This PR is follow up of #28213. On that PR we tried to decrease GPU usage, however the CI still randomly failed. So I added retry logic for the initialization of nccl and cusolver. If the initialization failed, we can retry to avoid the random failure.

* add generate_proposals_v2 op

* Fix lr setting of AdamW when lr is an instance of LRScheduler.
test=develop

* Fix static graph test mode in test_adamw_op.py.
test=develop

* [Dy2stat] Support to modify value of buffer tensor

* remove "defaultTest"

* fix name confliction

* update version pybind to v2.4.3, test=develop

* update unittests, test=develop

* check and fix tensor and scalar type promotion

* fix else branch error

* fix scalar method error

* fix test_math_op_path unittest

* add future division for unittest

* rm useless bin file

* Add median api.

* Add median api.

* Add median api.

* Add median api.

* Add median api.

* enable test_parallel_executor_fetch_isolated_var, test=develop

* add enable_static, test=develop

* set test_parallel_executor_fetch_isolated_var RUN_TYPE=DIST, develop=test

* Enhance multiclass_nms to support LoD for dygraph mode

* fix some error in multiclass_nms

* update GetLodFromRoisNum to GetNmsLodFromRoisNum

* add + - * / @ [] operator to ComplexVariable, also add unittest

* fix circular reference bug

* fit for py2.7

* remove reverse oprators which not supported now

* set static shape for shape tensor with constant

* remove debug code

* fix typo

* add ut

* refine code

* refine example

* fix fleetrun heter ps on paddlecloud

* update matrix nms op to api 2.0

* modify code according to review

* add fuse_bn_add_act pass

* add multiple exception type

* define all exception & polish compile pystack

* mapping paddle error to python exception

* polish static mode error format

* fix failed unittests

* fix dytostatic test_error

* fix check_nan_inf failed

* add unittest for coverage

* revert some code try to solve compile error

* refactor enforce & error change

* polish code & add unittest

* Join break cond with while cond

* remove usless code

* refine the if code

* Split into BreakTransfromOptimizer

* add BreakTransformOptimizer in ast_transformer

* add more comment

* fix DataLoader return same format between static & dynamic in single mode. test=develop

* Release 2.0rc cherry pick api rename #28108 (#28184)

* rename count_include_pad-->exclusive  return_indices-->return_mask

* remove track_running_stats

* fix typo.

* rename xxxd-->xxxxD

* solve conflicts

* 2.0rc api add all any (#28199)

* reduce trt warning message (#28011)

add paddle.enable_static() on sample code

alias recude_all-->all, reduce_any-->any

add import reduce_all and reduce_any in python/paddle/tensor/math.py

import all and any in python/paddle/tensor/__init__.py

remove all and any OP in python/paddle/tensor/logic.py, add all and any OP in python/paddle/tensor/math.py

fix import error

remove TestAllAPI temporary

* fix doc of recdue_all and reduce_any, test=document_fix

* fix typo

* fix unittest for all and any API
Co-authored-by: NPei Yang <peiyang@baidu.com>

* rename conv_transposeXd-->convXd_transpose (#28198)

* fix sample code of reduce_all and reduce_any
Co-authored-by: NPei Yang <peiyang@baidu.com>

* refine format

* update doc

* handle uninitialized tensor

* add ut

* Fix xpu notest;test=kunlun

* fix

* test=kunlun

* test=kunlun

* add sharding

Recently, test_parallel_executor_test_while_train randomly failed on CI. On all CI logs, it showed NCCL initialization failed or cusolver initialization failed. I found online that those failure is usually caused by GPU shortage. Those API calls CUDA APIs directly so it shouldn't be the problem of allocator. It may be somewhere in PaddlePaddle increases GPU usage.

However, I run this test for 1000 times on my machine and the CI machine, either of them can reproduce the random failure. Maybe there is something related to the environment only happened in test env.

To verify my assumption that somewhere in PaddlePaddle increases GPU usage and also fix this CI, I decreased the batch_size to see whether the random failure disappears in test env.

* fix strided_slice_op's GetExpectedKernelType when input tensor is at CUDAPinnedPlace

* add unittest for tensors in cuda pinned place

* skip test for cuda pinned place on cpu machines

* fix bug of fetch_async_op_handle

* revert some changes of test_buffer_shared_memory_reuse_pass

* revert some changes of test_buffer_shared_memory_reuse_pass

* remove some judgement

* fix len(outputs) == 1

* update hapi predict interface

* fix code style

* fix docs

* fix docs

* fix docs

* update docs

* fix codes style

* fix unittest

* fix unittest

* fix coverage

* modify ut cmakefile, test=develop

* rename manual_seed to seed

* rename xxx1d-->xxx1D, xxx2d-->xxx2D, xxx3d-->xxx3D

* rename manual_seed --> seed

* do not rename .cc, .cu and .h file

* rename manual_seed --> seed

* rename manual_seed --> seed

* rename manual_seed --> seed

* rename manual_seed --> seed

* disable_static on doc example code

* donot change manual_seed on generator

* add enable_static on sample code

* convert python/paddle/fluid/layers/nn.py to bak

* fix typo

* fix code style

* fix seed to manual_seed when call functions of Generator()

* fix bug

* add static_mode_white_list

* add Mac CI static list

* add Win CI white_list

* add Coverage and Py3 CI white_list, add test_unittest

* Add new api: is_tensor

* Add new api: is_tensor

* Add new api: is_tensor

* Add new api: is_tensor