* 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

* merge CMakeList.txt manual

* add platform for changethreadnum

* repair some bugs according to make error

* do nothing just flush CI

* forget change thread num

* add inplace_atol param for check_output_with_place

* Windows

* std:min and std::max should be change because of windows

* release gil before op run

* support npu grad test

* fix op_test

* add the matmul v2 grad kernel

* relief the test case time

* update the test case for the matmul double grad

* remove the unsed code for the matmul double grad

* update the test case for the double grad matmul

* remove the unused code in dot

* add conv op check for illegal input or attributes

* [NPU] update batch norm op, test=develop

* add NHWC support for bn, test=develop

* [NPU] fix for test_norm_op_npu

* [NPU] add norm_grad

* [NPU] add CheckAxis for axis

* [NPU] delete debug codes

* norm can not use L2Normalize, norm_grad can use L2NormalizeGrad

* [NPU] delete useless codes

* [NPU] optimize norm_grad OpMaker

* Update python import path

* [NPU] log_softmax_grad, test=develop

* remove debug files, test=develop

* update lookup_table_v2 for CANN 5.0.x, test=develop

* fix for reshape2

* added reviewers sugestions

* fix commit

* Open unittest

* fix unittest on Windows

* fix constructor

* open test_resnet_amp on Windows

* disable on Windows CPU CI for timeout

* disable on Windows CPU CI for timeout

* fix code style

* transfer the static.accurcay to v2 api

* remove the unused code

* [HIP] fix op not support AMD GPU bug, the flag PADDLE_WITH_ROCM is invalid

* [HIP] fix op not support AMD GPU bug, the flag PADDLE_WITH_ROCM is invalid

* [HIP] fix op not support AMD GPU bug

* support double in deformable conv

* add double for dcn v2

* add pool2d grad grad

* dbg

* add unittest

* update format

* add more unittests

* dbg

* add kernel, stride check

* add unitest for param out of range

* delete max limit check

* add depthwise_conv_npu_grad op

* add depthwise_conv_npu_grad op

* add depthwise_conv_npu_grad op

* add NHWC test case

* support numpy dtype and polish code of list index.

* polish code.

* where op add broadcast, when x_shape == y_shape, and x_shape != cond_shape

* add static api tests, and delete debug codes

* support lodtensorarray

* [NPU] Add huber_loss op

* [NPU] Add huber_loss op

* [NPU] Add huber_loss p[

* [NPU] Add huber_loss

* [NPU] add int64 kernels, test=develop

* update ci scripts to be able to trun WITH_ASCEND_INT64 on, test=develop

* Add AsExtra for transpose

* add AsExtra for lstm op

* add AsExtra for gru