* 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

* extract fuse pass logic to header file

* adjust namespaces

* Update paddle/fluid/framework/ir/mkldnn/activation_onednn_fuse_pass.h

update date
Co-authored-by: NTomasz Socha <tomasz.socha@intel.com>

* add inline remove static
Co-authored-by: NTomasz Socha <tomasz.socha@intel.com>

* add align check

* refine

* [Zero-Dim] support input 0D for paddle.moveaxis/quantile

* fix CI

* add reduce_sum_int64 and reduce_sum_int8 xpu kernels

* [PHI] add clip grad kernel with support type float32 and int32

* [PHI unittest] add clip_grad unit test

* adapt code to clang-format

* update xpu api output with clip_grad api

* remove int8 support of reduce_sum xpu kernel since it can not pass unit tests

* adapt license date, add code for XPUDataType convertion

* add int8 support of reduce_sum

* add reduce_sum unit tests for dtype int64, int8, and add more test cases

* update license date

* remove buggy bitwise and, or and xor xpu kernels, refine bitwise not xpu kernel

* change license date

* Add the cumsum 0d tensor

* xpu and cpu judge the 0d  tensor

* change to 2022 to 2023 in new commit

* fix the reverse logic

* revert to use default XPU stream for computing

XPUContext now has a null stream by default. If you want to use a separate stream
 (e.g. in async collective communication), you should create a dedicated XPUContext
and invoke its XPUContext::CreateStream()

* minor

* refine munmap freq for ref_cnt_mmap_allocator

* add shm reuse logic

* fix compile bug

* fix compile bug

* fix bug of file refcount

* fix compile bug

* fix compile bug

* refine code for delete shm case

* polish code

* refine shm cache pool size setting logic

* set buffer is 2

* refine shm cache size logic

* refine max shm cache

* refine shm cache size

* add zero dims test

* update code

* fix zero dims

* update code

* new exe supports CUDA Graph

* fix

* fix

* fix

* fix FLAGS_use_stream_safe_cuda_allocator in unittest

* insert output of coalesce_tensor op to skip_gc_var

* fix

* change feed_op to phi kernel

* fix ci bugs

* fix build bugs

* fix ci bugs

* fix compile bugs

* fix ci bugs

* perfect code

* perfect comment code

* fix install bugs

* modify code according comment

* remove visitor in feed_op

* modify according comment

* perfect code according comment

* add infershape

* fix py3 bugs

* fix getexpected kernel type

* fix getexpected kernel type

* fix ci bugs

* add registry for custom device

* fix py3 bugs

* fix floating point error

* fix py3 test bugs

* support elementwise base func

* fix compiling error and add test

* support vjp for div using comp

* remove additional change

* fix dy2st error with magic num

* fix dy magic num

* another magic

* another magic

* another magic

* add skip rename strategy

* support add vjp

* support add with new axis cal

* support sub vjp

* [prim] add multiply vjp rules

* [prim] add multiply vjp rules

* [prim] fix no infershape with composite in _append_backward_ops

* [prim] add expand vjp rule

* [prim] add exp vjp rule

* uncomment infer shape for reshape/sum static prim api

* [prim] fix tanh nullptr error

* remove some print message

* fix magic number in run_program relative tests @JiaBinYang

* [prim] add expand,multiply,exp vjp rules

* fix only support single direction reduce error

* infer reduce dims using out dims
Co-authored-by: NJiabinYang <360788950@qq.com>

* support the 'data_transform' for generating static graph ops

* reset 'pow' code

* change the 'GetKernelTypeForVar'

* Update warpctc for cuda-12

* Deprecate cudaProfilerInitialize for CUDA > 11

* Deprecate CUSPARSE_MV_ALG_DEFAULT for CUDA_VERSION >= 11040

* Add the missing thrust header