* make hapi support amp, and add unittest

* make unittest only support GPU

* update parameters for amp in hapi.Model

* update hapi.Model.prepare interface, and update unittest

* fix test_model.py unittest bug

* add grad clear in dygraph

* use_fp16_guard defaults to True, which could avoid nan

* add input check, and add internal doc link to low level api

* update doc, and decrease the sample num of dataset to avoid timeout

* make hapi amp param  support str 'O1' or 'O2'

* resume calling , modify the code of the check part

* upgrade the usage of Fleet API, and disable 'pure_fp16' param

test=document_fix

* update condition of logger print

* update EarlyStopping doc

* update EarlyStopping doc, test=document_fix

* add ReduceLROnPlateau

* enhance logger callback for benchmark

* Fix doc format for callbacks, metrics and Model
* Fix code sample and doc

* add early stopping

* add doc for early stopping

* fix sample code bugs

* update infer of mode, update doc, add unittests to increase coverage rate

* fix sample code for early stopping

* update sample code and unittests

* reduce time cost of test_callbacks unittest

* fix model.py code style error

* add lr scheduler

* return pil image as default according backend

* add visualdl callback

* refine vision models

* Move hapi form paddle/incubate to paddle

* Remove vision/datasets/utils.py and clean code

* Add sample code for conll05

* Print pull path when saving model

* Fix sample code after paramter_list of SGD is changed to parameters

* Fix bug in wmt16 datase

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

* Rename `Input` into `InputSpec`

* fix argument place of Input api

* Remove paddle.incubate.hapi.loss and reuse the paddle.nn.layer.loss in high level API

* 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

* 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.