Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10936241

plug finetune ：已在du reader- robust数据集上回归至最佳结果
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10916382

cv/language_guided_video_summarization增加finetune
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10790262

1. 在模型处理视频之前下载视频，防止网络抖动导致ffmpeg读取网络视频失败进而导致模型运行失败
2. 完善模型inference是的控制参数
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10906373

1. Abstract keys of dicts needed by nlp metric classes into the init method
2. Add Preprocessor.save_pretrained to save preprocessor information
3. Abstract the config saving function, which can lead to normally saving in the direct call of from_pretrained, and the modification of cfg one by one when training.
4. Remove SbertTokenizer and VecoTokenizer, use transformers' tokenizers instead
5. Use model/preprocessor's from_pretrained in all nlp pipeline classes.
6. Add model_kwargs and preprocessor_kwargs in all nlp pipeline classes
7. Add base classes for fill-mask and text-classification preprocessor, as a demo for later changes
8. Fix user feedback: Re-train the model in continue training scenario
9. Fix user feedback: Too many checkpoint saved
10. Simplify the nlp-trainer
11. Fix user feedback: Split the default trainer's __init__ method, which makes user easier to override
12. Add safe_get to Config class

----------------------------  Another refactor from version 36 -------------------------

13. Name all nlp transformers' preprocessors from TaskNamePreprocessor to TaskNameTransformersPreprocessor, for example:
      TextClassificationPreprocessor -> TextClassificationTransformersPreprocessor
14. Add a base class per task for all nlp tasks' preprocessors which has at least two sub-preprocessors
15. Add output classes of nlp models
16. Refactor the logic for token-classification
17. Fix bug: checkpoint_hook does not support pytorch_model.pt
18. Fix bug: Pipeline name does not match with task name, so inference will not succeed after training
       NOTE: This is just a stop bleeding solution, the root cause is the uncertainty of the relationship between models and pipelines
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10723513

    * add save_pretrained to preprocessor

* save preprocessor config in hook

* refactor label-id mapping fetching logic

* test ok on sentence-similarity

* run on finetuning

* fix bug

* pre-commit passed

* fix bug

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/preprocessors/nlp/nlp_base.py

* add params to init

* 1. support max ckpt num 2. support ignoring others but bin file in continue training 3. add arguments to some nlp metrics

* Split trainer init impls to overridable methods

* remove some obsolete tokenizers

* unfinished

* support input params in pipeline

* fix bugs

* fix ut bug

* fix bug

* fix ut bug

* fix ut bug

* fix ut bug

* add base class for some preprocessors

* Merge commit '379867739548f394d0fa349ba07afe04adf4c8b6' into feat/refactor_config

* compatible with old code

* fix ut bug

* fix ut bugs

* fix bug

* add some comments

* fix ut bug

* add a requirement

* fix pre-commit

* Merge commit '0451b3d3cb2bebfef92ec2c227b2a3dd8d01dc6a' into feat/refactor_config

* fixbug

* Support function type in registry

* fix ut bug

* fix bug

* Merge commit '5f719e542b963f0d35457e5359df879a5eb80b82' into feat/refactor_config

# Conflicts:
#	modelscope/pipelines/nlp/multilingual_word_segmentation_pipeline.py
#	modelscope/pipelines/nlp/named_entity_recognition_pipeline.py
#	modelscope/pipelines/nlp/word_segmentation_pipeline.py
#	modelscope/utils/hub.py

* remove obsolete file

* rename init args

* rename params

* fix merge bug

* add default preprocessor config for ner-model

* move a method a util file

* remove unused config

* Fix a bug in pbar

* bestckptsaver:change default ckpt numbers to 1

* 1. Add assert to max_epoch 2. split init_dist and get_device 3. change cmp func name

* Fix bug

* fix bug

* fix bug

* unfinished refactoring

* unfinished

* uw

* uw

* uw

* uw

* Merge branch 'feat/refactor_config' into feat/refactor_trainer

# Conflicts:
#	modelscope/preprocessors/nlp/document_segmentation_preprocessor.py
#	modelscope/preprocessors/nlp/faq_question_answering_preprocessor.py
#	modelscope/preprocessors/nlp/relation_extraction_preprocessor.py
#	modelscope/preprocessors/nlp/text_generation_preprocessor.py

* uw

* uw

* unify nlp task outputs

* uw

* uw

* uw

* uw

* change the order of text cls pipeline

* refactor t5

* refactor tg task preprocessor

* fix

* unfinished

* temp

* refactor code

* unfinished

* unfinished

* unfinished

* unfinished

* uw

* Merge branch 'feat/refactor_config' into feat/refactor_trainer

* smoke test pass

* ut testing

* pre-commit passed

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/models/nlp/bert/document_segmentation.py
#	modelscope/pipelines/nlp/__init__.py
#	modelscope/pipelines/nlp/document_segmentation_pipeline.py

