* implemented clip op bf16/fp32

* added skipping if not cpu or bf16

* CI rerun after bf16 package change

* added parentheses to ensure formatting

* change metaclass of Layer from pybind11_builtins.pybind11_type to type

* fix cast

* add ut

* sharding support dp

* sharding support mp

* sharding support pp

* fix warning error , test=document_fix

* fix warning error , test=document_fix

* fix warning error , test=document_fix

* fix warning error , test=document_fix

* fix warning error , test=document_fix

* fix warning error , test=document_fix

* fix warning error , test=document_fix

* add test_cumprod_op

* Revert "add test_cumprod_op"

This reverts commit c96cf6dff5d09ae7d8cc72c1e8ae4369a153aa19.

* recommit

* add error message

* test input(x) initialize

* test use cpu

* update test code

* add test type

* add test case

* solve ci problem

* add complex case test

* add complex case test

* fix review problem

* fix conflict

* fix some docs

* change test case

* change test case

* fix review problems again

* fix docs

* fix inclusivescan bug

This PR supports gradient clip (ClipGradByGlobalNorm) when training with AMP(auto mixed precision).

* fix scatter gather bug:

* fix windows ci

* conv3d

* remove const_cast

* modify ut

* disable dynamic shape for trt6.0

* remove trt5

* add asExtra for nce op

* fix unittest error in macos

* remove asExtra for is_test

* init matrix_rank op, add matrix_rank CPU code and test

* add GPU kernel, remove svd_eigen.h

* add CPU kernel when tol is tensor

* add cpu and gpu code when tol is tensor

* fix CI-ROCM error

* add matrix_rank API describe, fix PR-CI-Py3 error

* fix PR-CI-Windows error, add matrix_rank API test

* delete useless comments

* fix review

* add my code in svd_helper.h

* update doc commets

* remove spaces

* 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

* support shard reader

* support shard reader

* add parallel mode

* update process mesh

* add method to compute comm_group

* implement dist_embedding forward func

* implement dist matmul forward func

* implement dist reshape forward func

* add transpiler framework

* add transpiler forward

* implement transpiler forward

* implement transpiler backward & update

* add process

* add unitest

* chmod

* chmod

* chmod

* update unitest

* add unitest for gpt

* remove unused print

* rename transpiler --> partitioner

* rename transpiler --> partitioner

* chmod

* chmod

* bug fixed

* remove amp function

* update case for dp mode

* update case for dp mode

* [Auto Parallel] Integrate all parts with the newest code

* Integrate all parts of auto parallel and improve codes

* Integrate all parts by AutoParallelizer
* Add unit test for AutoParallelizer
* Improve auto completion module for pipeline parallel
* Add support for matmul_v2 in dist_matmul
* Correct the typo "stratergy" to "strategy"

* Modify distributed_strategy.proto to conform the main stream

* Restore parts of distributed_strategy to conform the develop branch
Co-authored-by: Nsandyhouse <lilong12@baidu.com>
Co-authored-by: NJZ-LIANG <jianzhongliang10@gmail.com>

multiply supports bool  

* add clip_by_norm fp16 kernel

* add ut

* update slice plugin

* add test

* fix code style

* fix trt6

* update test

* fix test

* add timeout

* update trt version

* update cmake

* can pass the fake test

* add files

* modify cmake to pass windows-ci

* for ci pass

* WITH_GLOO=ON

* for pass coverage test

* add cpuonly testcase

* add

* disable nccl when compile with cuda

* change python version in cpuonly

* add backend argument

* add required gpu

* add required:gpu

* fix scatter_add_nd and gather bug

* fix gather compile error

* add fleet api for program pass

* turn on apply pass for CI test

* fix disable fuse_all_optimizer bug

* try to test ci

* fix CI

* fill unspecified op role

* fix fuse_allreduce

* add ut to improve coverage

* remove useless change

* improve c++ coverage

* follow some comments

* test ir pass pipeline

* update doc

* reduce ut time again

The bug is that access to mean and var is incorrect, and the array will be out of bounds: the shape of mean and var is [batch_size], and the range of thread idx is 0~feature_size, so mean[idx] and var[idx] is incorrect.

When batch_size=1, the correct access is mean[0] and var[0], and a unit test with batch_size=1 is added.

* update, test=develop