* [Plugin] Fix Custom device in eager mode, test=develop

* update test case, test=develop

* update ut for coverage, test=develop

* cmake-lint

* update

* back fl

* delete ssl cert

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* make warning

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* unittest paral degree

* solve unittest

* heter & multi cloud commm ready

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* fl-ps v1.0

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* support N + N mode

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* delete print

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* experimental nvcc-lazy-module-loading

* remove two empty last line from two files

* merge all phi lib to several big static lib

* merge all phi lib to several big static lib

* Update Mac cmake version >=3.15

* notest;read test1

notest;read test2

notest;read test3

* fix inference link error

* fix inference link error

* fix windows link error

* fix cmake_policy

* fix build big size

* back fl

* delete ssl cert

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* make warning

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* unittest paral degree

* solve unittest

* heter & multi cloud commm ready

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* arm_brpc compile

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* only output is ok

* base is ok

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* add switch server bin

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* adapt brpc ssl

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* add onnxruntime predictor

* Add code comments

* support link paddle2onnx onnxruntime

* support onnxruntime with python

* support onnxruntime with python

* support onnxruntime with windows

* paddle2onnx compile with windows

* supoort windows compile

* supoort windows compile with onnxruntime

* supoort windows compile with paddle2onnx

* supoort mac compile

* compile with mac

* compile with mac

* add code comments

* fix remind word

* code optimization

* add test case

* add test case

* add inference demo_ci test case

* fix compile paddle2onnx with no python

* add inference demo_ci test case

* add inference demo_ci test case

* add inference infer_ut test case

* support c go api and test cases

* add converage test case

* add converage test case

* add capi test case

* add capi test case

* Adjust GPU Arches for Whl releases

* Adjusted CUDA arches

* fixed minor issue

* adjusted gpu arches

* update;test=cpu-py3

* [CustomRuntime] Add DeviceManager

* [CustomRuntime] Add DeviceInterface

* [CustomRuntime] Add Stream, Event, DeviceGuard, CallbackManager

* [CustomRuntime] Add plug-in device

* [CustomRuntime] Memory module support PluggableDevice

* [CustomRuntime] Add WITH_PLUGGABLE_DEVICE cmake option

* update

* [API] update API doc based on comments, test=develop
Co-authored-by: Nqili93 <qili93@qq.com>

* Add XPU compiler for paddle, test=develop

* clean code

* clean useless code

* clean useless code

* clean useless code

* test

* add include path

* use clang compiler

* xpu2.cmake

* XPU2 compiler passed

* update

* update after pten

* combination the WITH_XPU and WITH_XPU2

* update the fuse operation in WITH_XPU and WITH_XPU2

* update

* update

* update

* fix the merge error

* update

* update the code

* update the code

* add run_kp_kernel flag

* update

* update

* fix prepared type_ bug

* clean and update the code

* reset the kernel_primitives

* update

* clean the code

* delete useless comment

* fix the bug in WITH_XPU

* update

* update

* modify the abi

* delete some useless code

* Parameter automation in xpu compilation

* Parameter automation in xpu compilation

* delete kps in cmake

* delete useless comment

* clean the code

* clean the code

* compile for afs api

* with pslib

* add new API: paddle.clone;Tensor.element_size;nn.utils.parameters_to_vector

* fix comment

* add infrt code

refined with Paddle's code style.

* rename CinnRtConfig to InfRtConfig

* rename CinnRt to InfRt of some code

* rename CINNRT to INFRT

* remove unnecessary code

* replace CINN to INFRT in the source code

* replace all "cinn" in code to "infrt"

* remove some const_cast

This PR added some changes to match the CINN change for compilation. It also tried to fix JiangCheng's Problem in PR: https://github.com/PaddlePaddle/Paddle/pull/36100

These changes include:
1. Set `CINN_GIT_TAG` to a newer tag
2. CINN now just `make cinnapi -j`
3. We have to add `-DPY_VERSION=${PY_VERSION} -DWITH_TESTING=ON` to CINN cmake args
4. For CINN's third party dependencies, we could just include headers without target_link_libraries
5. Moved `cinn.cmake` from `paddle/cmake` to `paddle/cmake/external` to match old style. External folder contains `lite`, which is the same level of `cinn`
6. CINN added `-DNAMESPACE=cinn_gflags` in `gflags.cmake` to have different gflag namespaces between CINN and Paddle. It solved re-define problem.
7. Change namespace of `::google::` in gflags to `::GFLAGS_NAMESPACE`

Add CINN compile option in CMake.

Now you can use CINN in Paddle by `-DWITH_CINN=ON` when `cmake`

To test it, you can run `make cinn_lib_test -j` and `ctest -R cinn_lib_test`. 

