make dist unit test exclusive run

test=develop

test=develop

* 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
test=develop

* add clear model

* code style
test=develop

* code style
test=develop

* replace link

* update api.spec

* fix mistake

* open fuse optimization ops
test=develop

test=develop

* 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

* fix logical APIs

test=develop

test=document_preview

* fix isfinite

* update matmul comments

* update API.spec

test=document_preview

test=develop

* update API.spec

test=document_preview

test=develop

* update API.spec

test=document_preview

test=develop

refine launch_ps and role_maker

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

* 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
test=develop

* Fix unitest.
test=develop

* Add python api for lod append.
test=develop

* Fix API.spec
test=develop

* Fix format of doc.
test=develop

* Fix unitest.
test=develop

* Fix doc.
test=develop

* prun backward ops
test=develop

Modify auc doc. Add output variable description, previously was the scalar type, now changed to the tuple type.test=develop (#18771)

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.
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

* support sparse gradients
test=develop

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

The change includes 3 things:

1. Set CPU_NUM to 1 in the tests because the ParallelExecutor will print warning that CPU_NUM is not set and use default 1.

2. Old tests compare two RNNs, hand written simple RNN and same RNN built by Paddle, but initialized RNN weights in numpy random and Paddle random separately. Fixed it by setting weights and bias values.

3. Also set numpy random seed in the tests. Now the two RNNs diff can be smaller (rtol from 0.1, 0.2 to. 0.01) in the tests.

test=develop

test=develop

do some odd jobs, test=develop

* split different comm method for mnist distributed training

Test PaddingRNN on V100 GPU device.

Test configuration: large model, padding mode (which is the mode using recurrentOp), one GPU.
                   
GPU memory (MiB):   6414 (this PR)     vs   6837 (without this PR)
Speed (steps/s):         10.28 (this PR)    vs    9.89 (without this PR)
 

test=develop

* add check of executor, test=develop

* feature/auto_growth_allocator, test=develop

* add unittest of AlignedAllocator, test=develop

* try to turn on auto_growth to test on CI, test=develop

* fix segmentation fault in mixed_vector.h, test=develop

* add unittests, test=develop

* hash_op support int64 hash_size
* add corresponding UT

* remove ctr reader, all functions are satisfied in dataset

* make auc op compatible with 1 dim

test=develop

* fix int64_t

* update fill constant op unittest

* add empty line

2）add pip install coverage in Dockerfile.tmp
test=develop