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

* 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

refactor fleet api under paddle.fleet
update DistributedStrategy

* add paddle.fleet.DistributedStrategy for 2.0