Release Notes

MindSpore 2.8.0 Release Notes

Major Features and Improvements

Dataset

• [STABLE] The mindspore.dataset.dataloader.DataLoader API is added, aligning with industry-standard API design patterns and functions to enhance user experience. In addition, various commonly used methods for datasets, samplers, collate functions, and tools are added.

• [STABLE] The .send(...) and .recv(...) APIs are added for communication between nodes of the dataset object. These APIs enable the dataset data processing results to be transmitted between different nodes.

• [STABLE] When .map(...) is used for data augmentation and a custom PyFunc augmentation function is used in multi-process mode, if the PyFunc function execution is slow or hangs, a warning will be printed to notify the user: ** worker subprocess stack:....

Executor

• [STABLE] MindSpore now supports custom backends, allowing users to adapt to third-party backends.

Compiler

• [STABLE] In graph mode, augmented assignment statements are parsed into corresponding in-place operators, improving graph mode performance and unifying programming experience for pynative and graph mode. Currently only the Ascend backend is supported.

PyNative

• [STABLE] The recompute interface supports setting use_reentrant=False to enable gradient computation for complex type inputs. It also supports setting output_recompute to determine whether outputs should be re-computed.

• [STABLE] Added support for CPU Tensor conversion with DLPack.

• [STABLE] Storage supports shared memory between processes.

API Change

• [STABLE] mindspore.ops API has added three Native Sparse Attention(NSA) interfaces:

  mindspore.ops
  mindspore.ops.nsa_compress
  mindspore.ops.nsa_compress_attention
  mindspore.ops.nsa_select_attention

• [STABLE] The mindspore.mint API has added the mindspore.mint.nn.functional.cosine_embedding_loss and mindspore.mint.nn.CosineEmbeddingLoss interfaces. Most mint interfaces are currently still experimental and outperform ops in terms of performance under Graph Mode O0/O1 and PyNative Mode. O2 compilation mode (Graph Sinking), as well as CPU and GPU backends, are not currently supported and will be improved incrementally.

• [STABLE] The mindspore.mint.nn.functional.adaptive_max_pool2d and mindspore.mint.nn.AdaptiveMaxPool2d interfaces have been promoted from demo to stable status.

• [STABLE] The mindspore.Tensor.view interface now supports dtype as an input argument.

• [STABLE] The mindspore.mint.nn.functional.interpolate interface now supports setting scale_factor in bilinear/bicubic modes. The restriction preventing scale_factor from being set when align_corners=False in linear mode has been lifted.

• [STABLE] The mindspore.ops.grad and mindspore.ops.value_and_grad interfaces add the sens_param parameter, which is used to specify whether to configure sensitivity (gradient with respect to the output) in the input.

• [STABLE] When iterating over a dataset, if the output data contains the string type, the default output type will change from Tensor to numpy.ndarray.

• [BETA] Added mindspore.Tensor.to and mindspore.Tensor.to_, which convert a tensor's device and data type to the specified device and dtype.

• [BETA] Added mindspore.Tensor.delete_ for actively releasing the memory of the tensor on the device or host.

• [BETA] Added mindspore.Tensor.data, providing access to the raw data without tracking its computational history for autograd.

Backwards Incompatible Change

Dataset

• [STABLE] The following obsolete import methods are completely removed: import mindspore.dataset.vision.c_transforms as c_vision, import mindspore.dataset.vision.py_transforms as py_vision, import mindspore.dataset.transforms.c_transforms as c_transforms, import mindspore.dataset.transforms.py_transforms as py_transforms.

• [STABLE] The column_order parameter in the .map(...) operation is completely removed. You can use .project(...) to adjust the column order.

Bug Fixes

Dataset

• ID6JRL: Fixed an issue where an incorrect data dimension was caused when GeneratorDataset (when the custom dataset __getitem__ returns the self member variable of the dict type) and .batch(batch_size=1) was used.

Contributors

anyrenwei,Bellatan,caifubi,Carey,chaijinwei,changzherui,chengbin,chenshan2623,chujinjin,DavidFFFan,DeshiChen,dingjinshan,fangwenyi,fary86,fengyixing,fuchao,Gaoxiong,gaoyong10,guangpengz,guozhijian,haozhang,hedongdong,hhz886,HighCloud,huangbingjian,huangfuxin,huda,jiangna,jiangshanfeng,jiaorui,jijiarong,laoyu,leida,liangchenghui,LiangZhibo,lichen,lijiajie1234,limingqi107,LiNuohang,litingyu,liubuyu,liuchao,liuchuting,liuluobin,liuyanwei,lizhitong,looop5,luochao60,machangwei,maoyuanpeng1,Margaret_wangrui,mengxian,mwt,NaCN,panzhihui,Qiao_Fu,qiuleilei,qqqhhhbbb,r1chardf1d0,rainyhorse,rogeryu11,shaoshengqi,shen_haochen,shenwei41,shuqian0,tanghuikang,Tianci Xiao,tianxiaodong,wang_ziqi,wangjialin,wangyin,wujueying,wusuqin4,wuyanernuo,XianglongZeng,xiaopeng,xiaotianci,xuzhen,yanghaoran,yangting,yao_yf,yide12,yiguangzheng,yuanqi,yuchaojie,Yuheng Wang,YuJianfeng,Yule100,yuliangbin,YzLi,zhangbuxue,Zhanghanbo,zhanghanLeo,zhangyihui,zhangyinxia,zhangzhen,zhaochenjie,zhengzuohe,zhongmin,ZPaC,zyb,zyli2020,阿琛,曹彤,胡彬,宦晓玲,黄勇,李良灿,李林杰,刘飞扬,刘力力,宋佳琪,王振邦,熊攀,徐子康,严珞珈,杨晓春,张峻源,张栩浩