Add forward test with mem

python/paddle/v2/fluid/tests/test_dynrnn_gradient_check.py

@@ -211,5 +211,67 @@ class TestSimpleMul(unittest.TestCase):
         self.assertTrue(numpy.allclose(w_g_num, w_g, rtol=0.05))
 
 
+class TestSimpleMulWithMemory(unittest.TestCase):
+    DATA_WIDTH = 32
+    HIDDEN_WIDTH = 10
+    DATA_NAME = 'X'
+    PARAM_NAME = 'W'
+
+    class SimpleMulWithMemory(BaseRNN):
+        def __init__(self):
+            super(TestSimpleMulWithMemory.SimpleMulWithMemory, self).__init__({
+                TestSimpleMulWithMemory.DATA_NAME: {
+                    'shape': [TestSimpleMulWithMemory.DATA_WIDTH]
+                }
+            }, {'Mem': {
+                'shape': [TestSimpleMulWithMemory.HIDDEN_WIDTH]
+            }}, {
+                TestSimpleMulWithMemory.PARAM_NAME: {
+                    'shape': [
+                        TestSimpleMulWithMemory.DATA_WIDTH,
+                        TestSimpleMulWithMemory.HIDDEN_WIDTH
+                    ]
+                }
+            }, ['Out'])
+
+        def step(self, X, Mem, W, Out):
+            o = numpy.matmul(X, W)
+            assert isinstance(Mem, Memory)
+            o += Mem.ex
+            Mem.update(o)
+            assert isinstance(Out, Output)
+            Out.out(o)
+
+    @prog_scope()
+    def test_forward_backward(self):
+        py_rnn = TestSimpleMulWithMemory.SimpleMulWithMemory()
+
+        data = fluid.layers.data(
+            name=self.DATA_NAME, shape=[self.DATA_WIDTH], lod_level=1)
+        rnn = fluid.layers.DynamicRNN()
+        with rnn.block():
+            d = rnn.step_input(data)
+            mem = rnn.memory(value=0.0, shape=[self.HIDDEN_WIDTH])
+            hidden = fluid.layers.fc(input=d,
+                                     size=self.HIDDEN_WIDTH,
+                                     param_attr=self.PARAM_NAME,
+                                     bias_attr=False,
+                                     act=None)
+            o = fluid.layers.elementwise_add(x=hidden, y=mem)
+            rnn.update_memory(mem, o)
+            rnn.output(o)
+
+        out = rnn()
+        last = fluid.layers.sequence_pool(input=out, pool_type='last')
+
+        cpu = fluid.CPUPlace()
+        exe = fluid.Executor(cpu)
+
+        last_np, = exe.run(feed=py_rnn.to_feed(cpu), fetch_list=[last])
+        last_by_py, = py_rnn.exe().values()
+
+        self.assertTrue(numpy.allclose(last_np, last_by_py))
+
+
 if __name__ == '__main__':
     unittest.main()