Note:
1. You should set
```
export runtime_include_dir=${CINN_SOURCE_DIR}/cinn/runtime/cuda 
```
When run test, the `${CINN_SOURCE_DIR}` should be set based on your CINN directory.

2. CINN is under developing now, you may have to change `CINN_GIT_TAG` to the git commit you need.

* 1. add interface for fft;
2. add data type predicate;
3. fix paddle.roll.

* add fft c2c cufft kernel

* implement argument checking & op calling parts for fft_c2c and fftn_c2c

* add operator and opmaker definitions

* only register float and double for cpu.

* add common code for implementing FFT, add pocketfft as a dependency

* add fft c2c cufft kernel function

* fix bugs in python interface

* add support for c2r, r2c operators, op makers, kernels and kernel functors.

* test and fix bugs

* 1. fft_c2c function: add support for onesided=False;
2. add complex<float>, complex<double> support for concat and flip.

* 1. fft: fix python api bugs;
2. shape_op: add support for complex data types.

* fft c2c cufft kernel done with complie and link

* fix shape_op, add mkl placeholder

* remove mkl

* complete fft c2c in gpu

* 1. implement mkl-based fft, FFTC2CFunctor and common function exec_fft;
2. change the design, add input and output typename as template parameter for all FFTFunctors, update pocketfft-based implementation.

* complete fft c2c on gpu in ND

* complete fft c2c on gpu in ND

* complete fft c2c backward in ND

* fix MKL-based implementation

* Add frame op and CPU/GPU kernels.

* Add frame op forward unittest.

* Add frame op forward unittest.

* Remove axis parameter in FrameFunctor.

* Add frame op grad CPU/GPU kernels and unittest.

* Add frame op grad CPU/GPU kernels and unittest.

* Update doc string.

* Update after review and remove librosa requirement in unittest.

* Update grad kernel.

* add fft_c2r op

* Remove data allocation in TransCompute function.

* add fft r2c onesided with cpu(pocketfft/mkl) and gpu

* last fft c2r functor

* fix C2R and R2C for cufft, becase the direction is not an option in these cases.

* add fft r2c onesided with cpu(pocketfft/mkl) and gpu

* fix bugs in python APIs

* fix fft_c2r grad kernal

* fix bugs in python APIs

* add cuda fft c2r grad kernal functor

* clean code

* fix fft_c2r python API

* fill fft r2c result with conjugate symmetry (#19)

fill fft r2c result with conjugate symmetry

* add placeholder for unittests (#24)

* simple parameterize test function by auto generate test case from parm list (#25)

* miscellaneous fixes for python APIs (#26)

* add placeholder for unittests

* resize fft inputs before computation is n or s is provided.

* add complex kernels for pad and pad_grad

* simplify argument checking.

* add type promotion

* add int to float or complex promotion

* fix output data type for static mode

* fix fft's input dtype dispatch, import fft to paddle

* fix typos in axes checking (#27)

* fix typos in axes checking

* fix argument checking (#28)

* fix argument checking

* Add C2R Python layer normal and abnormal use cases (#29)

* documents and single case

* test c2r case

* New C2R Python layer normal and exception use cases

* complete rfft,rfft2,rfftn,ihfft,ihfft2,ihfftn unittest and doc string (#30)

* Documentation of the common interfaces of c2r and c2c (#31)

* Documentation of the common interfaces of c2r and c2c

* clean c++ code  (#32)

* clean code

* Add numpy-based implementation of spectral ops (#33)

* add numpy reference implementation of spectral ops

* Add fft_c2r numpy based implementation for unittest. (#34)

* add fft_c2r numpy implementation

* Add deframe op and stft/istft api. (#23)

* Add frame api

* Add deframe op and kernels.

* Add stft and istft apis.

* Add deframe api. Update stft and istft apis.

* Fix bug in frame_from_librosa function when input dims >= 3

* Rename deframe to overlap_add.

* Update istft.

* Update after code review.

* Add overlap_add op and stft/istft api unittest (#35)

* Add overlap_add op unittest.

* Register complex kernels of squeeze/unsquuze op.

* Add stft/istft api unittest.

* Add unittest for fft helper functions (#36)

* add unittests for fft helper functions. add complex kernel for roll op.

* complete static graph unittest for all public api (#37)

* Unittest of op with FFT C2C, C2R and r2c added (#38)

* documents and single case

* test c2r case

* New C2R Python layer normal and exception use cases

* Documentation of the common interfaces of c2r and c2c

* Unittest of op with FFT C2C, C2R and r2c added
Co-authored-by: lijiaqi <lijiaqi0612@163.com>

* add fft related options to CMakeLists.txt

* fix typos and clean code (#39)

* fix invisible character in mkl branch and fix error in error message

* clean code: remove docstring from unittest for signal.py.

