# Release Notes ## MindSpore 2.0.0 Release Notes ### Major Features and Improvements #### PyNative - [STABLE] Dynamic shape is fully supported on framework. For detailed operator support, refer to [Dynamic Shape Support Status of nn Interface](https://www.mindspore.cn/docs/en/r2.0/note/dynamic_shape_nn.html), [Dynamic Shape Support Status of functional Interface](https://www.mindspore.cn/docs/en/r2.0/note/dynamic_shape_func.html), and [Dynamic Shape Support Status of primitive Interface](https://www.mindspore.cn/docs/en/r2.0/note/dynamic_shape_primitive.html). #### AutoParallel - [STABLE] Build new MindFormers independent repositpry, providing distributed parallel suite, replacing mindspore.nn.transformer module. - [DEMO] Distributed parallel operator Gather supports the BatchDim attribute. - [DEMO] Streamline parallel supports specifying any dimension of the input data as the Batch dimension. ### API Change #### operator - Add operator primitive for `mindspore.ops.AdaptiveAvgPool2D` . - Add operator primitive for `mindspore.ops.BatchToSpaceNDV2` . - Add operator primitive for `mindspore.ops.CeLU` . - Add operator primitive for `mindspore.ops.ExtractVolumePatches` . - Add operator primitive for `mindspore.ops.FFTWithSize` . - Add operator primitive for `mindspore.ops.FillDiagonal` . - Add operator primitive for `mindspore.ops.FractionalMaxPool3DWithFixedKsize` . - Add operator primitive for `mindspore.ops.Im2Col` . - Add operator primitive for `mindspore.ops.MaskedScatter` . - Add operator primitive for `mindspore.ops.MatrixBandPart` . - Add operator primitive for `mindspore.ops.MatrixInverse` . - Add operator primitive for `mindspore.ops.MaxPoolWithArgmaxV2` . - Add operator primitive for `mindspore.ops.Ormqr` . - Add operator primitive for `mindspore.ops.RandpermV2` . - Add operator primitive for `mindspore.ops.ResizeBicubic` . - Add operator primitive for `mindspore.ops.Triu` . - Add operator primitive for `mindspore.ops.Zeta` . #### Backwards Incompatible Change - Interface: mindspore.ops.MultitypeFuncGraph Change: The interface parameter doc_url is used as a test feature in MindSpore 2.0.0.rc1 version. After the optimization of MindSpore 2.0.0 version, users do not need to configure this parameter, so this parameter is deleted in MindSpore 2.0.0 version.

Original Interface	Interface v2.0.0
mindspore.ops.MultitypeFuncGraph（name, read_value=False, doc_url=""）	mindspore.ops.MultitypeFuncGraph（name, read_value=False）

- Interface: mindspore.set_context(auto_tune_mode="GA,RL") Change: The AutoTune tool has been deprecated, delete auto_tune_mode option, new tuning tools will be planned in the future. - Interface: mindspore.set_context(mode=PYNATIVE_MODE) Change: The default value is changed from GRAPH_MODE to PYNATIVE_MODE. Description: If the running mode is not set and the diagram mode needs to be set, use the following method: mindspore.set_context(mode=GRAPH_MODE).

Original Interface	Interface v2.0.0-rc1
mindspore.set_context(mode=GRAPH_MODE)	mindspore.set_context(mode=PYNATIVE_MODE)

- Interface: mindspore.train.Model.train Change: The default value of dataset_sink_mode is changed from True to False. Description: If dataset_sink_mode is not set and the data sinking mode needs to be set, use the following method: Model.train(dataset_sink_mode=True).

Original Interface	Interface v2.0.0-rc1
Model.train(dataset_sink_mode=True)	Model.train(dataset_sink_mode=False)

- Interface: mindspore.export Change: The file_format parameter is changed from AIR to no default value. Description: If file_format is not set in the original mode, you need to set file_format additionally. In this case, use the following method: mindspore.export(net, *inputs, file_name, file_format="AIR", **kwargs).

