+
-gengdongjie, zhangyanhui, xiaoxiongzhu, wangshaocong, jianghui58, moran, wangtongyu6, 徐安越, qinzheng, 徐永飞, youshu, XinDu, yeyunpeng2020, yefeng, wangpingan, zjun, 胡安东, 刘力力, 陈宇, chenjianping, kairui_kou, zhangdanyang, hangq, mengyuanli, 刘崇鸣
+```cpp
+/// \brief Create a MSTensor.
+///
+/// \return Pointer to an instance of MindSpore Lite MSTensor.
+static MSTensor *CreateTensor(const std::string &name, TypeId type, const std::vector &shape, const void *data,
+ size_t data_len);
+```
-Contributions of any kind are welcome!
-
-## MindSpore 2.2.1 Release Notes
-
-### Bug Fixes
-
-- [#I7R3R5] Fixed the problem that the network precision of the ResNet-50 on the Ascend platform deteriorates.
-- [#I8A9RH] Fixed an issue where the DBNet(ResNet-50) network precision on the Ascend platform deteriorates.
-- [#I8B8IW] Fixed the segment error caused by out-of-bounds multi-dimensional tensor assignment.
-- [#I8J0F4] Fixed an issue where the multidimensional Tensor extension dimension fails to be executed in the dynamic graph.
-- [#I87P3P] Fixed an issue where the compilation cache fails to be loaded during secondary training on the Ascend platform.
-- [#I86GP9] Fixed an issue where the UNet3D network inference precision deteriorates on the Ascend platform.
-- [#I89B4K] Fixed an issue where the dynamic rank execution of dynamic graphs on the Windows platform is suspended.
-- [#I8CX0C] Fixed an issue where dynamic images occasionally fail in mixed precision mode on the Ascend platform.
-- [#I8BGCF] Fixed an issue where a segment error occurs when the command is executed in dynamic diagram mode of the AirNet network on the Ascend platform.
-- [#I8L5DS] Fixed an issue where the ResNet-50 image segmentation network dynamic image is executed slowly on the Ascend platform.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-yufan, dingcheng, lvzhangcheng, zhunaipan, fangwenyi, weiyang, changzherui, chujinjin, zangqingxiang, yuchaojie, wuweikang, tanghuikang, xiaoyao, huangbinjian, zhoupeichen, chenfei_mindspore, hedongdong, wangnan, zhengzuohe, yanghaoran, zouliqin, luoyang, liuchongmin, lujiale, machenggui, wangcong, lixiangyi, wangting, huangyong
-
-Contributions of any kind are welcome!
-
-## MindSpore Lite 2.2.1 Release Notes
-
-### Bug Fixes
-
-- [#I88055] Fixed a function issue caused by incorrect format setting of the gridsample operator in MindSpore Lite inference.
-- [#I8D80Y] The MindSpore Lite inference single-operator invoking process resources are not released and exits abnormally.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-zhanghaibo, wangsiyuan, wangshaocong, chenjianping
-
-Contributions of any kind are welcome!
-
-## MindSpore 2.2.0 Release Notes
-
-### Major Features and Improvements
-
-#### DataSet
-
-- [STABLE] The `row_size` parameter of data operation map/batch is extended to support passing list, which stands for [Input Shared Memory, Output Shared Memory], so as to flexibly control the size of shared memory in multi-process mode.
-- [STABLE] Provide 100% mindspore.dataset and mindspore.dataset.transforms samples for reference.
-- [STABLE] ConcatDataset supports global sampling. After combining data from multiple sources using concat operation, data can be globally sampled randomly to enhance data diversity.
-- [STABLE] When the model.train API is used for training, TimeMonitor(.., data_time=True) can be used to monitor data processing performance in real time.
-- [STABLE] Introduced the jemalloc library to solve the problem of slow memory rise due to untimely memory debris recovery in extreme scenarios.
-
-#### FrontEnd
-
-- [STABLE] Support adding decorator @lazy_inline to make a graph generated from cell being inlined lazily, which can improve the compilation performance effectively.
-- [STABLE] Optimize the function of mixed precision training, support automatic rewriting of Python scripts through rewrite to achieve mixed precision strategies, and support automatic parsing of functions, branch statements, and other syntax.
-- [STABLE] Mixed precision function optimization, ReWrite supports syntax parsing of class functions and branch statements, and extends O1 functionality.
-- [STABLE] Optimize the dynamic learning rate function and add APIs such as MultiStepLR; function get_lr and global_step decoupling, extending optimizer module functionality.
-- [STABLE] Optimize API code samples, API difference tables, and tutorials for using higher-order functions.
-
-#### Operator
-
-- [STABLE] Add new operator primitive `mindspore.ops.Dense`.
-- [STABLE] Add the random number operator state management feature, which allows the random number operator to save the state of the random number, and can be stably reproduced in scenarios such as model parallelism and recalculation. Currently, it only supports CPU/GPU platforms, and the involved random number operators include: `mindspore.ops.Multinomial`, `mindspore.ops.MultinomialWithReplacement`, `mindspore.ops.ParameterizedTruncatedNormal`, `mindspore.ops.StandardLaplace`, `mindspore.ops.StandardLaplace`, `mindspore.ops.Uniform`, `mindspore.ops.UniformInt`, `mindspore.ops.UniformReal`, `mindspore.ops.UniformInt`, `mindspore.ops.Dropout`, `mindspore.ops.RandomChoiceWithMask`, `mindspore.ops.RandomCategorical`, `mindspore.ops.RandomShuffle`, `mindspore.ops.RandamGamma`, `mindspore.ops.RandomPoisson` and `mindspore.ops.TruncatedNormal`.
-- [STABLE] When a GPU operator encounters an illegal input scenario, it supports asynchronously printing error logs in the CUDA kernel of the operator to the Host side and interrupting the execution of the current CUDA Stream, improving the efficiency of user operator problem positioning.
-
-#### PyNative
-
-- [STABLE] Support viewing mechanism in PyNative mode.
-- [STABLE] Function enhancement in PyNative mode: sens supports dict input type.
-
-#### Ascend
-
-- [STABLE] Supports user configurable operator high-precision/high-performance mode, users can use `context.set_context(ascend_config={"op_precision_mode": "/path/to/op_precision_config_file"})` to configure high-precision/high-performance modes for some TBE operators.
-- [BETA] Supports user configurable operators for fp16-in and fp32-out, users can use `context.set_context(ascend_config={"precision_mode": "force_fp32"})` to configure fp16-in and fp32-out for the TBE Cube operators.
-- [BETA] Remove the strong binding between `jit_level="O3"` and GE processes, so users no longer need to set `jit_level="O3"` when executing GE processes.
-
-#### Parallel
-
-- [STABLE] Support the gradient accumulation feature in non-pipeline parallel scenarios in semi-automatic/fully automatic mode. Users can enable gradient accumulation by writing `net = GradAccumulationCell(net, micro_size)`. The gradient accumulation feature is compatible with the lazy_inline feature.
-
-#### Inference
-
-Since version 2.2, the MindSpore main release package does not provide the inference interface enabling for the Ascend 310. If you need to use the inference interface, install the MindSpore Lite release package or download the MindSpore version earlier than 2.0. For details about how to install and use MindSpore Lite, see . HUAWEI Ascend 310 (Ascend) is an energy-efficient and highly integrated AI processor for edge scenarios. It supports inference on MindIR models. In the earlier version, MindSpore provides two methods for enabling inference on the Ascend 310 hardware:
-
-1. The MindSpore main release package provides the matching Ascend 310 version that supports C++ inference interfaces.
-2. The MindSpore Lite release package provides the matching Ascend version and supports C++ and Java inference.
-
-The C++ APIs provided by the two solutions are basically the same. In the future, MindSpore Lite is used instead of building and maintaining two sets of interfaces. The original 310 inference service built based on the MindSpore main release package can be switched to MindSpore Lite with a few modifications. For details, see .
-
-### Bug fixes
-
-- [I7SDA0] Fixed an issue where the accuracy of the CRNN network deteriorates on the NES platform.
-- [I7T4QK] Fixed an issue where the inference precision of the WGAN network deteriorates on the OptiX OSN 8800 platform.
-- [I7TJ8Z] Fixed an issue where the inference precision of the LGTM network deteriorates on the OptiX OSN 8800 platform.
-- [I7M58O] Fixed ASR-dynamic network training core dump issue on Ascend platform.
-- [I7L6B6] Fixed an issue where child processes do not exit in some scenarios when dataset is in multi-process mode.
-- [I7L7AE] Fixed an issue where dataset pipeline contains repeat operations and dynamic batchinfo.get_epoch_num() is incorrectly used in dataset.batch.
-- [I7UY7G] Rectify the file permission modification error in OBSMindDataset.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-bantao, Bingliang, BJ-WANG, Brian-K, caifubi, ccsszz, changzherui, chenfei_mindspore, chengfeng27, chenhaozhe, chenjianping, chenkang, chenweifeng, chuht, chujinjin, CShu0507, Cynthia叶, DeshiChen, douzhixing, Erpim, Etienne, fary86, fengxun, fengyixing, gaoshuanglong, Gaoxiong, gaoyong10, GaoZhenlong, Greatpan, GuoZhibin, guozhijian, hangq, hanhuifeng, haozhang, hedongdong, Henry Shi, HighCloud, Hongxing, huangbingjian, huanghui, huangxinjing, huangziling, hujiahui8, huoxinyou, HWalkingMan, jianghui58, jiangshanfeng, jiaorui, jijiarong, jjfeing, JuiceZ, jxl, KevinYi, kisnwang, KXiong, lanzhineng, Li Qingguo, LiangZhibo, lianliguang, ligan, lihao, Lihoon, limingqi107, ling, linqingke, liruyu, liubuyu, liuchao, liujunzhu, liuluobin, liupeng303, liutongtong9, liyan2022, liyejun, looop5, luochao60, luojianing, luoyang, machenggui, maning202007, Margaret_wangrui, MaZhiming, mengyuanli, moran, NaCN, nomindcarry, panshaowu, panzhihui, qinzheng, qiuzhongya, r1chardf1d0, shaojunsong, shenwei41, shenyaxin, shenzhangyi, Shira Zaloshinski, shunyuanhan, tangdezhi_123, tanghuikang, tan-wei-cheng, tan-wei-cheng-3260, TronZhang, TuDouNi, VectorSL, wang_ziqi, wanghenchang, wangpingan, wangshaocong, wangtongyu6, wtcheng, wujueying, XianglongZeng, xiaotianci, xiaoxin_zhang, xiaoxiongzhu, xiaoyao, xiaoyuanyuan, XinDu, xujinliang, xupan, yanghaoran, yangluhang, yangruoqi713, yangsijia, yangzhenzhang, yangzishuo, yanjiaming, Yanzhi_YI, yao_yf, yefeng, yeyunpeng2020, yide12, YijieChen, YingLai Lin, YingtongHu, yonibaehr, youshu, yuchaojie, YuJianfeng, zangqx, zhaizhiqiang, zhangbuxue, zhangchunlei, zhangdanyang, zhangdong, zhanghaibo, zhangminli, zhangqi, zhangqinghua, zhangyanhui, zhangyifan, zhangyongxian, zhangzhen, zhangzheng, zhanzhan, zhengzuohe, ZhihaoLi, zhoufeng, zhouyaqiang0, zhuguodong, zhupuxu, zichun_ye, zjun, ZPaC, zuochuanyong, zyli2020, 陈宇, 程超, 范吉斌, 冯浩, 冯一航, 胡彬, 宦晓玲, 黄勇, 雷元哲, 黎冠新, 李良灿, 李林杰, 刘崇鸣, 刘力力, 刘思铭, 刘勇琪, 吕浩宇, 没有窗户的小巷, 沈竞兴, 王禹程, 王振邦, 徐安越, 徐永飞, 俞涵, 张澍坤, 周超, 朱家兴
-
-Contributions of any kind are welcome!
-
-## MindSpore Lite 2.2.0 Release Notes
-
-### Major Features and Improvements
-
-#### FlashAttention Operator Fusion
-
-- [STABLE] The OptiX OSN Ascend series supports the FlashAttention large operator fusion of the LLAMA and stable diffusion models.
-
-## MindSpore 2.1.1 Release Notes
-
-### Bug fixes
-
-- [I7Q9RX] The Ascend platform supports adaptive identification of different hardware types.
-- [I7SDA0] Fixed an issue where the accuracy of the CRNN network deteriorates on the NES platform.
-- [I7T4QK] Fixed an issue where the inference precision of the WGAN network deteriorates on the OptiX OSN 8800 platform.
-- [I7TJ8Z] Fixed an issue where the inference precision of the LGTM network deteriorates on the OptiX OSN 8800 platform.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-changzherui, chenfei_mindspore, chenjianping, chenkang, chenweifeng, chujinjin, fangwenyi, GuoZhibin, guozhijian, hangq, hanhuifeng, haozhang, hedongdong, You Shu, Zhou Feng, Dai Yuxin
-
-Contributions of any kind are welcome!
-
-## MindSpore Lite 2.1.1 Release Notes
-
-### Major Features and Improvements
-
-- [STABLE] MindSpore Lite Cloud Inference adds support for Python 3.8 and Python 3.9
-
-## MindSpore 2.1.0 Release Notes
-
-### Major Features and Improvements
-
-#### FrontEnd
-
-- [BETA] JIT Fallback supports variable scenarios. In static graph mode, JIT Fallback supports return of Dict type and Scalar type, supports property setting of non-Parameter type objects, supports partial in-place modification operations of List, and supports third-party libraries such as NumPy. Moreover, it supports related operations of user-defined classes and supports Python basic operators and built-in functions to use more data types. It is compatible with features like control flow, side effects, automatic differentiation. For more details, please refer to [Static Graph Syntax Support](https://www.mindspore.cn/docs/en/r2.1/note/static_graph_syntax_support.html).
-
-- [BETA] In static graph mode, the error message of using undefined variables in the control flow scene is optimized. When using variables defined in if, while, and for control flow branches, the variables need to be initialized and defined before the control flow.
-
-- [STABLE] Add module ReWrite, support the ability to modify multiple network in batches based on customized rules.
-
-- [BETA] Add optim_ex module for optimizers, extend the current functionality, support parameter grouping for every parameter in the optimizer, and support parameter modification by assignment while training.
-
-- [STABLE] Optimize PyTorch and MindSpore API Mapping Table, specify the differences between APIs among functionality, parameter, input, output and specialized cases.
-
-#### PyNative
-
-- Optimize the performance of dynamic shape scenes in PyNative mode.
-
-#### DataSet
-
-- [STABLE] Optimize the memory structure of MindRecord data files. Memory consumption can be reduced 60% when loading 100TB+ data for training.
-- [STABLE] Support single-thread execution of data processing pipeline, and users can add code in the data pipeline for debugging.
-- [STABLE] Optimize the performance of TFRecordDataset to improve the performance of dataset loading by 60%+. Optimize the performance of batch to improve the performance by 30% for the scenarios with large number of batch.
-- [STABLE] Optimize API documentation of [mindspore.dataset](https://www.mindspore.cn/docs/en/r2.1/api_python/mindspore.dataset.html) and [mindspore.dataset.transforms](https://www.mindspore.cn/docs/en/r2.1/api_python/mindspore.dataset.transforms.html). Four new sample libraries have been added to show the effect of data enhancement, namely: [Load & Process Datasets Using Data Pipeline](https://www.mindspore.cn/docs/en/r2.1/api_python/mindspore.dataset.html#quick-start-of-dataset-pipeline), [Visual Transformation Sample Library](https://www.mindspore.cn/docs/en/r2.1/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.vision), [Text Transform Sample Library](https://www.mindspore.cn/docs/en/r2.1/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.text), [Audio Transform Sample Library](https://www.mindspore.cn/docs/en/r2.1/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.audio)
-
-#### AutoParallel
-
-- [STABLE] Support offload parameters or intermediate activations to the CPU or NVMe storage during training process. Users can enable this offload feature by configuring context to scale up the trainable model size.
-
-- [STABLE] Enhanced automatic parallel capability including:
-
- 1. Performance of automatic strategy for typical networks is no less than 90% of default configuration.
-
- 2. Support 3D hybrid parallel training: automatic operator-level strategy generation combined with manual configured pipeline partition.
-
-#### Runtime
-
-- [STABLE] Upgrade OpenMPI version to 4.1.4.
-- [STABLE] Upgrade NCCL version to 2.16.5.
-- [STABLE] Assign rank id continuously in same node when using dynamic cluster to launch distributed jobs.
-- [STABLE] No adaptation code is required for Scheduler node. The script of Scheduler could be identical to that of Worker.
-
-#### Ascend
-
-- [STABLE] Support dump assisted debug information for operator AIC Error scenario. The information includes the operator task name, stream ID, input/output/workspace address and so on.
-- [STABLE] Provide default processing mechanism, which skips its execution, for CANN operators for empty Tensor output scenarios.
