* Refine Type system

* Fixing type inference

* Fixed create_reader_op.cc

* Fix var_desc.h

* Fixed executor.cc

* Fix shape_inference.h

* Fixed create_reader_op.cc

* Fix tensor_util.h

* Fixed var_type_inference_test.cc

* Fix shape_inference.cc

* Fixed sum_op.c

* Fixed read_op.cc

* Fix var_type.h

* Fixed beam_search_decode_op.cc

* sendrecvop_utils.cc

* Fix operator.cc

* Fixed lookup_table_op.cc

* Fixed op_desc.cc

* Fixed get_places_op.cc

* Fixed lod_rank_table_op.cc

* Fixed beam_search_op.cc

* Fix var_desc.cc

* Fixed lod_tensor_to_array_op.cc

* Fixed while_op.cc

* Fix program_desc_test.cc

* tensor_array_read_write_op.cc

* Fix assign_op.cc

* Fix executor.cc

* Fix protobuf.cc

* Fix protobuf.cc

Add a new type `Reader` for `VarDesc`, which can holds more than one
LoDTensor.

* limit variable type to lod tensor in memory optimization transpiler

* refine policy

* support while operator

* fix random seed and training data order

* refine get_cfgs method to support multi while operators

* refine codes

* Rename XXDescBind --> XXDesc

* Fix Compile

* Move framework.proto to proto namespace

* Fix compile

* Fix compile

* Fix Compile

* Change all `data_type` in Python to `dtype`

* Change `date_type` in C++ to `dtype`

* Refine

* 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

* Stash

* Better debug message for IsInitialized

* Stash

* Better debug message for IsInitialized

* Complete array read/write op unittests

* 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 `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

* compelete restoring program_bind from program_desc

* Fix bugs

* fix compile errors

* fix errors and add unit tests

* rename some vars

* Follow comments

* init

* ensure ids in lookup table op must be a column vector

* add book4 configuration in test_layers

* debug test_book4

* add test_word2vec

* follow comments

* follow comments

* 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

* 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

* Implementation of simple conv_2d layer

* Fix bugs

* 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

* Compare OpDescBind directly

* 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

* Implementation of simple conv_2d layer

* Fix bugs

* Correct implement BlockDesc destructor

* Fix bugs

* Fix unit test error

* Follow comments

* Change ProgramDesc not a global variable

* Polish code style

* Correct implement BlockDesc destructor

* Unify program as parameter name

* init parameter base class

* optimize the Comments of optimizer

* basic implimentation of optimizer

* add test_optimizer

* add no_grad_set to interface

* update optimizer.py

* python code can run

* fix some problem

* add sync_with_cpp to Python Program and Block

* sync vars and ops in block from cpp

* optimize code and add some comment

* add more check for sync

* update optimizer with return value of Backward

* rm unused code

* infer shape when create gradient vairiable

* update test_optimizer

* update test_program.py

* update backward test

* follow comment