Original Interface	Interface v2.0.0-rc1
mindspore.export(net, inputs, file_name, file_format="AIR", *kwargs)	mindspore.export(net, inputs, file_name, file_format, *kwargs)

- Interface: mindspore.ops.norm Change: The ord parameter function is extended to support multiple forms.

Original Interface

Interface v2.0.0-rc1

  ops.norm(input_x, axis, p=2, keep_dims=False, epsilon=1e-12)
  >>> # Example:
  >>> input = Tensor(np.array([[[1.0, 2.0], [3.0, 4.0]],
  ...                          [[5.0, 6.0], [7.0, 8.0]]]).astype(np.float32))
  >>> output = ops.norm(input, [0, 1], p=2)

  ops.norm(A, ord=None, dim=None, keepdim=False, *, dtype=None)
  >>> # Example:
  >>> input = Tensor(np.array([[[1.0, 2.0], [3.0, 4.0]],
  ...                          [[5.0, 6.0], [7.0, 8.0]]]).astype(np.float32))
  >>> output = ops.norm(input, ord=2, dim=(0, 1))

- Interface: mindspore.Tensor.norm Change: The ord parameter function is extended to support multiple forms. Description: For details, see the example of ops.norm.

Original Interface	Interface v2.0.0-rc1
Tensor.norm(axis, p=2, keep_dims=False, epsilon=1e-12)	Tensor.norm(ord=None, dim=None, keepdim=False, *, dtype=None)

- Interface: mindspore.ops.dropout Change: The seed0 and seed1 parameters are deleted and seed=None parameter is added. Instead of returning Tensors and masks, only Tensors are returned. The input parameter training=True is added.

Original Interface

Interface v2.0.0-rc1

  ops.dropout(x, p=0.5, seed0=0, seed1=0)
  >>> # Example:
  >>> input = Tensor(((20, 16), (50, 50)),
  ...                mindspore.float32)
  >>> output, mask = dropout(x, p=0.5)

  ops.dropout(input, p=0.5, training=True, seed=None)
  >>> # Example:
  >>> input = Tensor(((20, 16), (50, 50)),
  ...                mindspore.float32)
  >>> output = ops.dropout(input, p=0.5，training=True)

- Interface: mindspore.ops.dropout2d Change: Return value is changed from Tensor and mask to Tensor only. The input parameter training=True is added.

Original Interface

Interface v2.0.0-rc1

  ops.dropout2d(x, p=0.5)
  >>> # Example:
  >>> input = Tensor(np.ones([2, 1, 2, 3]),
  ...                mindspore.float32)
  >>> output, mask = dropout2d(input, 0.5)

  ops.dropout2d(input, p=0.5, training=True)
  >>> # Example:
  >>> input = Tensor(np.ones([2, 1, 2, 3]),
  ...                mindspore.float32)
  >>> output = ops.dropout2d(input, 0.5, training=True)

- Interface: mindspore.ops.dropout3d Change: Return value is changed from Tensor and mask to Tensor only. The input parameter training=True is added.

Original Interface

Interface v2.0.0-rc1

  ops.dropout3d(x, p=0.5)
  >>> # Example:
  >>> input = Tensor(np.ones([2, 1, 2, 3]),
  ...                mindspore.float32)
  >>> output, mask = dropout3d(input, 0.5)

  ops.dropout3d(input, p=0.5, training=True)
  >>> # Example:
  >>> input = Tensor(np.ones([2, 1, 2, 3]),
  ...                mindspore.float32)
  >>> output = ops.dropout3d(input, 0.5, training=True)

- Interface: mindspore.ops.std Change: The interface is reconstructed, and the interface usage mode is more consistent with user habits. Description: If parameter `unbiased` has been set, use the following alternative: `unbiased=False` -> `ddof=0`, `unbiased=True` -> `ddof=1`.

