* modify cond while_loop to paddle.static.nn.cond

* modify crop_tensor to paddle.crop

* modify Variable to paddle.static.Variable

* remove nn.beam_search, nn.beam_search_decode, nn.gather_tree

* remove bpr_loss, center_loss, rank_loss, smooth_l1, teacher_student_sigmoid_loss, edit_distance, sampled_softmax_with_cross_entropy in nn.functional

* remove apis in nn.functional.learn_rate.py

* remove pool2d, pool3d, adaptive_pool2d, adaptive_pool3d in nn.functional

* remove apis in nn.functional.vision

* remove erf, soft_relu in nn.functional.activation

* remove apis in nn.functional.extension

* remove nn.functional.rnn

* remove hash from nn.functional.lod

* remove row_conv from nn.functional.extension

* remove one_hot, pad2d, pad_constant_like from nn.functional.common

* remove nn.gather_tree, nn.BilinearTensorProduct, nn.Pool2D, nn.Pad2D

* remove apis from optimizer.__init

* remove tensor.creation.fill_constant

* remove elementwise_mul in nn.functional.common and  modify to paddle.multiply

* remove  tensor.stat.reduce_mean

* remove reduce_all, reduce_any in tensor.logic

* remove apis in tensor.math

* remove apis in tensor.__init__

* remove has_inf, has_nan in tensor.search

* remove apis in framework.__init__

* remove apis in paddle.__init__

* remove apis in nn.functional.__init__

* modify removed alias apis to raw api in doc and unittests

* fix remove grid_sample bug

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* delete alias api relastions in doc

* reserve paddle.compat, paddle.sysconfig

* remove unittest for paddle.reduce_all, paddle.reduce_any

* modify removed alias apis to raw api in doc and unittests

* recover paddle.save and paddle.load

* resolve conflicts

* fix sample code missing paddle.enable_static() bug

* fix sample code missing paddle.enable_static() bug

* fix to_string sample code error

* fix doc and unittest of 2.0 lr_scheduler

* fix doc of 2.0 lr_scheduler

* fix unittest

* fix english doc of lr_scheduler

* fix api name of lr scheduler

* fix api name of lr scheduler

* add check for sparse parameters with weight_decay

* move sparse check to adam.py

* add embedding 2.0

* add embedding support input int32

* update doc

* update doc

* fix optimizer sample code

* add default value for adamw weight_decay

* fix adamw

* change LearningRateDecay to _LRScheduler

* fix adamw;notest

* fix load;notest

* remove file

* bug fix

* fix code style

* bug fix

* add ut

* adamw support weight_decay=0

* fix ut

* fix set_lr doc

* fix doc

* change parameters place

* support 2.0 lr_scheduler for 2.0 optimizer

* fix unittest

* fix doc

* fix unittest

* fix sample code, fix unittest

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>

* optimize adam speed by removing _finish_update test=develop

* fix SparseAdamFunctor param list test=develop

* Remove scale_op in expect_list of adam_op test=develop

* fix test optimizer loss assert error test=develop

* fix test optimizer loss assert error test=develop

* modify PADDLE_ENFORCE usage test=develop

* fix op_type in lamb_op.cc test=develop

* fix errors ostream format bug test=develop

* add betaPowOut in ngraph op test=develop

* fix ngraph::op api for gcc8 test=develop

* clean code test=develop

* modify struct into class test=develop

* remove code of beta1Tensor in lamb_op test=develop

* add Adam beta1/beta2 support Variable. test=develop

test=develop

test=develop

Currently, our tests run with 2 GPUs, the init time is absurdly long:
about 4s for each process.  Currently, we run each OP test on
different processes. This PR:

1. create cmake function py_test_modules which will generate the
Makefile that runs a list of Python unittest module in a single Python
process.

2. move all "python unittest compatible" (e.g., used the unittest
package, not just a regular python file). from fluid/tests to
fluid/tests/unittests.

3. cmake now will run all OP tests in fluid/tests/unittests in a
single process, except the time-consuming tests, they are separated
into different processes to utilize parallelism. Please make sure to
use the unittest package if you put the python test file in
fluid/tests/unittests

4. remove all exit(0) from fluid/tests/unittests/*.py, exit(0) is used
to disable unittest, we can not do it when running all tests in a
single process since it will terminate the process without running the
other tests. Instead, the test is disabled in
fluid/tests/unittests/CMakeLists.txt. FIXME is added for each disabled
item. Please disable the unittest from
fluid/tests/unittests/CMakeLists.txt, instead of adding exit(0) to the
Python file, for all Python file in fluid/tests/unittests/.

5. add an option WITH_FAST_BUNDLE_TEST. When OFF, will run the unit
tests in separate process so that they can be tested individually.

The English of the previous API is bad.

* "fix decode bug"

* "follow commnet"

* "fix error"

* "fix hook bug"

* fix based comment

* fix copyright

* fix based on comment

* add copyright hook

* add copyright hook

* refine copyright hook

* "test copyright hook"

* fix check style

* fix ci

* init commit

* change some dir name

* add adam op

moment1_out = beta1 * moment1 + (1 − beta1) * grad
moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad
moment1_hat =  moment1_out / (1 - beta1^t)
moment2_hat =  moment2_out / (1 - beta2^t)
param_out = param - learning_rate * moment1_hat / (sqrt(moment2_hat) +
epsilon)

* fix moment 2

* Adding the Adam optimization operator

* Adding more tests for Adam op