* 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>

* - disabled interpolate onednn

* - compilation fix

* - draft of batch_norm cache disabling

* - fixes to UT

* enable infer_ut on windows

* remove lib calculation & time

* unset http_proxy when download bos file on windows

Refactor the organization of layer_norm cuda impl so that it can be reused in fused attention op.

    Extract the layer_norm cuda impl form layer_norm_op.cu to layer_norm_kernel.cu.h.
    Define fused/attention_layer_norm.h, which can be used in fused attention op in next PR.

* Support getitem by Bool index

* delete some debug info of bool index

* support the case that the shape of bool index is different from indexed tensor

This reverts commit 6bacfb0e.

* adamw support cuda

* adamw support cuda

* Add cuda device count api

* update coda format

* fix unittest error

* update code format

* update comment

* add rmsprop npu

* add argsort npu

* add argsort npu

* modify according to review

* modify sharedatawith according to review

* modify reshape according to review

* rm dygraph=false

* [NPU] Support npu kernel for pad3d op

* fix for comment of zhouwei25

* fix some bugs according to qili93's comments

* add support and test for paddings in input

* delete VLOG used for debug

* use spin lock in auto growth allocator, test=develop

* use pthread spin lock, test=develop

* use lock guard, test=develop

* use malloc spin lock, test=develop

* use lock_guard, test=develop

* add trainer desc config to distributed strategy

* code style modified

* data_feed set lod

* add depthwise_conv2d npu

* add some tests

* Delete test_unique_op_npu.py

* delete trans input

* [NPU] Support npu op where and where grad

* fix use const_cast

* delete a test

* add (N,C,*) input support for GroupNorm

* --amend

* Add run function log

* test=document_fix

* add npu sin op

* [NPU] Support npu kernel for sin op

* modify support npu kernel for sin op

* modify support npu kernel for sin op

* modify nou sin op

* modify npu sin op

* add sin op npu

* add slim resnet50 quant model in pr-ci-inference

* enable resnet50_quant multi_thread4_trt_int8_bz1

* remove LOG(FATAL)

Add dimension check for inverse to avoid dividing by 0 error when input's shape is [0, 0, 0]. (#34996)

* fix batch_norm and instance norm when input is []