Release Notes
MindSpore 2.7.0 Release Notes
Major Features and Improvements
Dataset
[STABLE] The mindspore.dataset.vision.read_video interface now supports video decoding based on Ascend VDEC. Users can specify the video processing backend as
CPU
(Default) orAscend
through the mindspore.dataset.config.set_video_backend interface.[STABLE] Add mindspore.dataset.vision.VideoDecoder interface which provides the ability to decode frames at the given indices and retrieve metadata. Currently only the
Ascend
backend is supported.
Parallel
[STABLE] MindSpore now supports gather and reduce operations for tensors of non-uniform sizes. Users can use this functionality through the mint.distributed.all_gather_into_tensor_uneven and mint.distributed.reduce_scatter_tensor_uneven interfaces. mint.distributed.all_gather and mint.distributed.reduce_scatter also support gather and reduce operations for non-uniform sizes, respectively.
[BETA] The pipeline parallelism supports ZeroBubbleV scheduling, reducing pipeline parallel bubbles, and can be combined with overlap of forward and backward computation-communication phases, to enhance the proportion of communication and computation overlap.
[Stable] MindSpore optimizes the PP communication domain and significantly reduces HCCL_BUFFER_SIZE.
[BETA] MindSpore supports fine-grained optimization of the
HCCL_BUFFER_SIZE
, which can be set through the environment variableMS_DEV_HCCL_CONF
. Refer to Environment Variables for details.
Compiler
[BETA] Support mindspore.nn.Cell registration of forward hooks and backward hooks in graph mode.
Runtime
[STABLE] MindSpore supports reserving huge page memory. Users can enable this feature by passing the
huge_page_reserve_size
parameter in the mindspore.runtime.set_memory API.
Lite
MindSpore Lite delivers lightweight AI inference acceleration capabilities for diverse hardware devices, empowering smart applications. It provides developers with an end-to-end solution and offers algorithm engineers and data scientists a user-friendly development experience characterized by efficient execution and flexible deployment.
To better foster the thriving development of the AI software and hardware application ecosystem, MindSpore Lite has established an independent code repository to drive ecosystem growth. In the future, MindSpore Lite will work together with the MindSpore AI community enrich the AI software and hardware application ecosystem.
For further details, please visit the MindSpore Lite Code Repository.
API Change
[STABLE] As part of the task of mindspore.mint API integration task, the interface definitions and functionalities of several Tensor APIs have been aligned and optimized.
mindspore.Tensor
mindspore.Tensor.masked_scatter
mindspore.Tensor.bernoulli_
mindspore.Tensor.zero_
mindspore.Tensor.copy_
[STABLE] mindspore.ops API provides a new interface mindspore.ops.ring_attention_update. Currently, it is only supported on Atlas A2 Training Series Products.
[STABLE] Provide new interface mindspore.enable_dynamic to specify whether the shape of the parameter is dynamic shape or dynamic rank.
Contributors
Bellatan,caifubi,ccsszz,chaijinwei,chengbin,chenweifeng,chujinjin,DavidFFFan,DeshiChen,dingjinshan,fary86,fuchao,gaoyong10,GuoZhibin,guozhijian,haozhang,hedongdong,Henry Shi,hhz886,huangbingjian,huangziling,huda,Huilan Li,jiangchao_j,jianghui58,jiangshanfeng,jiaorui,jiaxueyu,jizewei,leida,lichen,limingqi107,LiNuohang,linux,liubuyu,liuluobin,looop5,luochao60,luoyang,maoyuanpeng1,Margaret_wangrui,mengxian,MengXiangyu,NaCN,One_East,panzhihui,Qiao_Fu,qiuleilei,qiuyufeng,r1chardf1d0,SaiYao,shaoshengqi,shen_haochen,shenwei41,shuqian0,St.Universe,suteng,TAJh,tanghuikang,tianxiaodong,wang_ziqi,wangyibo,wujueying,wusimin,XianglongZeng,xiaopeng,xiaotianci,xiaoyao,XinDu,xuzhen,yanghaoran,yangyingchun,yide12,yonibaehr,yuanqi,yuchaojie,YuJianfeng,YukioZzz,yuliangbin,zhangbuxue,zhangdanyang,zhanghanLeo,zhangyinxia,ZhangZGC,zhaochenjie,Zhi Feng Wu,zhuguodong,ZPaC,zyli2020,程超,胡犇,胡彬,宦晓玲,黄勇,李良灿,李林杰,刘飞扬,刘勇琪,刘子涵,王振邦,熊攀,杨卉,俞涵,云骑士,张栩浩,周一航
MindSpore 2.7.0-rc1 Release Notes
Major Features and Improvements
Ascend
[STABLE] Added GroupedMatmul optimization in O1 scenario, which supports fusion of element-wise operators to significantly reduce data handling overhead and improve computational efficiency. Users can turn it on by setting the environment variable
MS_DEV_GRAPH_KERNEL_FLAGS
to"--enable_cluster_ops=GroupedMatmul"
.[STABLE] Improved ease of use of memory tracker: Users can import tracker data for model memory analysis via mindspore.runtime.memory_replay(file_path); set the tracker data storage path by setting
export MS_ALLOC_CONF=memory_tracker_path:file_path
;reduce the size of saved data by settingexport MS_ALLOC_CONF=simple_tracker:true
to save only the last user of each memory block.[STABLE] Optimized the custom operator function of ops.Custom primitive for aclnn types in graph mode, with full support for inputs of non-Tensor type and automatic loading of RegInfo information, which significantly improves the ease of use and flexibility of aclnn custom operator.
