* add deformable_conv op on xpu (#29234)

* rebase develop

* update deformable_conv op on xpu

* update deformable_conv op on xpu

* update kunlun conv2d/softmax/elementwise implemetation (#29229)

* update conv2d & softmax to new xpu api
* test=kunlun

* remove useless comments
* test=kunlun

* remote softmax xpu op
* test=kunlun

* update kunlun softmax
* test=kunlun

* update xpu unitest
* test=kunlun

* fix elementwise_grad bug for kunlun
*test=kunlun

* support global pooling for kunlun (#29293)

* test=kunlun

* update reduce_sum op on xpu (#29367)

* update reduce_sum op on xpu

* update reduce_sum op on xpu

* support running on xpu

* fix expand/uniform_random && concat/transpose to new api on xpu (#29280)

* fix expand && concat/transpose to new api

* update uniform_random_op

* update xpu_header

* 1. fix elementwise ops'bug 2. fix softmax_with_cross_entropy_op 3. add biliner_interp_op (#29448)
Co-authored-by: Nroot <root@bjhw-sys-rpm0223.bjhw.baidu.com>
Co-authored-by: N卖鱼的哲学 <tangzhiyi11@users.noreply.github.com>
Co-authored-by: NQingshuChen <qingshu.chen714@gmail.com>
Co-authored-by: Ntaixiurong <taixiurong@126.com>
Co-authored-by: Nroot <root@bjhw-sys-rpm0223.bjhw.baidu.com>

Add elementwise XPU OP kernel for KUNLUN core, including (but still cannot process common broadcast

* add batch fc op, test=develop

* add batch_fc_op, test=develop

* fix untest, test=develop

* rm check_dygraph, test=develop

* fix comment, test=develop

* fix comment, test=develop

test=develop

There are mainly following fixes:

- take `DeviceContext` as the template parameter of math functors and OpKernel instead of `Place`
- remove `eigen_device` interface in base class  `DeviceContext`
- remove `GetEigenDevice` interface in `ExecutionContext` and base class `DeviceContext`
- remove unused `platform::EigenDeviceConverter`
- rename `REGISTER_OP_GPU_KERNEL` to `REGISTER_OP_CUDA_KERNEL`
- rename `USE_GPU_ONLY_OP` to `USE_CUDA_ONLY_OP`

* Fix documentation

* Address review comments

* conv_shift_op: initial implementation using Eigen

Limitations:
- both gradient outputs must be specified and are always computed
- explicit for loops => could be optimized in various ways
  (e.g., different memory layout)

* conv shift - gradient fixes

fix case when not all output gradients desired

* conv shift: minor cleanup

* conv shift - more minor cleanup

* conv shift: clean up & initial GPU implementation

* fix rebase issue

* Make `PADDLE_ENFORCE_EQ` supports custom class, like DDim

* add NDEBUG switch to PADDLE_ENFORCE

* Free will be added soon