* [AutoParallel] recompute tuning

* fix conflict

* update comment

* bug fix

* update rc algo

* tiny fix

* fix clear process_group

* remove comment

* update segment print

* fix import OpRole

* adapt amp pass and grad_clip pass for opt_tuner

* update tuning config

* fix import

* annotate recompute info on ops and upgrade recompute pass

* add op_namescope for seed op

* record reserved vars

* fix recompute var's dist_attr

* fix strategy unittest

* adapt for fp16

* update unittest

* revert copy opt

* update unittest

* rename set_recompute_segments

* fix unittest

* recompute dep filter param

* recompute dep for reshard

* isort all files

* revert conflicting files

* revert conflicting files

* revert conflicting files

* add depend

* add origin amp files

* fp16 distinguish None & False

* engine log

* dp add deps for graph exe

* add dep for grad clip

* dep ops in comm stream

* unitest

* add depend

* fp16 pass distinguish None & False

* engine log

* [AutoParallel] selective recompute

* add cmakelist

[Auto Parallel] Sharding Optimization：Partition Algorithm & Stage2 Parameter Bucket communication  (#47180)

* partition param by order

* add logging

* reorder opt

* config

* stage2 bucket

* update unitest

* expand op donot use naive data parallel

* fix unittest

* [Auto Parallel] Improve the c++ dist attr

* [Auto Parallel] Modify test_program.py

* [Auto Parallel] Add the missiong import

* fix engine build method

* fix import

* update engine cost

* update raise error

* update cmakelist

* revert optimizer

* revert optimizer

* fix unittest

* fix unittest
Co-authored-by: Ncaozhou <caozhou@radi.ac.cn>

* update config

* re-blacken python code

* temporarily disable date and diff_py_file

* skip a format

* add cost interface

* update inferface and add unittest

* update unittest

* update inferface

* [AutoParallel] add callbacks

* fix unittest

* fix dist_context

* fix engine

* fix cmakelist

* fix unittest's returns

* fix cmakelist

* for gpt-gen

* fix reshard

* adapt assign and shape op

* add dist_assign & unittest

* add conditional block unittest

* rename unittest

* [CodeStyle][F401] remove unused import in python/paddle/distributed

* remove pass

* empty commit

* Fix ValueError: list.remove(x): x not in list for meta_optimizer_names.

Fix ValueError: list.remove(x): x not in list for meta_optimizer_names.

* Fix split import.

Fix split import.

* add noqa after meta_optimizers in factory

* restort collective ops

* expand `import *`

* add noqa after required imports

* try to fix APIs without core.ops

* Revert "try to fix APIs without core.ops"

This reverts commit 6172beaf601e84bf61f2490c12c4739f0edaa5eb.

* fix an increment

* empty commit

* add noqa after required imports

* expand `import *`, fix ci error
Co-authored-by: NShuangchi He <34329208+Yulv-git@users.noreply.github.com>

* [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>

* trim trailing whitespace

* fix `.cmake-format.py`

* revert npu ut changes, avoid npu ci error

* bugfix (#45332)

* dist embedding support lookup table v1

* add unitest

* customize wait_comm

* group gradients

* bugfix

* update program

* add grad_clip pass

* add unittest

* add notes

* update func

* add dist_attr for new op

* add clip_grad

* fix comments

* add unittest

* update logger

* update reshard for auto search

* fix unittest bug

* update dist tensor

* update reshard output

* fix unittests bug

* merge develop

* bugfix

* remove scaling

* support rescale_grad opt

* fixed bug for pass & engine

* fixed bug for benchmark GPT-3

* add tuner & profiler

* add algorithms & config

* fix op_role

* fix engine

* update op_role

* fixed bug for pass & engine

* fixed bug for benchmark GPT-3

* 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

* adapt for 10 loss

* partitioner support optimizer

* [Auto Parallel] Add the parallel tuner

* [Auto Parallel] Improve the parallel tuner and fix some bugs

* upodate cost model

* update import Resharder by dist op

* update cost model

* fix comp cost bug

* update cost model

* [Auto Parallel] Amend the dist attr for #processses=1

* update cost model and tuner

* update cost model and tuner

* update cost model and tuner

* update cluster

* update reshard

* [Auto Parallel] Add the estimation from the cost model

* [Auto Parallel] Reimplement the backup and restore functions

* [Auto Parallel] Fix the bugs of the parallel tuner

* [Auto Parallel] Update the engine api and dist context

* [Auto Parallel] Work around the high order grad problem

* [Auto Parallel] Add some miscellaneous improvements

* [Auto Parallel] Add a unittest for DistributedContext
Co-authored-by: Ncaozhou <caozhou@radi.ac.cn>

* auto parallel support primitive op with data parallel

* add primitive change

* 5 loss 3D cylinder acc aligned

* add unitest

* [Auto Parallel] Replace the old planner by the new partition tuner

* [Auto Parallel] Improve the completion and distributed context

* [Auto Parallel] Fix some bugs of the compatible check of some dist ops

* [Auto Parallel] Fix some bugs

* add default_ctx in backward.py

* record grad_var_to_var with grad_times

* fix backward

* update annotation

* add complete_high_order_grad in complete_forward

* add dist slice op

* update grad_var_to_var type

* update partition_block init mapping before loss op

* update compatible for 'XShape' & update 'allreduce_vars'

* add dist reshape op when input dim equal to output dim

* update 'set_grad_var_shape' with grad_var_to_var

* fix dist slice

* fix set_grad_var_shape

* add dist pnorm op

* fix dist pnorm dist_attr

* fix engine startprogram & adapt highorder grad

* fix set_grad_var_shape when mp

* update unittest

* update cmakelist

* default strategy in engine: dp

* bug fix

* tiny fix

* flatten outputs

* fix default strategy

* init default ctx

* tiny fix

* test=allcase

* fix converter when sliced_shape is 1

* update unittest

*  add FP16 Pass 

* Support the auto completion of while_op

*  acc aligned

* fix code stype

* update unitest