* add activ

* refine unittest

* refine unittest

* refine unittest

* refine unittest

* refine code

* improve backward performance

* support different dtypes for elementwise ops

* Merged LoDTensor with Tensor,test=allcases

* Patched python level LoDTensor

* Patched python level LoDTensor

* Merge Tensor into DenseTensor

* Fixed namespace issues,test=allcases

* Fixed merge issues

* Fixed inference issues

* Fixed NPU test issues

* Fixed merge issues

* add mish operator and api

* remove redundant code and modify grad_atol of mish unittest

* modify mish code to be consistent with other activation implementation

* add activation

* update activation_op

* add unitest for activation

* fix acosh for init, test=develop

* add Logit API

* add unittest

* conflict

* pull conflit

* pull conflit logit

* fix unititest

* fix code style

* update docs style of

* update en doc

* fix docs en style

* fix docs en style1

* fix docs en style2

* fix docs en style3

* fix docs en style4

* fix docs en style5

* fix docs en style6

* fix docs en style7

* fix docs en style8

* update by review

* fix nan bug

* refine structure for cuda and rocm

* update

* update

* update

* update

* native commit for triple grad of sigmod

* Updated unittests files

* init functional jacobian api

* Updated trible_test func

* Updated gradient_checker & test_script

* finish test with dtype float32

* add float64 test case

* polish code

* use atol=1e-5 with dtype float64

* fix for ci

* set timeout for test_jacobian

* fix dygraph grad to support high differential

* polish API docstring

* Updated gradient checker and some related files

* fix double grad strip error for high differential

* fix double grad strip error for high differential

* Add Sigmoid triple grad tests

* fix dygraph double grad dtype error when calling for high differential senario

* Updated triple grad teses func

* Use np.random to initialize ddx

* Updated triple_grad_check func

* add todo for gradient checker and refine some comments

* remove additional code

* add test for warnging in backward.py

* add tanh triple grad

* format python code

* refine code
Co-authored-by: Nveyron95 <veyron_wu@163.com>
Co-authored-by: Nlevi131 <limaolin01@baidu.com>

* update

* update

* update

* try make CI pass

* doc typo

* update doc string

* native commit for triple grad of sigmod

* Updated unittests files

* init functional jacobian api

* Updated trible_test func

* Updated gradient_checker & test_script

* finish test with dtype float32

* add float64 test case

* polish code

* use atol=1e-5 with dtype float64

* fix for ci

* set timeout for test_jacobian

* fix dygraph grad to support high differential

* polish API docstring

* Updated gradient checker and some related files

* fix double grad strip error for high differential

* fix double grad strip error for high differential

* Add Sigmoid triple grad tests

* fix dygraph double grad dtype error when calling for high differential senario

* Updated triple grad teses func

* Use np.random to initialize ddx

* Updated triple_grad_check func

* add todo for gradient checker and refine some comments

* remove additional code

* add test for warnging in backward.py

* format python code
Co-authored-by: Nveyron95 <veyron_wu@163.com>
Co-authored-by: Nlevi131 <limaolin01@baidu.com>

Implement FunctionTraits to support two kinds of elementwise functor and remove some old codes for broadcast. (#35688)

Revert "Implement FunctionTraits to support two kinds of elementwise functor and remove some old codes for broadcast. (#35487)" (#35686)

Implement FunctionTraits to support two kinds of elementwise functor and remove some old codes for broadcast. (#35487)

Sigmoid: Out = Sigmoid(X)
SigmoidGrad: DX = DOut*(1-Out)*Out

[This Patch]
Out
DOut -> SigmoidGradGrad -> DOutNew
DDX                        DDOut

DDOut = (1-Out)*Out*DDX
DOutNew = (1-2*Out)*DOut*DDX

* add IsInitialized

* rm additional log and add tanh double grad

* rename is_initialized

* add leaky_relu forward and backward in activation_op.cu

* add relu forward kernel and backward kernel

* Modify relu native implementation

* fix GPU performance

* rewrite abs op

* rewrite abs op and remove abs in activation

* remove abs register in old codes

* fix abs_grad type error

* fix abs double_grad output name error

* modify abs_grad, abs_grad_grad functor for windows building

* format code style

* fix the bug of result is nan when the divisor is zero

* add missing abs attr and add abs for float16

register log double grad kernel for cpu and cuda

add abs support double grad for the api 2.0

* support elu activation double grad,test=develop

* delete the code commit in .cc,test=develop

* fix relu test unpass, test=develop

* add elu double grad kernel and unit test

* add caculate dX in elu double grad functor, test=develop

* update the commit code,test=develop

* register int/int64_t/float16 in  pow/square kernel,test=develop

* add abs/square/exp type,test=develop

improve pow op according to reviews:
1. Delete unnecessary judgement statements in PowGradOpDescMaker;
2. Improve test of test_api;

overload GetKernelTypeForVar

add stop_gradient=True when attr(factor) is tensor Variable, change examples in API pow.
test=develop,test=document_preview

* 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

backward of backward: leaky_relu