PyNative
[STABLE] mindspore.nn.Cell.register_forward_hook and mindspore.nn.Cell.register_forward_pre_hook added the
with_kwargs
argument (default: False) to support passing keyword arguments from theconstruct
call tohook_fn
.[STABLE] mindspore.Tensor.register_hook now supports registering hooks on output tensors of operators with multiple outputs.
[STABLE] Enhanced Cell custom bprop. Support verification and automatic conversion of data type and shape for return value and corresponding input.
[STABLE] Added Storage API for Tensor: Supports memory operations through Storage and related interfaces to achieve memory optimization.
[STABLE] Added C++ ms::pynative::PyboostRunner interface to facilitate customization of the operator to support PyNative's multi-stage pipelining runtime.
parallel
[STABLE] Configurable HCCL buffer size for communucation group: communication.create_group supports additional configuration
options
in passing communication domain, sets the communication domain HCCL communication domain cache size byhccl_config
key-value pair to avoid OOM under multiple communication domains.[STABLE] Supported CPU communication interfaces mint.distributed.all_reduce/mint.distributed.barrier/mint.distributed.send/mint.distributed.recv, through which users can use the corresponding collective communication operators functions.
[STABLE] In static graph mode, forward and reverse operators, AllGatherV and ReduceScatterV are supported.
[BETA] Support for data caching via member methods of mint.distributed.TCPStore function.
Training
[STABLE] Recomputation communication overlap: Supports mutual overlap of the communication between two cells for full recomputation, improving the performance of the recomputation scenario model.
[STABLE] Quick recovery from accuracy failure without reboot: Supports resuming training of loading a checkpoint file without restrarting the process when training result excepition occurs.
[STABLE] Silent data corruption detection: Supporting validation of
MatMul
results during forward and backward computations. User can enable it by setting the environment variableMS_SDC_DETECT_ENABLE
to1
. Using interfaces in themindspore.utils.sdc_detect
module to start/stop detection and get the detection result.
Inference
[STABLE] The vLLM MindSpore plugin has now been adapted to vLLM v0.8.3 and supports foundational features of the vLLM V1 new architecture, including inference capabilities such as Chunked Prefill and Automatic Prefix Caching. For service-oriented deployment, vLLM MindSpore adds support for hybrid DP/TP/EP parallel inference capabilities on DeepSeek-V3/R1, effectively improving both full and incremental inference efficiency while reducing device memory overhead.
Tool
MindInsight will no longer update or release new versions after version 2.3,and the related documents have been removed. The origin system optimization data visualization has been integrated into MindStudio Insight, and scalar visualization, parameter distribution visualization, and computational graphs visualization have been integrated into the MindStudio Insight plugins. For details, see the MindStudio Insight User Guide.
[STABLE] MindSpore Profiler supports msMonitor, enabling users to collect performance data through online monitoring tools.
[STABLE] MindSpore Profiler adds the record_shapes parameter, supporting users to collect shapes of operators issued by the framework side.
[STABLE] MindSpore Profiler adds sys resource parameters, supporting the ability to collect sys resource class information.
[STABLE] MindSpore Profiler adds host_sysparameter, supporting the ability to collect host information such as system call class, storage class, CPU information, etc.
[STABLE] MindSpore Profiler mstx module provides domain functionality, supporting users to finely control mstx data.
API Change
[STABLE] Some of the functional, nn and Tensor interfaces in the DEMO state in the mindspore.mint API are turned to STABLE. The mint interfaces are still mostly experimental, with better performance than ops interfaces in the graph compilation mode O0/O1 and PyNative mode. Currently O2 compilation mode (graph sink) and CPU, GPU backend are not supported, which will be gradually improved.
mindspore.mint
mindspore.mint.randomperm
mindspore.mint.randn
mindspore.mint.randint
mindspore.mint.triu
mindspore.mint.empty_like
mindspore.mint.empty
mindspore.mint.floor_divide
mindspore.mint.nn
mindspore.mint.nn.BatchNorm1d
mindspore.mint.nn.BatchNorm2d
mindspore.mint.nn.BatchNorm3d
mindspore.mint.nn.PixelShuffle
mindspore.mint.nn.Threshold
mindspore.mint.nn.functional
mindspore.mint.nn.functional.threshold
mindspore.mint.nn.functional.threshold_
mindspore.mint.nn.functional.pixel_shuffle
mindspore.Tensor
mindspore.Tensor.new_full
mindspore.Tensor.new_empty
mindspore.Tensor.floor_divide
mindspore.Tensor.exponential_
[STABLE] mindspore.ops API provides an new interface mindspore.ops.swiglu. Currently, only Ascend backend is supported.
