+
+ | Original interface | v2.1 interface |
+
+
+
+ mindspore.nn.Dense(in_channels,
+ out_channels,
+ weight_init='normal',
+ bias_init='zeros',
+ has_bias=True,
+ activation=None)
+
+ |
+
+ mindspore.nn.Dense(in_channels,
+ out_channels,
+ weight_init=None,
+ bias_init=None,
+ has_bias=True,
+ activation=None)
+
+ |
+
+
+
+ mindspore.nn.Conv1d(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init='normal',
+ bias_init='zeros')
+
+ |
+
+ mindspore.nn.Conv1d(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init=None,
+ bias_init=None)
+
+ |
+
+
+
+ mindspore.nn.Conv1dTranspose(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init='normal',
+ bias_init='zeros')
+
+ |
+
+ mindspore.nn.Conv1dTranspose(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init=None,
+ bias_init=None)
+
+ |
+
+
+
+ mindspore.nn.Conv2d(in_channels,
+ out_channels, kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init='normal',
+ bias_init='zeros',
+ data_format='NCHW')
+
+ |
+
+ mindspore.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init=None,
+ bias_init=None,
+ data_format='NCHW')
+
+ |
+
+
+
+ mindspore.nn.Conv2dTranspose(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ output_padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init='normal',
+ bias_init='zeros')
+
+ |
+
+ mindspore.nn.Conv2dTranspose(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ output_padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init=None,
+ bias_init=None)
+
+ |
+
+
+
+ mindspore.nn.Conv3d(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init='normal',
+ bias_init='zeros',
+ data_format='NCDHW')
+
+ |
+
+ mindspore.nn.Conv3d(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ has_bias=False,
+ weight_init=None,
+ bias_init=None,
+ data_format='NCDHW')
+
+ |
+
+
+
+ mindspore.nn.Conv3dTranspose(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ output_padding=0,
+ has_bias=False,
+ weight_init='normal',
+ bias_init='zeros',
+ data_format='NCDHW')
+
+ |
+
+ mindspore.nn.Conv3dTranspose(in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ pad_mode='same',
+ padding=0,
+ dilation=1,
+ group=1,
+ output_padding=0,
+ has_bias=False,
+ weight_init=None,
+ bias_init=None,
+ data_format='NCDHW')
+
+ |
+
+
+
+### Bug Fixes
+
+- [I6TKLW] Fix the issue of MobileNetV2 network performance degradation on the Ascend platform.
+- [I7CP5H] Fix the issue where ASR network training failed on the Ascend platform.
+- [I6QYCD] Fix the issue where the BERT-Large-Boost network fails to train in pynative mode on the Ascend platform.
+- [I7I3EZ] Fix the issue that caused run_check() failure due to changes to the enumeration interface in Pillow version 10.0.0. If encountered in a lower version of MindSpore, install versions of Pillow below 10.0.0 to avoid this issue.
+- [I7IZ8K] Fix accuracy issues with the assignsub interface in PyNative mode.
+- [I7HGY0] Fix the issue that the loss of the functional programming does not converge in the PyNative data_sink mode.
+- [I7J4N3] Fix the issue that the generation of Step Trace failed in Profiler dynamic Shape mode
+- [I7J4N3] Fix the issue that there is no data displayed in the MindInsight parallel strategy view.
+- [I79YY4] Fix SiLU operator error when high-order differential in PyNative mode.
+- [I6NQJQ] Fix the issue of probabilistic failure in dynamic shape scenarios of the ScatterUpdate operator in PyNative mode.
+- [I6Y4G5] Fix the issue of failure in dynamic Shape scenarios of the Conv3D operator in Graph mode.
