Add Sequential and ParameterList containers (#1676)

* add Sequential and ParameterList containers
test=develop

* update sample code of Sequential
test=develop

* update Sequential doc
test=develop

doc/fluid/api_cn/dygraph_cn.rst

@@ -30,6 +30,7 @@ fluid.dygraph
     dygraph_cn/NaturalExpDecay_cn.rst
     dygraph_cn/NCE_cn.rst
     dygraph_cn/NoamDecay_cn.rst
+    dygraph_cn/ParameterList_cn.rst
     dygraph_cn/no_grad_cn.rst    
     dygraph_cn/PiecewiseDecay_cn.rst
     dygraph_cn/PolynomialDecay_cn.rst
@@ -37,6 +38,7 @@ fluid.dygraph
     dygraph_cn/PRelu_cn.rst
     dygraph_cn/prepare_context_cn.rst  
     dygraph_cn/save_dygraph_cn.rst
+    dygraph_cn/Sequential_cn.rst
     dygraph_cn/SpectralNorm_cn.rst
     dygraph_cn/to_variable_cn.rst
     dygraph_cn/Tracer_cn.rst

doc/fluid/api_cn/dygraph_cn/ParameterList_cn.rst

+.. _cn_api_fluid_dygraph_ParameterList:
+
+ParameterList
+-------------------------------
+
+.. py:class:: paddle.fluid.dygraph.ParameterList(parameters=None)
+
+参数列表容器。此容器的行为类似于Python列表，但它包含的参数将被正确地注册和添加。
+
+参数：
+    - **parameters** (iterable，可选) - 可迭代的Parameters。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+    import numpy as np
+
+    class MyLayer(fluid.Layer):
+        def __init__(self, num_stacked_param):
+            super(MyLayer, self).__init__()
+            # 使用可迭代的 Parameters 创建 ParameterList
+            self.params = fluid.dygraph.ParameterList(
+                [fluid.layers.create_parameter(
+                    shape=[2, 2], dtype='float32')] * num_stacked_param)
+        def forward(self, x):
+            for i, p in enumerate(self.params):
+                tmp = self._helper.create_variable_for_type_inference('float32')
+                self._helper.append_op(
+                    type="mul",
+                    inputs={"X": x,
+                            "Y": p},
+                    outputs={"Out": tmp},
+                    attrs={"x_num_col_dims": 1,
+                           "y_num_col_dims": 1})
+                x = tmp
+            return x
+
+    data_np = np.random.uniform(-1, 1, [5, 2]).astype('float32')
+    with fluid.dygraph.guard():
+        x = fluid.dygraph.to_variable(data_np)
+        num_stacked_param = 4
+        model = MyLayer(num_stacked_param)
+        print(len(model.params))  # 4
+        res = model(x)
+        print(res.shape)  # [5, 2]
+        replaced_param = fluid.layers.create_parameter(shape=[2, 3], dtype='float32')
+        model.params[num_stacked_param - 1] = replaced_param  # 替换最后一个参数
+        res = model(x)
+        print(res.shape)  # [5, 3]
+        model.params.append(fluid.layers.create_parameter(shape=[3, 4], dtype='float32'))  # 添加参数
+        print(len(model.params))  # 5
+        res = model(x)
+        print(res.shape)  # [5, 4]
+
+

doc/fluid/api_cn/dygraph_cn/Sequential_cn.rst

+.. _cn_api_fluid_dygraph_Sequential:
+
+Sequential
+-------------------------------
+
+.. py:class:: paddle.fluid.dygraph.Sequential(*layers)
+
+顺序容器。子Layer将按构造函数参数的顺序添加到此容器中。传递给构造函数的参数可以Layers或可迭代的name Layer元组。
+
+参数：
+    - **layers** (tuple) - Layers或可迭代的name Layer对。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+    import numpy as np
+    data = np.random.uniform(-1, 1, [30, 10]).astype('float32')
+    with fluid.dygraph.guard():
+        data = fluid.dygraph.to_variable(data)
+        # 使用 iterable Layers 创建 Sequential 容器
+        model1 = fluid.dygraph.Sequential(
+            fluid.Linear(10, 1), fluid.Linear(1, 2)
+        )
+        model1[0]  # 访问第一个子层
+        res1 = model1(data)  # 顺序执行
+        # 使用 iterable name Layer 对创建 Sequential 容器
+        model2 = fluid.dygraph.Sequential(
+            ('l1', fluid.Linear(10, 2)),
+            ('l2', fluid.Linear(2, 3))
+        )
+        model2['l1']  # 访问 l1 子层
+        model2.add_sublayer('l3', fluid.Linear(3, 3))  # 添加子层
+        res2 = model2(data)  # 顺序执行
+
+