* add alpha_dropout in nn.functional and nn.layer, test=develop

* refine Interface and assertion, test=develop

* fix ci import error, test=develop

* fix alias and use layers.scale, test=develop

* fix doc and training params, test=develop

* refine details in doc, test=develop

* fix doc details, test=develop

refine Optimizer/Adam/Admax/RMSProp && add Admw

* buf fix

* update comment

* unify arguments place; notest

* fix ut, test=develop

* bug fix

* fix conflicts, test=develop

* add examples code

* bug fix

* fix comments

* fix sample code

* add sample code for Optimizer

* add adamax ut, test=develop

* fix rmsprop ut, test=develop

* add ut for optimizer.py and adamw.py

* remove TestAdamOptimizerBetaVariable

* update api && add ut

* update doc && fix ut

* add ut
Co-authored-by: Nmapingshuo <mps2012@yeah.net>

* update Conv2d Conv3d conv2d conv3d api

* add the new api and op for the argmax, argmin

* update conv_transpose api

* update the demo codes of paddle.grad.

* add static data

* solve the initial configuration about fleet and rolemaker
Co-authored-by: NseiriosPlus <tangwei12@baidu.com>

* add binary cross entropy with logit loss

add paddle.tensor.linalg.diag API, diag_v2 OP and CUDA kernel.

* add matmul_v2

* [API2.0] add dropout, dropout2d and dropout3d, test=develop

* refine Interface and assertion after review, test=develop

* fix alias p=1 and use scale, test=develop

* fix doc and training, test=develop

* fix doc in Dropout2D, test=develop

* fix the comment of scatter

* add bernoulli op

* fix cuda kernel and add unit test

* refine doc

* fix uniform

* add functional ctc_loss and CTCLoss class.

* modified docstring of ctc_loss and CTCLoss

* add error message

* support Baidu AI Accelerator
  * test=kunlun

* minor
 * test=kunlun

* support xpu op in separate file
 * test=kunlun

* update XPU error message and remove duplicated code

 * test=kunlun

* minor
 * test=kunlun

* minor
 * test=kunlun

* fix the example codes, test=develop

Update the error message for the margin_ranking_loss

* add cross_entropy to nn/layer and nn/functional, test=develop

* use functional/cross_entropy in layer/CrossEntropy

* use functional/cross_entropy in layer/CrossEntropy, test=develop

* refine load model by extra_var_info

* polish unittest for coverage

* consider the combination of different strategies to work together