-- [STABLE] Supplement debug information when network model fails to execute in graph mode. The debug information will saved in a CSV file in rank_${id}/exec_order/, recording the task ID and stream ID of each task.
-
-#### Profiler
-
-- [STABLE] The Profiler supports the collection of time-consuming data from all phases on the Host side.
-- [BETA] The Profiler supports the collection of memory data from all phases on the Host side.
-- [BETA] The Profiler supports the collection of data processing operator time consumption.
-
-### API Change
-
-- `mindspore.dataset.GraphData`, `mindspore.dataset.Graph`, `mindspore.dataset.InMemoryGraphDataset`, `mindspore.dataset. ArgoverseDataset` are no longer evolved and are deprecated. Use [MindSpore Graph Learning](https://gitee.com/mindspore/graphlearning) for related functional replacements. When replacing networks in Model repositories that use this API, please refer to [GCN](https://gitee.com/mindspore/graphlearning/tree/master/model_zoo/gcn) for GCN and [GAT](https://gitee.com/mindspore/graphlearning/tree/master/model_zoo/gat).
-- `mindspore.set_context` adds `jit_syntax_level` option, which is used to set JIT syntax support level. For more details, please refer to [set_context](https://www.mindspore.cn/docs/en/r2.1/api_python/mindspore/mindspore.set_context.html).
-- Change the `model.infer_predict_layout` interface, which has a new parameter skip_backend_compile with a default value of False. Set to True when the user wants to skip the backend compilation process to get the parameter slicing strategy.
-
-#### Operators
-
-- Add operator primitive for `mindspore.ops.ApplyAdamWithAmsgradV2`. It is recommended to call this operator through API `mindspore.nn.Adam`.
-- Add operator primitive for `mindspore.ops.UpsampleTrilinear3D`. It is recommended to call this operator through API `mindspore.ops.interpolate`.
-- Add operator primitive for `mindspore.ops.UpsampleNearest3D`. It is recommended to call this operator through API `mindspore.ops.interpolate`.
-
-#### API Deprecation
-
-- Deprecate operator primitive `mindspore.ops.ScatterNonAliasingAdd`. It is recommended to use operator primitive `mindspore.ops.TensorScatterAdd` as a replacement.
-
-#### Backwards Incompatible Change
-
-- Interface name: `mindspore.nn.Dense`, `mindspore.nn.Conv1d`, `mindspore.nn.Conv1dTranspose`, `mindspore.nn.Conv2d`, `mindspore.nn.Conv2dTranspose`, `mindspore.nn.Conv3d`, `mindspore.nn.Conv3dTranspose`
-
- Changes: Change initialization parameter strategy. The default value of weight_init is changed from "normal" to None, and the default value of bias_init is changed from "zeros" to None.
-
- Description: The default initialization method for weights has been changed from "normal" to internal HeUniform initialization. The default initialization method of bias is changed from "zeros" to internal Uniform initialization.
-
-
-
- | Original interface | v2.1 interface |
-
-
-
- mindspore.nn.Dense(in_channels,
- out_channels,
- weight_init='normal',
- bias_init='zeros',
- has_bias=True,
- activation=None)
-
- |
-
- mindspore.nn.Dense(in_channels,
- out_channels,
- weight_init=None,
- bias_init=None,
- has_bias=True,
- activation=None)
-
- |
-
-
-
- mindspore.nn.Conv1d(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init='normal',
- bias_init='zeros')
-
- |
-
- mindspore.nn.Conv1d(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init=None,
- bias_init=None)
-
- |
-
-
-
- mindspore.nn.Conv1dTranspose(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init='normal',
- bias_init='zeros')
-
- |
-
- mindspore.nn.Conv1dTranspose(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init=None,
- bias_init=None)
-
- |
-
-
-
- mindspore.nn.Conv2d(in_channels,
- out_channels, kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init='normal',
- bias_init='zeros',
- data_format='NCHW')
-
- |
-
- mindspore.nn.Conv2d(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init=None,
- bias_init=None,
- data_format='NCHW')
-
- |
-
-
-
- mindspore.nn.Conv2dTranspose(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- output_padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init='normal',
- bias_init='zeros')
-
- |
-
- mindspore.nn.Conv2dTranspose(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- output_padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init=None,
- bias_init=None)
-
- |
-
-
-
- mindspore.nn.Conv3d(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init='normal',
- bias_init='zeros',
- data_format='NCDHW')
-
- |
-
- mindspore.nn.Conv3d(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- has_bias=False,
- weight_init=None,
- bias_init=None,
- data_format='NCDHW')
-
- |
-
-
-
- mindspore.nn.Conv3dTranspose(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- output_padding=0,
- has_bias=False,
- weight_init='normal',
- bias_init='zeros',
- data_format='NCDHW')
-
- |
-
- mindspore.nn.Conv3dTranspose(in_channels,
- out_channels,
- kernel_size,
- stride=1,
- pad_mode='same',
- padding=0,
- dilation=1,
- group=1,
- output_padding=0,
- has_bias=False,
- weight_init=None,
- bias_init=None,
- data_format='NCDHW')
-
- |
-
-
-
-### Bug Fixes
-
-- [I6TKLW] Fix the issue of MobileNetV2 network performance degradation on the Ascend platform.
-- [I7CP5H] Fix the issue where ASR network training failed on the Ascend platform.
-- [I7I3EZ] Fix the issue that caused run_check() failure due to changes to the enumeration interface in Pillow version 10.0.0. If encountered in a lower version of MindSpore, install versions of Pillow below 10.0.0 to avoid this issue.
-- [I7IZ8K] Fix accuracy issues with the assignsub interface in PyNative mode.
-- [I7HGY0] Fix the issue that the loss of the functional programming does not converge in the PyNative data_sink mode.
-- [I7J4N3] Fix the issue that the generation of Step Trace failed in Profiler dynamic Shape mode
-- [I7J4N3] Fix the issue that there is no data displayed in the MindInsight parallel strategy view.
-- [I79YY4] Fix SiLU operator error when high-order differential in PyNative mode.
-- [I6NQJQ] Fix the issue of probabilistic failure in dynamic shape scenarios of the ScatterUpdate operator in PyNative mode.
-- [I6Y4G5] Fix the issue of failure in dynamic Shape scenarios of the Conv3D operator in Graph mode.
-
-### 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!
-
-## MindSpore Lite 2.1.0 Release Notes
-
-### Major Features and Improvements
-
-#### MindSpore Lite Cloud Inference
-
-- [STABLE] Supports high-performance inference for single-device large model and single-node multi-device distributed large model at Ascend backend.
-- [STABLE] Python API Ascend backend supports multiple models sharing workspace memory.
-- [STABLE] [The weights can be shared by multiple models through ModelGroup](https://mindspore.cn/lite/docs/en/r2.1/use/cloud_infer/runtime_cpp.html#multiple-models-sharing-weights). For example, weights can be shared between full models and incremental models in the large model scenario.
-
-#### API
-
-The [Python](https://www.mindspore.cn/lite/api/en/r2.1/mindspore_lite/mindspore_lite.ModelGroup.html) and [C++](https://mindspore.cn/lite/api/en/r2.1/generate/classmindspore_ModelGroup.html) ModelGroup interface is added. The interface definition is as follows:
-
-```python
-class ModelGroup
- def __init__(self, flags=ModelGroupFlag.SHARE_WORKSPACE)
- def add_model(self, models)
- def cal_max_size_of_workspace(self, model_type, context)
-```
-
-```C++
-// class ModelGroup
-ModelGroup(ModelGroupFlag flags = ModelGroupFlag::kShareWorkspace);
-Status AddModel(const std::vector &model_path_list);
-Status AddModel(const std::vector> &model_buff_list);
-Status AddModel(const std::vector &model_list);
-Status AddModel(const std::vector &model_list);
-```
-
-## 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 ops 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!
-
-## MindSpore 2.0.0-rc1 Release Notes
-
-### Major Features and Improvements
-
-#### FrontEnd
-
-- [BETA] Statement with "return", "return None" and with no return of function are supported in `GRAPH_MODE`.
-- [BETA] Object with `list` type are supported in `GRAPH_MODE`.
-- [BETA] Statement with "raise" are supported in variable condition situation in `GRAPH_MODE`.
-- [STABLE] Functional call supports data sinking mode.
-- [BETA] The Transformer layer in nn module is added to provide easy-to-use Transformer APIs. Batch_size does not need to be defined. Dynamic seq_length is supported.
-
-#### DataSet
-
-- [STABLE] In the Ascend environment，the timeout waiting time in data sink mode is adjusted to 1900s by default. This solves the problem that the GetNext operator may time out due to environment resource competition and large computing workload in data sinking mode.
-- [STABLE] MindRecord supports to query the schemas and number samples. MindRecord provides multi-process writing mode, allowing users to generate MindRecord data files in parallel.
-- [STABLE] The Dataset pipeline can process any Python object. For details, see [Supporting Python Objects in Dataset Pipeline](https://www.mindspore.cn/tutorials/en/r2.0/advanced/dataset/python_objects.html).
-
-#### AutoParallel
-
-- [STABLE] The strategies of whole parameters can be saved when saving strategy.
-- [STABLE] The Conv3D/MaxPool3D/AvgPool3D distributed operator is supported.
-- [STABLE] Support operator-level parallelism and optimizer-level parallelism under the PyNative with shard: parallel training and the Model API are decoupled to provide basic parallel expression capabilities.
-- [STABLE] Support operator-level parallelism, and optimizer-level parallelism under the Graph mode: parallel training and the Model API are decoupled to provide basic parallel expression capabilities.
-- [BETA] Supports customized distributed graph segmentation, improving the flexibility of distributed training.
-
-#### Runtime
-
-- [STABLE] Control flow supports subgraph sink.
-- [STABLE] Support CUDA 11.6.
-- [STABLE] Support for operator selection and execution of List/Tuple/Scalar type kernel to match native Python expression.
-- [STABLE] Kernel that is not supported by hardware can automatically select CPU kernel.
-- [STABLE] Support heterogeneous execution within subgraph.
-
-#### Ascend
-
-- [STABLE] Support overflow detection scheme and HCCL runtime overflow check.
-- [STABLE] Support dump of communication operators.
-
-#### Profiler
-
-- [STABLE] Rich Profiler collection item configuration, users can collect performance data in more detail.
-
-#### Dump
-
-- [BETA] Single card in PyNatvie mode supports operator overflow detection.
-- [BETA] Graph mode supports hccl operator dump.
-
-### API Change
-
-- [STABLE] Add computing APIs, such as MaxUnpool, ReplicationPad, and GaussianNLLLoss.
- For details, visit .
-- [STABLE] Extend inventory API functions, such as AvgPool, pad, norm, and interplate.
-
-#### operator
-
-- [BETA] Add operator primitive for `mindspore.ops.AdaptiveAvgPool3D`.
-- [BETA] Add operator primitive for `mindspore.ops.AffineGrid`.
-- [BETA] Add operator primitive for `mindspore.ops.Angle`.
-- [BETA] Add operator primitive for `mindspore.ops.BartlettWindow`.
-- [BETA] Add operator primitive for `mindspore.ops.Bernoulli`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselI0`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselI1`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselJ0`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselJ1`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselK0`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselK0e`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselK1`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselK1e`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselY0`.
-- [BETA] Add operator primitive for `mindspore.ops.BesselY1`.
-- [BETA] Add operator primitive for `mindspore.ops.Bincount`.
-- [BETA] Add operator primitive for `mindspore.ops.BlackmanWindow`.
-- [BETA] Add operator primitive for `mindspore.ops.ChannelShuffle`.
-- [BETA] Add operator primitive for `mindspore.ops.Cholesky`.
-- [BETA] Add operator primitive for `mindspore.ops.Col2Im`.
-- [BETA] Add operator primitive for `mindspore.ops.Complex`.
-- [BETA] Add operator primitive for `mindspore.ops.ComplexAbs`.
-- [BETA] Add operator primitive for `mindspore.ops.Cross`.
-- [BETA] Add operator primitive for `mindspore.ops.CTCLossV2`.
-- [BETA] Add operator primitive for `mindspore.ops.Cummin`.
-- [BETA] Add operator primitive for `mindspore.ops.Diag`.
-- [BETA] Add operator primitive for `mindspore.ops.Digamma`.
-- [BETA] Add operator primitive for `mindspore.ops.Expand`.
-- [BETA] Add operator primitive for `mindspore.ops.Fmax`.
-- [BETA] Add operator primitive for `mindspore.ops.Gcd`.
-- [BETA] Add operator primitive for `mindspore.ops.Geqrf`.
-- [BETA] Add operator primitive for `mindspore.ops.GLU`.
-- [BETA] Add operator primitive for `mindspore.ops.GridSampler2D`.
-- [BETA] Add operator primitive for `mindspore.ops.GridSampler3D`.
-- [BETA] Add operator primitive for `mindspore.ops.HammingWindow`.
-- [BETA] Add operator primitive for `mindspore.ops.Heaviside`.
-- [BETA] Add operator primitive for `mindspore.ops.Hypot`.
-- [BETA] Add operator primitive for `mindspore.ops.Igamma`.
-- [BETA] Add operator primitive for `mindspore.ops.IndexFill`.
-- [BETA] Add operator primitive for `mindspore.ops.InplaceIndexAdd`.
-- [BETA] Add operator primitive for `mindspore.ops.InplaceUpdateV2`.
-- [BETA] Add operator primitive for `mindspore.ops.Lcm`.
-- [BETA] Add operator primitive for `mindspore.ops.LeftShift`.
-- [BETA] Add operator primitive for `mindspore.ops.LogicalXor`.
-- [BETA] Add operator primitive for `mindspore.ops.Logit`.
-- [BETA] Add operator primitive for `mindspore.ops.LogSpace`.
-- [BETA] Add operator primitive for `mindspore.ops.LuUnpack`.
-- [BETA] Add operator primitive for `mindspore.ops.MatrixDiagPartV3`.
-- [BETA] Add operator primitive for `mindspore.ops.MatrixDiagV3`.
-- [BETA] Add operator primitive for `mindspore.ops.MatrixSetDiagV3`.
-- [BETA] Add operator primitive for `mindspore.ops.MaxPool3DWithArgmax`.
-- [BETA] Add operator primitive for `mindspore.ops.MaxUnpool2D`.
-- [BETA] Add operator primitive for `mindspore.ops.MaxUnpool3D`.
-- [BETA] Add operator primitive for `mindspore.ops.MultiMarginLoss`.
-- [BETA] Add operator primitive for `mindspore.ops.MultinomialWithReplacement`.
-- [BETA] Add operator primitive for `mindspore.ops.Mvlgamma`.
-- [BETA] Add operator primitive for `mindspore.ops.NanToNum`.
-- [BETA] Add operator primitive for `mindspore.ops.NextAfter`.
-- [BETA] Add operator primitive for `mindspore.ops.Orgqr`.
-- [BETA] Add operator primitive for `mindspore.ops.Polygamma`.
-- [BETA] Add operator primitive for `mindspore.ops.ResizeBilinearV2`.
-- [BETA] Add operator primitive for `mindspore.ops.RightShift`.
-- [BETA] Add operator primitive for `mindspore.ops.ScatterNdDiv`.
-- [BETA] Add operator primitive for `mindspore.ops.ScatterNdMul`.
-- [BETA] Add operator primitive for `mindspore.ops.SearchSorted`.
-- [BETA] Add operator primitive for `mindspore.ops.Sinc`.
-- [BETA] Add operator primitive for `mindspore.ops.Trace`.
-- [BETA] Add operator primitive for `mindspore.ops.Tril`.
-- [BETA] Add operator primitive for `mindspore.ops.TrilIndices`.
-- [BETA] Add operator primitive for `mindspore.ops.TriuIndices`.
-- [BETA] Add operator primitive for `mindspore.ops.UniqueConsecutive`.
-- [STABLE] Add operator primitive for `mindspore.ops.Cummax`.
-- [STABLE] Add operator primitive for `mindspore.ops.FillV2`.
-- [STABLE] Add operator primitive for `mindspore.ops.IsClose`.
-- [STABLE] Add operator primitive for `mindspore.ops.MatrixSolve`.
-- [STABLE] Add operator primitive for `mindspore.ops.Median`.
-- [STABLE] Add operator primitive for `mindspore.ops.MultilabelMarginLoss`.
-- [STABLE] Add operator primitive for `mindspore.ops.NonZero`.
-- [STABLE] Add operator primitive for `mindspore.ops.Pdist`.
-- [STABLE] Add operator primitive for `mindspore.ops.Polar`.
-- [STABLE] Add operator primitive for `mindspore.ops.RandomGamma`.
-- [STABLE] Add operator primitive for `mindspore.ops.RandomPoisson`.
-- [STABLE] Add operator primitive for `mindspore.ops.RandomShuffle`.
-- [STABLE] Add operator primitive for `mindspore.ops.Renorm`.
-- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdMax`.
-- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdMin`.
-- [STABLE] Add operator primitive for `mindspore.ops.Svd`.
-- [STABLE] Add operator primitive for `mindspore.ops.TripletMarginLoss`.
