cleanup test_ascend_group.py

Add startup bash files of test_ascend_group

Add Hccl program group

Pass device_ids info from launch to trainer

Build praser for Hcom* operators

Add distribution supported

Fix compilcation on CANN20.1 and older 

* test=develop, fix fleet.metrics(mse, rmse, mae)

* fix the rnn mask memory bug for out of read

* update the code for the rnn

* clean dockerfile

* clean dockerfile

* add auc into 'all' list

* alias acc, expose to users

* update sample code

Co-authored-by: NseiriosPlus <tangwei12@baidu.com>

* test=develop, add distributed_infer

As the title

* skip quantizing ops in cpu inference, test=develop

* added support for inference using qunatization aware trained dygraph

* added support for inference using qunatization aware trained dygraph
correct boost get usage

* Delete incorrect warning message (#30196)

* fix warning and no grad

* clean redundant API alias in 2.0 - part 2 (#30013)

* delete paddle.nn.functional.assign

* fix dynamic to static error

* just add the op error message for the matmul xpu (#30246)

 add the op error message for the matmul xpu

* Add Static Variable Clone (#30208)

Add clone method for static Variable so that this interface will be same as dygraph. It fixed some bugs in dy2stat

* use wget to replace curl to download the lcov file (#30229)

* use wget to replace curl to download the lcov file

* add cache for lcov

* fix test_pool3d_op timeout issue (#30248)

* Fix unittests bugs. (#30250)

* modify error message based on comments (#30189)

* modify error message based on comments

* edit code according to review.

* Correct spelling according to review.

* Fix bug for 'save mutiple method' (#30218)

* Fix bug for 'save mutiple method'

* To pass coverage.

* edit code to pass coverage.

* edit code to pass coverage.

* add unittest for coverage.

* change for coverage.

* edit for coverage.

* added support for inference using qunatization aware trained dygraph

* Alias from  paddle.fluid.layers.auc to paddle.static.auc (#30206)

* add alias from  fluid.layers.auc to static.auc

* Update __init__.py

* added support for inference using qunatization aware trained dygraph
correct boost get usage

* corrected boost get usage

* corrected naming issues and enforcing zero check

* correct paddle enforce message

* added more error checkings

* corrected error report message and optimized code

* corrected findvar usage

* corrected paddle_enforce in scope

* correct error messages

* correct error reporting format
Co-authored-by: NLielinJiang <50691816+LielinJiang@users.noreply.github.com>
Co-authored-by: NXiaoguangHu <46782768+XiaoguangHu01@users.noreply.github.com>
Co-authored-by: Nwawltor <fangzeyang0904@hotmail.com>
Co-authored-by: NHuihuang Zheng <zhhsplendid@gmail.com>
Co-authored-by: NYUNSHEN XIE <1084314248@qq.com>
Co-authored-by: NBai Yifan <me@ethanbai.com>
Co-authored-by: Ngongweibao <weibao.gong@gmail.com>
Co-authored-by: NWeiXin <weixin10@baidu.com>
Co-authored-by: NJiaqi Liu <liujiaqi06@baidu.com>

* softmax backward optimize

* fix bug on compiling inference shared lib with crypto;test=develop

* fix cmake bug when build inference lib using -DWITH_CRYPTO=OFF

* update cmake

* remove unnecessary enforce message

usleep function in <unistd.h> only takes argument less than 1,000,000. Current call can exceed this limit, we have to fix it. This PR can fix random CI error.

As the title

Set expected place in child thread for dataloader to avoid costing cuda memory on other card (#30338)

* set expected place in child thread for dataloader

* set device id when set tensor from numpy

* revert tensor_py change

* add compile guard

* fix ci

* fix bug

* optimize memcpy perf for kunlun

* remove useless unitest for kunlun mean

* minor

* Implemented AddQuantDequantPass in imperative quantization.

* Supported LeakyReLU Quantization

* For meeting coverage rate.

* Changed the file name of test of AddQuantDequant

* Implemented more Quantized NoWeightLayers.

* Fix the loss cannot align problem between static and dynamic model quantization, add swish as supported quantized layer in imperative quantization.

* remove noweight_list

* support 2.0 API such as Pool2D and ReLu