* Adding greater than and less than equal ops to compare op
* Changing the name of the less_than_equal and greater_than_equal op
* Also changing the name of the functors

* init commit

* change some dir name

* fix lod_tensor_array

* init test beam search decode op

* add test_beam_search_decode_op

* Conditional Block Forward

* Assign Operator.

Out=X, when type in [LoDTensor/SelectedRows/LoDTensorArray]

* Stash

* Add Scope::Rename

it is useful in gradient phase of an operator with block

* ConditionalBlock Grad Done

* Add comments

* yapf format code

* add split lod tensor operator

* add more test cast

* clean code

* add merge lod tensor operator

* fix bug

* clean code

* add grad operator

* make mask support GPU

* add comments

* Assign Operator.

Out=X, when type in [LoDTensor/SelectedRows/LoDTensorArray]

* Follow comments

* add lstm layer

* set hidden shape

* rename input parameter

* add dynamic lstm

* refine dynamic lstm layer

* change parameter using XavierInitializer by default

* refine dynamic lstm layer

* create learning rate variable for every parameter

* fix ci

* set parameter lr relatively to global lr

* "fix ci failed"

* "comment out seq_concate op to unblock PRs"

* 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

* Adding operator assignment

* Adding documentation to layers.py

* Removing file from another PR

* Fix attribute naming for momentum_op

* Fix minor typo in comment

* Fix attribute name

* Fix names in test_optimizer

* Fix python wrapper

* increament is default inplace

* 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

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