[Dy2stat] Fix lstm bug (#27631)

This PR fixed two bugs when converting LSTM in dy2stat:

is_unsupported has a condition can trigger Python syntax error
LSTM API's implementation in _rnn_static_graph doesn't include parameter initialization, which can cause dy2stat error.

python/paddle/fluid/dygraph/dygraph_to_static/convert_call_func.py

@@ -65,12 +65,17 @@ def is_unsupported(func):
     Checks whether the func is supported by dygraph to static graph.
     """
 
-    if any(func in m.__dict__.values() for m in BUILTIN_LIKELY_MODULES):
-        translator_logger.log(
-            2,
-            "Whitelist: {} is part of built-in module and does not have to be transformed.".
-            format(func))
-        return True
+    for m in BUILTIN_LIKELY_MODULES:
+        for v in m.__dict__.values():
+            func_in_dict = func == v
+            if isinstance(func_in_dict, (list, numpy.ndarray)):
+                func_in_dict = any(func_in_dict)
+            if func_in_dict:
+                translator_logger.log(
+                    2,
+                    "Whitelist: {} is part of built-in module and does not have to be transformed.".
+                    format(func))
+                return True
 
     if is_paddle_func(func):
         translator_logger.log(

python/paddle/fluid/layers/rnn.py

@@ -623,7 +623,7 @@ def _rnn_static_graph(cell,
         inputs = map_structure(rnn.step_input, inputs)
         states = map_structure(rnn.memory, initial_states)
         copy_states = map_structure(lambda x: x, states)
-        outputs, new_states = cell.call(inputs, copy_states, **kwargs)
+        outputs, new_states = cell(inputs, copy_states, **kwargs)
         assert_same_structure(states, new_states)
         if sequence_length:
             step_mask = rnn.step_input(mask)

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

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 numpy as np
+import paddle
+import unittest
+from paddle import nn
+
+
+class Net(nn.Layer):
+    def __init__(self, in_channels, hidden_size):
+        super(Net, self).__init__()
+        self.lstm = nn.LSTM(
+            in_channels, hidden_size, direction='bidirectional', num_layers=2)
+
+    @paddle.jit.to_static
+    def forward(self, x):
+        x, _ = self.lstm(x)
+        return x
+
+
+class TestLstm(unittest.TestCase):
+    def run_lstm(self, to_static):
+        paddle.jit.ProgramTranslator().enable(to_static)
+
+        paddle.disable_static()
+        paddle.static.default_main_program().random_seed = 1001
+        paddle.static.default_startup_program().random_seed = 1001
+
+        net = Net(12, 2)
+        x = paddle.zeros((2, 10, 12))
+        y = net(paddle.to_tensor(x))
+        return y.numpy()
+
+    def test_lstm_to_static(self):
+        dygraph_out = self.run_lstm(to_static=False)
+        static_out = self.run_lstm(to_static=True)
+        self.assertTrue(
+            np.allclose(dygraph_out, static_out),
+            msg='dygraph_out is {}\n static_out is \n{}'.format(dygraph_out,
+                                                                static_out))
+
+
+if __name__ == "__main__":
+    unittest.main()