* 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

* fix generate_proposal_labels in cascade-rcnn series model, test=develop

* fix example code & unittest, test=develop

* update code from review comments, test=develop

* fill_constant supports nan and inf

* add ut

* refine code of DygraphToStaticAst

* add __init__ function

* add stack pool2d roi_align xpu op,test=kunlun

* error message opt, test=kunlun

* add xpu unittest,test=kunlun

* skip check grad,test=kunlun

* fix boostget , test=kunlun

* error message opt for XPU, test=kunlun

* add rois_num for roi_align xpu OP, test=develop

* fix dataloader

* lookup_table_xpu op report errors;test=kunlun

* add adam xpu op;test=kunlun

* reset lookup

* change adam wrong;test=kunlun

Fix diag OP bug on Windows Python3.8 ，remove the std::min

fleet support paddle.optimzier

* bug fix

* fix fleet_base

* bug fix

* fix coverage

* transfer from paddle.fluid.layers.assign() into creation.py,test=develop

* fix ut fail,add support for paddle.assign,test=develop

* fix,test=develop

* fix UT coverage,test=coverage

* fix UT fail,test=coverage

* fix doc,test=develop

* Still has bugs.

* Fixed allclose_op bug, which cannot deal with some cases of fp64 inputs.

* improved CUDA kernel performance.

* Changed CUDA code.

* Fixed a bug in cuda kernel which cannot deal with large dimension input, and added an unittest for it.

* Add a test case for float32 input.

* fix multinomial doc

* fix multinomial error message

* little doc change

* fix Categorical class doc

* optimize format of error message

* fix CPU Kernel error message format

* fix isinf and isnan error in WindowsOPENBLAS CI

* delete inf and nan

* add manual_seed in sample code

* little error message change

* change error message to InvalidArgument

* add full point for error message and add manual_seed in CPU environment

* Add truncated_gaussian_random_op XPU kernel

* Add truncated_gaussian_random_op XPU kernel, test=kunlun

* little change, test=kunlun

* change boost_get to BOOST_GET_CONST

* change boost_get to BOOST_GET_CONST, test=kunlun

* little change, test=kunlun

* use Generator to generate random number and optimize format, test=kunlun

* little change, test=kunlun

* add TODO, test=kunlun

* Add gaussian_random XPU kernels

* commit kunlun, test=kunlun

* new version, test=kunlun

* change boost_get to BOOST_GET_CONST, test=kunlun

* use Generator to generate random number and optimize format, test=kunlun

* add TODO, test=kunlun

* support uniform_random op on Baidu Kunlun

* change dtype of attr shape from int to int64_t

* kunlun ci, test=kunlun

* new version, test=kunlun

* change boost_get to BOOST_GET_CONST

* change boost_get to BOOST_GET_CONST, test=kunlun

* use Generator to generate random number and optimize format

* run Kunlun CI, test=kunlun

* add TODO, test=kunlun