* open header for custom kernel

* add core utils

* tidy core code

* tify header

* tidy include

* tidy namespace

* resolve conflit

* fix unittest and coverage

* remove platform using

* resolve conflict

* resolve conflict

* fix digamma namespace error

* fix xpu full kernel error

* fix xpu full kernel error

* polish details

* add place for lib storage

* Migrate Dim and DDim from paddle::framework into pten namespace

* fix paddle::framework::Array

* fix framework::Array

* Add linalg.eigvals API

* pre-commit check

* Adjust code style

* Fix conflict

* Improve code style

* Modify the test code to ignore testing CUDA kernel

* Sort ouput data before checking in test code

* Set timeout value for UT

* Improve API example code to pass CI

* Fix bug for None fetch_list in Windows

* Delete grad Op

* use iwyu clean include, test=develop, test=win

* compilation error, test=develop

* fix compilation error2, test=develop

* fix compilation error3, test=develop

* fix compilation error4, test=develop

* fix compilation error5, test=develop

* fix compilation error6, test=develop

* fix compilation error7, test=develop

* fix compilation error8, test=develop

* fix compilation error8, test=develop

* fix compilation error10, test=develop

* fix compilation error11, test=develop

* polish framework error meg part2

* polish details

remove unused accuracy-diff warpctc-cudnn implementation

test=develop

test=develop

* clean code of dim and place, test=develop

* fix failed unittests, test=develop

* fix shape_check; test=develop

* fix format; test=develop

* fix format; test=develop

* fix ddim bug; test=develop

* fix c++ format; test=develop

* change function name; test=develop

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Dim<0> is for scalar (rank-0 tensor). Adding Dim<0> can simplify
a lot of code.

* 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

Fix #3386