[STABLE] mindspore.ops.svd of mindspore.ops API now extra supports Ascend backend.
[STABLE] mindspore.mint.nn.functional.silu and mindspore.mint.nn.SiLU now support input argument
inplace
.[STABLE] communication.create_group adds support for additional configuration
options
for communication domains. HCCL backend supports settinghccl_config
inoptions
to set the HCCL communication domain cache size for communication domains.[STABLE] mindspore.runtime API adds implementation of mindspore.runtime.empty_cache.
[STABLE] mindspore.runtime.set_memory now supports input argument
huge_page_reserve_size
.[STABLE] mindspore.runtime.set_cpu_affinity now supports input argument
module_to_cpu_dict
.[STABLE] minspore.nn.cell added the function to view/save model's state_dict. New interfaces are as follows:
mindspore.nn.Cell
cell.register_state_dict_post_hook
cell.register_state_dict_pre_hook
cell.state_dict
cell.register_load_state_dict_pre_hook
cell.register_load_state_dict_post_hook
cell.load_state_dict
[STABLE] minspore.nn.cell added the function to view/register model's buffer. New interfaces are as follows:
mindspore.nn.Cell
cell.register_buffer
cell.get_buffer
cell.get_sub_cell
cell.named_buffer
cell.buffers
Backwards Incompatible Change
-
The type of
affinity_cpu_list
changed from dictionary to list to customize the configuration of affinity CPU range segments for a single process only. Added a new parametermodule_to_cpu_dict
to support customized configuration of CPU affinity policies for hot module threads.2.6
2.7
>>> from mindspore.runtime import set_cpu_affinity
>>> set_cpu_affinity(True, {"device0": ["10-19", "23-40"]})>>> from mindspore.runtime import set_cpu_affinity
>>> set_cpu_affinity(True, ["10-19", "23-40"],
… {"main": [0,1,2,3],
… "runtime": [4,5,6],
… "pynative": [7,8,9]})
Contributors
baochong,Bellatan,BJ-WANG,caifubi,caiji_zhang,Carey,chaijinwei,changzherui,chengbin,chujinjin,DavidFFFan,DeshiChen,dingjinshan,Dring,ehaleva,Erpim,fary86,fengtingyan,fengyixing,fuchao,gaoyong10,gengdongjie,guangpengz,GuoZhibin,gupengcheng0401,haozhang,hedongdong,hhz886,huandong1,huangbingjian,huangziling,huda,HuilanLi,hujiahui8,jiangchao_j,jianghui58,jiangshanfeng,jiaorui,jiaxueyu,jizewei,jjfeing,jshawjc,kairui_kou,kingxian,kisnwang,lanzhineng,leida,LiangZhibo,lichen,limingqi107,LiNuohang,linux,liubuyu,liuchengji,liuluobin,liuyanwei,lkp,looop5,lujiale,luochao60,luoyang,maoyuanpeng1,Margaret_wangrui,mengxian,MengXiangyu,mengyuanli,NaCN,One_East,panshaowu,panzhihui,pengqi,Qiao_Fu,qiuleilei,qiuyufeng,rainyhorse,SaiYao,shaoshengqi,shen_haochen,shenhaojing,shenwei41,shuqian0,tanghuikang,tangmengcheng,tan-wei-cheng,tianxiaodong,uuhuu,wang_ziqi,WangChengzhao,wangshaocong,wangyibo,wujueying,XianglongZeng,xiaopeng,xiaotianci,xiaoyao,XinDu,xuzhen,yangguodong,yanghaoran,yangyingchun,Yanzhi_YI,yide12,yihangchen,YijieChen,yuanqi,yuchaojie,yuezenglin,YuJianfeng,YukioZzz,yuliangbin,yyuse,zhangbuxue,zhangdanyang,zhanghanLeo,zhangyinxia,ZhangZGC,zhanzhan,zhaochenjie,zhengzuohe,zhouyaqiang0,zhunaipan,zichun_ye,ZPaC,zyli2020,程超,范吉斌,胡犇,胡彬,宦晓玲,黄勇,李栋,李良灿,李林杰,李寅杰3,刘飞扬,刘力力,刘勇琪,刘子涵,梅飞要,宋佳琪,王泓皓,王禹程,王振邦,熊攀,徐安越,杨卉,杨明海,俞涵,虞良斌,云骑士,张栩浩,周一航