Add Static CostModel. Static data is based on op benchmark system

* add doc for show() in paddle.version

* fix format

* print cuda and cudnn in show API

* add paddle.version.cuda and paddle.version.cudnn API

* fix little bug

* fix bug

* add doc string

* fix mkdir error

* fix windows path

* fix new paddle/version path

* fix unittest

* fix format

本PR是fused_transformer的layer层代码，包含FusedFeedForward的layer层代码和FusedTransformerEncoderLayer的代码。

* support extern third_party lapack on Linux/Windows/Mac

* fix ci

[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

* add auto_parallel dir

* mv to paddle.distributed

* add shard_xx api

* add distributed attrs for var

* add ut, test=develop

* add dist

* update

* update

* update

* update

* update

* update, test=develop

* update, test=develop

* update, test=develop

* update, test=develop

* update, test=develop

* update, test=develop

* update, test=develop

* update

* update

* update

* update

* update

* update, test=develop

* update, test=develop

* update

* update

* delete unused proto

* resotre op_desc

* restore type_defs

* update var_desc

* remove dimss_mapping for proto_pybind

* update interface.py

* update framework.py

* update

* update

* add auto_parallel dir

* mv to paddle.distributed

* add shard_xx api

* add distributed attrs for var

* add ut, test=develop

* [WIP] Add the auto completion feature and related codes

* [WIP] Improve the auto completion and related codes

* [WIP] Make the auto completion to support data-parallel

* [WIP] Make the completion support mp and dp+mp

* [WIP] Refactor auto completion unit test for MLP

* [WIP] Refactor the implementation of DistributedOperatorImpl

* [WIP] Improve dims_mapping update rule and fix a bug

* [WIP] Support auto completion for one transformer decoder layer

* [WIP] Add a minor change

* [WIP] Fix a bug within the uint test

* Shard XShape tensor, add embedding completion and refactor code

* Add the distributed_operators dir to setup.py.in

* Improve the completion process and add the unittest for gpt

* fix process_mesh ut

* fix process_mesh ut

* update

* update, test=develop

* Add support for automatically completing distributed attrs of special ops

* update

* update

* update

* fix doc sample codes, test=develop

* improve coverage, test=develop

* add static_mode check, test=develop

* Model the cluster for cost model and physical mapping

* update, test=develop

* add set_placement, test=develop

* Add the check to make sure the candidate tensors' size is great than zero

* update doc, test=develop

* update doc, test=develop

* update doc, test=develop

* update doc, test=develop

* update, test=develop

* Auto mark dist attrs annotated by user

* update ndarray to nested list, test=develop

* update, test=develop

* Add auto-completion module for auto-parallel (based on PR#33804)

* Remove unnecessary files

* Remove unrelated files for the auto completion pr

* Update the unit test to improve the coverage

* Modify codes based on reviews

* Minor changes for CI

* Improve some codes based on new comments

* Fix bugs caused by shallow copy in attributes.py
* Imporve amend_distributed_attr_for_program in context.py
* Other changes for weihang's comments
Co-authored-by: Nsandyhouse <lilong12@baidu.com>

* add auto_parallel apis

* 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

* integrated gast library

* integrated gast library

* fix unittest and remove ast2.py

* remove 'gast' from __all__ in __init__.py

* add copyright in other files

* fix copyright

* add cuda event and stream api

* add cuda event and stream api

* add get_current_stream api

* add get_current_stream api

* init streams

* modify get_current_stream

* modify get_cuttent_stream

* add synchronize func

* add current_stream doc and test file

* move get_current_stream into CUDA macro

* move CudaEvent into CUDA macro

* move _get_current_stream and _device_synchronize into cuda macro

* modify the macro of cuda stream and event

* add test case for synchronize

* add paddle.devices.cuda module

* event and stream support hip

* add doc for stream and event class

* move cuda stream and event into single pybind

* add cuda_streams_py.cc to cmakelist

* add _device_synchronize and _get_current_stream to core module

* add test case for cudastream and cudaevent

* move __all__ in streams.py

* fix test fail

* add cuda to devices __all__

* fix current_stream doc writing error

* move devices to device direction, and merge device.py into __init__.py

* add required:gpu to sample codes

* remove cuda direction from device/__init__.py

* softmax mask fuse upper triangle

* cover not implemented cpu code

* Add wrap for functional api
* Refine the wraped api
* Add unit test for quant functional layers
* Update all unit tests for dygraph qat

