* is_training to is_test in dropout op

* handle dropout and batch_norm operator when prune pdesc in testing mode

* handle dropout and batch_norm operator when prune pdesc in testing mode

* add get_inference_program method

* fix dropout op

* fix ci

* test data after each batch training

* refine code

* refine test_book3

* fix ci

* follow comments

* Compare Operator

* Follow comments

* Use stable_sort in lod_rank_table

It is easy to debug and test when use `stable_sort`and the time
complexity is not changed.

* Add LoDTensorArray

* Add LoDRankTable

LoD Rank Table stores the `level` of `lod` which is ordered by sequence
length in descending order. It is useful when implement dynamic RNN and
is shared by dynamic RNN memory, dynamic RNN slice input and dynamic
RNN slice output operators.

* Add InferVarType

* Init commit

* Make executor use ProgramDescBind

* Change Attribute from BlockDesc to BlockDescBind

* Since we will get the program desc in RNN, just BlockDesc is not
  enough.

* Add DeviceContext to Executor API

* Rewrite RNN

* Pass Python

* AddBiasOp does not care num_flatten_dims

* Stash

* Fix MacOS Compile

* Pass RNN forward

* add python test

* refactor test

* Make compile pass

* add gradopmaker

* First draft done

* Polish code

* add grad op maker and grad infershape

* Polish code

* Fix backward.cc bug

* Fix infershape

* Rename function

* add backward test

* simplify recurrent test

* Update

* Pass unittest

* Add comments & refine test

* Add comments

* refactor test

* Complete Unittest

* fix StepScopes enforce

* Remove unused unittest

* no type error

* Update

* Make RNN Pass unittest

* Init commit

* Make executor use ProgramDescBind

* Change Attribute from BlockDesc to BlockDescBind

* Since we will get the program desc in RNN, just BlockDesc is not
  enough.

* improve unique_name, uniq id is related to prefix

* fix join

* add init_gflags interface

* refine code

* follow comments

* Add `dump_to_file()` for ProgrameDescBind in pybind

* Update

* Add utility.py

* typo

* Fix bugs

* Move add_feed/fetch_components to untility.py

* Compelete dump

* Follow comments

* Change output of Prune() from inference to pointer

* Expose Prune() to Python

* Compelete save/load API of inference model

* Fix errors

* Debuging

* Compelete unit tests

* follow comments

* Add print_operators_doc.cc

* Update Escape

* Correct a bug

* Remove OpInfoMap::Iterate

* Update the print_operators_doc.cc

* Escape tab

* Use auto&

* Use auto&

* Remove trailing ,

* clang-format C++

* Unify `set_feed_variable` to one method

* Move global scope to python, not in C++

* Remove template parameter for Tensor methods

* Also check the type is correct when data()
* Simplize holder_

* Fix accuracy_op

* Register Code

* Implement FC layer with helper

* Update LayerHelper

* Add debug string for Python ProtoBuf

and Rename `Sync` to `Flush`

* Add check of ProtoBuf initialization

* Layer wrapper for FC

* Fix unittest

* Fix CI

* Add code generator

* AttributeChecker Better error log and speicalize bool

Since lots of types can be cast to bool

* Complete mlp, fit_a_line

* Expose get global scope

* Make global scope not thread-safe

1. It is no need to make global scope thread-safe, since it will be
invoked in Python main thread.
2. Do not free the global scope when C++ exit. Let the OS free memories,
otherwise, we need to handle the destroy dependencies.

See
https://google.github.io/styleguide/cppguide.html#Static_and_Global_Variables

* Fix

* Implementation of simple conv_2d layer

* Stash

* Remove private data members in OpRegister

* Fix bugs

* Stash

* Expose FeedFetchList as VarType

* Change ProgramDesc not a global variable

* Polish code style

* Stash

* Correct implement BlockDesc destructor

* Correct implement BlockDesc destructor

* Unify program as parameter name

* Fix bugs

* Add unittest

* Fix unit test error

* Remove unused functions

* Add clone for Python Program

* Working on executor

* Stash

* Add glog as dependencies of ops

* Use VLOG to logging some information is helpful when we debug Paddle

* Expose VarDesc::persistable to Python

* Test executor

* Complete unittest

* Polish code

* Fix merge error

* Follow comment

* Polish Python Code

* Change ProgramDesc not a global variable

* Polish code style

* Correct implement BlockDesc destructor

* Unify program as parameter name

* Expose Executor to Python

* Follow comments

1. InferShape should be a method for `OpDescBind` not `Operator`, since
  `OpDescBind` is a compile-time concept.
2. Pre-create operators and store them into a map. Make InferShape
   faster

By shell command

```bash
sed -i 's#ifdef PADDLE_ONLY_CPU#ifndef PADDLE_WITH_GPU#g' `find ./paddle/ -name '*.h' -o -name '*.cc' -o -name '*.cpp' -o -name '*.c' -o -name '*.cu'`
sed -i 's#ifndef PADDLE_ONLY_CPU#ifdef PADDLE_WITH_GPU#g' `find ./paddle/ -name '*.h' -o -name '*.cc' -o -name '*.cpp' -o -name '*.c' -o -name '*.cu'`
```