cherry-pick : #46322, #46245
Sparse API 支持静态图

* set flag of clip_extra in save_inference_model to true (#46151)

* open the clip_extra flag in paddle.static.save_inference_model, test=allcase (#46456)

* Open the clip_extra flag in TracedLayer.save_inference_model (#46473)

* open the clip_extra flag in paddle.static.save_inference_model, test=allcase

* set the defalut value of clip_extra in TracedLayer from False to True, test=allcase

* update english doc of paddle.static.save_inference_model, test=document_fix (#46484)

* Fix clip_extra logic in remove_training_info (#46534)

* fix clip_extra code in remove_training_info

* revert rnn opmaker clear

* [MLU] fix sync_bn of mlu and add unittests

* [MLU] remove redunant code of pytest

* revert the change of remove_training_info

* update

* update

* [OpAtttr]Add attribute var interface for Operator class

* fix unittest

* fix unittest

* add runtime config in phi

* add runtime attr for op desc and op

* fix no proto error

* adjust opdesc set_attr impl

* try to remove conv_op extra attrs

* add init runtime attr map

* change extra header path

* fix runtime_attr

* fix trace_op

* fix bug of pass

* fix merge conflict

* fix dygraph attrs

* fix bug of pass

* fix dygraph bug

* fix unittest module

* delete extra attr default

* fix dropout kernel

* polish code

* fix extra output of instance_norm

* fix merge confilct

* fix op_desc bug

* add extra attr in yaml for conv3d_transpose

* don't remove extra input and output

* fix save_inference_model

* fix bug of batch_norm

* revert some change

* polish log

* polish code

* add code comment
Co-authored-by: NChen Weihang <chenweihang@baidu.com>

* add interpretercore

* refine backward program id

* add code

* refine program

* refine code

* create forward/backward_program by prog2graph2prog method

* test, do not care

* refine code

* refine code

* refine code

* test, do not care

* add interpretorcore

* add scope

* refine scope create method

* add jit for new_exe

* solve conflict

* delete unused code

* polish code

* polish code

* refine scope in inplace

* refine for datatransfer

* refine _rebuild_from_desc

* refine control eager deletion attr

* refine used_for_jit

* refine jit for infer

* op size0 use ori program

* polish code

* refine jit

* refine run_program_op ut

* refine inplace

* refine control

* refine graph helper

* refine control

* refine inplace

* refine buffer_share_inplace_pass

* polish code

* polish code

* refine usage for compilerProgram

* refine control

* test

* test core cache

* refine code

* refine io.py

* increase test_seq2seq timeout

* refine convert program

* refine interpretercore_cache release

* delete buildinplace

* refine partial_program && io

* refine code for io

* test

* test

* test

* [Eager]Support Lazy initialization for nn.Lazyer

* [Auto Parallel] Move the distributed info from python to c++

* [Auto Parallel] Add dist_attrs for VarDesc and OpDesc

* [Auto Parallel] Add the lost file

* [Auto Parallel] Make the dist attr be unique_ptr

* [Auto Parallel] Add the proto conversion

* [Auto Parallel] Improve the proto support

* [Auto Parallel] Fix the bugs for adding a device or a link

* [Auto Parallel] Add the C++ ProcessMesh and DistributedMapper

* [Auto Parallel] Improve the impl of these dist attrs

* [Auto Parallel] Pybind11 ProcessMesh and DeviceMesh

* [Auto Parallel] Fix the unittest problem

* [Auto Parallel] Explicitly add the src file for auto_parallel target

* [Auto Parallel] Add the proto depedency explicitly

* [Auto Parallel] Fix the cmake bug on windows and mac

* [Auto Parallel] Remove the pybind11 header file in process_mesh.h

* [Auto Parallel] Remove unused codes

* [Auto Parallel] Check whether the dist attr is null

* [Auto Parallel] Implement the assign operator for OpDesc explicitly

* [OpAttr]Support VarDesc* and vector<VarDesc*> in Attribute

* add unittest for inference predictor

* device_guard support xpu. test=kunlun

* sum op of xpu support LoDTensorArray. add test for while op of xpu. test=kunlun.

