* init trieconcat_op

* add basic implementation

* add test

* add more test

* update unit test

* add PackAllSteps test

* fix PackAllSteps

* all test passed

* clean code

* remove state inside helper

* rename prob to score

* optimize RemoveFromEnd

* use deconstructor to delete BeamNode recursively

* optimize interface

* add comment to interface

* optimizer data structure

* use template to define the type of score

* use template parameter for BeamHelper

* change father to parent

* rename TrieConcat to BeamSearchOutConcat

* use LoDTensorArray

* rename BeamSearchOutConcat to BeamSearchDecode

* refine code

* remain all candidate sentence in beam_search_decode_op, do not consider endid

* use unique_ptr

* fix compare bug

* fix lod compile problem

* "fix ci failed"

* "comment out seq_concate op to unblock PRs"

TensorSetConstant struct is used both in math_function.cc and math_function.cu. Somehow the release version can correctly handle it. But in debug version, set_constant_with_place() in math_function.cu uses the TensorSetConstant in math_function.cc and causes crash.

it is useful in gradient phase of an operator with block

It caused by a bug of std::call_once described in https://stackoverflow.com/questions/41717579/stdcall-once-hangs-on-second-call-after-callable-threw-on-first-call. It is likely caused by a deeper bug of pthread_once, which is discussed in https://patchwork.ozlabs.org/patch/482350/

* fix seq_concat with refactaring LoD

* fix failed unit test

* rename function name

* first commit

* Python API for while op

* Python Unittest for simple while_op forward

* fix out to be list

* Fix UT

* VarType

* Fix several bugs

* Fix bug

* Fix bug

* Fix Bug

* Fix bug

* Fix unittest

* Remove debug log

* Add comments

* add PADDLE_ENFORCE

* while_grad_op first commit

* Add `BlockDescBind::FindRecursiveOrCreateVar()` and fix bugs

* refine code

* fix unittest bug

* Fix attribute naming for momentum_op

* Fix minor typo in comment

* Fix attribute name

* Fix names in test_optimizer

* Fix python wrapper

* 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 skeleton for array_to_lod_tensor and lod_tensor_to_array

* Add VarType::LoDTensorArray

* Add PyBind of LoDTensorArray

* Add InferVarType

* Add first unittest

* Add ut

* Add unittest

* Add unittest

* Add unittests

* update

* init

* add infershape for lod_tensor_to_array_op

* compelete array_to_lod_tensor_op

* copy data

* clean code

* clean code

* Fix unittest data

* fix bugs

* fix compile error

* Refine TensorToArrayOp

* refactor array_to_lod_tensor

* Unittest

* fix bugs

* Fix unittest

* Fix unittest

* debug

* Debug

* Fix unittest

* Add grad for ops

* Debug

* Fix a bug

* fix a bug

* fix a bug

* add fill_constant_batch_size_like_op to rnn h_boot

* first commit

* merge develop; fix conflict

* update to main_program

CompareOp can run on CPU even other operators are running on GPU, since
opeatations like comparing control flags should be performed only on CPU

Follow comments

* 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 skeleton for array_to_lod_tensor and lod_tensor_to_array

* Add VarType::LoDTensorArray

* Add PyBind of LoDTensorArray

* Add InferVarType

* Add first unittest

* Add ut

* Add unittest

* Add unittest

* Add unittests

* update

* init

* add infershape for lod_tensor_to_array_op

* compelete array_to_lod_tensor_op

* copy data

* clean code

* clean code

* Fix unittest data

* fix bugs

* fix compile error

* Refine TensorToArrayOp

* refactor array_to_lod_tensor

* Unittest

* fix bugs

* Fix unittest

* Fix unittest

* debug

* Debug

* Fix unittest

* clean code

* refactor

* use ostream

* update test

* fix gpu build error

* make gpu test pass

Used for shrink memories state in DyRNN. The height of state could
be shrinked after running a step block.

* Chage `IndicateDataType` to `GetKernelType`. Make it easier to
  understand.
* Change `OpKernelKey` to `OpKernelType`
* Make operator developers can customize which kernel the operator will
  use in runtime.

* Compare Operator

* Follow comments