Release Notes

MindSpore 2.0.0 Release Notes

Major Features and Improvements

PyNative

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!