remove parallel_executor and execution_strategy (#6694)

Author: ccsuzzh

docs/api_guides/index_cn.rst

@@ -17,7 +17,6 @@ API 功能分类
     low_level/inference.rst
     low_level/memory_optimize.rst
     low_level/executor.rst
-    low_level/parallel_executor.rst
     low_level/compiled_program.rst
     low_level/parameter.rst
     low_level/distributed/index.rst

docs/api_guides/index_en.rst

@@ -17,7 +17,6 @@ This section introduces the Fluid API structure and usage, to help you quickly g
     low_level/inference_en.rst
     low_level/memory_optimize_en.rst
     low_level/executor_en.rst
-    low_level/parallel_executor_en.rst
     low_level/compiled_program_en.rst
     low_level/parameter_en.rst
     low_level/distributed/index_en.rst

docs/api_guides/low_level/executor.rst

@@ -4,7 +4,7 @@
 执行引擎
 ##########
 
-:code:`Executor` 实现了一个简易的执行器，所有的操作在其中顺序执行。你可以在 Python 脚本中运行 :code:`Executor` 。PaddlePaddle Fluid 中有两种执行器。一种是 :code:`Executor` 默认的单线程执行器，另一种是并行计算执行器，在 :ref:`api_guide_parallel_executor` 中进行了解释。``Executor`` 和 :ref:`api_guide_parallel_executor` 的配置不同，这可能会给部分用户带来困惑。为使执行器更加灵活，我们引入了 :ref:`api_guide_compiled_program` ， :ref:`api_guide_compiled_program` 用于把一个程序转换为不同的优化组合，可以通过 :code:`Executor` 运行。
+:code:`Executor` 实现了一个简易的执行器，所有的操作在其中顺序执行。你可以在 Python 脚本中运行 :code:`Executor` 。
 
  :code:`Executor` 的逻辑非常简单。建议在调试阶段用 :code:`Executor` 在一台计算机上完整地运行模型，然后转向多设备或多台计算机计算。
 

docs/api_guides/low_level/executor_en.rst

@@ -4,7 +4,7 @@
 Executor
 ################
 
-:code:`Executor` realizes a simple executor in which all operators will be executed in order. You can run :code:`Executor` in a Python script. There are two kinds of executors in PaddlePaddle Fluid. One is single-thread executor which is the default option for :code:`Executor` and the other is the parallel executor which is illustrated in :ref:`api_guide_parallel_executor_en` . The config of `Executor` and :ref:`api_guide_parallel_executor_en` is different, it may be a bit confusing for some users. To make the executor more facility, we introduce :ref:`api_guide_compiled_program_en` , :ref:`api_guide_compiled_program_en` is used to transform a program for various optimizations, and it can be run by :code:`Executor`.
+:code:`Executor` realizes a simple executor in which all operators will be executed in order. You can run :code:`Executor` in a Python script.
 
 The logic of :code:`Executor` is very simple. It is suggested to thoroughly run the model with :code:`Executor` in debugging phase on one computer and then switch to mode of multiple devices or multiple computers to compute.
 

docs/api_guides/low_level/layers/data_in_out_en.rst

@@ -25,6 +25,3 @@ Fluid supports obtaining data for the current batch in the training/inference ph
 
 The user can fetch expected variables from :code:`executor.run(fetch_list=[...], return_numpy=...)` . User can determine whether to convert the output data to numpy array by setting the :code:`return_numpy` parameter.
 If :code:`return_numpy` is :code:`False` , data of type :code:`LoDTensor` will be returned.
-
-For specific usage, please refer to the relevant API documentation :ref:`api_fluid_executor_Executor` and
-:ref:`api_fluid_ParallelExecutor`.

docs/api_guides/low_level/parallel_executor.rst

@@ -16,4 +16,3 @@
   block.md
   scope.md
   executor.md
-  parallel_executor.md

docs/api_guides/low_level/parallel_executor_en.rst

@@ -16,4 +16,3 @@ Core Concepts
   block.md
   scope.md
   executor.md
-  parallel_executor.md