* "add inplace attribute"

* "register inplace attribute"

* "change se-next model for memory-reuse"

* "fix typo"

* repick

* fix merge conflict

* "fix stupid error"

Do not use ctor

* Reduce line of codes.
* We can use virtual function for Maker now.
* The implementation does not care what maker holds, it is easier to
refactor later.

* Rename XXDescBind --> XXDesc

* Fix Compile

* Move framework.proto to proto namespace

* Fix compile

* Fix compile

* Fix Compile

There are mainly following fixes:

- take `DeviceContext` as the template parameter of math functors and OpKernel instead of `Place`
- remove `eigen_device` interface in base class  `DeviceContext`
- remove `GetEigenDevice` interface in `ExecutionContext` and base class `DeviceContext`
- remove unused `platform::EigenDeviceConverter`
- rename `REGISTER_OP_GPU_KERNEL` to `REGISTER_OP_CUDA_KERNEL`
- rename `USE_GPU_ONLY_OP` to `USE_CUDA_ONLY_OP`

* 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

* fix m_ops

* fix activation op

* Simplize Gradient Check

* Stash

* Extract apply_backward_pass to backward.py

Rename apply_backward_pass to append_backward_ops

* Use graph API to check gradient

* Fix ci

* Fix CI

* Fix backward for double precision

* Stash

* Fix CI

* Fix ci

* Ignore GRU test

* Ignore xe op

* Fix CI

* Fix softmax with xe gradient

The correct equation should be IG = OG * (d_softmax_with_xe())

* Fix typo

* Fix merge error

* Disable LRN

They are public now

* init Infershape

* add static InferShape interface

* refactor add-op infershape

* add AttrReader

* add all maker's infershape

* add all InferShape

* add python infer api

* add VarDesc interface

* add python VarDesc and OpDesc interface

* update python code

* use infershape function to do shape inference

* clean code

* do not use pointer

* refine code of op_proto_maker

* add get_dims to VarDesc

* refine the code

* remove the dependency from operator to op registry

* remove OpProtoAndCheckerMaker from operator

* restore complete_add_op

* add shape_infer_impl.h

* code optimization

* remove const return value

* add fake BlockDesc class

* optimize code

* remove infer function in op_info

* move InferShapeContextImpl to operator.h

* optimize the interface of InferShapeContextBase

* add temperary interface of new infershape

* change add_op, clip_op, conv2d_op and activation_op

* change all operators InferShape

* fix SetDim

* update cos_sim_op

* update crop_op

* update lookup_table_op

* allocate tensor when call GetDim in InferShapeContext

* update modified_huber_loss_op

* update rowwise_add_op

* update mean_op

* update sequence_avg_pool_op

* typo

* remove old InferShape interface

* can compile

* fix or unit test

* clean code

* clean code

* remove const before InferShapeContext

* change InferenceContextBase to pointer

* rename RunTime to Runtime, code clean

* No POD instance is forbidden in global scope.

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

* fix some enforce

* remove compatible_type to avoid compile error

* remove shared_ptr

* fix tensor error msg