[Speed] Make test_dyn_rnn faster (#16761)

* make test_dyn_rnn faster

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

@@ -24,15 +24,15 @@ from paddle.fluid.layers.control_flow import max_sequence_len
 from paddle.fluid.layers.control_flow import lod_tensor_to_array
 from paddle.fluid.layers.control_flow import array_to_lod_tensor
 from paddle.fluid.layers.control_flow import shrink_memory
+from fake_reader import fake_imdb_reader
 
 
 class TestDynRNN(unittest.TestCase):
     def setUp(self):
-        self.word_dict = paddle.dataset.imdb.word_dict()
+        self.word_dict_len = 5147
         self.BATCH_SIZE = 2
-        self.train_data = paddle.batch(
-            paddle.dataset.imdb.train(self.word_dict),
-            batch_size=self.BATCH_SIZE)
+        reader = fake_imdb_reader(self.word_dict_len, self.BATCH_SIZE * 100)
+        self.train_data = paddle.batch(reader, batch_size=self.BATCH_SIZE)
 
     def test_plain_while_op(self):
         main_program = fluid.Program()
@@ -42,7 +42,7 @@ class TestDynRNN(unittest.TestCase):
             sentence = fluid.layers.data(
                 name='word', shape=[1], dtype='int64', lod_level=1)
             sent_emb = fluid.layers.embedding(
-                input=sentence, size=[len(self.word_dict), 32], dtype='float32')
+                input=sentence, size=[self.word_dict_len, 32], dtype='float32')
 
             label = fluid.layers.data(name='label', shape=[1], dtype='float32')
 
@@ -109,7 +109,7 @@ class TestDynRNN(unittest.TestCase):
             sentence = fluid.layers.data(
                 name='word', shape=[1], dtype='int64', lod_level=1)
             sent_emb = fluid.layers.embedding(
-                input=sentence, size=[len(self.word_dict), 32], dtype='float32')
+                input=sentence, size=[self.word_dict_len, 32], dtype='float32')
 
             rnn = fluid.layers.DynamicRNN()
 

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

@@ -175,7 +175,7 @@ class TestTransformer(TestParallelExecutorBase):
             self.check_network_convergence(transformer, use_cuda=True)
             self.check_network_convergence(
                 transformer, use_cuda=True, enable_sequential_execution=True)
-        self.check_network_convergence(transformer, use_cuda=False, iter=5)
+        self.check_network_convergence(transformer, use_cuda=False, iter=2)
 
 
 if __name__ == '__main__':