* add trilinear interp. test=develop

* fix unittest. test=develop

* add python api and test_layers. test=develop

* refine API.spec. test=develop

* fix format. test=develop

* add python API test. test=develop

* format code. test=develop

* refine code strcuture. test=develop

* fix format

* fix doc. test=develop

* fix converage. test=develop

* fix format. test=develop

* Enhance fuse optimization op pass
test=develop

* Add call stack info during runtime and compile time
test=develop

* Rename operator_call_stack
test=develop

* Add unit test
test=develop

* follow comment
test=develop

* fix memory overlapping of fetch var (return of executor.run), test=develop

* fix wrong usage of ParallelExecutor in op_test, test=develop

* remove useless parameter and simplify code

* avoid tensor destruct untimely, test=develop

* add testcase independent of OpTest, test=develop

* fix overflow by int32 mul test=develop

* fix reference nullptr

* fix codestyle test=develop

* modify to point in ContextProjectFunctor test=develop

* modify to point in ContextProjectFunctor test=develop

* modify . to -> test=develop

* add var_conv_2d op test=develop

* edit api.spec test=develop

* ignore unittest if with_mkl=off test=develop

* fix python3 division test=develop

* fix ignore unittest bug test=develop

* remove useless code test=develop

* modify api.spec test=develop

* modify default_grad.spec test=develop

* test=develop support Tensor input for ctc_align_op

* test=develop add some comment

make listen and server as exclusive run 

* open gc by default, test=develop

* fix test_train_recognize_digits and disable gc when ngraph is enabled, test=develop

* fix conditional_block op eager deletion bug, test=develop

* add some comments to reviewers, test=develop

* add fusion_seqpool_cvm_concat test=develop

* simplify pass, test=develop

* fix code style, 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

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

make dist unit test exclusive run

test=develop

* remove legacy memory optimization codes, test=develop

* follow huihuang's comments,test=develop

* follow luotao's comments, test=develop

* open fuse optimization ops
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

* 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

* 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

* 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

* 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