* always convert numpy array to paddle.Tensor to avoid comparing numpy dtype with paddle dtype. (#40)

* always convert numpy array to paddle.Tensor to avoid comparing numpy dtype with paddle dtype.

* fix CI Errors: numpy dtype comparison, thrust when cuda is not available (#41)

1. always convert numpy array to paddle.Tensor to avoid comparing numpy dtype with paddle dtype.
2. promote floating point tensor to complex tensor ior fft_c2c and fft_c2r;
3. fix unittest to catch UnImplementedError and RuntimeError;
4. fix compile error by avoid using thrust when cuda is not available.
5.  fix sample code, use paddle.fft instead of paddle.tensor.fft

* remove inclusion of thrust, add __all__ list for fft (#42)

* Add api doc and update unittest. (#43)

* Add doc strings.
* Update overlap_add op unittest

* fix MKL-based FFT implementation (#44)

* fix MKL-based FFT implementation, MKL CDFT's FORWARD DOMAIN is always REAL for R2C and C2R

* remove code for debug (#45)

* use dynload for cufft (#46)

* use std::ptrdiff_t as datatype of stride (instead of int64_t) to avoid argument mismatch on some platforms.

* add complex support for fill_zeros_like

* use dynload for cufft

* Update doc and unittest. (#47)

* Add doc of frame op and overlap_add op.

* Update unittest.

* use dynload for cufft (#48)

1. use dynload for cufft
2. fix unittest;
3. temporarily disable Rocm.

* fix conflicts and merge upstream (#49)

fix conflicts and merge upstream

* fix compile error: only link dyload_cuda when cuda is available (#50)

* fix compile error: only link dyload_cuda when cuda is available

* fix dynload for cufft on windows (#51)

1. fix dynload for cufft on windows;
2. fix unittests.

* add NOMINMAX to compile on windows (#52)

 add NOMINMAX to compile on windows

* explicitly specify capture mode for lambdas (#55)

 explicitly specify capture mode for lambdas

* fix fft sample (#53)

* fix fft sample

* update scipy and numpy version for unittests of fft (#56)

update scipy and numpy version for unittests of fft

* Add static graph unittests of frame and overlap_add api. (#57)

* Remove cache of cuFFT & Disable ONEMKL (#59)

1. replace numpy.fft with scipy.fft as numpy<1.20 not support ortho norm
2. remove cache of cufft plans;
3. enhance error checking.
4. default WITH_ONEMKL to OFF
Co-authored-by: Njeff41404 <jeff41404@gmail.com>
Co-authored-by: Nroot <root@bjyz-sys-gpu-kongming9.bjyz.baidu.com>
Co-authored-by: NKP <109694228@qq.com>
Co-authored-by: lijiaqi <lijiaqi0612@163.com>
Co-authored-by: NXiaoxu Chen <chenxx_id@163.com>
Co-authored-by: Nlijiaqi0612 <33169170+lijiaqi0612@users.noreply.github.com>

* [NPU] add int64 kernels, test=develop

* update ci scripts to be able to trun WITH_ASCEND_INT64 on, test=develop

[Background]
Expansion in code size can be irreversible in the long run, leading to huge release packages which
not only hampers user experience but also exceeds a hard limit of pypi.

In such, NV_FATBIN section takes up 86% of the compiled dylib size, owing to the vast number of GPU
arches supported.

This PR aims to prune this NV_FATBIN.

[Solution]
In the new release strategy, two types of whl packages will be involved:

Cubin PIP package:
PIP package maintains a smaller window for GPU arches support, containing
sm_60, sm_70, sm_75, sm_80 cubins, covering Pascal - Ampere arches

JIT release package:
This is a backup for Cubin PIP package, containing compute_35, compute_50, compute_60,
compute_70, compute_75, compute_80, with best performance and GPU arches coverage.

However, it takes around 10 min to install due to the JIT compilation.

[How to use]
The new release strategy is disabled by default.
To compile for Cubin PIP package, add this to cmake: -DCUBIN_RELEASE_PIP
To compile for JIT release package, add this to cmake: -DJIT_RELEASE_WHL

* [NPU] update NPU ci tests, test=npu_aarch64

* [NPU] fix x86 build and add disable_ut for NPU, test=npu_aarch64

* [NPU] address review comments, test=develop

* polish windows compile for Ninja, fix random compile fail

* polish windows compile for Ninja, fix random compile fail

* Support Mac M1 make

* cmake version check

* Support sccache to speed up compilation on Windows

* Support sccache to speed up compilation on Windows

* fix ninja compilation bug on windows

* polish windows ci

* polish windows ci