* Add SVD Op and it's GPU and CPU kernel

* Remove CUDAPlace in test_svd_op, make the test available in CPU package

* modfity the file

* fix windows bug/ fix ROCM / fix test timeout

* for pass the CIs

* improve error report

* for code review

* some modification to test_svd_op

* change python code style

* expose the svd interface for document

* support KL label smooth

* update UT for KL label_smooth

* Revert "Revert "Add copy from tensor (#34406)" (#35173)"

This reverts commit 32c1ec42.

* add template instantiation

[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

* fix CI skip cc test error, test=develop

* remove test code, test=develop

This reverts commit ac33c0ca.

* add api

* temp save

* revert

* copytocpu async ok

* fix style

* copy sync ok

* fix compile error

* fix compile error

* api done

* update python async api

* fix compile

* remove async python api; add c++ async unittest

* remove python async api

* update unittest

* update unittest

* add C++ unittest for copytensor

* add unittest

* update namespace utils to class TensorUtils

* add unittest

* update unittest

* update unittest

* update code style

* update code style

* update unittest

* change bilinear thread for nano and tx2

* change bilinear thread for nano and tx2

* add any.hpp to utils and replace boost::any with self defined paddle::any

* add copy any.hpp to custom op depends

* modify any.hpp include path

* remove boost from setup.py.in

* add copy any.hpp to custom op depends

* move any.hpp to paddle/utils/ dirs

* move any.h to extension/include direction

* copy utils to right directions

* support Kunlun2

* support KL2

* support KL2

* 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

* add gpu implementation of shuffle batch
test=develop

* add thrust cuda patches
test=develop

* fix macro guard

* fix shuffle batch compile on windows/hip

* fix hip compilation error

* refine CMakeLists.txt

* fix windows compile error

* try to fix windows CI compilation error

* fix windows compilation again

* fix shuffle_batch op test on Windows

* support Ninja and establish dependencies relationship between paddle with third_party

* fix CI

* support Ninja