Original Interface	Interface v2.0.0-rc1
ops.std(input_x, axis=(), unbiased=True, keep_dims=False)	ops.std(input, axis=None, ddof=0, keepdims=False)

- Interface: mindspore.load_param_into_net Change: Parameters that are not loaded in the ckpt are added as return values.

Original Interface	Interface v2.0.0-rc1
net_param = load_param_into_net()	net_param, ckpt_param = load_param_into_net()

- Interface: mindspore.nn.BCELoss Change: The default value of `reduction` is changed from 'none' to 'mean'.

Original Interface

Interface v2.0.0-rc1

  BCELoss(weight=None, reduction='none')
  >>> # Example:
  >>> weight = Tensor(np.array([[1.0, 2.0, 3.0],
  ...                           [4.0, 3.3, 2.2]]),
  ...                 mindspore.float32)
  >>> loss = nn.BCELoss(weight=weight, reduction='mean')
  >>> logits = Tensor(np.array([[0.1, 0.2, 0.3],
  ...                           [0.5, 0.7, 0.9]]),
  ...                 mindspore.float32)
  >>> labels = Tensor(np.array([[0, 1, 0], [0, 0, 1]]),
  ...                 mindspore.float32)
  >>> output = loss(logits, labels)
  >>> print(output)
  >>> 1.8952923

  BCELoss(weight=None, reduction='mean')
  >>> # Example:
  >>> weight = Tensor(np.array([[1.0, 2.0, 3.0],
  ...                           [4.0, 3.3, 2.2]]),
  ...                 mindspore.float32)
  >>> loss = nn.BCELoss(weight=weight)
  >>> logits = Tensor(np.array([[0.1, 0.2, 0.3],
  ...                           [0.5, 0.7, 0.9]]),
  ...                 mindspore.float32)
  >>> labels = Tensor(np.array([[0, 1, 0], [0, 0, 1]]),
  ...                 mindspore.float32)
  >>> output = loss(logits, labels)
  >>> print(output)
  >>> 1.8952923

- Interface: mindspore.ops.split Change: The interface is reconstructed. The interface usage mode is more suitable for users. The sequence of the second and third parameters is adjusted, and the split_size_or_sections function is modified and extended.

Original Interface

Interface v2.0.0-rc1

  ops.split(input_x, axis=0, output_num=1)
  >>> # Example:
  >>> input = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]),
  ...                mindspore.int32)
  >>> output = ops.split(input, axis=1, output_num=4)

  ops.split(tensor, split_size_or_sections, axis=0)
  >>> # Example:
  >>> input = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]),
  ...                mindspore.int32)
  >>> output = ops.split(input, split_size_or_sections=1, axis=1)

- Interface: mindspore.Tensor.split Change: The interface is reconstructed. The interface usage mode is more suitable for users. The positions of the two parameters is adjusted, and the split_size_or_sections function is modified and extended. Description: For details, see the example of ops.split.

Original Interface	Interface v2.0.0-rc1
Tensor.split(axis=0, output_num=1)	Tensor.split(split_size_or_sections, axis=0)

- Interface: mindspore.ops.pad Change: Modify the parameter name paddings to padding, and the mode and value functions are added.

Original Interface

Interface v2.0.0-rc1

  ops.pad(input_x, paddings)
  >>> # Example:
  >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6],
  ...                            [0.4, 0.5, -3.2]]),
  ...                  mindspore.float32)
  >>> paddings = ((1, 2), (2, 1))
  >>> output = ops.pad(input_x, paddings)

  ops.pad(input_x, padding, mode='constant', value=None)
  >>> # Example:
  >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6],
  ...                            [0.4, 0.5, -3.2]]),
  ...                  mindspore.float32)
  >>> paddings = (2, 1, 1, 2)
  >>> output = ops.pad(input_x, paddings)

- Interface: mindspore.ops.meshgrid Change: The input parameter is changed from `inputs` to `*input`.

