* add check for input of slice

* add unittest

* add stride check for MaxPool

* add unittests

* fix 0-d tensor for arg_min_max op.

* fix xpu.

* fix zero dims

* fix

* Update arg_min_max_kernel.cc

* Update arg_min_max_kernel.cc

* Update arg_min_max_kernel.cc

* Update test_zero_dim_tensor.py

* Update test_zero_dim_tensor_xpu.py

* Update test_zero_dim_tensor.py

* Update arg_min_max_kernel.cc

* Update arg_min_max_kernel.cc

* Update arg_min_max_kernel.cc

* [Divide by 0 Error] add lu check

* [Divide by 0 Error] lu check migrate to c++

* [Divide by 0 Error] add eig check

* [Divide by 0 Error] eig check migrate to c++

* [Divide by 0 Error] Fix class name error

* [Divide by 0 Error] add norm check

* [Divide by 0 Error] fix x AttributeError

* [Divide by 0 Error] norm check migrate to c++

* A leap of try for cudaLaunchCooperativeKernel

* fix bugs

* Totally replace the lar cuda kernel

* Fix bugs

* fix code according to comments

* fix codes according to  review comments

* adding some function overload

* relocate the power operation.

* add bf16 support for index select relevant ops

* revert bf16 type change.

* add changes for more op

* fix code writting bugs

* add axis check for unbind

* add axis range check for unbind

* update unittest and axis validation for unbind

* add unittest invalid axis for unbind

* restore axis extract for unbind

* profile reduce kernel for fp16 and reduceHigherdim

* use reinterpret_cast

* fix for CI on ROCm

* add Macro for ROCm

* ROCm CI config

* ROCm CI config

* unit test repair

* pull

* add common_funcs.h

* reduceType

* Update reduce_function.h

* not higher

* rename

* implement of matmul using cublasLt instead of cublas

* cublasLt bugfix

* Update matmul_kernel_impl.h

* Update matmul_kernel_impl_via_blasLt.h

* for-loop-algo

* PR comments changes

* add macro

* ci unused variable isCublasLt

* ci unused variable isCublasLt macro

* split matmul to autotune

* rewrite the split kernel with segmented_array

* rewrite the split kernel with segmented_array

* rewrite the split kernel with segmented_array

* add some method for cuda_graph

* fix bugs for rocm

* change for ci-error

* i dont know why ci-model-benchmark gives a shit error, so i recover codes with original one to see if original codes work.

* add some changes for passing mode_benchmark and coverage ci

* fix ci error

* fix ci-rocm error

* add some changes for header

---------
Co-authored-by: Nzhangbopd <1299246947@qq.com>
Co-authored-by: NBo Zhang <105368690+zhangbopd@users.noreply.github.com>

* [CustomDevice] add custom device api

* update

* update

* test=document_fix

* update

* update

* add  examples

* fix incorrect output shape of broadcast

* add unittest

* fix mod 0 error

* fix div 0 error in floormod

* support 0d tensor for interpolate

* support 0d tensor for interpolate

* add xpu unittest for interp

* update unittest for interpolate

* fix coverage

* fix code style

* fix for coverage

* fix coverage

* decouple phi custom_op

* decouple phi custom_op, remove codes

* delete custom symbol of inference

* add axis check in UniqueRawInferMeta

* add unittest for negative axis

* simplify check for unique

* add elements count check in atan2

* add unittest and pre-check in inferMeta

* add dimension check

* add zero size check in matrix_power_kernel_impl.h

* add zero size check in matrix_power_kernel_impl.h

* add zero size check in unittest

* bug_fix

* bug_fix

* bug_fix

* bug_fix

* bug_fix

* bug fix

* bug_fix

* bug_fix

* add static check

* delete the dy codes

* support negative index in repeat_interleave

* add unittest

* Unify the gpu implementation of stack and unstack to reuse the optimization.

* Optimize the cuda implementation of unstack.

* Use GpuMemcpyAsync instead of memory::Copy.

* Fix error of calculating the index.

* Use FastDivMod to further imporve the performance of unstack.

* add pivots type check and fix batchsize error

* add unittest for batchsize = 0

* fix nullptr in lu_unpack

fix batchsize error in LU_Unpack
add nullptr check in OneFunctor

* remove exception in device code

* add pinv check

* add unitest

* update unitest

* roll back

* fix not call stupid bug

* use context

replace all TensorFromVector & TensorToVector

AssignKernel async copy

* Support stream priority for standalone executor

* Fix compile error

* Fix compile error

* Fix compile error

* Fix compile error

* Fix compile error

* make FLAGS_gemm_use_half_precision_compute_type=false defaultly

* fix comments

Co-authored-by: Nzhangbopd <1299246947@qq.com>

* fix for matmul_grad

* another fix for matmul_grad

* fix

* fix queeze_ bug

* fix slove use squeeze_kernel

* fix slove use squeeze_kernel

* fix slove use squeeze_kernel

* add test case

* [KUNLUN] add op: maxpool_with_index

* use DeviceContext::Alloc() instead of DenseTensor::mutable_data()

* fix file format

* solve clip unittest failure

* minor fix

* Revert "solve clip unittest failure" since the issue is fixed
in #49535

This reverts commit 1127adc66e79afe35ac3c00bb34e6aaa7cd7d78b.

* align with xdnn on the definition of mask in max_pool_with_index

* minor