* clang format

* add cumsum_grad op to xpu2_op_list

* 'fmin'

* 'fix'

* 'fix'

* test py3.8

* fix

* test gcc12

* test gcc12

* test gcc12

* test py3.8

* test py3

* fix

* fix

* add ubutnu20

* add ubutnu20

* add ubutnu20

* test py3

* fix error

* fix error

* fix error

* fix error

* fix error

* update py version

* fix

* test docker

* test docker

* test docker

* fix pip version,use pip3.9

* log error

* log add

* test ci

* add test log

* del

* fix

* test ci

* test ci

* update dockerfile

* update ci_dockerfile

* update ci_dockerfile

* update ci_dockerfile

* fix

* add git

* fix

* add mul doubel grad

* add sub_double_grad

* add add sub high test

* add mutiply test

* modify other unsqueeze

* delete api.yaml

* only for make ci run

* midify unsqueeze

* modify unsqueeze

* tmp

* modify operants gen

* change judgement for DropoutGradGPUKernelDriver

* add UnrollerWithoutVecSize and after this Loaddata to be refined

* pass unittest

* use same unroller with XPU

* BroadcastWithInt64Index

* BroadcastDataLoader template partial specialization

* fix compile errs in ROCms

* PR comment

* [phi] move sequence_pool kernel to phi

* mv kernels impl

* fix parameter error

* clean include

* fix compat filename

* [phi] move fluid sequence_pool_grad to phi

* [phi][compat] sig rm GradVarName

* [phi] fix sequence_pool out type

* [phi] rm impl, add const string

* [phi] fix const str

* fix sequence_pooling cmake

* [phi] mv sequence_pooling_test

* [phi] fix grad sig

* [phi] fix sequence_pool is_test error

* [phi] fix sequence_pooling gpu include

* [phi] mv to impl

* [phi] fix SequencePoolFunctor cu include

* [phi] modify out max_index int32_t

* [phi] add pooltype mapping determine

* [phi] fix sequence_pool_sig

* [phi] fix sequence_pool_sig sum

* [phi] try ci

* [phi] fix max_index optional

[Dy2St]Get grad names when call append backward to fix high order gradient (#53250)

* Update slim approve list

* Fix id, test=document_fix

* [static op generation] triangular_solve

* [phi] mv triangular_solve_grad to static_backward

* [phi] fix import

* [phi] mv to ops.yaml、 backward.yaml

* fix forward attr

* [phi] fix triangular_solve_grad args

* [CINN Support 0D-Tensor] CINN supports 0D-Tensor with trick temporarily

* Add unittest

* support fp16 for maxout op

* format code

* change api

* add test for static float16

* format code

* formatting code

* atol alignment

* experiment—1

* experiment-2

* experiment-3

* format code

* trans fused_feedward Compute function to phi

* add register info

* remove maxfunctor

* move fused feedward to phi

* remove sig file

* remove fliud include

* add include

* add include

* add sig file

* add output register info

* fix sig file

* Update fused_feedforward_sig.cc

* fix grad kernel

* update output register info

* fix

* open fused_feedforward static build

* add optional and fix code style

* fix output info for fused attention

* add optional param

* merge

* support OD level and skip dynamic loss scaling for bf16

* [Fix CppExtension Unittest] Change CUDAExtension to CppExtension if necessary

* Temporarily test cpp_extension under GPU

* Split mixed_extension unittest

* add jacobian and hessian in paddle.autograd

* disable unitest 'func_multi_input' for bug in high-order gradient of multiply

* add dimension checks

* add support for 0-D tensor

* change return type from Jacobian to Hessian in hessian function

* refine Jacobian _flatten function for single xs

* refine support for 0-D tensor

* 1. add 'func_multi_input' unitest for multiply_grad_kernel bug fixed
already.
2. support non-inplace math operation via magical method overwriting.

* add unitest for math operation and raise error when 0-D tensor is indexed

* add ndim check on ys and xs according to is_batched, and add one unitest

* refine docstring of jacobian and hessian

* move paddle.incubate.autograd.Jacobian/Hessian to paddle.incubate.autograd.functional.Jacobian/Hessian

* remove single_input unitest case because numerical differentiation is wrong

* remove 3 unitest for numerical result(reference result) is wrong

* 1. rename autodiff.py to autograd.py
2. increase TIMEOUT to 100

* cancel modification for functional Jacobian/Hessian

* 1. use tuple as return type instead of list
2. refine docstring

* add more unitest case to improve coverage

* remove 2 unitest of Hessian for numerical result is wrong

* remove 1 unitest of Hessian for numerical result is wrong

* remove 1 unitest of Hessian for numerical result is wrong

* change unit test to shape check

* correct doc and replace incubate API to stable API in _grad

* modify concat_grad add sum comp rule

* modify opcompat

* updata Adamw.py

out.backward()  -> loss.backward()

* Update adamw.py

* test,test=develop

* test,test=develop

* test,test=develop

* test,test=develop

* test,test=develop

* test,test=develop

* test,test=develop

* test,test=develop

* [XPU] remove scale_loss in parallel.py

* [XPU] throw Unimplemented when using Reducer

* update

* fix bug

* update cmake3.16 to 3.18

* test

* Update Dockerfile.ubuntu

* add pad op

* add_some_code

* modify some code

* add some code

* add some code

* modify some code

* add some code

* modify some code

* Update composite_backward_api.h

* modify some code

* add some code

* add some code

* add some code

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* polish

* rm paddle_install_dir

* update test=document_fix

* update test=document_fix

* update

* add test

* update

* update

* update

* update

* update

* update

* update

* update

* update

* add todo commont