# Release Notes
[](https://gitee.com/mindspore/mindspore/blob/v2.7.1/RELEASE.md)
## MindSpore 2.7.1 Release Notes
### Major Features and Improvements
#### Dataset
- [STABLE] The parallel acceleration capability of the `.map` operation and `.batch` operation in the dataset has been restructured. The original data transfer logic of `c++ thread(with GIL) -> python process pool -> worker[i]` has been optimized to `c++ thread -> msg & shm -> worker[i]`, reducing the data transfer chain and eliminating the dependency on the GIL lock, which significantly improves the efficiency of custom data processing.
#### Parallel
- [STABLE] In static graph mode, the `AlltoAllVC` forward and reverse operators are supported. Users can use this operator through the [mindspore.ops.AlltoAllVC](https://www.mindspore.cn/docs/en/r2.7.1/api_python/ops/mindspore.ops.AlltoAllVC.html) interface. In dynamic graph mode, the [mindspore.communication.comm_func.all_to_all_v_c](https://www.mindspore.cn/docs/en/r2.7.1/api_python/communication/mindspore.communication.comm_func.all_to_all_v_c.html) communication interface is supported. Users can use this operator through this interface.
- [STABLE] In dynamic graph mode, supports `TCPStore` function, supports the communication class [mindspore.mint.distributed.TCPStore](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.TCPStore.html), users can use the `TCPStore` function through this class.
- [STABLE] The [mindspore.communication.create_group](https://www.mindspore.cn/docs/en/r2.7.1/api_python/communication/mindspore.communication.create_group.html) interface now supports the `hccl_comm` parameter, allowing the reuse of externally created communication groups. Users can create communication groups through other means and pass them to MindSpore for network construction.
- [BETA] The [mindspore.mint.distributed.init_process_group](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.init_process_group.html) interface supports `init_method` or `store` parameters. Users can use this interface to initialize communication in a way that does not depend on the scheduler process.
#### Ascend
- [BETA] Operator automatic fusion is supported in PyNative asynchronous execution mode. Users can enable it by setting the environment variable: `export MS_DEV_PYNATIVE_FUSION_FLAGS="--opt_level=1"`.
- [BETA] The static graph now supports custom operator integration via the CustomOpBuilder method, and exposes the MS_CUSTOM_OPS_REGISTER macro for registering custom operator function classes.
- [BETA] Dynamic graph mode supports the use of custom memory pools via [mindspore.runtime.use_mem_pool](https://www.mindspore.cn/docs/en/r2.7.1/api_python/runtime/mindspore.runtime.use_mem_pool.html) to implement custom memory allocation within the context.
#### PyNative
- [STABLE] The [mindspore.mint.empty_like](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.empty_like.html) and [mindspore.mint.empty](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.empty.html) functions support the `pin_memory` parameter. If users set the `pin_memory` parameter to `True`, the returned Tensor will be allocated on pinned memory. Works only for CPU Tensors.
- [STABLE] View operator performance optimization. By streamlining execution workflows and refining critical implementations, we have significantly enhanced the execution efficiency of View operators in eager mode.
- [STABLE] The [mindspore.Tensor.to](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore/Tensor/mindspore.Tensor.to.html) interface is extended to support copying Tensor data between devices.
- [STABLE] Tensor storage supports CPU, GPU, and Ascend. Before 2.7.1, Tensor storage only supported Ascend hardware.
#### Tools
- [STABLE] The static graph supports the joint detection scheme for feature values. Users can enable this feature by configuring the environment variable MS_NPU_ASD_CONFIG.
- [STABLE] HCCS link fault detection is supported. The [stress_detect](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore.utils.html#mindspore.utils.stress_detect) interface now includes an HCCS detection type.
### API Change
- [STABLE] [mindspore.mint](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore.mint.html) API provides some new functional and Tensor interfaces. Most of the mint interfaces are currently still experimental interfaces. They perform better than ops interfaces in `jit_level="O0"/"O1"` and pynative mode. Currently, the graph sinking mode and CPU/GPU backend are not supported, and it will be gradually improved in the future.
| mindspore.mint |
| :------------------ |
| mindspore.mint.real |
| mindspore.mint.imag |
| mindspore.Tensor |
| -------------------------------- |
| mindspore.Tensor.sign_ |
| mindspore.Tensor.masked_scatter_ |
| mindspore.Tensor.index_copy_ |
| mindspore.Tensor.index_fill_ |
| mindspore.Tensor.sigmoid_ |
- [STABLE] [mindspore.mint.nn.functional.conv1d](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.nn.functional.conv1d.html) and [mindspore.mint.nn.Conv1d](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.nn.Conv1d.html) interfaces have transitioned from demo to stable.
- [STABLE] [mindspore.mint.stack](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.stack.html) and [mindspore.mint.concat](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.concat.html) interfaces now support Tensor inputs of different data types.
