Fix recurrent op not update grade issue (#18581)

* Fix recurrent op fails

For the variable used in outter block,
copy sub block's grad variable to outter block

test=develop

Signed-off-by: zhaoyuchen <zhaoyuchen01@baidu.com>

* Fix unicode error

test=develop

Signed-off-by: zhaoyuchen <zhaoyuchen01@baidu.com>

* Refine test code

test=develop

Signed-off-by: zhaoyuchen <zhaoyuchen01@baidu.com>

* Fix seq2seq case fails

test=develop

Signed-off-by: zhaoyuchen <zhaoyuchen01@baidu.com>

* remove unreasonable code.

test=develop

Signed-off-by: zhaoyuchen <zhaoyuchen01@baidu.com>

* Refine code according to comment

test=develop

Signed-off-by: zhaoyuchen <zhaoyuchen01@baidu.com>

python/paddle/fluid/layers/control_flow.py

@@ -576,7 +576,7 @@ class StaticRNN(object):
                     if in_var_name not in local_inputs:
                         params.append(in_var_name)
 
-        parameters = [parent_block.var(name) for name in params]
+        parameters = [parent_block.var(name) for name in set(params)]
 
         step_scope = parent_block.create_var(
             type=core.VarDesc.VarType.STEP_SCOPES)

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

@@ -464,5 +464,143 @@ class RecurrentOpNoMemBootTest(RecurrentOpTest1):
         return rnn()
 
 
+class RecurrentOpSubBlockTest(RecurrentOpTest1):
+    '''
+    Test RNNOp with subblock variable
+    equation:
+        y_ = emb * w1
+        h_t = \concat([x, h_{t-1}])
+        h_t = h_t * w2
+        h_t = \\unsqueeze(h_t, 1)
+        h_t = \dot_attention(h_t, y_)
+        h_t = \squeeze(h_t, 1)
+        y = h_t
+    vars:
+        - x
+        - w1
+        - w2
+    memories:
+        - h
+    outputs:
+       - y
+    '''
+
+    class PySimpleRNN5(PyRNNBase):
+        def __init__(self, input_shape, output_shape):
+            super(RecurrentOpSubBlockTest.PySimpleRNN5, self).__init__(
+                input_shape, output_shape)
+
+            seq_len, batch_size, input_dim = input_shape
+            self.w1 = np.random.uniform(
+                -0.1, 0.1, size=(input_dim, input_dim)).astype("float32")
+            self.w2 = np.random.uniform(
+                -0.1, 0.1, size=(input_dim * 2, input_dim)).astype("float32")
+
+            self.emb = np.random.uniform(
+                -0.1, 0.1, size=(seq_len, batch_size,
+                                 input_dim)).astype("float32")
+
+            men_dim = (seq_len, batch_size, input_dim)
+            self.mems = np.zeros(shape=men_dim).astype("float32")
+            self.oy = np.matmul(self.emb, self.w1)
+
+        def step(self, step_id, x):
+            def dot_attention(query, memory):
+                attn = np.matmul(query, memory.transpose((0, 2, 1)))
+                weight = softmax(attn)
+                weight_memory = np.matmul(weight, memory)
+                return weight_memory, weight
+
+            def softmax(x):
+                return np.exp(x) / sum(np.exp(x))
+
+            if step_id == 0:
+                pre_mem = np.zeros_like(x)
+            else:
+                pre_mem = self.mems[step_id - 1]
+            concat_in = np.concatenate([x, pre_mem], 1)
+            new_mem = np.matmul(concat_in, self.w2)
+
+            new_mem = np.expand_dims(new_mem, 1)
+            new_mem, _ = dot_attention(new_mem, self.oy)
+            new_mem = np.squeeze(new_mem, 1)
+
+            self.mems[step_id] = new_mem
+            self.y[step_id] = self.mems[step_id]
+
+    input_dim = 2
+    batch_size = 3
+    sent_len = 3
+
+    def setUp(self):
+        self.setup_program()
+
+        self.data_field = {"x", "emb", "w1", "w2"}
+
+        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
+        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
+        self.py_rnn = RecurrentOpSubBlockTest.PySimpleRNN5(self.input_shape,
+                                                           self.output_shape)
+
+        with fluid.program_guard(self.main_program, self.startup_program):
+            rnn_out = self.create_rnn_op()
+            self.output = layers.mean(rnn_out)
+
+    def create_rnn_op(self):
+        x = layers.data(
+            shape=[self.sent_len, self.batch_size, self.input_dim],
+            dtype='float32',
+            name='x',
+            append_batch_size=False)
+        x.stop_gradient = False
+
+        emb = layers.data(
+            name='emb',
+            shape=[self.sent_len, self.batch_size, self.input_dim],
+            dtype='float32',
+            append_batch_size=False)
+        emb.stop_gradient = False
+
+        w1 = layers.data(
+            shape=[self.input_dim, self.input_dim],
+            dtype='float32',
+            name='w1',
+            append_batch_size=False)
+        w1.stop_gradient = False
+        w2 = layers.data(
+            shape=[self.input_dim * 2, self.input_dim],
+            dtype='float32',
+            name='w2',
+            append_batch_size=False)
+        w2.stop_gradient = False
+
+        rnn = layers.StaticRNN()
+
+        def dot_attention(query, memory):
+            attn = layers.matmul(query, memory, transpose_y=True)
+            weight = layers.softmax(attn)
+            weight_memory = layers.matmul(weight, memory)
+
+            return weight_memory, weight
+
+        y = layers.matmul(emb, w1)
+        with rnn.step():
+            pre_h = rnn.memory(
+                shape=(self.sent_len, self.input_dim),
+                batch_ref=x,
+                init_value=0.0)
+            step_in = rnn.step_input(x)
+            concat_in = layers.concat([step_in, pre_h], 1)
+            new_h = layers.matmul(concat_in, w2)
+            new_h = layers.unsqueeze(new_h, [1])
+            new_h, _ = dot_attention(new_h, y)
+            new_h = layers.squeeze(new_h, [1])
+
+            rnn.update_memory(pre_h, new_h)
+            rnn.step_output(new_h)
+
+        return rnn()
+
+
 if __name__ == '__main__':
     unittest.main()