* add interpretercore for jit engine

* add ut

* [CustomDevice] add custom_device_resource_pool & device_event_custom_device

* update

* update

* update

* update

* add reduce_mean,reduce_sum primitive ops
* add ne_p gt_p primitive operators
* add ge_p abs_p primitive oparators
* add cast primitive operators
* add pow,square prim2oirg rules
* add elementwise_div orig2prim rule

* unify  core_avx and core_noavx

* fix except error

* revert mac compile logic

* revert dylib to so

* add core_noavx branch

* remove core_noavx

* replace paddle_core by lib paddle

* polish var name

* replace paddle_core by libpaddle

* update custom device commit

* polish code by comments

* refactor mp.

* update setup.py.

* update mp_layers.py for compatibility.

* add documents for mp_layers.py

* update init.py

* update collective.py.

* update.

* update mp_ops.py

* update.

* update code style.

* update code style.

* back fl

* delete ssl cert

* .

* make warning

* .

* unittest paral degree

* solve unittest

* heter & multi cloud commm ready

* .

* .

* fix gloo compile warning

* skip forward save

* fix bug

* more ci for jit skip forward

* [Auto Parallel] Use c++ dist attr in the completion process

* [Auto Parallel] Add minor changes

* [Auto Parallel] Use c++ dist attr in the completion process

* [Auto Parallel] Add minor changes

* [Auto Parallel] Add the serialization process for dist attrs

* [Auto Parallel] Remove unnecessary comments

* [Auto Parallel] Fix some bugs

* [Auto Parallel] Fix the code style

* [Auto Parallel] Remove unnecessary impls

* [Auto Parallel] Fix the importing error

* [Auto Parallel] Fix the copy from bugs of op dist attr

* [Auto Parallel] Replace the use of constexpr if

* [Auto Parallel] Redesign the shard_tensor, shard_op and ProcessMesh

* [Auto Parallel] Change API of the completion unittest

* [Auto Parallel] Fix the bug when set_attr an int

* [Auto Parallel] Add the unittest for the serialization

* [Auto Parallel] Add some unit tests

* [Auto Paralle] Unify the strategy

* [Auto Parallel] Improve the engine api

* [Auto Parallel] Reset the changes made to the framework

* [Auto Parallel] Change the engine unittest

* [Auto Parallel] Update API of the completion and partitioner

* [Auto Parallel] Update unit tests using engine api

* update shard annotation

* [Auto Parallel] Remove the modifications of other modules

* [Auto Parallel] Add docs for APIs

* add new strategy

* [Auto Parallel] Replace the logger

* [Auto Parallel] Restore the test_program.py

* [Auto Parallel] Change the import rules

* [Auto Parallel] Add the examples for Engine

* [Auto Parallel] Do some minor changes

* [Auto Parallel] Remove yaml dependency

* [Auto Parallel] Fix the unittests

* add valid after train

* bug fix
Co-authored-by: Nzhaoyingli <zhaoyingli@baidu.com>
Co-authored-by: Ncaozhou <caozhou@radi.ac.cn>
Co-authored-by: Ncaozhou <48191911+Caozhou1995@users.noreply.github.com>

* refine PADDLE_WITH_MKLDNN code

* fix data_norm_op

* polish addmm_op

* - mul & matmul changes

- fix

- bs16 correction of strides

* - cosmetic fixes

* - lint

* - fix

* - fix

* - format -> mem_desc

* - fix

* - fix

* - fix

* - fix

* - fix

* clear extra attrs of elementwise op in opmaker

* fix op_debug_string_test

* fix bug of grad_add

* fix sort of runtime attrs

Support 0 shapes input Tensor for MKL slice kernel

* first commit

* fix some bugs in code

* fix bugs

* to optimize merge one dimension feature

* saved_tensors_hooks

* make eager log readable

* fix compile error

* recover test

* invoke ci again

* delete eigen header in data_type.h

* fix complie bug

* refactor

* trim trailing whitespace

* fix `.cmake-format.py`

* revert npu ut changes, avoid npu ci error

* merge python lib
* Update third_party.cmake
* Update CMakeLists.txt

* Delay TensorRT registry
* Add unused define
* Fix TensorRT test
* fix function to reference
* Update trt_plugin.h

* support bmm and bmm_grad in xpu

* add error removal

* test=kunlun

* refactor code for better structure

* test=kunlun

* add fp16 kernel for bmm

* test=kunlun

* add convert rules for fill_any_like op in paddle science

* add unit test for fill_any_like op in paddle science

* modify fill_any_like convert rule

* modify fill_any_like convert rule dtype