-
-#### Deleted APIs
-
-- The `mindspore.compression` feature was deprecated at MindSpore 1.8 and is removed in this version.
- The following `mindspore.nn.quant` interfaces are also removed simultaneously: `mindspore.nn.FakeQuantWithMinMaxObserver`, `mindspore.nn.Conv2dBnFoldQuantOneConv`, `mindspore.nn.Conv2dBnFoldQuant`, `mindspore.nn.Conv2dBnWithoutFoldQuant`, `mindspore.nn.Conv2dQuant`, `mindspore.nn.DenseQuant`, `mindspore.nn.ActQuant`, `mindspore.nn.TensorAddQuant`, `mindspore.nn.ActQuant`, `mindspore.nn.MulQuant`. Please use [MindSpore Golden Stick](https://gitee.com/mindspore/golden-stick) instead to implement QuantAwareTraining in MindSpore.
-- The `mindspore.dataset.close_pool`, `mindspore.dataset.to_device`, and `mindspore.dataset.set_dynamic_columns` interfaces are discarded in earlier version and being removed in this version.
-
-#### Backwards Incompatible Change
-
-- 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
-
-- [I66PE6] fix AssignSub primitive abnormal input leads to coredump.
-
-- [I6F5E6] fix data_sink function timeout on Ascend.
-
-### Others
-
-- Windows support is still being optimized,this version does not support now.It will be available for download in version 2.0.
-
-### 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!
-
-## MindSpore Lite 2.0.0-rc1 Release Notes
-
-### Major Features and Improvements
-
-#### MindSpore Lite Cloud Inference
-
-The original MindSpore Lite is mainly used for edge devices such as mobile phones and head units. Cloud inference is added to support scenarios with multiple backend hardware resources on the cloud, supports Ascend and NVIDIA GPU inference cards, and efficiently utilizes multi-core resources on the cloud.
-
-The original cloud inference integrated through MindSpore training can be changed to MindSpore Lite. For details, see [Quick Start to Cloud-side Inference](https://mindspore.cn/lite/docs/en/r2.0/quick_start/one_hour_introduction_cloud.html). To retain the original integration method, see [Inference](https://mindspore.cn/docs/en/r2.0/faq/inference.html).
-
-- [STABLE] Support MindIR model files.
-- [STABLE] Third-party Onnx, TensorFlow, and Caffe models can be converted to MindIR model files using the MindSpore Lite conversion tool.
-- [STABLE] One release package supports multiple hardware backends: Ascend, NVIDIA GPU, CPU.
-- [STABLE] Supports the `Model` interface and `ModelParallelRunner` concurrent inference interface.
-- [STABLE] Supports C++, Python, and Java inference interfaces.
-
-#### API
-
-- Due to the defects of the original Python API that many configuration parameters and complex usage, the usability of The Python APIs are optimized in version 2.0. The optimizations include class construction methods and class attribute adjustment. In addition, the Python APIs in version 2.0 and later will be integrated into the cloud-side inference scenario, which are incompatible with Python APIs of the earlier versions. For details, see [Python API](https://www.mindspore.cn/lite/api/en/r2.0/mindspore_lite.html).
-
-## MindSpore 2.0.0-alpha Release Notes
-
-### Major Features and Improvements
-
-#### PyNative
-
-- The default mode of MindSpore is switched to PyNative. If you want to manually set the mode, please refer to [Computational Graph](https://www.mindspore.cn/tutorials/en/r2.0.0-alpha/advanced/compute_graph.html).
-- Support dynamic shape without padding, three networks are supported as demos: Transformer-GPU, YOLOV5-GPU, ASR-Ascend. Transformer-GPU and YOLOV5-GPU can be downloaded from [models](https://gitee.com/mindspore/models/tree/dynamic_shape). Only the following operators are available on Ascend backend: Add、Assign、BatchMatMul、BiasAdd、BiasAddGrad、Cast、Conv2D、Conv2DBackpropFilter、Conv2DBackpropInput、CTCLoss、Div、Dropout、DropoutDoMask、Equal、ExpandDims、Gather、GetNext、LayerNorm、LayerNormGrad、LessEqual、Load、Log、LogicalAnd、LogicalNot、LogicalOr、LogSoftmax、LogSoftmaxGrad、MatMul、Maximum、Mul、Neg、NotEqual、NPUAllocFloatStatus、NPUClearFloatStatus、OneHot、RealDiv、Reciprocal、ReduceMean、ReduceSum、ReLU、ReluGrad、Reshape、Select、Softmax、StridedSlice、Sub、Tile、Transpose、UnsortedSegmentSum、ZerosLike. The remaining operators have not been fully verified, please use them as appropriate.
-
-#### DataSet
-
-- The TFRecordDataset API can directly read TFRecord files compressed by GZIP or ZLIB.
-- The NumpySlicesDataset API can process data of different dimensions at the same time.
-- Optimize the structure of error log to display more clear call stack information for debugging.
-- Fixed `mindspore.dataset.config.set_seed` does not take effect for random seeds in distributed training scenarios.
-
-#### AutoParallel
-
-- Supports more operators with distributed implements.
-
- Element Wise Operators:AddN, BitwiseAnd, BitwiseOr, BitwiseXor, CumProd, HShrink, HSigmoid, IsFinite, Mish, MulNoNan, Rint, SeLU, SoftShrink, TruncateDiv, TruncateMod, Xdivy Xlogy, InplaceAdd, InplacSub, InplaceUpdate, Cdist, L2Loss, Lerp.
-
- Math Operators:SquaredDifference, Erfinv, MaskedFill, SplitV, Gamma, KLDivLoss, LinSpace.
-
- Scatter Operators:ScatterAdd,ScatterDiv,ScatterMax,ScatterMul,ScatterNdAdd,ScatterNdSub,ScatterNdUpdate,ScatterSub,TensorScatterAdd,TensorScatterDiv,TensorScatterMax,TensorScatterMax,TensorScatterMul,TensorScatterAdd,TensorScatterUpdate.
-
-- Add new apis `transform_checkpoints` and `transform_checkpoint_by_rank` to transfer the distributed checkpoint files by strategy files. Please refer to [Distributed Resilience Training and Inference](https://www.mindspore.cn/tutorials/experts/en/r2.0.0-alpha/parallel/resilience_train_and_predict.html).
-
-### API Change
-
-#### operator
-
-- [STABLE] Add operator primitive for `mindspore.ops.AdaptiveMaxPool3D`.
-- [STABLE] Add operator primitive for `mindspore.ops.AdjustHue`.
-- [STABLE] Add operator primitive for `mindspore.ops.BartlettWindow`.
-- [STABLE] Add operator primitive for `mindspore.ops.BesselJ0`.
-- [STABLE] Add operator primitive for `mindspore.ops.BesselJ1`.
-- [STABLE] Add operator primitive for `mindspore.ops.BesselK0`.
-- [STABLE] Add operator primitive for `mindspore.ops.BesselK0e`.
-- [STABLE] Add operator primitive for `mindspore.ops.BesselK1`.
-- [STABLE] Add operator primitive for `mindspore.ops.BesselK1e`.
-- [STABLE] Add operator primitive for `mindspore.ops.BesselY0`.
-- [STABLE] Add operator primitive for `mindspore.ops.BesselY1`.
-- [STABLE] Add operator primitive for `mindspore.ops.Betainc`.
-- [STABLE] Add operator primitive for `mindspore.ops.Bincount`.
-- [STABLE] Add operator primitive for `mindspore.ops.BlackmanWindow`.
-- [STABLE] Add operator primitive for `mindspore.ops.Bucketize`.
-- [STABLE] Add operator primitive for `mindspore.ops.CombinedNonMaxSuppression`.
-- [STABLE] Add operator primitive for `mindspore.ops.CompareAndBitpack`.
-- [STABLE] Add operator primitive for `mindspore.ops.Complex`.
-- [STABLE] Add operator primitive for `mindspore.ops.DataFormatVecPermute`.
-- [STABLE] Add operator primitive for `mindspore.ops.EuclideanNorm`.
-- [STABLE] Add operator primitive for `mindspore.ops.Expand`.
-- [STABLE] Add operator primitive for `mindspore.ops.ExtractGlimpse`.
-- [STABLE] Add operator primitive for `mindspore.ops.FillDiagonal`.
-- [STABLE] Add operator primitive for `mindspore.ops.FractionalAvgPool`.
-- [STABLE] Add operator primitive for `mindspore.ops.FractionalMaxPool`.
-- [STABLE] Add operator primitive for `mindspore.ops.Gcd`.
-- [STABLE] Add operator primitive for `mindspore.ops.HammingWindow`.
-- [STABLE] Add operator primitive for `mindspore.ops.Histogram`.
-- [STABLE] Add operator primitive for `mindspore.ops.HSVToRGB`.
-- [STABLE] Add operator primitive for `mindspore.ops.Lcm`.
-- [STABLE] Add operator primitive for `mindspore.ops.LeftShift`.
-- [STABLE] Add operator primitive for `mindspore.ops.ListDiff`.
-- [STABLE] Add operator primitive for `mindspore.ops.LogSpace`.
-- [STABLE] Add operator primitive for `mindspore.ops.Lstsq`.
-- [STABLE] Add operator primitive for `mindspore.ops.MatrixDiagPartV3`.
-- [STABLE] Add operator primitive for `mindspore.ops.MatrixDiagV3`.
-- [STABLE] Add operator primitive for `mindspore.ops.MatrixExp`.
-- [STABLE] Add operator primitive for `mindspore.ops.MatrixPower`.
-- [STABLE] Add operator primitive for `mindspore.ops.MaxPool3DWithArgmax`.
-- [STABLE] Add operator primitive for `mindspore.ops.MaxUnpool2D`.
-- [STABLE] Add operator primitive for `mindspore.ops.MultilabelMarginLoss`.
-- [STABLE] Add operator primitive for `mindspore.ops.NextAfter`.
-- [STABLE] Add operator primitive for `mindspore.ops.Orgqr`.
-- [STABLE] Add operator primitive for `mindspore.ops.ReduceStd`.
-- [STABLE] Add operator primitive for `mindspore.ops.RGBToHSV`.
-- [STABLE] Add operator primitive for `mindspore.ops.RightShift`.
-- [STABLE] Add operator primitive for `mindspore.ops.SampleDistortedBoundingBoxV2`.
-- [STABLE] Add operator primitive for `mindspore.ops.ScaleAndTranslate`.
-- [STABLE] Add operator primitive for `mindspore.ops.ScatterAddWithAxis`.
-- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdDiv`.
-- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdMax`.
-- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdMul`.
-- [STABLE] Add operator primitive for `mindspore.ops.STFT`.
-- [STABLE] Add operator primitive for `mindspore.ops.Trace`.
-- [STABLE] Add operator primitive for `mindspore.ops.UpsampleNearest3D`.
-- [STABLE] Add operator primitive for `mindspore.ops.UpsampleTrilinear3D`.
-- [STABLE] Add distributed weight conversion interface `mindspore.parallel.transform_checkpoints`.
-- [STABLE] Add distributed weight conversion interface `mindspore.parallel.transform_checkpoint_by_rank`.
-
-#### Backwards Incompatible Change
-
-##### Python API
-
-- The `mindspore.ms_function` interface is renamed to `mindspore.jit`, and `mindspore.ms_function` will be deprecated and removed in a future version.
-- The `mindspore.ms_class` interface is renamed to `mindspore.jit_class`, and `mindspore.ms_class` will be deprecated and removed in a future version.
-- The `mindspore.ops.ms_kernel` interface is renamed to `mindspore.ops.kernel`, and `mindspore.ops.ms_kernel` will be deprecated and removed in a future version.
-- The `mindspore.dataset.map` interface parameter `column_order` does not take effect, use`mindspore.dataset.project`.
-- The `mindspore.dataset.close_pool` and `mindspore.dataset.to_device` and `mindspore.dataset.set_dynamic_columns` are deprecated and removed in this version.
-
-### Bug fixes
-
-- Fixed an issue where the mixed precision functional interface could not modify the backend driver in graph mode
-- Fixed the problem that users can automatically transfer device_id in the single-P scenario for the following networks:（mobilenetv1/fasterrcnn/yolov3/yolov4/yolov5/unet/openpose/simplepose/crnn/gnmtv2/faceattribute/facequality/facedetection）
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
-
-Contributions of any kind are welcome!
-
-## MindSpore 1.10.1 Release Notes
-
-### Bug fixes
-
-- Fixed the issue that the specified axis is not considered in logsumexp anti-overflow processing
-- Fixed the compilation dependency of proto file
-- Fixed the issue that the print operator printing result is not normal
-- Fixed the issue that the equal operator is out of range
-- Fixed the problem that when function wrapped by @jit，the cell id is not correct
-- Fixed the GNN scenario data type verification error
-- Fixed the problem that the dataset.map multi-process degenerates into threads
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-archer2049, caifubi, chenfei_mindspore, gaoshuanglong, Greatpan, guozhijian, huoxinyou, Kxiong, lanzhineng, lijunbin, liubuyu, liuchuting, luochao60, lyqlola, nomindcarry, TuDouNi, xiaotianci, xupan, yangshuo, yefeng, YingtongHu, yuchaojie, zhoufeng, ZPaC, 刘勇琪, 吕昱峰, 王禹程, 于振华.
-
-Contributions of any kind are welcome!
-
-## MindSpore 1.10.0 Release Notes
-
-### Major Features and Improvements
-
-#### DataSet
-
-- [STABLE]The timeout waiting time is adjusted in data sinking mode. The default value is 600s after adjusted. This solves the isuses that the GetNext operator may timeout due to environment resource competition and large computing workload when training in sink mode.
-
-### Bug fixes
-
-- Fixed an issue where some Primitive operators in AMP cannot be instantiated in graph mode and the interface is unavailable.
-- Fixed an issue of DynamicRNN execution failure in LSTM network under the scenario of computational force segmentation on Ascend platform.
-- Fixed DEVICE_ID cannot be set by single card train scripts parameters in mobilenet, fasterrcnn, yolo, etc.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
-
-Contributions of any kind are welcome!
-
-## MindSpore Lite 1.10.0 Release Notes
-
-### Bug fixes
-
-- Fixed potential accuracy problem of arithmetic type CPU kernels at dynamical shape case.
-- Fixed the Incorrect Write Address of the Deconv Quantization Operator.
-
-## MindSpore 1.9.0 Release Notes
-
-### Major Features and Improvements
-
-#### FrontEnd
-
-- [STABLE] Add the object-oriented and functional combination programming paradigm, add mixed-precision APIs for combination programming paradigms such as `mindspore.amp.LossScaler`, `mindspore.amp.DynamicLossScaler`, `mindspore.amp.StaticLossScaler`, `mindspore.amp.auto_mixed_precision` and `mindspore.amp.all_finite`.
-
-### API Change
-
-#### operator
-
-- [STABLE] Add nn interface for `nn.AdaptiveAvgPool3d`.
-- [STABLE] Add functional interface for `ops.adaptive_avg_pool3d`.
-- [STABLE] Add functional interface for `ops.addcdiv`.
-- [STABLE] Add functional interface for `ops.addcmul`.
-- [STABLE] Add GPU and CPU support for `ops.approximate_equal`.
-- [STABLE] Add GPU support for `ops.atanh`.
-- [STABLE] Add GPU support for `ops.bessel_i0`.
-- [STABLE] Add Ascend support for `ops.bessel_i0e`.
-- [STABLE] Add GPU support for `ops.bessel_i1`.
-- [STABLE] Add Ascend and GPU support for `ops.bessel_i1e`.
-- [STABLE] Add GPU support for `ops.bessel_j0`.
-- [STABLE] Add GPU support for `ops.bessel_j1`.
-- [STABLE] Add GPU support for `ops.bessel_k0`.
-- [STABLE] Add GPU support for `ops.bessel_k0e`.
-- [STABLE] Add GPU support for `ops.bessel_k1`.
-- [STABLE] Add GPU support for `ops.bessel_k1e`.
-- [STABLE] Add GPU support for `ops.bessel_y0`.
-- [STABLE] Add GPU support for `ops.bessel_y1`.
-- [STABLE] Add functional interface for `ops.bias_add`.
-- [STABLE] Add GPU support for `ops.bitwise_and`.
-- [STABLE] Add GPU support for `ops.bitwise_or`.
-- [STABLE] Add GPU support for `ops.bitwise_xor`.
-- [STABLE] Add Ascend support for `ops.grid_sample`.
-- [STABLE] Add CPU support for `ops.inplace_update`.
-- [STABLE] Add Ascend and GPU support for `ops.isclose`.
-- [STABLE] Add Ascend support for `ops.isnan`.
-- [STABLE] Add GPU support for `ops.lerp`.
-- [STABLE] Add functional interface for `ops.random_poisson`.
-- [STABLE] Add functional interface for `ops.reverse_sequence`.
-- [STABLE] Add GPU support for `ops.scatter_mul`.
-- [STABLE] Add functional interface for `ops.scatter_nd_max`.
