is {}'.format(dygraph_loss,
+                                                                   static_loss))
+
+
+if __name__ == '__main__':
+    unittest.main()