* polish parallel api impl & doc details

* add unittest for coverage

* remove spawn test in py2.7

* add parallel api into white list

* support elementwise add, activation, matmul on Baidu Kunlun
* test=kunlun

* minor
* test=kunlun

* reconstuct the xpu directory
* test=kunlun

* minor
* test=kunlun

* minor
* test=kunlun

* minor
* test=kunlun

* minor
* test=kunlun

* minor
* test=kunlun

* modify ci api white list, add annotation

* modify ci api white list, add annotation

* move gpu_not_white list to wlist.json

* add null line

* Add example for cache and buffered, test=develop, test=document_fix

* add set_default_type for tensor

* fix doc

* fix doc

* Add RNN related apis in paddl.nn
test=develop

* new rnn api, cell almost done

* add new progresses in rnn APIs for 2.0

* refine rnn APIs and docstrings.

* add unittets

* disable gpu tests when paddle is not compiled with cuda support

* remove unnecessary imports

* fix docstring

* add to no_sample wlist

* backport to python2 to avoid yield from

* add **kwargs, fix typos

* update docstrings for birnn

* rename argument for SimpleRNN and SimpleRNNCell, fix sample code

* add default value for initial_states in fluid.layers.birnn
Co-authored-by: Nguosheng <guosheng@baidu.com>

* Move paddle/incubate/hapi/metrics to paddle/metric
* Add Precision, Recall and Auc metric

* Refine Model

1. Take the network (instance of Layer) as the input of Model.
2. Refine set_dict/load_dict of Layer.
3. Refine Input interface, so update code sample about Input

* Add hapi.text and corresponding unit test.
test=develop

* Remove hapi.text apis' reuse parameter args for coverage.
test=develop

* Fix TransformerCell and TransformerBeamSearchDecoder example codes.
test=develop

* Fix example codes in hapi.text.
test=develop

* Add some apis in hapi.text into example code white list.
test=develop

* Fix example code of DynamicDecode in hapi.text.
text=develop

* Rename Model.self as model in test_text.py
test=develop

* Merge hapi into Paddle

Hapi is a high level API for training and inference.
The main modules include Model, Loss, Metrics, Dataset.
Also includes common modules and models in NLP and computer vision, such as BERT, ResNet.

These modules are developed by:
0YuanZhang0, guoshengCS heavengate, LielinJiang, qingqing01, xyzhou-puck huangjun12, wangxiao1021, zhangyang.

* add dygraph parallel env doc, test=develop

* polish details, test=develop, test=document_fix

* fix examples error in other apis, test=develop

* fix more example error in other api, test=develop

* add white list for gpu examples, test=develop, test=document_fix