-- [STABLE] Add functional interface for `ops.scatter_nd_min`.
-- [STABLE] Add GPU support for `ops.SparseToDense`.
-- [STABLE] Add functional interface for `ops.square`.
-- [STABLE] Add GPU support for `ops.standard_laplace`.
-- [STABLE] Add functional interface for `ops.std`.
-- [STABLE] Add Ascend and GPU support for `ops.trunc`.
-- [STABLE] Add functional interface for `ops.unsorted_segment_sum`.
-- [STABLE] Add functional interface for `ops.xdivy`.
-- [STABLE] Add GPU support for `ops.xlogy`.
-- Deprecate `ops.poisson` and use `ops.random_poisson` instead.
-- Deprecate `ops.SparseApplyAdagrad` and use `ops.SparseApplyAdagradV2` instead.
-
-### Bug fixes
-
-- [BUGFIX] The logic of the auto mixed precision (amp) O2 level is revised. In addition to the `BatchNorm1d` and `BatchNorm2d` operators, the other two operators `BatchNorm3d` and `LayerNorm` are added. The four operators still use the float32 data type when calculating.
-
-- [BUGFIX] Fix the problem that when processing string type data, if `output_numpy=True` is specified when calling the `create_dict_iterator` or `create_tuple_iterator` interface, the obtained data will be of type `numpy.bytes_`. After this fixing, these interfaces will directly return `numpy.str_` type data, and users do not need to perform string decoding operations on it. Likewise, when performing user defined processing functions, the received data will also be of type `numpy.str_` directly, matching the original source data type.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, liyanliu, lizhenyu, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, panfengfeng, panyifeng, Payne, peixu_ren, Pengyongrong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanyuan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
-
-Contributions of any kind are welcome!
-
-## MindSpore 1.8.1 Release Notes
-
-### API Change
-
-#### operator
-
-- [STABLE] Add GPU and CPU support for ops.ApplyAdagradDA.
-- [STABLE] Add CPU support for ops.ApplyAdagradV2.
-- [STABLE] Add Ascend dynamic shape support for ops.ApplyCenteredRmsProp.
-- [STABLE] Add CPU support for ops.ApplyFtrl.
-- [STABLE] Add CPU support for ops.ApplyGradientDescent.
-- [STABLE] Add CPU support for ops.ApplyPowerSign.
-- [STABLE] Add GPU and CPU support for ops.ApplyProximalAdagrad.
-- [STABLE] Add Ascend dynamic shape support for ops.ApplyRmsProp.
-- [STABLE] Add functional interface for ops.max.
-- [STABLE] Add functional interface for ops.atan2.
-- [STABLE] Add GPU support for ops.cummax.
-- [STABLE] Add GPU and CPU support for ops.cummin.
-- [STABLE] Add GPU support for ops.diag.
-- [STABLE] Add functional interface for ops.expand_dims.
-- [STABLE] Add functional interface for ops.gather_elements.
-- [STABLE] Add GPU support for ops.grid_sample.
-- [STABLE] Add Ascend support for ops.hardswish.
-- [BETA] Add GPU support for ops.index_fill.
-- [BETA] Add CPU support for ops.inplace_update.
-- [BETA] Add GPU support for nn.InstanceNorm1d.
-- [BETA] Add GPU support for nn.InstanceNorm2d.
-- [BETA] Add GPU support for nn.InstanceNorm3d.
-- [STABLE] Add functional interface for ops.log1p.
-- [STABLE] Add GPU and CPU support for ops.masked_fill.
-- [BETA] Add GPU support for ops.matrix_diag_part.
-- [BETA] Add GPU support for ops.matrix_diag.
-- [BETA] Add GPU support for ops.matrix_set_diag.
-- [STABLE] Add GPU support for ops.max_pool3d.
-- [STABLE] Add functional interface for ops.nll_loss.
-- [STABLE] Add functional interface for ops.one_hot.
-- [STABLE] Add functional interface for ops.pad.
-- [STABLE] Add CPU support for ops.random_gamma.
-- [STABLE] Add functional interface for ops.amax.
-- [STABLE] Add functional interface for ops.mean.
-- [STABLE] Add functional interface for ops.amin.
-- [STABLE] Add functional interface for ops.prod.
-- [STABLE] Add Ascend, GPU, and CPU support for ops.renorm.
-- [BETA] Add Ascend, GPU, and CPU support for ops.tensor_scatter_elements.
-- [STABLE] Add GPU support for ops.scatter_max.
-- [STABLE] Add GPU support for ops.scatter_min.
-- [STABLE] Add functional interface for ops.scatter_nd.
-- [STABLE] Add GPU support for ops.scatter_nd_max.
-- [STABLE] Add functional interface for ops.scatter_update.
-- [STABLE] Add CPU support for ops.binary_cross_entropy_with_logits.
-- [STABLE] Add functional interface for ops.smooth_l1_loss.
-- [STABLE] Add CPU support for ops.space_to_batch_nd.
-- [STABLE] Add GPU and CPU support for ops.SparseApplyAdagrad.
-- [STABLE] Add GPU and CPU support for ops.sparse_segment_mean.
-- [STABLE] Add functional interface for ops.squeeze.
-- [STABLE] Add CPU support for ops.standard_laplace.
-- [BETA] Add Ascend, GPU, and CPU support for nn.ReflectionPad1d.
-- [BETA] Add Ascend, GPU, and CPU support for nn.ReflectionPad2d.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.SiLU.
-- [STABLE] Add functional interface for ops.transpose.
-- [STABLE] Add CPU support for ops.uniform_candidate_sampler.
-- [STABLE] Add functional interface for ops.uniform.
-- [STABLE] Add GPU support for ops.unique_with_pad.
-- [STABLE] Add functional interface for ops.unstack.
-- [BETA] Add GPU and CPU support for ops.interpolate.
-- [STABLE] Add CPU support for ops.xdivy.
-- [STABLE] Add CPU support for ops.xlogy.
-
-## MindSpore 1.8.0 Release Notes
-
-### Major Features and Improvements
-
-#### FrontEnd
-
-- [BETA] Add `mindspore.train.Model.fit` API, add `mindspore.train.callback.EarlyStopping` and `mindspore.train.callback.ReduceLROnPlateau` in Callback.
-- [BETA] Support custom operator implemented by Julia.
-- [BETA] Support custom operator implemented by MindSpore Hybrid DSL.
-- [STABLE] The export() interface supports the export of a model using a custom encryption algorithm, and the load() interface supports the import of a model using a custom decryption algorithm.
-- [BETA] [Unified_Dynamic_and_Static_Graphs] [Usability] Constant-type data (tuple/list/dict is supported in Version 1.8) can be set to be variable during graph compiling.
-- [BETA] [Unified_Dynamic_and_Static_Graphs] JIT fallback is used to support the control flow capability in the constant scenario.
-- [STABLE] [Unified_Dynamic_and_Static_Graphs] The Python raise statement is supported in the graph mode constant scenario.
-- [STABLE] [Unified_Dynamic_and_Static_Graphs] The Python assert statement is supported in the graph mode constant scenario.
-- [STABLE] [Unified_Dynamic_and_Static_Graphs] The Python print statement is supported in the graph mode constant scenario.
-- [STABLE] [Unified_Dynamic_and_Static_Graphs] The str.format() method is supported in the graph mode.
-- [STABLE] [Unified_Dynamic_and_Static_Graphs] The slice method can be used to assign a value to the list in the graph mode.
-- [STABLE] [Unified_Dynamic_and_Static_Graphs] The instances of custom classes can be created and invoked in the graph mode.
-- [STABLE] [Unified_Dynamic_and_Static_Graphs] Obtaining the properties of a class from the Cell array and the custom class array is supported.
-- [STABLE] [Unified_Dynamic_and_Static_Graphs] isinstance supports scenario expanding in the graph mode.
-- [STABLE] Rename the custom operator decorator 'ms_hybrid' to 'ms_kernel'.
-- [BETA] Custom operator Hybrid DSL is supported on the backend of CPU.
-- [BETA] Custom operator Ascend backend adds custom scheduling primitive syntax support.
-
-#### PyNative
-
-- [STABLE] Implement the AdamWeightDecay operator to replace the original small operator combination mode.
-- [STABLE] In PyNative mode, execute the optimizer by unifying the dynamic and static graphs.
-- [STABLE] Optimize the execution performance of PyNative bprop graph and ms_function.
-
-#### Auto Parallel
-
-- [STABLE] Docking the AllToAll single-operator mode. Support AllToAll Operator in the graph compilation level O0.
-- [STABLE] Whole-graph offloading supports MPI launching. In Whole-graph offloading, launching with MPI is supported.
-- [STABLE] Seeds of model weights provide parallel interface configuration. If you do not set the random number of seeds through the mindspore.set_seed command, the weights initialized by each parameter is determined by the current fragment index. If the random number of seeds are configured, the initialization results of the same shape and weight of the same segmentation policy are the same.
-- [STABLE] The HCCL shields internal full-mesh and non-full-mesh connections. Both fully-connected AllToAllv and hierarchical AllToAllv are allowed in one training session.
-- [BETA] CPU optimizer fusion. Multiple optimizer operators are combined according to data types through cross-parameter fusion, improving performance. Currently, It has been verified on CPU AdamWeightDecay optimizer. You can use the flatten_weights method in the network cell class to enable this function.
-
-#### Executor
-
-- [STABLE] Provide southbound API.
-- [STABLE] Multi-actor fusion execution to optimize the execution performance during runtime.
-- [STABLE] Nopop operators (eg. reshape) execute elimination.
-- [STABLE] Embedded cache architecture switches unified distributed runtime.
-- [STABLE] Parameter Server switches unified distributed runtime.
-- [STABLE] Support Parameter Server mode training on CPU.
-
-#### DataSet
-
-- [STABLE] When using the map operation for dataset objects and the parameters like: num_parallel_workers > 1 and python_multiprocessing=True, the multi-process mechanism is optimized, so that the data channel and child processes are mapped one by one, avoiding excessive file handle occupation, and closing_pool interface is also deleted.
-- [STABLE] Add a batch of Vision, Text and Audio data augmentation operations.
-- [STABLE] Fix a bug where the flat_map method of the Dataset class does not flatten the result.
-- [STABLE] Unify import paths of dataset augmentation APIs to provide more easier way to use. Refer to [latest api usages](https://www.mindspore.cn/docs/en/r1.8/api_python/mindspore.dataset.vision.html).
-
-### API Change
-
-#### operator
-
-- [STABLE] Add GPU support for ops.adaptive_avg_pool2d.
-- [BETA] Add Ascend, GPU, and CPU support for ops.adaptive_max_pool2d .
-- [BETA] Add CPU support for ops.approximate_equal.
-- [STABLE] Add CPU support for ops.argmin.
-- [BETA] Add CPU support for ops.assign_sub.
-- [STABLE] Add GPU support for ops.bernoulli.
-- [BETA] Add CPU support for ops.bessel_i0.
-- [BETA] Add CPU support for ops.bessel_i0e.
-- [BETA] Add CPU support for ops.bessel_i1.
-- [BETA] Add CPU support for ops.bessel_i1e Add CPU support.
-- [STABLE] Add CPU support for ops.bessel_j0.
-- [STABLE] Add CPU support for ops.bessel_j1.
-- [STABLE] Add CPU support for ops.bessel_k0.
-- [STABLE] Add CPU support for ops.bessel_k0e.
-- [BETA] Add CPU support for ops.bessel_k1.
-- [BETA] Add CPU support for ops.bessel_k1e.
-- [STABLE] Add CPU support for ops.bessel_y0.
-- [STABLE] Add CPU support for ops.bessel_y1.
-- [STABLE] Add CPU support for ops.bitwise_and.
-- [STABLE] Add CPU support for ops.bitwise_or.
-- [STABLE] Add CPU support for ops.bitwise_xor.
-- [STABLE] Add functional interface for ops.broadcast_to.
-- [BETA] Add GPU and CPU support for ops.ceil.
-- [BETA] Add GPU support for ops.col2im.
-- [BETA] Add functional interface for ops.concat.
-- [STABLE] Add GPU support for ops.cosh.
-- [STABLE] Add Ascend and CPU support for ops.ctc_greedy_decoder.
-- [BETA] Add GPU and CPU support for ops.DataFormatDimMap.
-- [BETA] Add GPU and CPU support for ops.dropout2d.
-- [BETA] Add CPU support for ops.dropout3d.
-- [BETA] Add CPU support for ops.erf.
-- [BETA] Add CPU support for ops.erfc.
-- [STABLE] Add functional interface for ops.expand_dims.
-- [STABLE] Add GPU and CPU support for ops.fast_gelu.
-- [STABLE] Add Ascend dynamic shape support for ops.flatten.
-- [BETA] Add GPU and CPU support for ops.ger.
-- [STABLE] Add Ascend, GPU, and CPU support for ops.gumbel_softmax.
-- [BETA] Add GPU and CPU support for ops.hardshrink.
-- [BETA] Add CPU support for ops.index_add.
-- [BETA] Add CPU support for ops.inplace_add.
-- [BETA] Add CPU support for ops.inplace_sub.
-- [STABLE] Add CPU support for ops.intopk.
-- [STABLE] Add GPU and CPU support for ops.inv.
-- [STABLE] Add GPU and CPU support for ops.invert.
-- [BETA] Add CPU support for ops.isclose.
-- [STABLE] Add CPU support for ops.lerp.
-- [BETA] Add CPU support for ops.linspace.
-- [BETA] Add functional interface for ops.log_softmax.
-- [BETA] Add Ascend, GPU, and CPU support for ops.norm.
-- [BETA] Add CPU support for ops.lrn.
-- [BETA] Add GPU support for ops.masked_select.
-- [BETA] Add GPU and CPU support for ops.matrix_band_part.
-- [BETA] Add GPU and CPU support for ops.matrix_solve.
-- [BETA] Add CPU support for ops.meshgrid.
-- [STABLE] Add CPU support for ops.mish.
-- [BETA] Add GPU support forops.nonzero.
-- [STABLE] Add GPU and CPU support for ops.padding.
-- [BETA] Add Ascend dynamic shape support for ops.pow.
-- [BETA] Add functional interface for ops.range.
-- [BETA] Add Ascend dynamic shape support for ops.round.
-- [STABLE] Add Ascend dynamic shape support for ops.scatter_add.
-- [STABLE] Add Ascend dynamic shape support for ops.scatter_div.
-- [BETA] Add GPU support for ops.scatter_max.
-- [BETA] Add GPU support for ops.scatter_min.
-- [BETA] Add CPU support for ops.scatter_nd_add.
-- [STABLE] Add GPU and CPU support for ops.scatter_nd_div.
-- [STABLE] Add GPU and CPU support for ops.scatter_nd_min.
-- [STABLE] Add GPU and CPU support for ops.scatter_nd_mul.
-- [BETA] Add CPU support for ops.scatter_nd_sub.
-- [STABLE] Add Ascend dynamic shape support for ops.scatter_update.
-- [BETA] Add Ascend dynamic shape support for ops.select.
-- [BETA] Add GPU and CPU support for ops.selu.
-- [BETA] Add GPU and CPU support for ops.soft_shrink.
-- [BETA] Add CPU support for ops.softsign.
-- [STABLE] Add GPU support for ops.tan.
-- [BETA] Add Ascend and CPU support ops.tensor_scatter_add.
-- [STABLE] Add GPU and CPU support for ops.tensor_scatter_div.
-- [STABLE] Add GPU and CPU support for ops.tensor_scatter_mul.
-- [BETA] Add Ascend and CPU support for ops.tensor_scatter_sub.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.AdaptiveAvgPool1d.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.AdaptiveMaxPool1d.
-- [BETA] Add Ascend, GPU, and CPU support for nn.BiDense.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.ConstantPad1d.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.ConstantPad2d.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.ConstantPad3d.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.Hardtanh.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.HuberLoss.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.RReLU.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.Tanhshrink.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.Threshold.
-- [STABLE] Add Ascend, GPU, and CPU support for nn.ZeroPad2d.
-- [BETA] Add GPU support for ops.unique_consecutive.
-- [STABLE] Add CPU support for ops.unsorted_segment_max.
-- [STABLE] Add CPU support for ops.unsorted_segment_min.
-- [STABLE] Add GPU support for ops.unsorted_segment_prod.
-
-#### Backwards Incompatible Change
-
-##### Python API
-
-- DVPP simulation algorithm is no longer supported. Remove `mindspore.dataset.vision.c_transforms.SoftDvppDecodeRandomCropResizeJpeg` and `mindspore.dataset.vision.c_transforms.SoftDvppDecodeResizeJpeg` interfaces.
-- Add `on_train_epoch_end` method in LossMonitor, which implements printing metric information in the epoch level when it is used in `mindspore.train.Model.fit`.
-- TimeMonitor printing content changes, and the printed content is added to "train" or "eval" to distinguish between training and inference phases.
-- `filter_prefix` of `mindspore.load_checkpoint` interface: empty string ("") is no longer supported, and the matching rules are changed from strong matching to fuzzy matching.
