* 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

* - Added partial draft of pooling acquire

- Workspace support

- compilation fix

- Added draft of pooling backward reimplementation

- Segfault fix

- reverted 'any' for diff_dst crewation in pooling

- Lint fixes

test=develop

- lint fixes

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- Further lint fixes

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* - Fixes after review

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* - Lint fixes

test=develop

* - Even more lint fixes

test=develop

test=develop

* make auc op compatible with 1 dim

* feature/buffer_shared_inplace, test=develop

* refine code, test=develop

* fix elementwise_add op cpu inplace and sum inplace bug, test=develop

* add unittest and debug log, test=develop

* fix parallel_executor scope bug, polish code, test=develop

* fix sum op, activation op, single_in_place_inference bug, test=develop

* remove kLocalExecScopeName, test=develop

* fix unittest,test=develop

* fix out_var first version bug, test=develop

* follow comments,test=develop

* clean code of dim and place, test=develop

* fix failed unittests, test=develop

* test=develop, fix docker with paddle nccl problem

* test=develop, fix/gcc_4.8_ubt_link_error

* test=develop, fix code format

* fix transform matrix bug, test=develop

* modify API.spec

* 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
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* add the missing files.
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* Fix topk cannot handle 1D vector bug

Add path to handle 1D vector

test=develop
Signed-off-by: Nzhaoyuchen <zhaoyuchen01@baidu.com>

* refine code

test=develop
Signed-off-by: Nzhaoyuchen <zhaoyuchen01@baidu.com>

* Refine Infershape in activation_op for double_grad.

* test=develop
support Tensor input for chunk_eval op

* test=develop
fix testcase for chunk_eval op

* test=develop
fix typos in nn.py

* rename mkldnn set/get_cur_thread_id() to set/get_cur_mkldnn_session_id()

test=develop

* update session id definition and adjust logic for default behavior

test=develop

* reset logic in mkldnn reuse as most of cases work in default.

test=develop

1. Since allreduce op has 4 reduce types, We split these four reduce types into four ops
2. We also refined the collective op code, e.g. we separated the collective op kernel into CPUKernel and CUDAKernel, and remove the device specified DeviceContext parameter in template as we already knew the target DeviceContext
3. We remove the newly added Collective op role to reduce the complexity of program and graph analysis

* add not_been_used_vars to no_grad_set
test=develop

* modify roi_perspective_transform_op to output mask and transform matrix

* modify comment

* modify comment

* modify API.spec

* update API.spec

* remove no use header, test=develop

* resolve conflict