* parameter_server_optimizer support auto_strategy

* add auto parallel L1 implementation
test=develop

* consider the combination of different strategies to work together

* add feature to fleet2.0 role_maker, distribute_strategy, test=develop

* move paddle.fleet to paddle.distributed.fleet

* Directory migration, test=develop

* Change imperative from paddle init to paddle framework, test=develop

* Fixed jit bug, test=develop

* default static mode, test=develop

* fixed format and create parameter belongs to framework, test=develop

* Fixed import package, test=develop

* fix __init__ format, test=develop

* fixed alias problem

* fixed paddle.enable_imperative problems, test=develop

* Add unittest

* delete install_check comment

* Fixed unittest timeout

* fixed unittest error

* move Program default_xx_program to static package

* optimize unittest method

* fixed framework __init__ format

* fixed jit path

* delete alias

* move jit to paddle

* Fixed unittest format

* fixed paddle.default_main_program

* Fixed save load API in paddle __init__.py

* fixed ci paddle.imperative.to_variable

* split meta optimizer files
* add graph execution in execution, update two properties in DistributedStrategy, unit tests for these features

Integrated Trainer of Parameter Server (API add `fluid.contrib.layers.sparse_embedding` only) (#22957)

* Integrated Trainer of Parameter Server

* refine strategy compiler and meta optimizers
make async as a_sync

* add more settings for distributed strategy
Basically, DistributedStrategy has several parts of configurations:
- BuildStrategy: the same as paddle.fluid.BuildStrategy, but the distributed arguments are moved out of BuildStrategy
- ExecutionStrategy: the same as paddle.fluid.ExecutionStrategy
- collective communication configs: nccl_comm_num, hierarchical allreduce and so on
- distributed algorithms: async_update(mainly used in PS), lars, lamb and so on

* fix gen nccl id bug

refactor fleet api under paddle.fleet
update DistributedStrategy