-
-#### Import Optimization
-
-APIs in `mindspore.context`, `mindspore.parallel`, `mindspore.profiler` and `mindspore.train` can be directly used in `mindspore`. The original usage can still be supported.
-
-For examples:
-
-- `mindspore.context.set_context` can be simplified to `mindspore.set_context`.
-- `mindspore.parallel.set_algo_parameters` can be simplified to `mindspore.set_algo_parameters`.
-- `mindspore.profiler.Profiler` can be simplified to `mindspore.Profiler`.
-- `mindspore.train.callback.Callback` can be simplified to `mindspore.train.Callback`.
-
-The API pages are aggregated to .
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
-
-Contributions of any kind are welcome!
-
-## MindSpore Lite 1.8.0 Release Notes
-
-### Major Features and Improvements
-
-#### API
-
-- [STABLE] Add C++ and Python APIs for model conversion.
-- [STABLE] Add Python APIs for model inference.
-
-#### Post-Training Quantization
-
-- [STABLE] Support perlayer quantization, and built-in CLE to optimize perlayer quantization accuracy.
-
-## MindSpore 1.7.0 Release Notes
-
-### Major Features and Improvements
-
-#### OS
-
-- [STABLE] Support Python 3.8 (Linux/Windows/Mac).
-- [STABLE] Installation improved with more detailed install guide and automated shell scripts.
-- [STABLE] Support operator computing with multi-thread under Windows.
-- [STABLE] Compatible with GCC from version 7.3 to 9.x.
-
-#### FrontEnd
-
-- [STABLE] Support dynamic weight decay for optimizers, that is weight decay value will change according to the increasing step during training.
-- [STABLE] Add four methods to create Tensor, which are `mindspore.numpy.rand()`, `mindspore.numpy.randn()`, `mindspore.numpy.randint()`, and `mindspore.ops.arange()`.
-- [STABLE] Add `mindspore.train.callback.History` in Callback.
-- [BETA] Support custom operator implemented by Julia operator.
-- [STABLE] Support accessing attributes and methods of user-defined classes through `mindspore.ms_class` class decorator.
-- [STABLE] Support training when a network has side effect operations and control flow statements at the same time.
-- [STABLE] Support for more complex control flow syntax, such as a for loop statement in the body of a while loop.
-- [STABLE] Improve the performance of networks with complex syntax control flow statements by decreasing the num of subgraphs.
-
-#### PyNative
-
-- [STABLE] Add Hook functions in PyNative mode, including register_forward_pre_hook, register_forward_hook of the forward hook interface, register_backward_hook of the reverse hook interface.
-- [STABLE] Optimize the execution performance of PyNative mode, and execute the front-end Python and the back-end C++ in parallel.
-
-#### Auto Parallel
-
-- [STABLE] Support TopK routing, data parallel and optimizer state parallel when enable MoE.
-- [STABLE] Support AllGather/ReduceScatter communication operator fusion. Support AllReuduce fusion by the data volume size in DATA_PARALLEL mode.
-- [STABLE] Support ops.clip_by_global_norm in the parallel mode.
-- [STABLE] Support AdaSum optimizer in the parallel mode.
-- [STABLE] Support automatic optimizer state parallel.
-- [STABLE] Support AlltoAll configurable. Support automatically add virtualdataset cell.
-- [STABLE] Support automatically inference trainable parameters in pipeline parallel training.
-- [STABLE] Support clusters where the device number is not the power of 2.
-- [STABLE] Support sharding propagation in auto-parallel mode.
-- [STABLE] Support optimizer offload under the unified runtime.
-- [STABLE] Support Adafactor operator on CPU.
-- [STABLE] Support sharding at H/W axis for Conv2d/Conv2DTranspose operator. Support operators such as ResizeBilinear，ROIAlign, CropAndResize, BoundingBoxEncode, IOU and RandomChoiceWithMask.
-
-#### Executor
-
-- [BETA] [Failure Recovery Under Data Parallel Training](https://www.mindspore.cn/tutorials/experts/en/r1.7/parallel/train_gpu.html) Support auto failure recovery under data parallel training mode.
-- [BETA] Support searching for the number of threads under the CPU to obtain the optimal number of threads for execution. The entire search process takes 50 steps, and the overall performance will reach a stable state after 50 steps. When testing performance, data after 50 steps need to be used as a standard.
-
-#### DataSet
-
-- [STABLE] Add dataset operations mapping between TensorFlow.data module and MindSpore.dataset module, [check list](https://www.mindspore.cn/docs/en/r1.7/note/api_mapping/tensorflow_api_mapping.html#tf-data).
-- [STABLE] Python multiprocessing optimization and make processes exit normally.
-- [STABLE] Support [Dataset Autotune](https://www.mindspore.cn/tutorials/experts/en/r1.7/debug/dataset_autotune.html) for tuning the speed of dataset pipeline automatically.
-- [BETA] [Dataset Offload](https://www.mindspore.cn/docs/en/r1.7/design/dataset_offload.html) support new data augmentation operations: RandomColorAdjust, RandomSharpness, TypeCast.
-- Output a single data column when `__getitem__/__next__` methods of GeneratorDataset return a single NumPy object.
-- Use `ulimit -u 10240` to increase the number of threads/processes available to the current user when specify too many processes or threads for loading dataset may cause RuntimeError: can't start new thread.
-
-### API Change
-
-#### Backwards Incompatible Change
-
-##### Python API
-
-- Modify the gradient return value type of the hook corresponding to the register_backward_hook function, and change the gradient return value to the tuple type uniformly.([!31876](https://gitee.com/mindspore/mindspore/pulls/31876))
-- Deprecated usage: `import mindspore.dataset.engine.datasets as ds`. Use `import mindspore.dataset as ds` instead as recommended in [mindspore doc](https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.dataset.html).
-- Add `mindspore.ms_class` interface, as class decorator for user-defined classes. It allows MindSpore to identify user-defined classes and access their attributes and methods([!30855](https://gitee.com/mindspore/mindspore/pulls/30855))
-- Deprecate `mindspore.SparseTensor` and use `mindspore.COOTensor` instead. ([!28505](https://gitee.com/mindspore/mindspore/pulls/28505))
-- Add Tensor init arg `internal` for internal use.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
-
-Contributions of any kind are welcome!
-
-## MindSpore Lite 1.7.0 Release Notes
-
-### Major Features and Improvements
-
-#### Post quantization
-
-- [STABLE] Support post quantization to run dynamic quantization algorithm.
-- [BETA] Support post quantized model to run on NVIDIA GPU.
-
-# MindSpore 1.6.0
-
-## MindSpore 1.6.0 Release Notes
-
-### Major Features and Improvements
-
-#### OS
-
-- [STABLE] Support macOS with CPU(X86)
-- [BETA] Supoport macOS with CPU(M1)
-
-#### FrontEnd
-
-- [STABLE] Support JIT Fallback feature in Graph mode.
-- [STABLE] Support compile cache feature in Graph mode.
-- [STABLE] Add new optimizers, including ASGD and Rprop.
-- [STABLE] Add new initializers, including Identity, Orthogonal, Dirac, Sparse and VarianceScaling.
-- [STABLE] Support resuming training when an exception occurs in the process.
-- [STABLE] Change `mindspore.nn.LSTMCell` from single-layer LSTM to single-cell LSTM.
-- [BETA] Introduce `mindspore.ops.Custom` to customize your own operators for Ascend(AICore, AICPU), GPU, CPU backends, and the custom type can be one of TBE, AKG, pure Python function or prebuild binary(called aot operator).
-
-#### PyNative
-
-- [STABLE] Support heterogeneous feature in PyNative mode.
-- [STABLE] Optimize memory allocation in PyNative mode.
-
-#### Auto Parallel
-
-- [STABLE] Support configuring the output shard strategy of the MatMul distributed operator.
-- [STABLE] Support multi-instances parallel.
-- [STABLE] Support activation slice communication and calculation overlap in Transformer.
-- [STABLE] Support heterogeneous parallel tensor swap.
-- [STABLE] Add implementations of distributed operator of ResizeNearestNeighbor.
-- [STABLE] Add a communication operator named NeighborExchangeV2 that supports data exchange between adjacent 8 rank ids.
-- [STABLE] Pipeline parallel support GPU platform.
-- [STABLE] Add cell-level data parallel interface.
-- [STABLE] Support gradient AllReduce fusion according to the amount of data.
-- [STABLE] Support a sharding strategy search algorithm called sharding propagation.
-
-#### Executor
-
-- [STABLE] Support multigraph sink and subgraph sink of MindRT.
-- [STABLE] Support memory swap to break the device memory size limit on Ascend platform.
-- [STABLE] Support dynamic deployment of distributed training cluster(GPU).
-- [BETA] Support automatic failover of parameter server.
-
-#### DataSet
-
-- [STABLE] Support overwrite feature in MindRecord.
-- [STABLE] Log improvement and more friendly to users.
-- [BETA] Support new feature [Dataset Offload](https://www.mindspore.cn/docs/programming_guide/en/r1.6/enable_dataset_offload.html) to speed up data processing by heterogeneous computing.
-- [BETA] Support new feature [Dataset Autotune](https://www.mindspore.cn/docs/programming_guide/en/r1.6/enable_auto_tune.html) to adjust parallelism of dataset pipeline automatically.
-
-#### GraphKernel Fusion
-
-- [STABLE] Support kernel fusion and generation for CPU backend.
-
-#### Federated Learning
-
-- [STABLE] FL-Client framework and model decoupling.
-- [BETA] Support Cross-silo federated learning framework.
-
-#### Debug
-
-- [STABLE] Support dump in cell level(Ascend).
-- [STABLE] Support dump Tensor statistics(Ascend/GPU).
-- [STABLE] Support displaying corresponding code lines for fusion nodes.
-- [STABLE] Support passing dump flag in Ascend backend in order to dump correct operators after fusion transformation.
-
-### API Change
-
-#### Backwards Incompatible Change
-
-##### Python API
-
-###### `mindspore.dataset.MindDataset` interface changes input parameter dataset_file([!27542](https://gitee.com/mindspore/mindspore/pulls/27542))
-
-`MindDataset` contains the input parameter `dataset_file`, which is in the singular format. It can receive a single file path or a list that stores multiple file paths. Thus It is preferred to change the input parameter `dataset_file` into plural format. In addition, the input parameters of most dataset API, such as `TFRecordDataset`, are in plural formart (`dataset_files`). To ensure consistency, the input parameter `dataset_file` of MindDataset is changed to plural formart as `dataset_files`, we can see the updated version in api of [mindspore.dataset.MindDataset](https://www.mindspore.cn/docs/en/master/api_python/dataset/mindspore.dataset.MindDataset.html#mindspore.dataset.MindDataset).
-
-###### Delete `mindspore.Tensor`'s property `virtual_flag`([!26989](https://gitee.com/mindspore/mindspore/pulls/26989))
-
-###### Delete `mindspore.Parameter`'s property `is_init`([!26989](https://gitee.com/mindspore/mindspore/pulls/26989))
-
-###### Delete `mindspore.nn.ROC`'s interface `roc`([!25713](https://gitee.com/mindspore/mindspore/pulls/25713))
-
-###### The `shard()` interface of primitive is changed from `shard(strategy)` to `shard(in_strategy=None, out_strategy=None)`
-
-###### The `set_auto_parallel_context()` interface of context is changed from
-
-###### `set_auto_parallel_context(parallel_mode=AUTO_PARALLEL, auto_parallel_search_mode="dynamic_programming")` to `set_auto_parallel_context(parallel_mode=AUTO_PARALLEL, search_mode="dynamic_programming")`
-
-#### Collect Data and Create Landscape
-
-##### Python API
-
-###### `mindspore.train.callback.SummaryCollector` interface's parameter `collect_specified_data` add new operations `collect_landscape` ([!26229](https://gitee.com/mindspore/mindspore/pulls/26229))
-
-`collect_landscape` can collect the parameters needed to create the loss landscape. we can see the updated version in api of [mindspore.train.callback.SummaryCollector](https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.SummaryCollector.html#mindspore.SummaryCollector).
-
-###### `mindspore.train.callback` add new interface `SummaryLandscape` ([!26229](https://gitee.com/mindspore/mindspore/pulls/26229))
-
-`SummaryLandscape` can help you to collect loss landscape information. It can create landscape in PCA direction or random direction by calculating loss. We can see the updated version in api of [mindspore.train.callback.SummaryLandscape](https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.SummaryLandscape.html#mindspore.SummaryLandscape).
-
-### Bug fixes
-
-#### Executor
-
-- Fix process hanging while calling MPI_comm_create in asymmetric pipeline split scenario. ([!28707](https://gitee.com/mindspore/mindspore/pulls/28707))
-- Fix the execution error when the weights are shared between graph mode and PyNative mode.([!26635](https://gitee.com/mindspore/mindspore/pulls/26635))
-- Fixed the probability coredump when free memory under PyNative mode.([!25472](https://gitee.com/mindspore/mindspore/pulls/25472))
-
-#### Dataset
-
-- Fix memory increase abnormally when running dataset for a long time. ([!26237](https://gitee.com/mindspore/mindspore/pulls/26237))
-- Fix saving MindRecord files with Chinese path on Windows. ([!28378](https://gitee.com/mindspore/mindspore/pulls/28378))
-
-## MindSpore Lite
-
-### Major Features and Improvements
-
-#### Converter and runtime
-
-- [STABLE] Add more fusion patterns in the converter tool to improve runtime performance.
-- [STABLE] Support take OpenGL texture as input and output of inference.
-- [STABLE] Refactor the JAVA API.
-- [BETA] Support inference on Ascend310.
-
-#### x86 backend optimization
-
-- [STABLE] Optimize kernels for x86 using Advanced Vector Extensions(AVX512).
-
-#### ARM backend optimization
-
-- [STABLE] Support heterogeneous parallel inference, including splitting operators, constructing heterogeneous subgraphs, and heterogeneous parallel scheduling between CPUs and GPUs.
-- [STABLE] Add more FP16 operators.
-
-#### Post quantization
-
-- [STABLE] Post quantization supports debugging.
-- [STABLE] Full quantization supports choosing non-quantized nodes.
-- [STABLE] Mixed bit quantization supports auto-tune.
-
-#### Training on Device
-
-- [STABLE] Support user-defined algorithm models to access the federated learning framework.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, [wangnan39@huawei.com](mailto:wangnan39@huawei.com), wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, [zhanghaibo5@huawei.com](mailto:zhanghaibo5@huawei.com), zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
-
-Contributions of any kind are welcome!
-
-# MindSpore 1.5.2
-
-## MindSpore 1.5.2 Release Notes
-
-### Bug fixes
-
-- Fix code specification, pclint, codedex alarm.
-- Repair NN Abnormal output of graphnorm operator.
-- Fixed the problem of poor performance in scenes with dynamic rnngrad batch size of 16 times.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
-
-Contributions of any kind are welcome!
-
-# MindSpore 1.5.1
-
-## MindSpore 1.5.1 Release Notes
-
-### Bug fixes
-
-- Fix code specification, pclint, codedex alarm.
-- Fix yolov4 network probabilistic segment error.
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
-
-Contributions of any kind are welcome!
-
-# MindSpore 1.5.0
-
-## MindSpore 1.5.0 Release Notes
-
-### Major Features and Improvements
-
-#### NewModels
-
-- [STABLE] Add CV model on Ascend: Fast-SCNN
-- [BETA] Add CV models on Ascend: midas_V2, attgan, FairMOT, CenterNet_resnet101, SEResNext, YOLOV3-tiny, RetinaFace
-- [STABLE] Add CV models on GPU: ssd_mobilenetv1_fpn, shufflenetv1, tinyDarkNet, CNN-CTC, unet++, DeepText, SqueezeNet
-- [STABLE] Add NLP models on GPU: GRU, GNMT2, Bert-Squad
-- [STABLE] Add recommend models on GPU: NCF
-- [BETA] Add CV models on GPU: FaceAttribute, FaceDetection, FaceRecongnition SENet,
-- [BETA] Add Audio models on GPU: DeepSpeech2
-- [STABLE]`model_zoo` has been separated to an individual repository`models`
-
-#### FrontEnd
-
-- [STABLE] Support`while` and`break`,`continue` statements of training network in`GRAPH_MODE`.
-- [BETA] Support export MindIR file after model training in cloud side and evaluate in edge side by import the MindIR file.
-- [STABLE] Support forward mode auto-diff interface Jvp(Jacobian-Vector-Product).
-- [STABLE] Support backward mode auto-diff interface Vjp(Vector-Jacobian-Product).
-
-#### Auto Parallel
-
-- [STABLE] Support distributed pipeline inference.
-- [STABLE] Add implementation of the sparse attention and its distributed operator.
-- [STABLE] Add implementations of distributed operator of Conv2d/Conv2dTranspose/Conv2dBackpropInput/Maxpool/Avgpool/Batchnorm/Gatherd.
