* add hard_swish activation op (new op)
test=develop

* remove redundancy files

* modify document content of HardSwish OP

* add API test in test_layers.py

* add dynamic_graph for test_hard_swish

test=develop

test=develop

* 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

* Refine Infershape in activation_op for double_grad.

* fix softrelu doc

test=develop

* update API doc

test=develop

* Update backward.py:
     - If there is no input grad var in all outputs of previous ops, do not append this op into graph.
     - Only apply this stragety when double backward.
* Update some double backward op.
* Update sum_op to judge whether a tensor is empty by numel or IsInitialized().

* double backward sqrt

* refine unittest. test=develop

* refine test. test=develop

* remove alpha in unittest. test=develop

* add double grad for square. test=develop

* formax code. test=develop

* fix for grad sum. test=develop

* refine shape. test=develop

* refine extract. test=develop

* Add conv2d_grad_grad_op
* Extracte the cuDNN conv algo searching code in conv_cudnn_helper.h.
    - Now use it in conv2d_grad_grad.
    - Will simply the searching code in conv2d and conv2d_grad in next PR.
* Enhance and fix bug in unit testing of gradient_checker.
* Support to fetch empty variables，return None in Python.

backward of backward: leaky_relu

Support backward of backward for Relu and add a new gradient checker by comparing theoretical and numerical Jacobian. (#16862)

* Support backward of backward and a new gradient checker
* Rename decorators.py to decorator_helper.py, since Python on Windows CI has decorators package.

1. Add ReluDoubleGradMaker when register relu_grad.
2. Add a new gradient checker by comparing theoretical and numerical Jacobian.  Check double gradients by double_grad_check.

test=develop

add rsqrt op

* temoprarily disable the code of use kCUDNN, test=develop

* add TODO comment, test=develop

test=develop

test=develop

test=develop

test=develop

* staged.

* polish code

* polish code. test=develop

* polish code. test=develop

* api change. test=develop

* fix default value. test=develop

* fix default value. test=develop

* staged.

* polish code

* polish code. test=develop

* polish code. test=develop

* api change. test=develop

* fix default value. test=develop

* fix default value. test=develop

test=develop

test=develop

* expose square
test=develop

* fix activation
test=develop

* Add square API
test=develop

* add necessary op

* code refine

* fix API.spec
test=develop

* fix unit test
test=develop

* add unit test sparse_grad_clip
test=develop

* fix API.spec
test=develop

* remove mac test for test_gradient_clip
test=develop

* remove selectedrows_mul_tensor
test=develop

* add is_test to pooling and activations

add prop_kind support for layers activation. conv and pooling

add a pass that sets is_test to true

add transpiler version of is_test pass

test=develop

* patch test and pass

test=develop

* add pass to analyzer.h

test=develop

* add is_test attr description & pass only on mkldnn

in:
activation_op.cc
batch_norm_op.cc
conv_op.cc
dropout_op.cc
lrn_op.cc
pool_op.cc
sequence_pool_op.cc
softmax_op.cc

* fix is_test handling for activation pool and conv

* change description of is_test for all layers again

* remove GetAttr(use_mkldnn) from pass

* rename correct_mkldnn_test_phase to is_test

and remove dependency on MKLDNN
test=develop

* review fix magic number

* two if(..)s into one

* Check is_test once and pass mkldnn forward prop kind

* dereference shared_ptr with * (without get())

test=develop

* add is_test_pass back

test=develop

* add_infer_var_type
test=develop

* InferVarTypeHelper-> VarTypeInferenceHelper
test=develop

* PassInputTypeAndDTypeOnOutput
 test=develop

* follow comment
test=develop

test=develop

* set the right shape of selected_rows
test=develop

* enhance check

* fix activation_op

* remove cast

* use ShareDimInfo replace SetDim and ShareLod

* use ShareDimAndLod
test=develop

* follow comment

test=develop

* check whether the input has lod
test=develop

* Split ShareDimAndLod

test=develop

* checkout clip.py
test=develop