* add cpu_context.

* update

* update

* update

* update

* update

* fix ci problem

* fix npu ci problem

* update

* fix ci compile

* Migrate bfloat16/float16/complex from platform into pten::common

* fix typo

* fix code style

* Merged LoDTensor with Tensor,test=allcases

* Patched python level LoDTensor

* Patched python level LoDTensor

* Merge Tensor into DenseTensor

* Fixed namespace issues,test=allcases

* Fixed merge issues

* Fixed inference issues

* Fixed NPU test issues

* Fixed merge issues

* add pten::Place data structure.

* update ci problem

* fix ci problem

* update

* using platform::Place=pten::Place

* remove BOOST_GET_CONST for CPUPlace and GPUPlace

* compile pass 25%.

* compile pass 45%

* compile pass 60%

* remove boost_get for xpu npu mlu and ipu

* compile pass on cpu and gpu.

* fix compile problem

* fix compile error.

* update

* fix ci problem

* update

* ci approve

* fix ci problem

* fix ci eager test problem

* remove BOOST_GET_CONST

* fix npu compile

* Merged LoDTensor with Tensor,test=allcases

* Patched python level LoDTensor

* Fixed example code failure

* Polished function names, removed duplicated forward declarations

* remove hybird dir

* resolve conflit

* first commit

* fix wrong filename

* fix the wrong spell name

* fix gpu config warper

* modify according to pr advices

* fix GpuLauchConfig1D api bugs

* change the config for dropout grad

* fix bugs

* modification according to pr advices

* modification according to pr advices

* refactor matmul directory in pten

* fix merge conflict

* add dot_grad kernel

* add dot_grad kernel in pten

* add matmul_grad kernel

* update the code

* delete useless code in fluid

* fix some bug of running matmul grad kernel

* fix merge conflict

* refactor some code

* refactor code

* Added shared_ptr<Allocation> member & corresponding interfaces to Storage

* Removed original pten::Allocation from Storage and adjusted the interfaces accordingly

* Fixed issues with storage offset

* Used place to malloc allocation for TensorStorage

* [Unify Tensors PR #3]Ported framework::Tensor interfaces to pten::DenseTensor

* Fixed issues with place

* Added comments

* Moved mutable_data with stream argument to DenseTensor

* Added set_offset interface

* Fixed CI issues,test=allcases

* [Unify Tensors PR #4] Port LoDTensor interfaces to DenseTensor

* Removed friend class EigenTensor/EigenMatrix/EigenVector from Tensor

* Modified framework::Tensor to inherit from DenseTensor

* Reverted changes too pten_layout() interface

* Removed friend classes

* Rearranged cfunction calls from tensor.data<void>() to tensor.data()

* Fixed CI issues

* Fixed lite issues

* Fixed data() interface issues,test=allcases

* Resolved IsInitialized() issues

* Fixed ResetHolder() issues

* Fixed MKLDNN & Storage issues

* Resolved ShareBufferWith() issues

* Fixed LoD issues

* add maxunpool3d op

* update doc for maxunpool3d op

* update doc for maxunpool3d op

* update doc for maxunpool3d op

* update sample code for maxunpool3d

* add maxunpool1d op

* update some code for maxunpool1d

* add cpu kernel of lstsq

* update

* modify code style

* modify unittest

* remove support for complex

    将cuSparse的handle与DeviceContext进行绑定，避免op中进行创建和销毁
    添加对cuSparse中dense和sparse转换的API进行封装
    添加对封装的API的单测

From --ptxas-options=-v, SegmentOpsKernel uses 66 registers in a block.
There are two ways to resolve this problem:
    Reduce the threads per block launch configuration
    add __launch_bound__ to give information to nvcc compiler for reducing registers usage
this PR chooses __launch_bound__ solution because changing gpu_launch_config may affect other ops.