-- [STABLE] Support configuring the dataset strategy on distributed training and inference mode.
-- [STABLE] Add high level API of the Transformer module.
-
-#### Executor
-
-- [STABLE] Support AlltoAll operator.
-- [STABLE] CPU operator (Adam) performance optimization increased by 50%.
-- [BETA] Support Adam offload feature, reduce the static memory usage of Pangu large model by 50%.
-- [STABLE] MindSpore Ascend backend supports configuration operator generation and loading cache path.
-- [STABLE] MindSpore Ascend backend supports lazy build in PyNaitve mode and compilation performance improved by 10 times.
-- [STABLE] The function or Cell decorated by ms_function supports gradient calculation in PyNative mode.
-- [STABLE] The outermost network supports parameters of non tensor type in PyNative mode.
-
-#### DataSet
-
-- [BETA] Add a new method for class Model to support auto data preprocessing in scenario of Ascend 310 inference.
-- [STABLE] Add a new drawing tool to visualize detection/segmentation datasets.
-- [STABLE] Support a new tensor operation named ConvertColor to support color space transform of images.
-- [STABLE] Enhance the following tensor operations to handle multiple columns simultaneously: RandomCrop, RandomHorizontalFlip, RandomResize, RandomResizedCrop, RandomVerticalFlip.
-- [STABLE] Support electromagnetic simulation dataset loading and data augmentation.
-- [STABLE] Optimize the error logs of Dataset to make them more friendly to users.
-
-#### Federated Learning
-
-- [STABLE] Change the deployment environment of FL-Client.
-
-#### Running Data Recorder
-
-- [STABLE] RDR saves collected data files within directories named by Rank ID on distributed training on Ascend, GPU and CPU.
-
-#### GraphKernel Fusion
-
-### API Change
-
-#### Backwards Incompatible Change
-
-##### Python API
-
-###### New Recomputation Configuration for AutoParallel and SemiAutoParallel Scenarios
-
-Configuring the recomputation of the communication operations generated by the model parallel and optimizer parallel to save the memory on the
-devices. Users can pass `mp_comm_recompute` and `parallel_optimizer_comm_recompute` to enable the recomputation of the communication operations.
-
-### Bug fixes
-
-#### FrontEnd
-
-- Fix bug of too many subgraphs when network include`for` statement.([!23669](https://gitee.com/mindspore/mindspore/pulls/23669))
-
-#### Executor
-
-- RunTask failed when parameter_broadcast is enabled in PyNative mode. ([!23255](https://gitee.com/mindspore/mindspore/pulls/23255))
-- An illegal memory access was encountered in the dynamic shape net on GPU.
-- Fix tune failed for DynamicRnn. ([!21081](https://gitee.com/mindspore/mindspore/pulls/21081))
-
-#### Dataset
-
-- Optimize thread monitoring to solve the problem of running multiple multiprocessesing on Windwos. ([!23232](https://gitee.com/mindspore/mindspore/pulls/23232))
-- Fix bugs of Dataset tensor operations in lite mode. ([!21999](https://gitee.com/mindspore/mindspore/pulls/21999))
-- Fix memory increasing when using create_dict_iterator in for loop. ([!22529](https://gitee.com/mindspore/mindspore/pulls/22529))([!22529](https://gitee.com/mindspore/mindspore/pulls/22529))
-
-## MindSpore Lite
-
-### Major Features and Improvements
-
-#### Converter and runtime
-
-1. Optimize TDNN-like streaming model by reusing the result of last inference.
-2. Support dynamic filter Convolution.
-3. Support serializing float32 weight into float16 weight for reducing size of model file.
-4. Provide unified runtime API for developer reusing their code between cloud side and end side.
-5. Now developer can configure built-in pass as custom passes.
-6. Now user can specify format and shape of model inputs while converting model.
-7. Support multiple devices inference, includeing CPU, NPU, GPU. User can set devices in mindspore::Context.
-8. Support mixed precision inference. User can set inference precision by LoadConfig API.
-9. Support custom operator registration and enable inference on third-party hardware.
-
-#### ARM backend optimization
-
-1. Support the nchw data format of some Operators, such as Conv, InstanceNorm, etc. The performance of some models convertered from onnx and caffe is greatly improved.
-2. Fix bugs of memory leak on NPU.
-
-#### Post quantization
-
-1. Weight quantization supports mixed bit quantization.
-2. Full quantization supports data pre-processing.
-3. Adjust the quantization parameters from the command line to the configuration file.
-
-#### Training on Device
-
-1. Unify lite external api with MindSpore.
-2. Implement static memory allocator and common workspace for TOD，save memory 10-20%.
-3. Provide getgradients and setgradients interface，get and set optimizer params interfaces to support MOE Model.
-4. Support user specified output node when export IOD Model.
-5. Support more text networks (tinybert,albert) and operators.
-
-#### Codegen
-
-1. Support kernel register for custom op. Third-party hardware like NNIE can be accessed through it.
-
-### API Change
-
-#### API Incompatible Change
-
-##### C++ API
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
-
-Contributions of any kind are welcome!
-
-# MindSpore 1.4.0
-
-## MindSpore 1.4.0 Release Notes
-
-### Major Features and Improvements
-
-#### NewModels
-
-#### FrontEnd
-
-#### Auto Parallel
-
-- Add distributed operators: Conv2D/Conv2DTranspose/Conv2DBackpropInput/MaxPool/AvgPool/BatchNorm/GatherD
-- Support to configure shard strategy for dataset
-
-#### Executor
-
-#### DataSet
-
-- Add SlicePatchesOperation for Remote Sensing feature（[!18179](https://e.gitee.com/mind_spore/repos/mindspore/mindspore/pulls/18179)）
-
-#### FederatedLearning
-
-#### Running Data Recorder
-
-#### GraphKernel Fusion
-
-#### Profiler
-
-- [STABLE] Support MS_DIAGNOSTIC_DATA_PATH for profiler feature.(Ascend/GPU)
-
-#### Dump
-
-- [STABLE] Support MS_DIAGNOSTIC_DATA_PATH for dump feature.(Ascend/GPU/CPU)
-
-### API Change
-
-#### Backwards Incompatible Change
-
-##### Python API
-
-##### Command Line Interface
-
-###### Dump Config
-
-Previously, we need to set the dump path in dump config file. To make the dump feature easier to use on cloud, we support new environment parameter `MS_DIAGNOSTIC_DATA_PATH`.
-
-| 1.3.0 | 1.4.0 |
-| ------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------- |
-| `path` is a mandatory field. | `path` field is optional. If `path` field is not provided or is empty string, `MS_DIAGNOSTIC_DATA_PATH` should be set in environment. |
-
-### Bug fixes
-
-#### FrontEnd
-
-#### Executor
-
-#### Dataset
-
-- Fix module 'signal' has no attribute 'SIGCHLD' problem under windows platform. ([!21232](https://gitee.com/mindspore/mindspore/pulls/21232))
-
-## MindSpore Lite
-
-### Major Features and Improvements
-
-#### Converter and runtime
-
-#### x86 backend optimization
-
-#### ARM backend optimization
-
-#### Cuda backend optimization
-
-#### OpenCL backend
-
-#### Post quantization
-
-#### Training on Device
-
-#### Codegen
-
-### API Change
-
-#### API Incompatible Change
-
-##### C++ API
-
-#### New features
-
-##### Java API
-
-### Bug fixes
-
-#### Deprecations
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
-
-Contributions of any kind are welcome!
-
-# MindSpore 1.3.0
-
-## MindSpore 1.3.0 Release Notes
-
-### Major Features and Improvements
-
-#### NewModels
-
-- [STABLE] Add CV models on Ascend: CPM, FCN8s, SSD-ResNet50-FPN, EAST, AdvancedEast.
-- [STABLE] Add NLP models on Ascend: DGU, TextCNN, SentimentNet(LSTM).
-- [STABLE] Add CV models on GPU: Faster-RCNN, FCN8s, CycleGAN, AdvancedEast.
-- [BETA] Add CV models on Ascend: CycleGAN, PoseNet, SimCLR.
-- [BETA] Add NLP models on Ascend: DGU, EmoTect, Senta, KT-Net.
-- [BETA] Add NLP models on GPU: DGU, EmoTect.
-- [BETA] Add EPP-MVSNet: a novel deep learning network for 3D reconstruction from multi-view stereo, which has won the first place in Tanks & Temples leaderboard(until April 1, 2021)(GPU).
-
-#### FrontEnd
-
-- [STABLE] The default running mode of MindSpore is changed to Graph mode.
-- [STABLE] Support interface `run_check` to check whether MindSpore is working properly or not.
-- [STABLE] Support saving custom information in the checkpoint file.
-- [STABLE] Normal class adds mean parameter.
-- [STABLE] Support export YOLOv3-DarkNet53 and YOLOv4 ONNX model.
-- [STABLE] Support 40+ operator export ONNX model.
-- [STABLE] The Metric module supports `set_indexes` to select the inputs of `update` in the specified order.
-- [STABLE] Switch `_Loss` to an external API `LossBase` as the base class of losses.
-
-#### Auto Parallel
-
-- [STABLE] Add distributed operators: Select/GatherNd/ScatterUpdate/TopK.
-- [STABLE] Support basic pipeline parallelism.
-- [STABLE] Optimize sharding strategy setting of `Gather`.
-- [STABLE] Optimize mix precision and shared parameter scenarios.
-- [STABLE] Optimize distributed prediction scenarios.
-
-#### Executor
-
-- [STABLE] Support unified runtime in GPU and CPU backend.
-- [STABLE] MindSpore GPU support CUDA11 with cuDNN8.
-- [STABLE] MindSpore GPU inference performance optimization by integrating TensorRT.
-- [STABLE] MindSpore built on one Linux distribution can now be used on multiple Linux distributions with the same CPU architecture (e.g. EulerOS, Ubuntu, CentOS).
-- [STABLE] MindSpore Ascend support group convolution.
-
-#### DataSet
-
-- [STABLE] Support caching over MindRecord dataset.
-- [STABLE] Support new shuffle mode for MindRecord dataset.
-- [STABLE] Support a cropper tool for MindSpore Lite to allow the user to customize MindData binary file according to their script.
-- [STABLE] Support share memory mechanism to optimize the multi-processing efficiency of GeneratorDataset/Map/Batch.
-- [STABLE] Add features for the GNN dataset to support molecular dynamics simulation scenarios.
-
-#### FederatedLearning
-
-- [STABLE] Support Cross-device federated learning framework.
-- [STABLE] Support FL-Server distributed networking including TCP and HTTP communication.
-- [STABLE] Support FL-Server distributed federated aggregation，support autoscaling and fault tolerance.
-- [STABLE] Develop FL-Client framework.
-- [STABLE] Supports local differential privacy algorithms.
-- [STABLE] MPC-based security aggregation algorithm.
-- [STABLE] MindSpore Lite Device-side Inference & Training Interconnection with FL-Client.
-
-#### Running Data Recorder
-
-- [STABLE] Provide records of multi-stage computational graphs, memory allocation information and graph execution order when a "Launch kernel failed" occurs. (CPU)
-
-#### GraphKernel Fusion
-
-- [STABLE] Add options to control the optimization level.
-- [STABLE] Enhance the generalization ability on GPU. GraphKernel is enabled by default in 40+ networks which cover the field of NLP, CV, Recommender, NAS and Audio. The result shows their throughput is significantly improved, and you are Recommended enabling GraphKernel in your network.
-
-#### Debug
-
-- [STABLE] Unified dump function.
-
-### API Change
-
-#### Backwards Incompatible Change
-
-##### Python API
-
-###### `mindspore.dataset.Dataset.device_que` interface removes unused parameter `prefetch_size`([!18973](https://gitee.com/mindspore/mindspore/pulls/18973))
-
-Previously, we have a parameter `prefetch_size` in `device_que` to define the prefetch number of records ahead of the user's request. But indeed this parameter is never used which means it is an ineffective parameter. Therefore, we remove this parameter in 1.3.0 and users can set this configuration by [mindspore.dataset.config.set_prefetch_size](https://www.mindspore.cn/docs/api/en/r1.3/api_python/mindspore.dataset.config.html#mindspore.dataset.config.set_prefetch_size).
-
-
-
-| 1.2.1 | 1.3.0 |
-
-
-|
-
-```python
-device_que(prefetch_size=None, send_epoch_end=True, create_data_info_queue=False)
-```
-
- |
-
-
-```python
-device_que(send_epoch_end=True, create_data_info_queue=False)
-```
-
- |
-
-
-
-###### `mindspore.nn.optim.thor` interface changes to lowercase `thor` and adds two parameters `enable_clip_grad` and `frequency`([!17212](https://gitee.com/mindspore/mindspore/pulls/17212))
-
-The parameter `enable_clip_grad` is used for gradient clipping and another parameter `frequency` is used to control the update interval of second order information matrix.
-
-
-
-| 1.2.1 | 1.3.0 |
-
-
-|
-
-```python
-THOR(net, learning_rate, damping, momentum, weight_decay=0.0, loss_scale=1.0, batch_size=32,
- use_nesterov=False, decay_filter=lambda x: x.name not in [], split_indices=None)
-```
-
- |
-
-
-```python
-thor(net, learning_rate, damping, momentum, weight_decay=0.0, loss_scale=1.0, batch_size=32,
- use_nesterov=False, decay_filter=lambda x: x.name not in [], split_indices=None, enable_clip_grad=False,
- frequency=100)
-```
-
- |
-
-
-
-##### Dump Config
-
-Previously, we could only dump tensor data for one or all steps. To make the dump feature easier to use, we changed the dump configuration format and dump structure. View the [New Dump Tutorial](https://www.mindspore.cn/docs/programming_guide/en/r1.3/dump_in_graph_mode.html).
-
-| 1.2.1 | 1.3.0 |
-| ------------------------------------------------------ | ------------------------------------------------------------------------------------------- |
-| `iteration` is an int. | `iteration` is a string. |
-| `op_debug_mode` is in `async_dump_settings` field. | `op_debug_mode` is in `common_dump_settings` field. `async_dump_settings` is removed. |
-
-### Bug fixes
-
-#### FrontEnd
-
-- Fix exception when use import module in while body such as 'F.xxx'.([!17635](https://e.gitee.com/mind_spore/repos/mindspore/mindspore/pulls/17635))
-- Fix the exception of 'exceeding limit call depth' in compile graph process when using while expression with grad operation. ([!18662](https://e.gitee.com/mind_spore/repos/mindspore/mindspore/pulls/18662))
-
-#### Executor
-
-- Fix reallocate memory bug for communication op.([!14492](https://gitee.com/mindspore/mindspore/pulls/14492))
-- Replace memcpy_async op with tensor_move op.([!15204](https://gitee.com/mindspore/mindspore/pulls/15204))
-- Fix the build error when multiple python versions are installed in the environment. ([!19165](https://gitee.com/mindspore/mindspore/pulls/19165))
-- The warning when the te/topi/hccl version does not match is optimized, and fix the repeated warning. ([!18704](https://gitee.com/mindspore/mindspore/pulls/18704))
-- Fix the error in a cluster with more than 8 pcs in pynative mode. ([!16376](https://gitee.com/mindspore/mindspore/pulls/16376))
-- Fix graph ring problem in UB fusion.([!16109](https://gitee.com/mindspore/mindspore/pulls/16109))
-- Fix AllGather op select problem when the shape is not divisible by 16. ([!18878](https://gitee.com/mindspore/mindspore/pulls/18878))
-
-#### Dataset
-
-- Fix an out-of-memory error when ImagefolderDataset gets an illegal directory. ([!16196](https://gitee.com/mindspore/mindspore/pulls/16196))
-- Fix bugs of vision transformations in lite mode. ([!14722](https://gitee.com/mindspore/mindspore/pulls/14722),[!14774](https://gitee.com/mindspore/mindspore/pulls/14774),[!15050](https://gitee.com/mindspore/mindspore/pulls/15050))
-- Fix default numbers of parallel workers of MindData for those CPUs with fewer cores. ([!15921](https://gitee.com/mindspore/mindspore/pulls/15921))
-- Fix MindRecord writing failed probabilistically in multiprocessing. ([!15242](https://gitee.com/mindspore/mindspore/pulls/15242))
-
-## MindSpore Lite
-
-### Major Features and Improvements
-
-#### Converter and runtime
-
-1. Support Caffe model running on Hi3516D.
-2. Support delegate mechanism to run your models(part or whole) on user specified executor.
-3. Support control flow models.
-4. Support cross-compiling for iOS, so that we can inference models on iOS devices.
-
-#### x86 backend optimization
-
-1. Optimize kernels for x86 using Advanced Vector Extensions(AVX).
-
-#### ARM backend optimization
-
-1. Optimize fp16 kernels.
-2. Support arm32 fp16 instruction acceleration on ARMv8.2.
-
-#### Cuda backend optimization
-
-1. Support NV GPU backend base on delegate mechanism(use TensorRT as delegate).
-
-#### OpenCL backend
-
-1. Optimize the strategy of workgroup and blocksize to improve performance.
