Revert "use static variable to do cache instead of thread local in thread frequent switching case (#18428)" (#18879)

This reverts commit ce38bb53.

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* remove legacy memory optimization codes, test=develop

* follow huihuang's comments,test=develop

* follow luotao's comments, 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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* replace link

* update api.spec

* fix mistake

Also fix a dependency error which may cause compile error

* open fuse optimization ops
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* first version memory optimize pass, test=develop

* remove move_tensor_sharing_pass, test=develop

* refine code comments, add unittests, test=develop

* turn off memory_optimize by default, test=develop

* follow huihuang's comments, test=develop

* follow chengduoZH's comments, test=develop

* fix grammar error, add const qualifier, fix pass_test exception message, test=develop

* follow chengduoZH's comments 2nd, test=develop

* test=develop fix cpu roi_align_op backward bug

* fix logical APIs

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* fix isfinite

* update matmul comments

* update API.spec

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* update API.spec

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* update API.spec

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refine launch_ps and role_maker

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

* change INT8 to template so that checking dst_dt with if-else could be removed. CI will be enabled after fixing reviews

* reverse user_residual_memory_p and user_bias_memory_p declaration scope
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* split test_dist_se_resnext.py into 4 testcases

* extend matmul op to support multiple head multiplication

With the support of multiple head, the multiplication of two big matrixes is
split into multiplication of several (head_number) small matrixes. e.g. if
Mat A is [3, 24] and Mat B is [24, 4], when multiple A and B with head_number
as 4, Mat A will be split as 4 matrix of [3, 6] and Mat B will be 4 matrix of
[6, 4]. The result of final matrix will be 4 matrix of [3, 4], i.e. [3, 16].

* Make lod reset op support for append lod level.

* Fix API.spec
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* Fix unitest.
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* Add python api for lod append.
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* Fix API.spec
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* Fix format of doc.
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* Fix unitest.
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* Fix doc.
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* prun backward ops
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Modify auc doc. Add output variable description, previously was the scalar type, now changed to the tuple type.test=develop (#18771)

* update paddle-trt for:
    1. fix bug: when batch > 2, core in split plugin.
    2. add leaky_relu trt5.0 support (yolov3 from 65ms to 42ms.)
    3. add new attr to dropout.
    4. shuffle channel, swish, relu6 support
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* 1. fix ci
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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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* modify install GPU97

* modify install GPU97

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- compileation fix

- Yet another compilation fix

- Even yet another compilation fix

- Surprise! Again compilation fix

- lint fixes

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- Fix to workspace acquire of LRN

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- Fix to hash of BWD LRN

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- fix to lrn BWD PD acquire

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- Fixing LRN PD creation

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- cosmetic fix in comment

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- Fixes after review

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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

* support sparse gradients
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* rewrite the conv_op using cudnn_conv_helper

* add workspace limit for v7 test=develop

* fix test=develop

* add half float test=develop

* fix test=develop

* fix test=develop

* revise code style test=develop

* fix test=develop

* supports distributed classification training
* update API.spec
* fix evenly division in python3
* change "index_range" to "index_num" in shard_index operator
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