Remove pipeline_optimizer and RoundRobin (#2258)

doc/fluid/api/optimizer.rst

@@ -28,7 +28,6 @@ paddle.optimizer
     optimizer/ModelAverage.rst
     optimizer/Momentum.rst
     optimizer/MomentumOptimizer.rst
-    optimizer/PipelineOptimizer.rst
     optimizer/RecomputeOptimizer.rst
     optimizer/RMSPropOptimizer.rst
     optimizer/SGD.rst

doc/fluid/api/optimizer/PipelineOptimizer.rst

-..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
-    !DO NOT EDIT THIS FILE MANUALLY!
-
-.. _api_fluid_optimizer_PipelineOptimizer:
-
-PipelineOptimizer
------------------
-
-..  autoclass:: paddle.fluid.optimizer.PipelineOptimizer
-    :members:
-    :inherited-members:
-    :exclude-members: apply_gradients, apply_optimize, backward, load
-    :noindex:
-

doc/fluid/api/transpiler.rst

@@ -10,4 +10,3 @@ fluid.transpiler
     transpiler/HashName.rst
     transpiler/memory_optimize.rst
     transpiler/release_memory.rst
-    transpiler/RoundRobin.rst

doc/fluid/api/transpiler/RoundRobin.rst

-..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
-    !DO NOT EDIT THIS FILE MANUALLY!
-
-.. _api_fluid_transpiler_RoundRobin:
-
-RoundRobin
-----------
-
-..  autoclass:: paddle.fluid.transpiler.RoundRobin
-    :members:
-    :inherited-members:
-    :noindex:
-

doc/fluid/api_cn/optimizer_cn.rst

@@ -31,7 +31,6 @@ paddle.optimizer
     optimizer_cn/ModelAverage_cn.rst
     optimizer_cn/Momentum_cn.rst
     optimizer_cn/MomentumOptimizer_cn.rst
-    optimizer_cn/PipelineOptimizer_cn.rst
     optimizer_cn/RecomputeOptimizer_cn.rst
     optimizer_cn/RMSPropOptimizer_cn.rst
     optimizer_cn/SGD_cn.rst

doc/fluid/api_cn/optimizer_cn/PipelineOptimizer_cn.rst

-.. _cn_api_fluid_optimizer_PipelineOptimizer:
-
-PipelineOptimizer
--------------------------------
-
-
-.. py:class:: paddle.fluid.optimizer.PipelineOptimizer(optimizer, cut_list=None, place_list=None, concurrency_list=None, queue_size=30, sync_steps=1, start_cpu_core_id=0)
-
-:api_attr: 声明式编程模式（静态图)
-
-
-
-使用流水线模式进行训练。
-Program会根据切分列表cut_list进行分割。如果cut_list的长度是k，则整个program（包括反向部分）将被分割为2*k-1个section。 所以place_list和concurrency_list的长度也必须是2*k-1。 
-
-.. note::
-
-    虽然我们在流水线训练模式中采用异步更新的方式来加速，但最终的效果会依赖于每条流水线的训练进程。我们将在未来尝试同步模式。
-
-参数:
-    - **optimizer** (Optimizer) - 基础优化器，如SGD
-    - **cut_list** (list of Variable list) - main_program的cut变量列表
-    - **place_list** (list of Place) - 对应section运行所在的place
-    - **concurrency_list** (list of int) - 指定每个section的并发度列表
-    - **queue_size** (int) -  每个section都会消费其输入队列(in-scope queue)中的scope，并向输出队列(out-scope queue)产出scope。 此参数的作用就是指定队列的大小。 可选，默认值：30
-    - **sync_steps** (int) - 不同显卡之间的同步周期数。可选，默认值：1
-    - **start_cpu_core_id** (int) - 指定所使用的第一个CPU核的id。可选，默认值：0
-
-**代码示例**
-
-.. code-block:: python
-
-        import paddle.fluid as fluid
-        import paddle.fluid.layers as layers
-        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)
-
-
-
-

doc/fluid/api_cn/transpiler_cn.rst

@@ -13,4 +13,3 @@ fluid.transpiler
     transpiler_cn/HashName_cn.rst
     transpiler_cn/memory_optimize_cn.rst
     transpiler_cn/release_memory_cn.rst
-    transpiler_cn/RoundRobin_cn.rst

doc/fluid/api_cn/transpiler_cn/RoundRobin_cn.rst

-.. _cn_api_fluid_transpiler_RoundRobin:
-
-RoundRobin
--------------------------------
-
-
-.. py:class:: paddle.fluid.transpiler.RoundRobin(pserver_endpoints)
-
-:api_attr: 声明式编程模式（静态图)
-
-
-
-该方法使用 ``RoundRobin`` 的方式将变量散列到多个parameter server终端。
-
-`RondRobin <https://en.wikipedia.org/wiki/Round-robin_scheduling>`_
-
-参数:
-  - **pserver_endpoints** (list) - endpoint （ip:port）的 list 
-
-返回：实例化后的RoundRobin的对象
-
-返回类型：RoundRobin
-
-**代码示例**
-
-.. code-block:: python
-
-          import paddle.fluid.transpiler.RoundRobin as RoundRobin
-
-          pserver_endpoints = [“127.0.0.1:6007”, “127.0.0.1:6008”]
-          vars = [“var1”,”var2”,”var3”,”var4”,”var5”]
-          rr = RoundRobin(pserver_endpoints)
-          rr.dispatch(vars)
-
-
-.. py:method:: dispatch(varlist)
-
-该方法使用RoundRobin的方式将多个参数散列到多个parameter Server终端。
-
-参数:
-  - **varlist** (list) - 参数 (var1, var2, var3) 的 list
-
-返回：基于varlist中var的顺序，返回参数服务器(ip:port)的列表， 列表中的数据量和varlist的数据量一致。
-
-返回类型：list
-
-**代码示例**
-
-.. code-block:: python
-
-          pserver_endpoints = [“127.0.0.1:6007”, “127.0.0.1:6008”]
-          vars = [“var1”,”var2”,”var3”,”var4”,”var5”]
-          rr = RoundRobin(pserver_endpoints)
-          rr.dispatch(vars)
-
-
-.. py:method:: reset()
-
-该方法将重置RoundRobin内置的计数， 计数将重置为0。
-
-返回：无。
-
-**代码示例**
-
-.. code-block:: python
-
-          pserver_endpoints = [“127.0.0.1:6007”, “127.0.0.1:6008”]
-          vars = [“var1”,”var2”,”var3”,”var4”,”var5”]
-          rr = RoundRobin(pserver_endpoints)
-          rr.reset()
-
-