-2. Support OpenCL dynamic infershape.
-3. Support INT32 type ops.
-
-#### Post quantization
-
-1. Support fp32 training model converts to quantization training model.
-
-#### Training on Device
-
-1. Support fp32 training model export to quantization model after training process end.
-2. Unify APIs and output package name of training and inference.
-3. Simplify implementation of Train Session.
-4. Optimize train and infer compile, reduce libmindspore-lite-train.so memory.
-5. Training memory optimization: memory reduce 10-50% compare with r1.2.
-6. Training performance optimization: for 1*1 special input shape Cov2DGradInput and SparseSoftmaxCrossEntropyWithLogits operator optimization, improved 10%-20%.
-7. Support more networks(transformer, albert).
-
-#### Codegen
-
-1. Support deployment on HarmonyOS for device.
-
-### API Change
-
-#### API Incompatible Change
-
-##### C++ API
-
-###### Unify LiteSession and TrainSession, Merge LiteSession And TrainSession.([!17356](https://gitee.com/mindspore/mindspore/pulls/17356))
-
-Previously, Training on Device use TrainSession while Inference on Device use LiteSession. To simplify implementation, we move TrainSession functions to LiteSession as virtual function. and move APIs previous defined in train_session.h to lite_session.h.
-
-```cpp
-class MS_API LiteSession {
-...
-static LiteSession *CreateTrainSession(const std::string &filename, const lite::Context *context,
- bool train_mode = false, const lite::TrainCfg *cfg = nullptr);
- static LiteSession *CreateTransferSession(const std::string &filename_backbone, const std::string &filename_head,
- const lite::Context *context, bool train_mode = false,
- const lite::TrainCfg *cfg = nullptr);
-virtual int Train() { return mindspore::lite::RET_ERROR; }
-virtual int Eval() { return mindspore::lite::RET_OK; }
-virtual int SetupVirtualBatch(int virtual_batch_multiplier, float lr = -1.0f, float momentum = -1.0f) {
- return mindspore::lite::RET_ERROR;
- }
-virtual std::vector GetPredictions() const {
- std::vector outputs;
- return outputs;
- }
-...
-```
-
-###### Add Export API for Training on device, obsolete SaveToFile API.([!17356](https://gitee.com/mindspore/mindspore/pulls/17356))
-
-Previously, Training on Device uses SaveToFile API to save the training model to file. Export API was added in this release to support more format, more model type(train or interface part of the model), and save weight quant model of train.
-
-```cpp
-virtual int Export(const std::string &file_name, lite::ModelType model_type = lite::MT_TRAIN,
- lite::QuantizationType quant_type = lite::QT_DEFAULT, lite::FormatType = lite::FT_FLATBUFFERS) {
- return mindspore::lite::RET_ERROR;
- }
-```
-
-###### Add GetFeatureMaps and UpdateFeatureMaps interface for Training on device.([!18344](https://gitee.com/mindspore/mindspore/pulls/18344))
-
-When Training on the device, we may need to update the model featuremap and get model featuremap.particularly in MindSpore Federated Scenario.
-
-```cpp
-virtual std::vector GetFeatureMaps() const {
- std::vector features;
- return features;
- }
- virtual int UpdateFeatureMaps(const std::vector &features) { return mindspore::lite::RET_ERROR; }
-```
-
-#### New features
-
-##### Java API
-
-###### new static method for creating LiteSession by MSConifg in LiteSession.class
-
-Previously, if we want to create a LiteSession object, we need to call two APIs:
-
-```js
-MSConfig config;
-// config options ...
-LiteSession liteSession = new LiteSession();
-boolean ret = liteSession.init(config);
-if (!ret) {
- // handle init LiteSession failed ...
-}
-```
-
-now we can create a LiteSession object with new API just like:
-
-```js
-MSConfig config;
-// config options ...
-LiteSession liteSession = createSession(config);
-if (liteSession == null) {
- // handle create LiteSession failed ...
-}
-```
-
-###### new static method for creating LiteSession byModelBuffer and MSConfig in LiteSession.class
-
-Previously, if we want to inference a model, we need to call APIs like:
-
-```js
-MSConfig config;
-// config options ...
-LiteSession liteSession = new LiteSession();
-boolean initSessionRet = liteSession.init(config);
-if (!initSessionRet) {
- // handle init LiteSession failed and return ...
-}
-Model model = new Model();
-boolean loadModelRet = model.loadModel(modelMappedByteBuffer);
-if (!loadModelRet) {
- // handle load model failed and return ...
-}
-boolean compileModelRet = liteSession.compileGraph(model);
-if (!loadModelRet) {
- // handle compile model failed and return ...
-}
-model.free();
-// liteSession is ready to inference model, call runGraph in LiteSession.class ...
-```
-
-now we can use new API just like:
-
-```js
-MSConfig config;
-// config options ...
-LiteSession liteSession = createSession(modelMappedByteBuffer, config);
-if (liteSession == null) {
- // handle init LiteSession failed and return ...
-}
-// liteSession is ready to inference model, call runGraph in LiteSession.class ...
-```
-
-New createSession method is an API that integrates four old APIs: LiteSession.init, Model.loadModel, LiteSession.compileGraph and model.free. It is simple and efficient as it reduces one modelBuffer copy operation.
-
-###### new methods getFeaturesMap and updateFeatures for in LiteSession.class
-
-Recently, we add a new C++ api in LiteSession class, Correspondingly we add a new java API in LiteSession.java.
-
-```java
-public List getFeaturesMap() {
- List ret = this.getFeaturesMap(this.sessionPtr);
- ArrayList tensors = new ArrayList();
- for (Long msTensorAddr : ret) {
- MSTensor msTensor = new MSTensor(msTensorAddr);
- tensors.add(msTensor);
- }
- return tensors;
- }
- public boolean updateFeatures(List features) {
- long[] inputsArray = new long[features.size()];
- for (int i = 0; i < features.size(); i++) {
- inputsArray[i] = features.get(i).getMSTensorPtr();
- }
- return this.updateFeatures(this.sessionPtr, inputsArray);
- }
-```
-
-###### new methods export to replace saveToFile API in LiteSession.class
-
-Recently, we add a new C++ api in LiteSession class, Correspondingly we add a new java API in LiteSession.java.
-
-```java
-public boolean export(String modelFileName, int modelType, int quantizationType) {
- return this.export(this.sessionPtr, modelFileName, modelType, quantizationType);
- }
-```
-
-###### new train related API moved to LiteSession.class from TrainSession.class
-
-Align with update of C++ api in LiteSession class, add new java API to LiteSession.java Correspondingly.
-
-```java
-public class LiteSession {
-...
-public static LiteSession createTrainSession(String modelName, final MSConfig config, boolean trainMode){...}
-public boolean train() {...}
-public boolean eval() {...}
-...
-```
-
-### Bug fixes
-
-1. Fix the bug that the train session does not release memory cause of refcount bug.
-
-#### Deprecations
-
-### Contributors
-
-Thanks goes to these wonderful people:
-
-Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.
-
-Contributions of any kind are welcome!
-
-# MindSpore 1.2.1
-
-## MindSpore 1.2.1 Release Notes
-
-### Major Features and Improvements
-
-#### FrontEnd
-
-- [STABLE] Add MaskedSelect aicpu operation.(Ascend)
-
-#### Auto Parallel
-
-- [STABLE] Support distributed checkpoint loading.(Ascend/GPU)
-
-# MindSpore 1.2.0
-
-## MindSpore 1.2.0 Release Notes
-
-### Major Features and Improvements
-
-#### NewModels
-
-- [STABLE] Add CV models on Ascend: 3D Unet, Unet++, SSD-Resnet50-fpn, SSD-VGG16, crnn_seq2seq_ocr for BSI, CTPN, resnet18, DPN
-- [STABLE] Add CV models on GPU: Faster-RCNN
-- [STABLE] Add NLP models on Ascend: NAML, Fasttext, GRU, LSTM
-- [BETA] Add TPRR: Thinking Path Re-Ranker, an original ranked-base framework for Multi-Hop Question Answering which has won the first place in HotpotQA leaderboard.(Ascend)
-
-#### FrontEnd
-
-- [STABLE] Support side effects expression to ensure that the perform order of user's semantics is correct.(Ascend/GPU/CPU)
-- [STABLE] Support calculating the gradient for network that contain non-Tensor input parameters（int, float, bool, mstype,int, mstype.float, mstype.uint, mstype.bool_, tuple, list, dict）.(Ascend/GPU/CPU)
-- [STABLE] Support the inverse of a bool Tensor.(Ascend/GPU/CPU)
-- [STABLE] Uniform the interface `isinstance`.(Ascend/GPU/CPU)
-- [STABLE] Support negative indexes.(Ascend/GPU/CPU)
-- [STABLE] Support 110+ Numpy-like interfaces in mindspore.numpy.(Ascend/GPU/CPU)
-- [STABLE] Support export/load mindir model with a size greater than 2 GB.
-- [STABLE] The optimizer supports gradient centralization.(Ascend)
-- [STABLE] Support support auc metric, rou metric, bleu score metric, confusion matrix metric, cosine similarity metric, dice metric, hausdorff distance metric, occlusion sensitivity metric, perplexity metric, mean surface distance metric, root mean surface distance metric.
-- [STABLE] Support use EmbeddingLookup with cache.(Ascend)
-- [STABLE] Add MaskedSelect aicpu operation.(Ascend)
-
-#### Auto Parallel
-
-- [STABLE] Support AllGather and ReduceScatter fusion.(Ascend)
-- [STABLE] Support gradient accumulation feature in auto parallel mode.(Ascend/GPU)
-- [STABLE] Support running parallel optimizer with gradient accumulation.(Ascend)
-- [STABLE] Add the configuration of communication operators' fusion.(Ascend)
-- [STABLE] Support distributed checkpoint loading.(Ascend/GPU)
-
-#### Executor
-
-- [STABLE] Support inference with Nvidia GPU.
-- [STABLE] Support data parallelism in PyNative mode.(Ascend/GPU)
-- [STABLE] Optimize LSTM inference memory consumption in Graph mode with CPU.
-
-#### Sponge
-
-- [STABLE] Add SPONGE modules for molecular dynamics simulation, including Bond, Angle, Dihedral, Non Bond 14, NeighborList, Particle Mesh Ewald, Langevin MD and LIUJIAN MD.(GPU)
-
-#### DataSet
-
-- [STABLE] If the libnuma library is installed in the environment, you can run `export DATASET_ENABLE_NUMA=True` or `export MS_ENABLE_NUMA=True` to configure NUMA binding. In multi-card training scenarios, the training data processing speed can be improved, thereby improving the network training efficiency.
-- [STABLE] Unify API Tensor structure of Training/Inference interfaces in C++ SDK.
-- [STABLE] Optimize duplicated Decode in data preprocess using cache, improve preprocess efficiency.
-- [STABLE] Support eager mode to run data augmentation in Python & C++.
-- [STABLE] Support more data augmentation operators(e.g. Affine, Perspective) in MindSpore-Lite.
-- [STABLE] Support light pipeline to process MindData in MindSpore-Lite training.
-- [STABLE] Support more data preprossing operators based on DVPP hardware module and can be used on on Ascend310 platform.
-- [STABLE] Support copy-free property for data in Ascend310 inference process scenarios.
-
-#### Running Data Recorder
-
-- [STABLE] Support running data recorder (RDR) for exception demarcation.
-- [STABLE] Provide records of multi-stage computational graphs, memory allocation information, graph execution order, stream execution order and task debug information when a "run task error" or "distribute task failed" occurs. (Ascend)
-- [STABLE] Provide records of multi-stage computational graphs, memory allocation information and graph execution order when a "SyncStream error" occurs. (GPU)
-
-#### 3D Feature
-
-- [STABLE] Support 3D ops: Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter, Conv3DTranspose, BiasAdd, BiasAddGrad, PReLU, Transpose, Reshape, transdata, StrideSlice, MaxPool3D, MaxPool3DGrad, BinaryCrossEntropy, SigmoidCrossEntropyWithLogits, SigmoidCrossEntropyWithLogitsGrad, SoftmaxCrossEntropyWithLogits, SigmoidCrossEntropyWithLogits, SigmoidCrossEntropyWithLogitsGrad, BatchNorm3d, BatchNorm3dGrad, Dropout3d.
-- [STABLE] Support RMSELoss loss function, MAELoss loss function, FocalLoss loss function, DiceLoss binary loss function, and MultiClassDiceLoss multi-type loss function for 2D/3D network.
-- [STABLE] Add optimizer: AdamApplyOne(3D), ApplyMomentum(3D), SGD(3D).
-
-### API Change
-
-#### Backwards Incompatible Change
-
-##### Python API
-
-###### `mindspore.numpy.array()`, `mindspore.numpy.asarray()`, `mindspore.numpy.asfarray()`, `mindspore.numpy.copy()` now support GRAPH mode, but cannot accept `numpy.ndarray` as input arguments anymore([!12726](https://gitee.com/mindspore/mindspore/pulls/12726))
-
-Previously, these interfaces can accept numpy.ndarray as arguments and convert numpy.ndarray to Tensor, but cannot be used in GRAPH mode.
-However, currently MindSpore Parser cannot parse numpy.ndarray in JIT-graph. To support these interfaces in graph mode, we have to remove `numpy.ndarray` support. With that being said, users can still use `Tensor` to convert `numpy.ndarray` to tensors.
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```python
->>> import mindspore.numpy as mnp
->>> import numpy
->>>
->>> nd_array = numpy.array([1,2,3])
->>> tensor = mnp.asarray(nd_array) # this line cannot be parsed in GRAPH mode
-```
-
- |
-
-
-```python
->>> import mindspore.numpy as mnp
->>> import numpy
->>>
->>> tensor = mnp.asarray([1,2,3]) # this line can be parsed in GRAPH mode
-```
-
- |
-
-
-
-###### mindspore.numpy interfaces remove support for keyword arguments `out` and `where`([!12726](https://gitee.com/mindspore/mindspore/pulls/12726))
-
-Previously, we have incomplete support for keyword arguments `out` and `where` in mindspore.numpy interfaces, however, the `out` argument is only functional when `where` argument is also provided, and `out` cannot be used to pass reference to numpy functions. Therefore, we have removed these two arguments to avoid any confusion users may have. Their original functionality can be found in [np.where](https://www.mindspore.cn/docs/en/master/api_python/numpy/mindspore.numpy.where.html#mindspore.numpy.where)
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```python
->>> import mindspore.numpy as np
->>>
->>> a = np.ones((3,3))
->>> b = np.ones((3,3))
->>> out = np.zeros((3,3))
->>> where = np.asarray([[True, False, True],[False, False, True],[True, True, True]])
->>> res = np.add(a, b, out=out, where=where) # `out` cannot be used as a reference, therefore it is misleading
-```
-
- |
-
-
-```python
->>> import mindspore.numpy as np
->>>
->>> a = np.ones((3,3))
->>> b = np.ones((3,3))
->>> out = np.zeros((3,3))
->>> where = np.asarray([[True, False, True],[False, False, True],[True, True, True]])
->>> res = np.add(a, b)
->>> out = np.where(where, x=res, y=out) # instead of np.add(a, b, out=out, where=where)
-```
-
- |
-
-
-
-###### Turn `ops.MakeRefKey` into an internal interface ([!12010](https://gitee.com/mindspore/mindspore/pulls/12010))
-
-Previously MakeRefKey is an external interface that is not used, now make it an internal interface with the same usage. We do not recommend users to use this interface, and we will remove the relevant introduction of this interface from the official website.
-
-###### `ops.ApplyFtrl`, `ops.ApplyMomentum`, `ops.ApplyRMSProp`, `ops.ApplyCenteredRMSProp` change the output on Ascend backend from multiple to a single. ([!11895](https://gitee.com/mindspore/mindspore/pulls/11895))
-
-Previously the number of outputs of these operator is different on different backends. To unify their definition we change their output on Ascend backend from multiple to a single.
-
-##### `P.FusedBatchNorm`, `P.FusedBatchNormEx` deleted ([!12115](https://gitee.com/mindspore/mindspore/pulls/12115))
-
-The FusedBatchNorm and FusedBatchNormEx interface has been deleted. Please use the batchnorm operator to replace it.
-
-##### `MetaTensor` deleted ([!10325](https://gitee.com/mindspore/mindspore/pulls/10325))
-
-The MetaTensor interface has been deleted. The function of MetaTensor has been integrated into tensor.
-
-###### `ControlDepend` is deleted, use `Depend` instead. The decorator `@C.add_flags(has_effect=True)` does not work. ([!13793](https://gitee.com/mindspore/mindspore/pulls/13793))
-
-Previously, we used ControlDepend to control the execution order of multiple operators. In version 1.2.0, mindspore introduces the auto-monad side effects expression to ensure that the perform order of user's semantics is correct. Therefore, ControlDepend is deleted and Depend is recommended.