- [BETA] The [mindspore.ops.Morph](https://www.mindspore.cn/docs/en/r2.7.1/api_python/ops/mindspore.ops.Morph.html) operator primitive now supports custom backward propagation functions (bprop), allowing users to define corresponding gradient computation logic for the custom forward function.
- [BETA] [mindspore.recompute](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore/mindspore.recompute.html) supports being used in static graph, user can call the api in the function which is decorated by @jit.
- [BETA] [mindspore.enable_dynamic](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore/mindspore.enable_dynamic.html) supports symbolic deduction, meaning that the input shapes are allowed to use [mindspore.Symbol](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore/mindspore.Symbol.html).
### Contributors
Bellatan,caifubi,chaijinwei,changzherui,chengbin,chujinjin,DavidFFFan,DeshiChen,Dring,ehaleva,fary86,gaoyong10,guangpengz,GuoZhibin,guozhijian,haozhang,hedongdong,Henry Shi,hhz886,huangbingjian,huangzhuo,huangziling,huda,Huilan Li,huoxinyou,jiangshanfeng,jiaorui,jiaxueyu,jizewei,kairui_kou,kingxian,kisnwang,leida,liangchenghui,lichen,limingqi107,Linhai,LiNuohang,linux,liubuyu,liudongxu,liuluobin,liuyanwei,lizhitong,looop5,luochao60,machenggui,maoyuanpeng1,Margaret_wangrui,mengxian,MengXiangyu,mengyuanli,Metaqiang,NaCN,nepdada,panzhihui,Qiao_Fu,qiuleilei,qqqhhhbbb,qujianwei,shaoshengqi,shen_haochen,shenwei41,shuqian0,tanghuikang,uuhuu,wang_ziqi,wangjialin,wujueying,XianglongZeng,Xiaoda,xiaopeng,xiaotianci,xiaoyao,XinDu,xuzhen,yanghaoran,yao_yf,yefeng,yide12,yiguangzheng,YijieChen,yuanqi,yuchaojie,YuJianfeng,YukioZzz,yuliangbin,yyyyrf,zhangbuxue,zhangdanyang,zhanghanLeo,zhangyinxia,ZhangZGC,zhanzhan,zhaochenjie,zhengzuohe,zhunaipan,ZPaC,zyli2020,范吉斌,龚昊宇,胡彬,宦晓玲,李栋,李良灿,李林杰,刘崇鸣,刘飞扬,刘力力,刘子涵,宋佳琪,孙昊辰,王泓皓,王振邦,俞涵,张栩浩,张学同,周一航
## MindSpore 2.7.0 Release Notes
### Major Features and Improvements
#### Dataset
- [STABLE] The [mindspore.dataset.vision.read_video](https://www.mindspore.cn/docs/en/r2.7.1/api_python/dataset_vision/mindspore.dataset.vision.read_video.html) interface now supports video decoding based on Ascend VDEC. Users can specify the video processing backend as `CPU` (Default) or `Ascend` through the [mindspore.dataset.config.set_video_backend](https://www.mindspore.cn/docs/en/r2.7.1/api_python/dataset/mindspore.dataset.config.set_video_backend.html) interface.
- [STABLE] Add [mindspore.dataset.vision.VideoDecoder](https://www.mindspore.cn/docs/en/r2.7.1/api_python/dataset_vision/mindspore.dataset.vision.VideoDecoder.html) 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](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.all_gather_into_tensor_uneven.html) and [mint.distributed.reduce_scatter_tensor_uneven](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.reduce_scatter_tensor_uneven.html) interfaces. [mint.distributed.all_gather](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.all_gather.html) and [mint.distributed.reduce_scatter](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.reduce_scatter.html) 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 variable `MS_DEV_HCCL_CONF`. Refer to [Environment Variables](https://www.mindspore.cn/docs/en/r2.7.1/api_python/env_var_list.html) for details.
#### Compiler
- [BETA] Support [mindspore.nn.Cell](https://www.mindspore.cn/docs/en/r2.7.1/api_python/nn/mindspore.nn.Cell.html) 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](https://www.mindspore.cn/docs/en/r2.7.1/api_python/runtime/mindspore.runtime.set_memory.html) 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](https://gitee.com/mindspore/mindspore-lite).
### API Change
- [STABLE] As part of the task of [mindspore.mint](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore.mint.html) 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](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore.ops.html) API provides a new interface [mindspore.ops.ring_attention_update](https://www.mindspore.cn/docs/en/r2.7.1/api_python/ops/mindspore.ops.ring_attention_update.html). Currently, it is only supported on Atlas A2 Training Series Products.
