* Add FasterTokenizer Operator (#34491)

Add Tokenizer related functionalities for Transformer model in order that the process of training and predicting is consistent.

* support the text string as an input Tensor
* support the "VOCAB"unordered_map<wstring, int> as an input Tensor to lookup tokens
* Tokenizer used for BERT. This tokenizer applies an end-to-end, text string to wordpiece tokenization.
* It first applies basic tokenization, followed by wordpiece tokenization.

* optimize fast tokenizer

* remove const_cast
Co-authored-by: Nzhoushunjie <zhoushunjie@baidu.com>
Co-authored-by: Nwawltor <fangzeyang0904@hotmail.com>

* clip op extra information when export model,test=ocr

* rename clip_extra parameter to kwargs in save_inference_model, test=ocr

* 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 inplace passes and tests

* update

* fix use_cuda undefined
fix compile error of op compat

* add more ut

* fix CPU CI error

* check adam unique

* fix mac/windows ci, improve coverage

* fix ci error

* follow weihang's comment

* fix BlockDesc::MoveFrom

* follow qiuliang's comment

* update

* follow huihuang's comments

* add auto_parallel apis

This PR added optional boolean is_parameter and stop_gradient in the VarDesc proto, and remove them during save_inference_model

* use PYTHON_C_API in dygraph, test=develop

* Unset ReserveSpace for inference program.

* Support training from an inference program.

* add complex64 and complex128 type; add +-*/@ and slice opreator for complex types

* add test cases for complex elementwise, matmul and getitem unittest

* add test cases for complex types

* add test cases for complex matmul unittest

* polish api program error message, test=develop

* fix condition error, test=develop

* fix test prune error, test=develop

* fix coverage problem, test=develop

* add double grad implementation for dygraph, test=develop

* polish code, add uts, test=develop

* fix place bug, test=develop

* polish codes, add more uts for coverages, test=develop

* add no_grad_set, test=develop

* add star gan ut, test=develop

* follow comments, test=develop

* refactor dygraph,test=develop

* fix failed unittest,test=develop

* polish code,test=develop

* check windows ci error,test=develop
try to fix windows ci error by np.allclose,test=develop

* polish vlog and profiler, test=develop

* try to fix preceding ops order,test=develop

* test transformer in windows ci, test=develop

* use python c-api to speed up tracer.trace,test=develop

* test=develop, fix docker with paddle nccl problem

* test=develop, add ut for debug string and gradient_accumulator

* test=develop, add tests for layer/gradient_accumulator/prepared_op

* test=develop, fix complie error for test_prepared_op

* test=develop, add more ut for dygraph

* test=develop, create API.spec for dygraph api change

* optimize grad maker; test=develop

* optimize grad maker

* test

* grad make optim; test=develop

* fix unittest bugs; test=develop

* add dygraph grad op maker and split_op

* grad op maker refactor; test=develop

* add dygraph grad maker; test=develop

* fix op deformable_conv_v1_op bug; test=develop

* fix deformable_conv prroi pool bugs;

* fix new op grad op maker bug; test=develop

* fix split by ref bug; test=develop

* fix dygraph auto prune bug; test=develop

* fix test_trace bug; test=develop

* fix fused emb seq pool bug; test=develop

* remove useless code in op_desc file; test=develop

* remove useless code, StrVarBaseNode; test=develop

* fix review issues; test=develop

* fix rank_loss grad maker; test=develop

* remove flag in VarBase; test=develop

* fix distributed_notify_op compile bug ; test=develop

* fix reshape op double grad; test=develop

* fix expand as op; test=develop

* add impertive type_defs.h for demo_train; test=develop

* fix inference lib cmake; test=develop

* fix inference lib; test=develop

* fix infernce_lib; test=develop

* fix inference cmake; test=develop

* fix inference lib; test=develop

* fix inference lib; test=develop

* remove condition dygraph grad maker, modify local name; test=develop

* fix split grad maker bug; test=develop

* fix pyramid_op bug; test=develop

* change travis time out limit; test=develop

* restore travis; test=develop

* change timeout limit; test=develop

* add new save load interface; test=develop

* add new save interface; test=develop

* add save load interface ;

* fix save load error;

* fix dygraph set dict bug;

* add save load unit test; test=develop

* fix test_imperative_optimizer bug; test=develop

* fix unitest optimizer bug; test=develop

* fix code coverage; test=develop

* fix converage; test=develop

* add document for apis; test=develop

* fix unitest error; test=develop

* fix save load unit test error; test=develop

* fix error message; test=develop

* change set_parameter set_optimizer to save_dygraph; test=develop

* add load_graph check; test=develop

* fix api spec; test=develop

* update operator compatible info, test=develop

* revert cmake/version.cmake, test=develop

* add unit_tests and fix bugs, test=develop

* update ../paddle/fluid/framework/framework.proto, test=develop

* fix bug of paddle/fluid/inference/api/analysis_predictor.cc, test=develop

* update paddle/fluid/framework/version_test.cc, test=develop

* add comments and rename interfaces, test=develop

The new "fluid.data" changes old "fluid.layers.data":

1. Add shape and dtype check.
2. Remove "append_batch_size" parameter. We won't offer this in the new data layer because other deep learning platforms don't have this kind of data layer pre-processing. It may confuse users.
3. Remove "stop gradient" parameter because the data layer doesn't do back-propagation

TODO：
Now data layer feeded by executor is checked, will we want to check the feed data of readers in the future?

* Remove Desc in Forward Pass

* Refactor VarBase

* Add dbg info

* Only check type in imperative mode

* Polish code and support optimizer

test=develop

* Fix stop gradient problem in PyLayer

test=develop

test=develop

test=develop

test=develop

test=develop

test=develop

test=develop

Fix code to support cpplint syntax check

test=develop

test=develop

* hide operator API

* update

* update api.spec

* fix merge

* fix test

* Support int8 type.

Fix str and bytes problem
Fix sorted problem
Fix math problem
Fix CI problem

* remove block api

* remove clone_variable

* hide block inner apis

* update

* fix tests