* not run startup program in constructor of StandaloneExecutor

* clear interface of standalone executor

* clean debug code

* Decoupling ipu sharding and modeling (#665)

* feat(shard): decoupling shard setting with modeling.

* fix(shard): split test cases to avoid failure.

* fix(shard): add function docs and fix typo.

* test(shard): add tests.

* test(shard): more test case.

* fix(): change ipu_index/stage default value to -1.

* fix format
Co-authored-by: Nczr-gc <96037699+czr-gc@users.noreply.github.com>

* Replace np.bool/np.bool8 with np.bool_

* Replace np.object with np.object_

* Replace np.complex with np.complex128

* Replace np.float with np.float64

* Replace np.int with np.int_

* Rerun pre-commit for newer pre-commit configuration

* Use builtin bool instead of np.bool_ based on the context

* use yapf to format all python file

* yapf exclude two unittests file for they rely on writing and reading file, and format will break them

* disable diff_py_file because too many diff files cause command following failed

* polish append op using

* fix var error

* fix group norm impl

* optimize cinn find graph by graph address

* graph_key use int64_t instead of program string

* fix framework _to_readable_code python code

* rename get_readable_comile_key to get_serialize_comile_key

* fit for printing cinn_launch op

* update boost::variant caster for bytes

fix en docs of some Apis (gradients, scope_guard, cuda_places, name_scope, device_guard, load_program_state, scale, ParamAttr and WeightNormParamAttr) (#41604)

* Update scope_guard; test=document_fix

* gradients; test=document_fix

* gradients; test=document_fix

* name_scope; test=document_fix

* cpu_places; test=document_fix

* WeightNormParamAttr; test=document_fix

* cuda_places; test=document_fix

* load_program_state; test=document_fix

* device_guard; test=document_fix

* device_guard; test=document_fix

* ParamAttr; test=document_fix

* scale; test=document_fix

* scale; test=document_fix

* update code example；test=document_fix
Co-authored-by: NChen Long <1300851984@qq.com>

* gradients; test=document_fix

* fix VarType; test=document_fix

* fix vartype; test=document_fix

* cumsum; test=document_fix

* t; test=document_fix

* [Eager] add _fallback_legacy_dygraph for npu/xpu/rocm

* fix import

* [Eager]Polish enable/disable_legacy_dygraph logic

* merge yunfei PR

* merge other pr

* [Eager]Enhance eager_trace_op logic to support Optimizer Op

* fix AsDispensable

* [Eager]Fix 17 unittest and open check_eager=True

* remove print

* fix unittests

* fix op_testa

* fix coverage CI failed

* fix ci

* Update ResNet test cases

* [Eager] Support uva, adamax, fill_diagonal_, to_list and so on.

* Fix CI

* Updated CUDA defined statement

* Fix CI

* Update headers, Fix CI

* Remove useless setting

* Updated func name to Fix windows-CI

* Remove tensor uva related codes

* Remove uva related code

* recover original test

* fix inplace fill_ and zero_ API

* add eager unittest

* refactor eager flags

* fix flags error when we switch from eager to dygraph

* fix ci problem

* fix ci

* fix ci

* merge develop and fix code style

* merge develop and fix code style

* fix op test error

* fix op test error

* fix op test error

* fix op test error

* fix op test error

* merge develop

* switch eager mode and change it

* set default is eager

* set default is eager

* fix error; test=develop

* fix some error; test=develop

* update

* upd

* update code; test=develop

* update

* fix some bug; test=develop

* fix bug; test=develop

* fix bug; test=develop

* fix bug; test=develop

* fix error; test=develop

* format; test=develop
Co-authored-by: NJiabinYang <360788950@qq.com>

* fix 'is with a literal'

* fix typo

* merge legacy to fluid

* Remove legacy code

* Remove legacy code

* Remove DataType test

* Using Tensor directly instead of using EagerTensor

* support gradient_accumulation

* make test_imperative_lod_tensor_to_selected_rows longer

* make test_imperative_lod_tensor_to_selected_rows longer

* refine code

* Rename all EagerTensor to Tensor

* Rename some EagerTensor to Tensor

* rename EagerTensor to EagerVariable

* add more test

* merge develop and refine code