test=develop

* 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

* 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

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

- optimization of TID to string

test=develop

test=develop

* test=develop fix cpu roi_align_op backward bug

* 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

test=develop

- compileation fix

- Yet another compilation fix

- Even yet another compilation fix

- Surprise! Again compilation fix

- lint fixes

test=develop

- Fix to workspace acquire of LRN

test=develop

- Fix to hash of BWD LRN

test=develop

- fix to lrn BWD PD acquire

test=develop

- Fixing LRN PD creation

test=develop

- cosmetic fix in comment

test=develop

- Fixes after review

test=develop

* support sparse gradients
test=develop

* 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
test=document_preview
test=develop

test=develop

test=develop

add license

* fix clip_by_norm doc, test=develop

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

* hash_op support int64 hash_size
* add corresponding UT

* remove ctr reader, all functions are satisfied in dataset

* Add GPU implementation for `prelu` backward pass

test=develop

* Fix logic error in `prelu` GPU backward and simplify a bit

test=develop

* Fix `prelu` backward CUDA implementation

test=develop

CPU version was not used actually, so test passed