* fix CUDA Graph H2D bug again

* fix no return bug

* refine structure for cuda and rocm

* update

* update

* update

* update

* support data_format='NHWC' for prelu channel mode

* [NPU] reorganization for device API abstraction

* [NPU] delete old files

* [NPU] fix npu_collective_helper

* [NPU] fix collective_helper

* [NPU] fix ut

* [NPU] mod memory allocation and hccl_helper

* [NPU] fix place_type

* [NPU] split enfoce.h

* move acl* call into npu_info

* merge conflict

* fix merge

* merge conflict

* merge conflict

* Added GradTensorHolder to Eager Dygraph

* Added accumulation codes to Eager Dygraph

* Fix windows-ci issue

* Fix NPU-CI issue

* Fixed CI-Coverage issue

* add scalar and scalar_array

* remove DenseTensor include from Scalar and ScalarArray

* remove inner header from scalar_array

* refactor the method of fill_constant and add some comment

* Added HCCL backend support in dynamic graph mode

* fix segmentation fault

* add ut

* try to fix CUDA Graph H2D copy bug

* remove useless code

* fix ci

* fix ROCM CI

* fix CUDA_VERSION

* improve CI coverage

* add new API: paddle.linalg.triangular_solve

* add new API/OP: paddle.linalg.triangular_solve

* add new API/OP: paddle.linalg.triangular_solve

* fix comment

* Revert "Align CTC grad scale same with ESPNet (#34729)"

This reverts commit 10f9644c.

* ctc grad compute on gpu

* Add paddle.linalg.eig op

* remove comments

* remove comments

* extend batch_size to the origin

* add real times complex functor & destroy the backward complex output bug

* terminate output diff when input real tensors

* correct tiny doc errors

* move functions from eig_helper to svd_helper and remove eig_helper

* remove tensor.Resize

* remove no longer used code

* use existing lapack functions

* reply review comments 21/27

* remove .cu as this op is only executed on CPU

* remove const_cast & add const in argument list for read-only references

* fix sample code error in CI

* remove template typename Tbase and more

* remove eig exposure in paddle.*

* add 'name=None' in eig python implementation

* handle the unittest

* try to solve the unittest

* solve CI coverage

* remove no longer used code

* polish API doc and more

* reply review comments

* polish unittest, commit plan B

* polish unittest

* Add linalg.solve op, test=develop

* Fix a bug caused by accidental deletion

* updated description and fix a bug: missing a comma

* Add linalg.solve op, test=develop

* updated solve op backward logic

* updated solve op backward logic again

* Add linalg.solve Op, test=develop

* Updated and modified to fit CI requirements

* Fix a bug

* 1)Add more test cases; 2)Fix a wrong usage in reduces operation; 3)Remove redundant code

* Remove redundant comments

* 1)Removed redundant code; 2)Updated to enhance code robustness

* Removed redundant code

* Updated API documents

* Pass compat of conv_transpose_bias_mkldnn_fuse_pass

* Fix a bug of strided_slice op, about the axes parameter access memory out of bounds

* Fix a bug of transpose op, about accessing memory out of bounds of the perm param

* op:transpose_op supports bool type

* support extern third_party lapack on Linux/Windows/Mac

* fix ci

* Create stateful OneDNNAXPYHandler object.

This makes it possible to call it multiple times without recreating the
oneDNN primitives every time.

* Prepare SGDOpKernel to reuse its implementation from OneDNN kernel.

* OneDNN SGD kernel.

* Update call to use new OneDNNAXPYHandler object api.

* Setup seed in proper place.

* Enable OneDNN kernel only for single case.

* For dense param and sparse grad.

* Small refactor.

* Enable oneDNN by op attr or by cmd line flag.

* Use int64_t type for number of elements.

* Support dense param and grad from OneDNN kernel.

* Enable SGD OneDNN kernel when use MP BF16 optimizer.

* Force non-copyable/movable OneDNNAXPYHandler.

* Reuse OneDNNAXPYHandler for spare tensors in SUM op.

* Fix SFINAE rules.

* Remove recording event inside AXPY.

* Get rid of internal primitive caching.

* Stop use PP cache mechanims to store mem and primitive obj.
* Handler obj store and reuse needed desc & prim

* Do not derive from MKLDNNHandlerT

* Optimization of pool2d grad, first commit.

* remove useless print codes

* refine codes

* refine codes

* seal more operation into template specialization

* fix template struct error in MaxPool2dGrad.

* Fix header including error

* refine code with comment

* Seal the param-preparation codes into function for common use.

* Seal the param-preparation codes into function for common use.

* Seal the param-preparation into funciton and make it common for other kernels

* polish code and erase useless template speicalization

* Rerun triger

* rerun trigger