Original Interface

Interface v2.0.0-rc1

  ops.meshgrid(inputs, indexing='xy')
  >>> # Example:
  >>> x = Tensor(np.array([1, 2, 3, 4]).astype(np.int32))
  >>> y = Tensor(np.array([5, 6, 7]).astype(np.int32))
  >>> z = Tensor(np.array([8, 9, 0, 1, 2]).astype(np.int32))
  output = ops.meshgrid((x, y, z), indexing='xy')

  ops.meshgrid(*inputs, indexing='xy')
  >>> # Example:
  >>> x = Tensor(np.array([1, 2, 3, 4]).astype(np.int32))
  >>> y = Tensor(np.array([5, 6, 7]).astype(np.int32))
  >>> z = Tensor(np.array([8, 9, 0, 1, 2]).astype(np.int32))
  output = ops.meshgrid(x, y, z, indexing='xy')

- Interface: mindspore.ops.max Change: Return value exchange sequence. The value is changed from "index, value" to "value, index".

Original Interface

Interface v2.0.0-rc1

  ops.max(x, axis=0, keep_dims=False)
  >>> # Example:
  >>> input = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]),
  ...                mindspore.float32)
  >>> index, output = ops.max(input)
  >>> print(index, output)
  >>> 3 0.7

  ops.max(input, axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)
  >>> # Example:
  >>> input = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]),
  ...                mindspore.float32)
  >>> output, index = ops.max(input, axis=0)
  >>> print(output, index)

- Interface: mindspore.ops.min Change: Return value exchange sequence. The value is changed from "index, value" to "value, index".

Original Interface

Interface v2.0.0-rc1

  ops.min(x, axis=0, keep_dims=False)
  >>> # Example:
  >>> input = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]),
  ...                mindspore.float32)
  >>> index, output = ops.min(input)
  >>> 0 0.0

  ops.min(input, axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)
  >>> # Example:
  >>> input = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]),
  ...                mindspore.float32)
  >>> output, index = ops.min(input, keepdims=True)
  >>> 0.0 0

- Interface: mindspore.ops.random_gamma Change: The seed2 parameter is deleted and seed=0 is changed to None. The framework behavior is unified and complies with the actual application scenarios and habits of users.

Original Interface	Interface v2.0.0-rc1
ops.random_gamma(shape, alpha, seed=0, seed2=0)	ops.random_gamma(shape, alpha, seed=None)

- Interface: mindspore.ops.standard_laplace Change: The seed2 parameter is deleted and seed=0 is changed to None. The framework behavior is unified and complies with the actual application scenarios and habits of users.

Original Interface	Interface v2.0.0-rc1
ops.standard_laplace(shape, seed=0, seed2=0)	ops.standard_laplace(shape, seed=None)

- Interface: mindspore.ops.standard_normal Change: The seed2 parameter is deleted and seed=0 is changed to None. The framework behavior is unified and complies with the actual application scenarios and habits of users.

Original Interface	Interface v2.0.0-rc1
ops.standard_normal(shape, seed=0, seed2=0)	ops.standard_normal(shape, seed=None)

- Interface: mindspore.ops.bernoulli Change: The default value of seed is changed from -1 to None. Meets the actual application scenario.

Original Interface	Interface v2.0.0-rc1
ops.bernoulli(x, p=0.5, seed=-1)	ops.bernoulli(input, p=0.5, seed=None)

- Interface: mindspore.data_sink Change: Deleted the steps parameter. Parameter name jit is changed to jit_config, and new input_signature parameter is added. The usability is improved to meet the requirements of actual application scenarios.

Original Interface	Interface v2.0.0-rc1
mindspore.data_sink(fn, dataset, steps, sink_size=1, jit=False)	mindspore.data_sink(fn, dataset, sink_size=1, jit_config=None, input_signature=None)

- Interface: mindspore.ops.conv2d Change: Extend Interface Function. Add the bias parameter and modify the parameter name and parameter sequence.

