Add dygraph.DataParallel cn doc (#2411)

* add DataParallel cn doc

* polish details

* fix method declarative error

* polish format

* fix sample code error

* append to white list

doc/fluid/api_cn/dygraph_cn.rst

@@ -17,6 +17,7 @@ fluid.dygraph
     dygraph_cn/Conv3DTranspose_cn.rst
     dygraph_cn/CosineAnnealingDecay_cn.rst
     dygraph_cn/CosineDecay_cn.rst
+    dygraph_cn/DataParallel_cn.rst
     dygraph_cn/declarative_cn.rst
     dygraph_cn/Dropout_cn.rst
     dygraph_cn/Embedding_cn.rst

doc/fluid/api_cn/dygraph_cn/DataParallel_cn.rst

+.. _cn_api_fluid_dygraph_DataParallel:
+
+DataParallel
+------------
+
+.. py:class:: paddle.fluid.dygraph.DataParallel(layers, strategy)
+
+:api_attr: 命令式编程模式（动态图)
+
+通过数据并行模式执行动态图模型。
+
+目前，``DataParallel`` 仅支持以多进程的方式执行动态图模型。使用方式如下：
+
+``python -m paddle.distributed.launch –selected_gpus=0,1 dynamic_graph_test.py``
+
+其中 ``dynamic_graph_test.py`` 脚本的代码可以是下面的示例代码。
+
+参数：
+    - **Layer** (Layer) - 需要通过数据并行方式执行的模型。
+    - **strategy** (ParallelStrategy) - 数据并行的策略，包括并行执行的环境配置。
+
+返回：支持数据并行的 ``Layer``
+
+返回类型：Layer实例
+
+**代码示例**：
+
+.. code-block:: python
+
+    import numpy as np
+    import paddle.fluid as fluid
+
+    place = fluid.CUDAPlace(fluid.dygraph.ParallelEnv().dev_id)
+    with fluid.dygraph.guard(place):
+
+        # prepare the data parallel context
+        strategy = fluid.dygraph.prepare_context()
+
+        linear = fluid.dygraph.Linear(1, 10, act="softmax")
+        adam = fluid.optimizer.AdamOptimizer(
+            learning_rate=0.001, parameter_list=linear.parameters())
+
+        # make the module become the data parallelism module
+        linear = fluid.dygraph.DataParallel(linear, strategy)
+
+        x_data = np.random.random(size=[10, 1]).astype(np.float32)
+        data = fluid.dygraph.to_variable(x_data)
+
+        hidden = linear(data)
+        avg_loss = fluid.layers.mean(hidden)
+
+        # scale the loss according to the number of trainers.
+        avg_loss = linear.scale_loss(avg_loss)
+
+        avg_loss.backward()
+
+        # collect the gradients of trainers.
+        linear.apply_collective_grads()
+
+        adam.minimize(avg_loss)
+        linear.clear_gradients()
+
+.. py:method:: scale_loss(loss)
+
+缩放模型损失值 ``loss`` 。在数据并行模式中，损失值 ``loss`` 需要根据并行训练进程的数目进行缩放。
+
+如果不在数据并行模式下，会直接返回原 ``loss`` 。
+
+参数：
+    - **loss** (Variable) - 当前模型的损失值。
+
+返回：缩放后的损失值 ``loss``
+
+返回类型：Variable
+
+**代码示例**
+
+.. code-block:: python
+
+    import numpy as np
+    import paddle.fluid as fluid
+
+    place = fluid.CUDAPlace(fluid.dygraph.ParallelEnv().dev_id)
+    with fluid.dygraph.guard(place):
+
+        # prepare the data parallel context
+        strategy = fluid.dygraph.prepare_context()
+
+        linear = fluid.dygraph.Linear(1, 10, act="softmax")
+        adam = fluid.optimizer.AdamOptimizer(
+            learning_rate=0.001, parameter_list=linear.parameters())
+
+        # make the module become the data parallelism module
+        linear = fluid.dygraph.DataParallel(linear, strategy)
+
+        x_data = np.random.random(size=[10, 1]).astype(np.float32)
+        data = fluid.dygraph.to_variable(x_data)
+
+        hidden = linear(data)
+        avg_loss = fluid.layers.mean(hidden)
+
+        # scale the loss according to the number of trainers.
+        avg_loss = linear.scale_loss(avg_loss)
+
+        avg_loss.backward()
+
+        # collect the gradients of trainers.
+        linear.apply_collective_grads()
+
+        adam.minimize(avg_loss)
+        linear.clear_gradients()
+
+
+.. py:method:: apply_collective_grads()
+
+AllReduce（规约）参数的梯度值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import numpy as np
+    import paddle.fluid as fluid
+
+    place = fluid.CUDAPlace(fluid.dygraph.ParallelEnv().dev_id)
+    with fluid.dygraph.guard(place):
+
+        # prepare the data parallel context
+        strategy = fluid.dygraph.prepare_context()
+
+        linear = fluid.dygraph.Linear(1, 10, act="softmax")
+        adam = fluid.optimizer.AdamOptimizer(
+            learning_rate=0.001, parameter_list=linear.parameters())
+
+        # make the module become the data parallelism module
+        linear = fluid.dygraph.DataParallel(linear, strategy)
+
+        x_data = np.random.random(size=[10, 1]).astype(np.float32)
+        data = fluid.dygraph.to_variable(x_data)
+
+        hidden = linear(data)
+        avg_loss = fluid.layers.mean(hidden)
+
+        # scale the loss according to the number of trainers.
+        avg_loss = linear.scale_loss(avg_loss)
+
+        avg_loss.backward()
+
+        # collect the gradients of trainers.
+        linear.apply_collective_grads()
+
+        adam.minimize(avg_loss)
+        linear.clear_gradients()

scripts/api_white_list.txt

@@ -7,3 +7,4 @@ transpiler_cn/release_memory_cn.rst
 transpiler_cn/RoundRobin_cn.rst
 optimizer_cn/Dpsgd_cn.rst
 io_cn/ComposeNotAligned_cn.rst
+dygraph_cn/DataParallel_cn.rst
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