- [STABLE] Provide new interface [mindspore.enable_dynamic](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore/mindspore.enable_dynamic.html) 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)](https://www.mindspore.cn/docs/en/r2.7.1/api_python/runtime/mindspore.runtime.memory_replay.html); set the tracker data storage path by setting `export MS_ALLOC_CONF=memory_tracker_path:file_path`；reduce the size of saved data by setting `export 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](https://www.mindspore.cn/docs/en/r2.7.1/api_python/nn/mindspore.nn.Cell.html#mindspore.nn.Cell.register_forward_hook) and [mindspore.nn.Cell.register_forward_pre_hook](https://www.mindspore.cn/docs/en/r2.7.1/api_python/nn/mindspore.nn.Cell.html#mindspore.nn.Cell.register_forward_pre_hook) added the `with_kwargs` argument (default: False) to support passing keyword arguments from the `construct` call to `hook_fn`.
- [STABLE] [mindspore.Tensor.register_hook](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore/Tensor/mindspore.Tensor.register_hook.html#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](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore/Tensor/mindspore.Tensor.storage.html) API for Tensor: Supports memory operations through Storage and related interfaces to achieve memory optimization.
- [STABLE] Added C++ [ms::pynative::PyboostRunner](https://www.mindspore.cn/tutorials/en/r2.7.1/custom_program/operation/cpp_api_for_custom_ops.html#class-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](https://www.mindspore.cn/docs/en/r2.7.1/api_python/communication/mindspore.communication.create_group.html#mindspore.communication.create_group) supports additional configuration `options` in passing communication domain, sets the communication domain HCCL communication domain cache size by `hccl_config` key-value pair to avoid OOM under multiple communication domains.
- [STABLE] Supported CPU communication interfaces [mint.distributed.all_reduce](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.all_reduce.html)/[mint.distributed.barrier](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.barrier.html)/[mint.distributed.send](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.send.html)/[mint.distributed.recv](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.recv.html), through which users can use the corresponding collective communication operators functions.
- [STABLE] In static graph mode, forward and reverse operators, [AllGatherV](https://www.mindspore.cn/docs/en/r2.7.1/api_python/ops/mindspore.ops.AllGatherV.html) and [ReduceScatterV](https://www.mindspore.cn/docs/en/r2.7.1/api_python/ops/mindspore.ops.ReduceScatterV.html) are supported.
- [BETA] Support for data caching via member methods of [mint.distributed.TCPStore](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.distributed.TCPStore.html) 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 variable `MS_SDC_DETECT_ENABLE` to `1`. Using interfaces in the `mindspore.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](https://www.hiascend.com/document/detail/zh/mindstudio/80RC1/GUI_baseddevelopmenttool/msascendinsightug/Insight_userguide_0002.html).
- [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](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore.mint.html) 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](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore.ops.html) API provides an new interface [mindspore.ops.swiglu](https://www.mindspore.cn/docs/en/r2.7.1/api_python/ops/mindspore.ops.swiglu.html). Currently, only Ascend backend is supported.
- [STABLE] [mindspore.ops.svd](https://www.mindspore.cn/docs/en/r2.7.1/api_python/ops/mindspore.ops.svd.html#mindspore.ops.svd) of [mindspore.ops](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore.ops.html) API now extra supports Ascend backend.
- [STABLE] [mindspore.mint.nn.functional.silu](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.nn.functional.silu.html#mindspore.mint.nn.functional.silu) and [mindspore.mint.nn.SiLU](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mint/mindspore.mint.nn.SiLU.html) now support input argument `inplace`.
- [STABLE] [communication.create_group](https://www.mindspore.cn/docs/en/r2.7.1/api_python/communication/mindspore.communication.create_group.html#mindspore.communication.create_group) adds support for additional configuration `options` for communication domains. HCCL backend supports setting `hccl_config` in `options` to set the HCCL communication domain cache size for communication domains.
- [STABLE] [mindspore.runtime](https://www.mindspore.cn/docs/en/r2.7.1/api_python/mindspore.runtime.html) API adds implementation of [mindspore.runtime.empty_cache](https://www.mindspore.cn/docs/en/r2.7.1/api_python/runtime/mindspore.runtime.empty_cache.html#mindspore.runtime.empty_cache).
- [STABLE] [mindspore.runtime.set_memory](https://www.mindspore.cn/docs/en/r2.7.1/api_python/runtime/mindspore.runtime.set_memory.html#mindspore.runtime.set_memory) now supports input argument `huge_page_reserve_size`.
- [STABLE] [mindspore.runtime.set_cpu_affinity](https://www.mindspore.cn/docs/en/r2.7.1/api_python/runtime/mindspore.runtime.set_cpu_affinity.html) now supports input argument `module_to_cpu_dict`.
- [STABLE] [minspore.nn.cell](https://www.mindspore.cn/docs/en/r2.7.1/api_python/nn/mindspore.nn.Cell.html) 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](https://www.mindspore.cn/docs/en/r2.7.1/api_python/nn/mindspore.nn.Cell.html) 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
- [runtime.set_cpu_affinity](https://www.mindspore.cn/docs/en/r2.7.1/api_python/runtime/mindspore.runtime.set_cpu_affinity.html)
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 parameter `module_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