Original Interface	Interface v2.0.0-rc1
conv2d(inputs, weight, pad_mode="valid", padding=0, stride=1, dilation=1, group=1)	conv2d(input, weight, bias=None, stride=1, pad_mode="valid", padding=0, dilation=1, groups=1)

- Interface: mindspore.dataset.vision.Pad Change: Adjust the input parameter padding of Pad, RandomCrop, and RandomCropWithBbox. When the input length of Padding is 2, the first value is used to fill the left/upper boundary, the second value is used to fill the right/lower boundary, and the first value is used to fill the left/right boundary. Fill the upper/lower boundary with the second value. Description: The padding parameter whose size is 2 is not compatible with the effect of the earlier version. The padding parameter needs to be explicitly represented (left, right, top, and bottom).

Original Interface

Interface v2.0.0-rc1

  mindspore.dataset.vision.Pad(padding=(1,2))
  Indicates that the left/upper part of the image is filled with 1 pixel,
  and the right/down part is filled with 2 pixels.

  mindspore.dataset.vision.Pad(padding=(1,2,1,2))
  Indicates that the left/upper part of the image is filled with 1 pixel,
  and the right/down part is filled with 2 pixels.

- Interface: mindspore.dataset.Dataset.map Change: Delete the column_order parameter. In most cases, output_columns and column_order have the same value. Therefore, column_order does not need to be transferred. To adjust the sequence of data columns, use mindspore.dataset.Dataset.project. Description: 1. If the column sequence does not need to be changed, delete the column_order parameter. 2. If you need to specify the data column sequence, delete the column_order parameter and add a project method to the end of the parameter for column transformation (as in the following example).

Original Interface

Interface v2.0.0-rc1

  >>> dataset = dataset.map(operations=[transforms],
  ...                       input_columns=["column_a"],
  ...                       output_columns=["column_b", "column_c"],
  ...                       column_order=["column_c", "column_b"])

  >>> dataset = dataset.map(operations=[transforms],
  ...                       input_columns=["column_a"],
  ...                       output_columns=["column_b", "column_c"])
  >>> dataset = dataset.project(["column_c", column_b"])")

- Interface: mindspore.dataset.Dataset.batch Change: Delete the column_order parameter. In most cases, output_columns and column_order have the same value. Therefore, column_order does not need to be transferred. To adjust the sequence of data columns, use mindspore.dataset.Dataset.project. Description: 1. If the column sequence does not need to be changed, delete the column_order parameter. 2. If you need to specify the data column sequence, delete the column_order parameter and add a project method to the end of the parameter for column transformation (as in the following example).

Original Interface

Interface v2.0.0-rc1

  >>> dataset = dataset.batch(batch_size=4,
  ...                         input_columns=["column_a"],
  ...                         output_columns=["column_b", "column_c"],
  ...                         column_order=["column_c", "column_b"])

  >>> dataset = dataset.batch(batch_size=4, input_columns=["column_a"]
  ...                         output_columns=["column_b", "column_c"])
  >>> dataset = dataset.project(["column_c", column_b"])")

- Interface: mindspore.dataset.Dataset.batch Change: Split the batch method into two methods: batch and padded_batch. The pad_info parameter is moved from the batch method to the padded_batch method. Description: To use the pad_info parameter, use the padded_batch method instead.

Original Interface	Interface v2.0.0-rc1
>>> dataset = dataset.batch(batch_size=4, ... drop_remainder=True, pad_info=...)	>>> dataset = dataset.padded_batch(batch_size=4, ... drop_remainder=True, pad_info=...)

### Bug fixes - [I62I3J] fix inference failure of BGCF network on Ascend 310 - [I7C2W3] fix error issuse of null pointer when enabling multiple loss in parallel pipeline scenarios ### 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!