* standard_api

* add hardtanh

* Einsum Support Complex

* code fix

* add unittest for complex grad with einsum

* set rtol=1e-4

* fix

* add extra attr property set

* add type_info for all context

* add onednn context to all context

* fix context compile error

* simplify conv kernel args

* pass runtime attr into dev_ctx

* fix marco error

* clear conv_grad_kernel extra args

* merge conv_grad_grad into conv_grad

* clear conv2d_grad_grad extra attrs

* clear yaml and eager extra attr

* fix conv1d error

* change to thread local

* fix npu compile failed

* try to fix windows compile failed

* add conv2d onednn phi kernel

* fix ci bugs (#36)

* fix compile bugs (#38)

* fix extra input transform bug (#39)

* support dynamic created attr (#40)

* reset extra info gen code

* rm conv_grad_grad kernel

* reimpl pass attr adapting

* add int attr support

* remove vector inputnames creating

* fix map at error

* Update paddle/phi/kernels/onednn/conv_grad_kernel.cc
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>

* remove useless extra attrs

* replace mkldnn_engine by onednn_engine
Co-authored-by: NYuanRisheng <yuanrisheng@baidu.com>
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>

* standard api

* fix ci bugs

* fix ci bugs

* fix ce bugs

* add unittest for einsum-v2-trace and diagonal

* repeat labels.

* einsum support repeated labels.

* forward is ok for diagonal and undiagonalized.
TODO: check backward is ok by our theorem.

* backward is ok!

* fix by PR suggestions.

* fix ci error

* fix ci error

* fix ci warning

* [CustomDevice] GetCCLComm add custom device support

* update

* update

* update

* replace executor in conditional_block_op.run with standalone_executor

* add block_id as the argument of standalone executor's method run; add print for program

* fix scope bug about conditional block op

* fix bug: unnecessary return of fetch value

* fix typo

* fix: quantization will set variable persistable, and these variables must exist in global scope

* add interpretercore cache for conditional block op but not activate in default

* fix bug: local scope reuse for conditional block op

* reset scope when conditional block op runs

* fix typo

* fix typo and code style

* add build scope for conditional block op

* add skip for transfer_layout kernel

* refind code

* fix reset_scope

* fix reset_scope

* refine code

* refine code

* refine code

1. remove flag use in conditional_block_op
2. pass execution_config to BuildOpFuncList instead of individual parameter

* refine code

* remove the use of FLAGS_control_flow_use_new_executor_cache

* change FLAGS_control_flow_use_new_executor to false

* - halfway transforming transpose grad

- Fixes

- buildable

* - lint

* rerunning the process

* fix reduce_any kernel data race on sharedMem

* use bit operation instead of div & mod

* unbranch

* modified according to PR comments

* Fix paddle.pow() bugs

* Add unittest cases

* Fix ut cases

* Add ut cases on multiple devices

fix uninitialized, tautological-constant-out-of-range-compare and literal-conversion warning on macos (#47341)

* support selected_rows kernel for multiply in dygraph

* delete useless code of slice in fluid

* fix complie bug

* move slice_array from fluid to phi

* fix strided_slice_op_npu

* Add infer prune function

* Update phi.cmake

* Update operators.cmake

* add fusion op

Enable to record whether the conv algo is got by exhaustive search to fix autotune cache bug. (#47065)

* [Dy2Static] Remove GradTransformer
1. fix einsum infershape bugs.
2. remove grad_transformer and unify paddle.grad and paddle.static.gradient.
3. add dygraph_and_dy2static_only decorator for dy2static.

* fix bugs

* rename

* add embedding range check

* change head file

* change head file

* fix

* [XPU] update xpu cmake to 1016. test=kunlun

* fix special case of transpose op. test=kunlun

* Fix bug of reduce_sum op. When input.numel() > INT32_MAX, its result
is wrong.

* support pure bfloat16

* support bf16 linear

* update PR to pass CI

* tiny fix where_grad_kernel.cu

* Support bfloat16 type for reducer and sharding.

* Fix some bug.

* Polish code.

* Polise code.

* Add bfloat16 datatype in fill_grad kernels.
Co-authored-by: Nsneaxiy <sneaxiy@126.com>

* namespace modify

* update by comment

* add sparse reshape

* change the dtype in all test cases to int64

* just one test case

* modify comments

* Update test_sparse_reshape_op.py

* chang the type of "shape"  from  vector<int64_t>  to  IntArray

* check whether sp_out.to_dense() is the cause  of error

* print sp_out

* Update reshape_kernel.cc

* use numpy to generate the equal paddle tensor

* just check dense_tensor.numpy()

* check cpu and cuda versions

* Update test_sparse_reshape_op.py

* supply all test cases for cpu forward coo kernel

* test forward coo cuda kernel

* change configuration of cuda kernel

* keep only one test case

* test coo cpu kernel (forward and backward)

* row major or column major ???

* test cuda coo forward kernel

* complete declaration and registration

* Update __init__.py

* rebuild

* retrigger CI

* add cudaMalloc and cudaMemcpy  in  ReshapeCooKernel  and change back to row major order in a cuda dense tensor

* midify minor error

* test only cpu coo forward kernel

* add all test cases for coo forward kernel  (both cpu and gpu)

* test all forward kernels (coo, csr; cpu, gpu)

* add all test cases for all kinds of kernels

* just retrigger CI

* Update sparse_ops.yaml

* Update sparse_ops.yaml

* Update sparse_ops.yaml

* resolve conflicts

* Update sparse_ops.yaml

* don't specify tensor place

* new shape has -1 or 0 in it

* Update unary_grad_kernel.h

* correct lvalue error

* code style

* Update sparse_backward.yaml

* Update sparse_ops.yaml

* Update unary_kernel.h

* Update unary.py

* Update sparse_backward.yaml

* Update unary.py

* code style

* code style

* code style

* Update unary.py

* specify tensor place explicitly

* do not use numpy array

* use numpy array in unit test again

* modify example code in docstring

test=kunlun

* draft with debug print