* merge master

* unifnished

* Merge branch 'feat/fix_bug_pipeline_name' into feat/refactor_config

* fix bug

* fix ut bug

* support ner batch inference

* fix ut bug

* fix bug

* support batch inference on three nlp tasks

* unfinished

* fix bug

* fix bug

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/models/base/base_model.py
#	modelscope/pipelines/nlp/conversational_text_to_sql_pipeline.py
#	modelscope/pipelines/nlp/dialog_intent_prediction_pipeline.py
#	modelscope/pipelines/nlp/dialog_modeling_pipeline.py
#	modelscope/pipelines/nlp/dialog_state_tracking_pipeline.py
#	modelscope/pipelines/nlp/document_segmentation_pipeline.py
#	modelscope/pipelines/nlp/faq_question_answering_pipeline.py
#	modelscope/pipelines/nlp/feature_extraction_pipeline.py
#	modelscope/pipelines/nlp/fill_mask_pipeline.py
#	modelscope/pipelines/nlp/information_extraction_pipeline.py
#	modelscope/pipelines/nlp/named_entity_recognition_pipeline.py
#	modelscope/pipelines/nlp/sentence_embedding_pipeline.py
#	modelscope/pipelines/nlp/summarization_pipeline.py
#	modelscope/pipelines/nlp/table_question_answering_pipeline.py
#	modelscope/pipelines/nlp/text2text_generation_pipeline.py
#	modelscope/pipelines/nlp/text_classification_pipeline.py
#	modelscope/pipelines/nlp/text_error_correction_pipeline.py
#	modelscope/pipelines/nlp/text_generation_pipeline.py
#	modelscope/pipelines/nlp/text_ranking_pipeline.py
#	modelscope/pipelines/nlp/token_classification_pipeline.py
#	modelscope/pipelines/nlp/word_segmentation_pipeline.py
#	modelscope/pipelines/nlp/zero_shot_classification_pipeline.py
#	modelscope/trainers/nlp_trainer.py

* pre-commit passed

* fix bug

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/preprocessors/__init__.py

* fix bug

* fix bug

* fix bug

* fix bug

* fix bug

* fixbug

* pre-commit passed

* fix bug

* fixbug

* fix bug

* fix bug

* fix bug

* fix bug

* self review done

* fixbug

* fix bug

* fix bug

* fix bugs

* remove sub-token offset mapping

* fix name bug

* add some tests

* 1. support batch inference of text-generation,text2text-generation,token-classification,text-classification 2. add corresponding UTs

* add old logic back

* tmp save

* add tokenize by words logic back

* move outputs file back

* revert veco token-classification back

* fix typo

* Fix description

* Merge commit '4dd99b8f6e4e7aefe047c68a1bedd95d3ec596d6' into feat/refactor_config

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/pipelines/builder.py

根据在线demo前端的要求，多输出一个color图片用于展示
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10926624

This bug is a result of float rounding when saving key-value pairs to log files, which is reported by a user.
Now the solution is to remove the rounding operation of all values, instead of only the lr value, which I think may be too specific.

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10684029

* remove some logic which may result in strange error when get hub info failed

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10924091

This reverts commit e970a6eb43.

nlp csanmt translation fix finetuning bug
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10923166

    * [to #42322933] nlp csanmt translation fix finetuning bug

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10919277

* support new asr paraformer model

* support asr conformer model

按在线demo前端的要求，将输出改成单独一个numpy格式的图片
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10912907

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10913762

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10836131

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10894248

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10873454

* add en-zh en-es es-en base translation models
 * add en-zh en-es es-en base translation models
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10895782

    * 新增英中/英西/西英-base机器翻译模型

* 新增英中/英西/西英-base机器翻译模型

Title: merge master github to gitlab 

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10884192

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10868091

1. ExternalDataset的init部分中，引入datasets包自带的_EXTENSION_TO_MODULE会有版本兼容性的问题，比如2.5.2版本就修改了数据结构，与老版本不兼容；
2. 某些cv数据集跳过打印logger.error
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10893702

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10890156

    * [to #46408569]fix: pipeline and trainer user-agent add not replacement.

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10885659

接入图像深度估计模型，新增model、pipeline、test
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10857764

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10886461

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10837490

CodeGeex code translation and generation

ut failed due to a known run.py environment setup issue that is being fixed. nothing to do with the change itself.

[pipelines] support wenet

note: ut failed is due to a run.py enveironment setup issue that is being fixed. nothing to do with the change.

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10856839

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10868583

1. add ExternalDataset methods for csv/txt/json/jsonl files on the oss storage
2. add user-define delimiter for csv in meta.
3. supports internal dataset
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10872422

ofa增加asr任务infer
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10761019

为diffusion模型加入dpm solver支持，相比ddim scheduler快2～6倍。
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10826722

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10851834

    * [to #46342279]fix: fix some time test_skin_retouching.py failed

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10814720

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10773667

It is recommented that each pipleine should implement `_batch`  to make a list of preprocessed data into a batched data dict.

Then  by paasing batch_size=n  we can use batch inference in pipline, for example
```python
img_captioning = pipeline(
            Tasks.image_captioning,
            model='damo/ofa_image-caption_coco_large_en')

results = img_captioning(
            [{
                'image': 'data/test/images/image_captioning.png'
            } for _ in range(6)],
            batch_size=2)
```

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10051193