* replace and remove complex64/128 types in custom OP and other files

* fix custom_tensor_test fail bug

* fix custom_conj_test fail bug

* fix dispatch_test_op build fail bug

* Unify all external API error message mechanism and enhance third-party API error msg

* fix some comment

* fix some comment

* Remove paddle_custom_op dynamic libraries, change link to FLUID_CORE on windows, and check copy_to

* fix CI

* revert data_generator

* test

* add setup.py

* add pp dygraph, test=develop

* add pipeline layer

* remove useless ext headers

* fix boost header compile failed

* remove fluid for auto_checkpoint.

* fix bug.

* support dp & mp

* add model parallel support in dygraph

* Remove old custom OP to reduce whl package volume

* [Custom OP]Remove old custom OP to reduce whl package volume

* support macos

* Ascend rc (#30483)

* Fix compilcation on CANN20.1 and older (#30494)

Fix compilcation on CANN20.1 and older

* Add distribution supported (#30578)

Add distribution supported

* Build praser for Hcom* operators (#30627)

Build praser for Hcom* operators

* Pass device_ids info from launch to trainer. (#30632)

Pass device_ids info from launch to trainer

* Add Hccl program group (#30642)

Add Hccl program group

* Add startup bash files of test_ascend_group. (#30645)

Add startup bash files of test_ascend_group

* cleanup (#30646)

cleanup test_ascend_group.py

* [Feature] Build parser to support distributed training (#30658)

[Feature] Build parser to support distributed training

* fix compilation on ascend-20.1 (#30722)

fix compilation on ascend-20.1

* Dev/fix ascend string (#30749)

Dev/fix ascend string

* code style (#30781)

code style

* Merge ascend_optimizer and ascend_parser. (#30776)

Merge ascend_optimizer and ascend_parser.

* Ascendrc add converted op : [range/equal/range/uniform_random/expand/squeeze], fix cast op bug  (#30797)

Ascendrc add converted op : [range/equal/range/uniform_random/expand/squeeze], fix cast op bug

* Add paddle ascend distribution training supported (#30796)

Add paddle ascend distribution training supported

* pass cxx_flags to gloo cmake (#30857)

* Destroy session first. (#30954)

Destroy session first.

* merge

* fix, test=develop

* fix, test=develop

* fix style, test=develop

* fix, test=develop

* fix

* fix log fatal, test=develop

* fix enforce style, test=develop

* fix, test=develop

* fix, test=develop

* fix rccl, test=develop

* fix test, test=develop

* fix, test=develop

* fix, test=develop

* fix, test=develop

* fix node_num, test=develop

* fix ids str, test=develop

* fix ids str, test=develop

* fix ids str, test=develop

* fix, test=develop

* fix, test=develop

* fix, test=develop

* fix, test=develop

* fix, test=develop

* fix, test=develop

* fix, test=develop

* fix, test=develop

* fix style code, test=develop

* fix style code, test=develop

* fix style code, test=develop

* fix style code, test=develop
Co-authored-by: Nhutuxian <hutuxian2011@sina.cn>
Co-authored-by: Ngongweibao <weibao.gong@gmail.com>
Co-authored-by: NVoid Main <voidmain1313113@gmail.com>
Co-authored-by: NLeo Chen <chenqiuliang@baidu.com>
Co-authored-by: Ndingsiyu <18369187719@163.com>
Co-authored-by: NOleNet <olenet@126.com>

* add custom init grad for backward function

* add custom init grad for backward function

* handle when the grad_tensor is none

* handle when the grad_tensor is none

* fix the args type error on windows platform

* modify the args order and doc

* format code

* add grad_tensor to xpu

* modify the grad_tensor type check

* add paddle.backward api to support multi tensors gradient compute

* add paddle.backward api to support multi tensors gradient compute

* add paddle.atuograd module and backward api

* change tensor.backward func args

* modify tensor backward api

* remove create_graph intputs args

* add doc and examplex code for backward api

* when have the same tensor, throw error

* modify test Init func args

* modify the execute.Init func args in test files

* add paddle.autograd package in setup.py.in

* modify error msg, remove _run_backward method in class Tensor

* add test cases for backward api

* fix whl package push pypi

* add rst

* Remove old custom OP to reduce whl package volume

* [Custom OP]Remove old custom OP to reduce whl package volume