* update

* update test=develop

* update compile set test=develop

* update compile set test=develop

* update test=develop

* update test=develop

* update test=develop

* update compile setting test=develop

* update compile setting test=develop

* update run demo test=develop

* update test=develop

* update test=develop

* fix test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update format test=develop

* update format test=develop

* update style test=develop

* update style test=develop

* change style test=develop

* change style test=develop

* change style test=develop

* add dataset unittest test=develop

* update test=develop

* update for record test=develop

* udpate style for record test=develop

* update for record test=develop

* update for record test=develop

* update for record test=develop

* fix format test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* add collective communication library in fleet to replace mpi
* test=develop

* print table stat
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* notes
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* notes
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* general table

* add sparse table
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* no cvm
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* add no_cvm
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* add note
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* code style
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* code style
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* code style
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* code style
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* code style
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* add key of optimizer
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* solve pslib stop core
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* barrier
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* add notes
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* general table

* add sparse table
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* no cvm
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* add no_cvm
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* add note
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* code style
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* code style
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* code style
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* code style
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* code style
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* add key of optimizer
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* fix cache table bug
* add save_paddle_inference_model
* fix hdfs util bug
* test=develop

* copy some feasigns and corresponding embeddings from one sparse table to another
* copy all feasigns and corresponding embeddings from one sparse table to another
* copy all dense params from one table to another
* copy some local vars to other local vars

* no longer need to define all embedding layers (no one less) of all slots in each program. make trainer_param repeated in ps.proto.
* add find_distributed_lookup_table_grads instead of hard code GRAD
* support embedding stop gradient. push sparse has error before fix this.* 
* fix fill sparse, skip slots which do not have embedding. each slot's embedding in a sparse table should be used in all training programs before fix this.
* fix pull sparse, skip slots which do not have embedding.
* fix collect feasign label info, skip slots which do not have embedding.
* support when there are multi sparse tables in one or multi training programs, each program can pull/push its own related sparse tables instead of all sparse tables.
* test=develop

* support change shuffle thread num
* support change train thread num
* fix receive shuffle data of each channel
* data norm stop gradient
* add check thread_tensor type and root_tensor type when merge metric
* remove sleep in shuffle, add config
* add config of pslib client to client communication
* fix xbox str
* add data norm op testcase
* add flush in trainer finalize

* support preload thread
* sleep before fleet wrapper exit for pslib core dump
* optimize hdfs log
* fix master+patch bug

* add get_last_save_xbox_base/get_last_save_xbox
* fix fleet_util bug of load paddle model
* add doc string in fleet api

add save cache model api in fleet& add slots shuffle in dataset module & add metric op to calculate ctr related metrics (#18871)

* add ctr related metric layer test=develop

* add save cache and slots shuffle test=develop

* add save cache and slots shuffle test=develop

* fix error

* fix error

* fix style for ci

* fix for comments

* change SlotsShuffle input to std::strinf for generality

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix stylr

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* change non-const reference to pointer

* fix style

* fix style

* fix style test=develop

* fix style  test=develop

* add return ins num in ctr metric op

* change dtype to float in metric_op.py

* fix error test=develop

* fix style test=develop

* fix API spec

* fix API spec

* fix API spec test=develop

* add UT test=develop

* dump slot

* test

* proto

* dump slot

* test

* proto

* code style

* code style

* code style

* style

* add delete after unseen days

* add unseen days

* code style

* conflict solve
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* add clear model

* code style
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* code style
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Fix FleetWrapper:
1. fix shrink dense: just scale show
2. add datanorm scale: divide datanorm's gradient by batch_size

The change includes 2 things:

1. save delta model and shrink table are control by the same parameter before, now add delete_after_unseen_days to control shrink table.
2. value in sparse table has no slot before, now add slot in sparse table, and add DownpureCtrAccessor to support the new meta.
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（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

* add save/load model, shrink table, cvm, config file & fix pull dense bug
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* fix global shuffle bug, fix pull dense bug, fix release memeory bug, fix shrink error
add client flush, add get data size
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* fix global shuffle bug
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* fix global shuffle bug
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* fix code style
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* fix code style & modify pslib cmake
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* fix error of _role_maker
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* fix code style
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* fix code style
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* fix code style
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* fix code style
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* fix code style
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* fix windows compile error of fleet
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* fix global shuffle bug

* add comment
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* update pslib.cmake
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* fix fill sparse bug
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* fix push sparse bug
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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