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/10856839

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

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10834768

1. add damoyolo-t & damoyolo-m models
2. fix the configuration overlap error
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10816561

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10636269

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10728515

    * make mglm isolated test because mpu init problem

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10713843

    * [to #42322933] solve memory error for translation finetune

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10708723

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10712244

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10680402

主要做了如下修改：
1. 加入了同义词词典
2. 对SQL进行后处理，如果包含排序，则将空列转化成Primary列
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10670121

修复了 mplug evaluation 使用了错误的 metrics 的问题，将部分中文处理代码独立到 utils 中，为 mplug 添加 trainer
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10612875

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10608664

the new master

Title: [to #42322933] add event tracking 

1. add event tracking for dataset downloading pv/uv
2. change datasets version: <=2.5.2
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10593016

修复ocr预处理逻辑不一致问题
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10581697

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10585502

1.修复token classification preprocessor finetune结果错误问题
2.修复word segmentation output 无用属性
3. 修复nlp preprocessor传use_fast错误
4. 修复torch model exporter bug
5. 修复文档撰写过程中发现trainer相关bug
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10573269

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10572842

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10572162

1. Fix bug in model exporting
2. Skip some long trainings in test level 2
3. Refine some comments
4. Fix a bug that mode is not correct when saving checkpoints
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10564716

* mglm init

* add mglm requirements

Co-authored-by: Yufeng <zhuyufeng@gmail.com>
Co-authored-by: wenmeng.zwm <wenmeng.zwm@alibaba-inc.com>

To keep consistent between training and evaluation, change the eps and momentum of BN.
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10554451

此前文生图模型没有加载configuration.json中的参数 影响默认配置
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10558026

1. 修复 bloom 和 gpt_neo 模型更新 transformers 4.23 后后处理报错的问题
2. 统一使用 ModelOutput 作为模型输出
3. gpt_neo checkpoint 已上线，修改 ut 为 level2
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10553103