+
+### Contributors
+
+Thanks goes to these wonderful people:
+
+alashkari,anzhengqi,archer2049,B.L.LAN,baihuawei,bichaoyang,BJ-WANG,Bokai Li,Brian-K,caifubi,caiyimeng,cathwong,changzherui,ChenDonYY,chenfei_mindspore,chengang,chengbin,chenhaozhe,chenjianping,chenkang,chenweifeng,chuht,chujinjin,davidanugraha,DavidFFFan,DeshiChen,douzhixing,emmmmtang,Erpim,Ethan,fangwenyi,fangzehua,fangzhou0329,fary86,fengyixing,gaoshuanglong,Gaoxiong,gaoyong10,gengdongjie,gongdaguo1,Greatpan,GuoZhibin,guozhijian,hangq,hanhuifeng,haozhang,hedongdong,Henry Shi,heterogeneous_to_backoff_2_0,huangbingjian,huanghui,huangxinjing,hujiahui8,hujingsong,huoxinyou,jachua,jiahongQian,jianghui58,jiangzhenguang,jiaorui,jiaoy1224,jijiarong,jjfeing,JoeyLin,json,JuiceZ,jxl,kairui_kou,KevinYi,kisnwang,KXiong,laiyongqiang,lanzhineng,liangchenghui,liangzelang,LiangZhibo,lianliguang,lichen,ligan,lijunbin,limingqi107,ling,linqingke,liubuyu,liuchao,liuchuting,liujunzhu,liuluobin,liutongtong9,liuyang811,lixiao,liyan2022,liyejun,liyuxia,looop5,luochao60,luojianing,luoyang,luoyuan,lyqlola,maning202007,maoyaomin,Margaret_wangrui,mayadong,MaZhiming,melody,mengyuanli,michaelzhu_70ab,Mohammad Motallebi,moran,NaCN,nomindcarry,OwenSec,panfengfeng,panshaowu,panzhihui,pkuliuliu,qinzheng,qiuzhongya,qujianwei,r1chardf1d0,Renyuan Zhang,RobinGrosman,shaojunsong,shenwei41,Soaringfish,tangdezhi_123,tanghuikang,tan-wei-cheng,TinaMengtingZhang,TronZhang,TuDouNi,VectorSL,wang_ziqi,wanghenchang,wangnan39,wangpingan,wangshaocong,wangshengnan123,wangtongyu6,weichaoran,wind-zyx,wqx,wtcheng,wujueying,wYann,XianglongZeng,xiaohanzhang,xiaotianci,xiaoyao,XinDu,xulei,xumengjuan1,xupan,xwkgch,yanghaoran,yangluhang,yangruoqi713,yangshuo,yangsijia,yangzhenzhang,yanzhenxiang2020,Yanzhi_YI,yao_yf,yefeng,yeyunpeng2020,Yi_zhang95,yide12,YijieChen,YingLai Lin,YingtongHu,youshu,yuchaojie,yuedongli,YuJianfeng,zangqx,ZengZitao,zhangbuxue,zhangdanyang,zhangdong,zhangfanghe,zhangqi,zhangqinghua,zhangyanhui,zhangyinxia,zhangyongxian,zhangzhaoju,zhanzhan,zhengzuohe,ZhidanLiu,zhixinaa,zhoufeng,zhouyaqiang0,zhuguodong,zhupuxu,zhuyuxiao,zichun_ye,zjun,zlq2020,zong_shuai,ZPaC,zuochuanyong,zyli2020,陈宇,范吉斌,冯一航,胡彬,宦晓玲,黄勇,雷元哲,李良灿,李林杰,刘崇鸣,刘力力,刘勇琪,吕浩宇,吕昱峰（Nate.River）,没有窗户的小巷,沈竞兴,十六夜,王程浩,王禹程,王振邦,徐安越,徐永飞,杨旭华,于振华,俞涵,张清华,张澍坤,张栩浩,张学同,赵英灼,周超,周洪叶,朱家兴
+
+Contributions of any kind are welcome!
+
+## MindSpore Lite 2.1.0 Release Notes
+
+### Major Features and Improvements
+
+### MindSpore Lite Cloud Inference
+
+- [STABLE] Supports high-performance inference for single-device large model and single-node multi-device distributed large model at Ascend backend.
+- [STABLE] Python API Ascend backend supports multiple models sharing workspace memory.
+- [STABLE] [The weights can be shared by multiple models through ModelGroup](https://mindspore.cn/lite/docs/en/r2.1/use/cloud_infer/runtime_cpp.html#multiple-models-sharing-weights). For example, weights can be shared between full models and incremental models in the large model scenario.
+
+#### API
+
+The [Python](https://www.mindspore.cn/lite/api/en/r2.1/mindspore_lite/mindspore_lite.ModelGroup.html) and [C++](https://mindspore.cn/lite/api/en/r2.1/generate/classmindspore_ModelGroup.html) ModelGroup interface is added. The interface definition is as follows:
+
+```python
+class ModelGroup
+ def __init__(self, flags=ModelGroupFlag.SHARE_WORKSPACE)
+ def add_model(self, models)
+ def cal_max_size_of_workspace(self, model_type, context)
+```
+
+```C++
+// class ModelGroup
+ModelGroup(ModelGroupFlag flags = ModelGroupFlag::kShareWorkspace);
+Status AddModel(const std::vector