* gpups default config and dataset

* codestyle

* add unittest

* code style

* add dymf to gpups

* codestyle

* add static.nn.cvm import

* PSERVER_DEBUG

* add fs config to worker desc

* update unittest

* unittest

* remove gpups unittest

* remove gpups unittest

* static check

* back fl

* delete ssl cert

* .

* make warning

* .

* unittest paral degree

* solve unittest

* heter & multi cloud commm ready

* .

* .

* fl-ps v1.0

* .

* support N + N mode

* .

* .

* .

* .

* delete print

* .

* .

* .

* .

* fix bug

* .

* .

* update paddle.distributed.launch

* add sample code

* update shell

* fix typo

* fix typo

* update docs

* rm code

* fix doc 2

* fix doc 3

* fix doc 4
Co-authored-by: Nroot <root@sgjur-pod004-1.ipu.graphcore.cn>

* 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

* fix fetch list

* fix unittest

* gpups default config and dataset

* codestyle

* add unittest

* code style

* add nproc_per_node for DistributedFusedLamb

* fix nproc_per_node communicator bug

* fix ring_id = 1 init bug

* fix ci

* fix test_parallel_executor_mnist.py

* add fetch_list

* fix evaluate log

* tiny fix

* fix bugs of reducer

* update

* update

* fix gradient merge

* bug fix

* update annotation

* 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

* back fl

* delete ssl cert

* .

* make warning

* .

* unittest paral degree

* solve unittest

* heter & multi cloud commm ready

* .

* .

* fl-ps v1.0

* .

* support N + N mode

* .

* .

* .

* .

* delete print

* .

* .

* .

* .

* prepare only once

This reverts commit e680d581.

* 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>

* [IPU] support paddle.distributed.launch with IPUs

* add device_num to env_args_mapping

* use original id in dist_op_context.grad_op_id_to_op_id

* del assert

* remove redundant map

* fix_sharding3_offload

* fix_fp16dtype_bug

* auto parallel support primitive op with data parallel

* add primitive change

* 5 loss 3D cylinder acc aligned

* add unitest

* slice data in dist_loader & flag to scale grad

* bug fix

* update unittest

* enable static

* fix recompute in mp

* fix recompute

* update