* add half_async in the communicator
* fix DistributedStrategy

* add thread scope stat accurate metrics test=develop

* fix style

* fix style

* fix style

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix conflict

* fix style

* fix style test=develop

* fix error test=develop

* fix error test=develop

（1）support patch data （merge slots of instances of same line id, modify dense layer which
changes its size）
（2）add fleet load_one_table interface, support load from paddle model and load from pslib model
（3）fix push sparse bug which cause push sparse cost more time（about 10% in my testcase）
（4）when some slots are not in one of your network (join/update, etc.)，data feed、collect label info、push/pull sparse will skip these slots， instead of throw error.
（5）add more debug info in TrainFilesWithProfiler

* polish_executor_and_add_ctx_cache

* add save/load model, shrink table, cvm, config file & fix pull dense bug
test=develop

* fix global shuffle bug, fix pull dense bug, fix release memeory bug, fix shrink error
add client flush, add get data size
test=develop

* fix global shuffle bug
test=develop

* fix global shuffle bug
test=develop

* fix code style
test=develop

* fix code style & modify pslib cmake
test=develop

* fix error of _role_maker
test=develop

* fix code style
test=develop

* fix code style
test=develop

* fix code style
test=develop

* fix code style
test=develop

* fix code style
test=develop

* fix windows compile error of fleet
test=develop

* fix global shuffle bug

* add comment
test=develop

* update pslib.cmake
test=develop

* fix fill sparse bug
test=develop

* fix push sparse bug
test=develop

test=develop

test=develop

add data_generator into paddle.fluid.incubate.data_generator, add op run log in hogwild_device_worker and downpour_device_worker
test=develop

test=develop

add dist_multi_trainer for distributed training, add trainer_factory and device_worker_factory so that we can easily extend new training mode, add pull dense worker which is a singleton for parameter fetching