* test=develop
Add the op of unique_with_counts, the op is calc the unqiue input of data, and output the corresponding indices and count of data.

* test=develop
Check the input and dtype in the op of unique_with_counts

* test=develop
test=document_preview
update the API.spec for `unique_with_counts`, at the same time, optimize the python api in the op of `unique_with_count`

* test=develop
test=document_preview
Fix some python api problem in the op of `unique_with_counts`, and change the error messsage in this op.

* Fix some API problem in the op of `unique_with_counts`
test=develop
test=document_preview

* test=develop
test=document_preview
Fix the api sample of op `unique_with_counts`, and update api.spec

test=develop

- Extracted key generation from FWD and GRAD into separate function

test=develop

- Compilation fix

test=develop

- another compilation

test=develop

* fix depthwise conv gpu kernel bug, test=develop
* add more depthwise conv test, test=develop

GPU allocation uses fraction of available memory, also fix the GetUsed without lock

* fix security issue, test=develop

* bug fix, test=develop

* throw an exception when null pointer data with non-zero length PaddleBuf is passed, test=develop

* Fix Mask rcnn predictor
    1. refine memory optim algorithm to support the model with the block op.
    2. output diff : modify the affine channel fuse
    3. add condition_block_infer op
add interface for setting trt calib table dir
test=develop

* add the missing files.
test=develop

* 1 add trt fp16 support
test=develop

* update parallel.py
test=develop

* support center loss
* change tensor copy  api to high level api tensorcopy

* test=develop rewrite the center_loss cuda_kernel to make it faster
and add document of the center loss api,also update test function

* test=document_preview test=develop
update document of center loss

* test=document_preview test=develop
modify API.spec modify test code remove nouse const_cast

Update conv2d transpose link

test=develop

Optimize the error report information when loadcombine fail to open model files test=develop (#18888)

* upgrade ngraph to v0.24 test=develop

* simplify io test=develop

test=develop

test=develop

- optimization of TID to string

test=develop

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

This reverts commit ce38bb53.

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

Also fix a dependency error which may cause compile error

* 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

* test=develop fix cpu roi_align_op backward bug

* 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

test=develop

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
test=develop

* 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)