* adapt gradient merge

* fix op_role

* fix strategy

* adapt lazy init and fix pass

* add unittest

* update comment

* fix amp and sharding

* remove clip_by_norm

* support iterable dataset for auto parallel

* add split_data proto

* fix unittest bug

* fix recompute bug

* update cmake

* bugfix

* remove scaling

* support rescale_grad opt

* add unitest

* add clip_grad

* fix comments

* add unittest

* update logger

* add collate_fn

* fix number of inputs

* fixed bug for pass & engine

* fixed bug for benchmark GPT-3

* add tuner & profiler

* add algorithms & config

* [dy2st]Add ProgramHelper to polish build program logic in autoparallel.Engine

* refine code

* avoid sync with cpp in partition op

* delay eval & predict mode

* bugfix for gradient merge pass

* add comm init control by socket

* avoid single card instance failure

* add 'to_static' in engine api

* fix cmakelist

* fix op_role

* fix engine

* update op_role

* fixed bug for pass & engine

* fixed bug for benchmark GPT-3

* [Auto Parallel] Use a fast completion for data parallelism

* remove unuse cuSparse function

* [Auto Parallel] Fix some bugs of the fast dp completion

* [Auto Parallel] Add the cmake statements

* [Auto Parallel] Make the unittest adapt to the new interface

* [Auto Parallel] Modify the timeout of the unittest

* [Auto Parallel] Remove unnecessary comments
Co-authored-by: Nzhouwei25 <zhouwei25@baidu.com>

* fix fetch list

* fix unittest

* add fetch_list

* fix evaluate log

* tiny fix

* 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

* prepare only once

* [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>

* slice data in dist_loader & flag to scale grad

* bug fix

* update unittest

* enable static

* [Auto Parallel] Refactor the engine api and parallelizer

* [Auto Parallel] Fix the default dist op for the slice op

* [Auto Parallel] Fix the format of planer.py

* [Auto Parallel] Fix a bug

* [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

* adapot dist op

* [Auto Parallel] Support the auto completion of while_op

* add dist_fill_constant_batch_size_like

* align infer  accuracy

* fix code stype

* update unitest

* add dist_saver and update engine

* add dist_saver and update engine

* [Auto Parallel] Support the auto completion of while_op

* [Auto Parallel] Improve the completion algorithms

* [Auto Parallel] Fix bugs for ernie inference

* [Auto Parallel] Remove attrs which cannot be pickled

* [Auto Parallel] make the dims_mappings of LodTensorArray vars empty

* [Auto Parallel] Fix bugs for the ernie inference in the pipeline parallel

* [Auto Parallel] Remove unncessary comments

* [Auto Parallel] Fix a bug of the CMakeLists

* [Auto Parallel] Use the newest APIs to write the unit test

* [Auto Parallel] Remove unnecessary statements

* engine support pp

* fix format

* avoid multi print

* fix convert

* bug fix

* add pp unittest

* [Auto Parallel] Add the high-level Engine API

* Update the test cmakefile