* modify for wincheck-inference case

* modify according to zhouwei's comment

* open with_trt and block failed unittests in windows

* test

* support data_format='NHWC' for prelu channel mode

* [Auto Parallel]  Add the unified cluster representation

* [Auto Parallel] Add the graph class for physical mapping

* [Auto Parallel] Add the simple physical mapper

* Set the timeout of the mapper

* Merge the upstream develop unittests cmake files

* Fix a bug of the process group

* Remove mapper unittest from platforms which is not GPU

* Move the instantiation of process group after resharding

* Add the local id for devices

* Update the rank mapping format

* Add some comments

* Remove the related files about mapping

* Update the unittest for auto mapping

* Remove unused rank_mapping unittest

* Improve the unittest coverage

* Improve the unittest coverage

* add scope_guard

* 1. fix control flow cases 2. fix calc_gradient

* add expand_v2/expand_as_v2 for kunlun

* update expand_as_v2

* update expand_as_v2

* support float16/bool

* update xpu.cmake

* amp.decorate optimizers set to None is ok

* refine unittest

* add unittest and refine example code

* refine unittest

* native commit for triple grad of sigmod

* Updated unittests files

* init functional jacobian api

* Updated trible_test func

* Updated gradient_checker & test_script

* finish test with dtype float32

* add float64 test case

* polish code

* use atol=1e-5 with dtype float64

* fix for ci

* set timeout for test_jacobian

* fix dygraph grad to support high differential

* polish API docstring

* Updated gradient checker and some related files

* fix double grad strip error for high differential

* fix double grad strip error for high differential

* Add Sigmoid triple grad tests

* fix dygraph double grad dtype error when calling for high differential senario

* Updated triple grad teses func

* Use np.random to initialize ddx

* Updated triple_grad_check func

* add todo for gradient checker and refine some comments

* remove additional code

* add test for warnging in backward.py

* format python code

* support multi input in triple gradient checker

* Add matmul triple grad kernel

* Updated comments of TODO

* Supported some special tests

* Change code-format to follow CI std

* Updated gradient_checker.py

* Fix conflicts

* Removed unnecessary printing log

* Change code style to follow CI std

* support batch in jacobian and hessian

* add batch jacobian and batch hessian

* Add batch_jacobian test, draft version

* [New features] Add elementwise_mul triple grad kernel (#37152)

* Add elementwise_mul triple grad kernel

* Removed InplaceInferer and polished code

* Add numerical_batch_jacobian,numerical_batch_hessian and tests

* Support batch_jacobian and batch_numerical

* Use pre-commit to check code format

* Update doc, polish code, add unit test

* Reset the TIMEOUT properties of test_jacobian to pass CI
Co-authored-by: Nlevi131 <limaolin01@baidu.com>
Co-authored-by: NJiabin Yang <360788950@qq.com>

Co-authored-by: NChen Long <1300851984@qq.com>

* [Auto Parallel]  Add the unified cluster representation

* [Auto Parallel] Add the graph class for physical mapping

* [Auto Parallel] Add the simple physical mapper

* Set the timeout of the mapper

* Merge the upstream develop unittests cmake files

* Fix a bug of the process group

* Remove mapper unittest from platforms which is not GPU

* Move the instantiation of process group after resharding

* Add the local id for devices

* Update the rank mapping format

* Add some comments

* Remove the related files about mapping

* Remove unused rank_mapping unittest

* Improve the unittest coverage

* fix data parallel when VOCAB var in program

* test

* test

* rm test

* update

* update

* update

* add unittest

* update

* update save

* Fix bugs when bias is none for static graph for fused_attention op.

reset_inplace_version removes all inplace related records to VarBase/VariableWrapper, the essential purpose of which is to let you use inplace operations as if using its non-inplaced version, which of course will cause unexpected consequences if not used with care.

This is essentially a hack interface to satisfy one specific request

* add tdm sample

* add tdm sample in c++

* update tdm sample

* modify sample count

* fix conflict

* add set_date

* fix cmake error

* fix bug of proto

* update index_dataset proto

* update cmake

* fix error cmake

* fix cmake mkldnn

* fix cmake proto

* update cmake proto

* update cmake

* update rec

* update dataset

* update dataset

* update dataset

* updata dataset

* updata dataset

* updata coverage

* updata ci

* goback4

* fix npu ci

* add xxhash dep

* fix dropout static when axis != None

* update dropout test

* add dropout test

* fix test

* Update test_dropout_op.py

* Update test_dropout_op.py

* fix testcase

* fix testcase

* Update test_dropout_op.py

* fix testcase

* fix testcase

* optimize perf

* add new test

* fix testcase

* [NPU] add int64 support for argsort op

* [NPU] delete debug codes

* [NPU] add NPU kernel for prior_box op

* [NPU] delete debug codes

* fix. test=develop

* fix. test=develop

* fix. test=develop

* fix. test=develop

* fix. test=develop

* [heterps]bug fix for _run_from_dataset

* fix heter_server.cc

* fix launch_utils.py

* fix heter_section_worker.cc

* fix. test=develop

* fix. test=develop

* add new API paddle.nn.initializer.Dirac

* fix doc

* fix program cache key

* bug fix

* fix cache problem

* remove unused code

* hot fix for dataloader thread error

* polish comment

* fix type in comment, test=document_fix

* add zeropad2d v0.1

* add zeropad2d v0.2

* add zeropad2d v0.3

* add zeropad2d v0.3

* add zeropad2d v0.3

* add zeropad2d v0.4

* add zeropad2d v0.5

* add zeropad2d v0.5 codestyle

* add zeropad2d v0.5 codestyle

* add zeropad2d v0.6 functional

* add zeropad2d v0.6 functional

* add zeropad2d v0.6 functional

* skip compiled program

* fix ut

* pybind core

* set use psgpu

* matmul_convert_int8

* matmul_convert_int8

* matmulconvert_int8

* Matmul_int8_convert: tensor*tensor

* Matmul_int8_convert: tensor*tensor

* Matmul_int8_convert: tensor*tensor

* adapt auto search

* adapt auto search

* fix matmulv2 compatible

* del debug

* [Auto Parallel]  Add the unified cluster representation

* Add the local id for devices

* Add some comments

* run dy2stat pure fp16 in Linear model

* no use self._pure_fp16_inputs

* add test and fix Adam error in dy2stat pure fp16 training

* use paddle.optimizer.Adam

* run test in gpu

* change test time for CI

* enlarge atol for test_resnet_pure_fp16

* refine code and enlarge atol

* make custom_white_list and custom_black_list take effect for AMP and pure fp16

* check tracer is not None

* use default atol

* change filter_size

* change atol and add some NOTE

* fix inplace bug

* fix custom grad input error

* add unittest

* fix inplace bug

Add support for bias is none for fused_attention op.

`paddle.utils.download` ：change to call `extractall` on tar/zip compressd file  to speed up the uncompress process when they includes many files

--- result of decompression speed comparison ---
1. dataset：https://paddlenlp.bj.bcebos.com/datasets/cnn_dailymail/cnn_stories.tgz, decompression time
：5m50s vs 20s
2. dataset：https://paddlenlp.bj.bcebos.com/datasets/cnn_dailymail/dailymail_stories.tgz, decompression time：33m20s vs 47s