cherry-pick: add api desc for pipeline training (#18293) (#18304)

cherry-pick for #18293

python/paddle/fluid/optimizer.py

@@ -2640,6 +2640,61 @@ class ExponentialMovingAverage(object):
 
 
 class PipelineOptimizer(object):
+    """
+    Pipeline Optimizer
+    Train with pipeline mode. The program will be splited by cut_list.
+    If the len of cut_list is k, then the whole program (including 
+    backward part) will be splited to 2*k-1 sections. So the length of place_list
+    and concurrency_list must be also 2*k-1.
+    Note: Though the asynchronous mode is applied in pipeline training to speed up,
+    the final performance depends on the training progress of each pipeline heavily.
+    And we will try the synchronous mode in the future
+    Args:
+        optimizer (Optimizer): The based optimizer, such as SGD
+        cut_list (list of Variable list): The cut variable of the main_program
+        place_list (list of Place): The place where the section will run on
+        concurrency_list (list of int): The concurrency degree
+        queue_size (int): Each section will consume scopes from its in-scope queue 
+                        and produce scopes to out-scope queue. And this parameter 
+                        specify the scope queue size. [Optional. Default: 30]
+        sync_steps (int): The synchronization steps between different cards. [Optional. Default: 1]
+        start_cpu_core_id (int): specify the first cpu core id. [Optional. Default:0]
+    Examples:
+        .. code-block:: python
+        x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0)
+        y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0)
+        emb_x = layers.embedding(input=x, param_attr=fluid.ParamAttr(name="embx"), size=[10,2], is_sparse=False)
+        emb_y = layers.embedding(input=y, param_attr=fluid.ParamAttr(name="emby",learning_rate=0.9), size=[10,2], is_sparse=False)
+        concat = layers.concat([emb_x, emb_y], axis=1)
+        fc = layers.fc(input=concat, name="fc", size=1, num_flatten_dims=1, bias_attr=False)
+        loss = layers.reduce_mean(fc)
+        optimizer = fluid.optimizer.SGD(learning_rate=0.5)
+        optimizer = fluid.optimizer.PipelineOptimizer(optimizer,
+                cut_list=[[emb_x, emb_y], [loss]],
+                place_list=[fluid.CPUPlace(), fluid.CUDAPlace(0), fluid.CPUPlace()],
+                concurrency_list=[1, 1, 4],
+                queue_size=2,
+                sync_steps=1,
+                )
+        optimizer.minimize(loss)
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+        filelist = [] # you should set your own filelist, e.g. filelist = ["dataA.txt"]
+        dataset = fluid.DatasetFactory().create_dataset("FileInstantDataset")
+        dataset.set_use_var([x,y])
+        dataset.set_batch_size(batch_size)
+        dataset.set_filelist(filelist)
+        exe.train_from_dataset(
+                    fluid.default_main_program(),
+                    dataset,
+                    thread=2,
+                    debug=False,
+                    fetch_list=[],
+                    fetch_info=[],
+                    print_period=1)
+    """
+
     def __init__(self,
                  optimizer,
                  cut_list=None,