-
-In most scenarios, if operators have IO side effects (such as print) or memory side effects (such as assign), they will be executed according to the user's semantics. In some scenarios, if the two operators A and B have no order dependency, and A must be executed before B, we recommend using Depend to specify their execution order. See the API documentation of the Depend operator for specific usage.
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```python
- In some side-effect scenarios, we need to ensure the execution order of operators.
- In order to ensure that operator A is executed before operator B, it is recommended
- to insert the Depend operator between operators A and B.
-
- Previously, the ControlDepend operator was used to control the execution order.
- Since the ControlDepend operator is deprecated from version 1.1, it is recommended
- to use the Depend operator instead. The replacement method is as follows::
-
- a = A(x) ---> a = A(x)
- b = B(y) ---> y = Depend(y, a)
- ControlDepend(a, b) ---> b = B(y)
-```
-
- |
-
-
-```python
- In most scenarios, if operators have IO side effects or memory side effects,
- they will be executed according to the user's semantics. In some scenarios,
- if the two operators A and B have no order dependency, and A must be executed
- before B, we recommend using Depend to specify their execution order. The
- usage method is as follows::
-
- a = A(x) ---> a = A(x)
- b = B(y) ---> y = Depend(y, a)
- ---> b = B(y)
-```
-
- |
-
-
-
-After the introduction of the auto-monad side effect expression feature, the decorator `@C.add_flags(has_effect=True)` does not work. If the decorator is used in the script, please modify. Take the overflow identification operator (without side effects) as an example, the modification method is as follows:
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```python
-@C.add_flags(has_effect=True)
-def construct(self, *inputs):
- ...
- loss = self.network(*inputs)
- init = self.allo_status()
- self.clear_status(init)
- ...
-```
-
- |
-
-
-```python
-def construct(self, *inputs):
- ...
- loss = self.network(*inputs)
- init = self.allo_status()
- init = F.depend(init, loss)
- clear_status = self.clear_status(init)
- ...
-```
-
- |
-
-
-
-##### C++ API
-
-###### C++ API support dual ABI now.([!12432](https://gitee.com/mindspore/mindspore/pulls/12432))
-
-1.1.1 supports only the old ABI. Currently, both the new and the old are supported.
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```cmake
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-```
-
- |
-
-
-```cmake
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0) # old ABI are supported
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=1) # new ABI are supprrted, too
- # write nothing, use new ABI as default
-```
-
- |
-
-
-
-###### Context refactor.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
-
-The `Context` class is refactored. For details, see the API docs.
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```cpp
-GlobalContext::SetGlobalDeviceTarget(kDeviceTypeAscend310); // set device target is ascend310
-GlobalContext::SetGlobalDeviceID(0); // set device id is 0
-auto model_context = std::make_shared(); // create a model context
-ModelContext::SetInsertOpConfigPath(model_context, "./aipp.cfg") // set aipp config file is ./aipp.cfg
-```
-
- |
-
-
-```cpp
-auto model_context = std::make_shared(); // create a model context
-auto ascend310_info = std::make_shared();
-model_context.MutableDeviceInfo().push_back(ascend310_info ); // set device target is ascend310
-ascend310_info->SetDeviceID(0); // set device id is 0
-ascend310_info->SetInsertOpConfigPath("./aipp.cfg"); // set aipp config file is ./aipp.cfg
-```
-
- |
-
-
-
-###### LoadModel interface changes.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
-
-`LoadModel` is renamed `Load`. No exception is thrown new but the return status should be checked.
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```cpp
-try {
- auto graph = Serialization::LoadModel(model_file_path, kMindIR);
-} catch (...) { ... }
-```
-
- |
-
-
-```cpp
-Graph graph;
-auto ret = Serialization::Load(model_file_path, kMindIR, &graph);
-if (ret != kSuccess) { ... }
-```
-
- |
-
-
-
-###### Model ctor changes.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
-
-`Model` uses a non-parameter ctor now, and arguments are passed in through `Build`.
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```cpp
-Model net(net_cell, model_context);
-auto ret = net.Build();
-if (ret != kSuccess) { ... }
-```
-
- |
-
-
-```cpp
-Model net;
-auto ret = net.Build(net_cell, model_context);
-if (ret != kSuccess) { ... }
-```
-
- |
-
-
-
-###### MSTensor::CreateTensor returns a native pointer now.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
-
-`MSTensor::CreateTensor` and `MSTensor::CreateRefTensor` returns a native pointer now, need to be destroy by `DestroyTensorPtr`.
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```cpp
-auto tensor = MSTensor::CreateTensor(xxx, xxx, ...);
-auto name = tensor.Name();
-```
-
- |
-
-
-```cpp
-auto tensor = MSTensor::CreateTensor(xxx, xxx, ...);
-auto name = tensor->Name();
-MSTensor::DestroyTensorPtr(tensor);
-```
-
- |
-
-
-
-#### New features
-
-##### Python API
-
-- Add SPONGE functions: `mindspore.ops.operations.BondForceWithAtomEnergy`, `mindspore.ops.operations.AngleForceWithAtomEnergy`, `mindspore.ops.operations.DihedralForceWithAtomEnergy`, `mindspore.ops.operations.Dihedral14LJCFForceWithAtomEnergy`, `mindspore.ops.operations.LJForceWithPMEDirectForce`, `mindspore.ops.operations.PMEExcludedForce`, `mindspore.ops.operations.PMEReciprocalForce`,`mindspore.ops.operations.BondEnergy`, `mindspore.ops.operations.AngleEnergy`,`mindspore.ops.operations.DihedralEnergy`, `mindspore.ops.operations.Dihedral14LJEnergy`, `mindspore.ops.operations.Dihedral14CFEnergy`,`mindspore.ops.operations.LJEnergy`, `mindspore.ops.operations.PMEEnergy`. All operators are supported in `GPU`.
-
-#### Deprecations
-
-##### Python API
-
-###### `nn.MatMul` is now deprecated in favor of `ops.matmul` ([!12817](https://gitee.com/mindspore/mindspore/pulls/12817))
-
-[ops.matmul](https://www.mindspore.cn/docs/en/master/api_python/ops/mindspore.ops.matmul.html#mindspore.ops.matmul) follows the API of [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html) as closely as possible. As a function interface, [ops.matmul](https://www.mindspore.cn/docs/en/master/api_python/ops/mindspore.ops.matmul.html#mindspore.ops.matmul) is applied without instantiation, as opposed to `nn.MatMul`, which should only be used as a class instance.
-
-
-
-| 1.1.1 | 1.2.0 |
-
-
-|
-
-```python
->>> import numpy as np
->>> from mindspore import Tensor, nn
->>>
->>> x = Tensor(np.ones((2, 3)).astype(onp.float32)
->>> y = Tensor(np.ones((3, 4)).astype(onp.float32)
->>> nn.MatMul()(x, y)
-```
-
- |
-
-
-```python
->>> import numpy as np
->>> from mindspore import Tensor, ops
->>>
->>> x = Tensor(np.ones((2, 3)).astype(onp.float32)
->>> y = Tensor(np.ones((3, 4)).astype(onp.float32)
->>> ops.matmul(x, y)
-```
-
- |
-
-
-
-### Bug fixes
-
-#### FrontEnd
-
-- fix the null pointer problem of evaluator in control flow.([!13312](https://gitee.com/mindspore/mindspore/pulls/13312))
-- fix parameter naming conflict bug for CellList and SequentialCell. ([!13260](https://gitee.com/mindspore/mindspore/pulls/13260))
-
-#### Executor
-
-- fix executor pending task not execute in some heterogeneous cases.([!13465](https://gitee.com/mindspore/mindspore/pulls/13465))
-- add passes to support frontend IR unification, including following operations: SliceGrad([!11783](https://gitee.com/mindspore/mindspore/pulls/11783)), ApplyFtrl, ApplyMomentum, ApplyRMSProp, CenteredRMSProp([!11895](https://gitee.com/mindspore/mindspore/pulls/11895)), AvgPoolGrad([!12813](https://gitee.com/mindspore/mindspore/pulls/12813)), BatchNorm([!12115](https://gitee.com/mindspore/mindspore/pulls/12115))
-
-#### Dataset
-
-- Fix getter functions(e.g. GetDatasetSize) terminated abnormally when use python multi-processing. ([!13571](https://gitee.com/mindspore/mindspore/pulls/13571), [!13823](https://gitee.com/mindspore/mindspore/pulls/13823))
-- Fix unclear error log of data augmentation operators. ([!12398](https://gitee.com/mindspore/mindspore/pulls/12398), [!12883](https://gitee.com/mindspore/mindspore/pulls/12883), [!13176](https://gitee.com/mindspore/mindspore/pulls/13176))
-- Fix profiling performs abnormally when sink_size = False, as saving data is later than profiling analysis. ([!13944](https://gitee.com/mindspore/mindspore/pulls/13944))
-
-## MindSpore Lite
-
-### Major Features and Improvements
-
-#### Converter and runtime
-
-1. Support TensorFlow model in Converter except aware-training model.
-2. Add fusion pattern for same horizontal operators in Converter.
-3. Support Jar in x86_64 system for integrating into server with Java backend conveniently.
-4. Provide unified runtime API for developer reusing their code between cloud side and end side.[BETA]
-5. Improve control-flow capabilities continually: Support GRU fusion in Converter; Support weight-quant for control-flow model; Support control-flow model inference with half precision; Support nested control-flow model.[BETA]
-
-#### ARM backend optimization
-
-1. Add NLP dependent float16 operators(like lstm) to enhance inference performance.
-2. Optimize operators: lstm, gru, depthwise.
-3. Add 6 NPU operators(like FullConnection), and fix some bugs about buildIR failed.
-
-#### OpenCL backend
-
-1. Add new ops: add 10+ ops，total 72 ops；
-2. Performance optimization: by memory layout optimize，block tiling，Performance improved by 30% compared to version 1.1 at Adreno GPU.
-3. Initialization time optimization: initialization time improve 100% vs MSLITE Version1.1 by store kernel cache as binary.
-4. Support Java call on Mali or Adreno GPU.
-
-#### Post quantization
-
-1. Support quantization of gather and lstm ops.
-2. Support quantizatizing TF Lite models with sub-graph node.
-3. Add quantiztion strategy to decide quantize ops or not，less accuracy loss and higher compression rate.
-
-#### Training on Device
-
-1. Virtual batching, use mini-batch to minic large batch in theorical with few RAM consumption.
-2. Converter unify, do not compile tod and iod converter separately.
-3. Performance optimization to BWD ops.
-4. TrainLoop with Off-The-Shelf Functionality blocks, like LR scheduler, Loss Monitor, Ckpt Saver, Accuracy Monitor.
-5. Integration of code with Minddata lite.
-6. Support more networks (googlenet, densenet, shufflenetv2, nin, vgg) and operators.
-
-#### Codegen
-
-1. Support 79 ops for the ARM platform and all CMSIS ops for Arm Cortex-M Series.
-2. Multiplatform support, including Android, IoT Devices.
-3. Support offline model weight preprocessing while compiling.
-4. Support offline memory reuse computing for minimum runtime buffer size.
-5. Support kernel register for custom op. Third-party hardware like NNIE can be accessed through it.
-
-### API Change
-
-#### API Incompatible Change
-
-##### C++ API
-
-###### Add header file named lite_types.h for some common data structs. ([!12262](https://gitee.com/mindspore/mindspore/pulls/12262))
-
-Previously, some common data structs such as `CpuBindMode` and `DeviceType` are in context.h, this may cause cross-dependency between headers. So we create a new header named lite_types.h for some common data structs and move `CpuBindMode` and `DeviceType` from context.h into lite_types.h.
-
-
-
-| lite_types.h |
-
-
-|
-
-```cpp
-namespace mindspore::lite {
-/// \brief CpuBindMode defined for holding bind cpu strategy argument.
-typedef enum {
- NO_BIND, /**< no bind */
- HIGHER_CPU, /**< bind higher cpu first */
- MID_CPU /**< bind middle cpu first */
-} CpuBindMode;
-
-/// \brief DeviceType defined for holding user's preferred backend.
-typedef enum {
- DT_CPU, /**< CPU device type */
- DT_GPU, /**< GPU device type */
- DT_NPU /**< NPU device type */
-} DeviceType;
-} // namespace mindspore::lite
-```
-
- |
-
-
-
-###### Add some new interfaces in ms_tensor.h for unified runtime API.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
-
-Previously, users could not create `MSTensor` or modify ``MSTensor, all `MSTensor` are created and managed by framework. However users need to create or modify MSTensor sometimes such as pre-processing input data. So we provide two new interfaces in ms_tensor.h: `CreateTensor` interface for creating `MSTensor` by user and `set_shape` interface for modifying the shape of `MSTensor`.
-
-
-
-| CreateTensor |
-
-
-|
-
-```cpp
-/// \brief Create a MSTensor.
-///
-/// \return Pointer to an instance of MindSpore Lite MSTensor.
-static MSTensor *CreateTensor(const std::string &name, TypeId type, const std::vector &shape, const void *data,
- size_t data_len);
-```
-
- |
-
-
-
-
-
-| set_shape |
-
-
-|
-
-```cpp
-/// \brief Set the shape of MSTensor.
-virtual void set_shape(const std::vector &shape) = 0;
-```
-
- |
-
-
-
-Previously, users could access to data of `MSTensor` by interface named `MutableData`. However `MutableData` is not only returning data of tensor but also allocating data for tensor if its data is nullptr. So we provide a new interfaces in ms_tensor.h named `data` for returning data of tensor without allocating automatically.
-
-
-
-| data |
-
-
-|
-
-```cpp
-/// \brief Get the pointer of data in MSTensor.
-///
-/// \note The data pointer can be used to both write and read data in MSTensor. No memory buffer will be
-/// allocated.
-///
-/// \return the pointer points to data in MSTensor.
-virtual void *data() = 0;
-```
-
- |
-
-
-
-###### Delete `DimensionSize()` in ms_tensor.h.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
-
-The interface named `DimensionSize` is fuinctionally overlapped with the interface named `shape`. For the simplicity of the interface, we delete `DimensionSize` and recommend users to use the new interface named `shape` instead.
-
-
-
-| DimensionSize() |
-
-
-|
-
-```cpp
-/// \brief Get size of the dimension of the MindSpore Lite MSTensor index by the parameter index.
-///
-/// \param[in] index Define index of dimension returned.
-///
-/// \return Size of dimension of the MindSpore Lite MSTensor.
-virtual int DimensionSize(size_t index) const = 0;
-```
-
- |
-
-
-
-###### Move allocator from namespace mindspore::lite to namespace lite for unified runtime API.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
-
-Previously, class `Allocator` is in namespace mindspore::lite. Considering unified allocator interface for unified runtime API, we move `Allocator` to namespace mindspore.
-
-
-
-| 1.1.0 | 1.2.0 |
-
-
-|
-
-```cpp
-namespace mindspore::lite {
-/// \brief Allocator defined a memory pool for malloc memory and free memory dynamically.
-///
-/// \note List public class and interface for reference.
-class Allocator;
-}
-```
-
- |
-
-
-```cpp
-namespace mindspore {
-/// \brief Allocator defined a memory pool for malloc memory and free memory dynamically.
-///
-/// \note List public class and interface for reference.
-class Allocator;
-}
-```
-
- |
-
-
-
-### Bug fixes
-
-1. Fix the bug that the array in kernel registrar is not initialized.
-2. Fix segment fault caused by releasing of OpParameter in Crop kernel in mistake.
-3. Fix the bug that the MINDIR aware-training model is finally interpreted as weight-quant model.
-
-## Contributors
-
-Thanks goes to these wonderful people:
-
-Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa.
-
-Contributions of any kind are welcome!
-
-# MindSpore 1.1.1 Release Notes
-
-## MindSpore
-
-### Major Features and Improvements
-
-#### NewModels
-
-- [STABLE] BGCF: a Bayesian Graph Collaborative Filtering(BGCF) framework used to model the uncertainty in the user-item interaction graph and thus recommend accurate and diverse items on Amazon recommendation dataset.(Ascend)
-- [STABLE] GRU: a recurrent neural network architecture like the LSTM(Long-Short Term Memory) on Multi30K dataset.(Ascend)
-- [STABLE] FastText: a simple and efficient text classification algorithm on AG's news topic classification dataset, DBPedia Ontology classification dataset and Yelp Review Polarity dataset.(Ascend)
-- [STABLE] LSTM: a recurrent neural network architecture used to learn word vectors for sentiment analysis on aclImdb_v1 dataset.(Ascend)
-- [STABLE] SimplePoseNet: a convolution-based neural network for the task of human pose estimation and tracking on COCO2017 dataset.(Ascend)
-
-#### FrontEnd
-
-- [BETA] Support Tensor Fancy Index Getitem with tuple and list. (Ascend/GPU/CPU)
-
-### Backwards Incompatible Change
-
-#### Python API
-
-##### `ops.AvgPool`, `ops.MaxPool`, `ops.MaxPoolWithArgmax` change attr name from 'ksize', 'padding' to 'kernel_size', 'pad_mode' ([!11350](https://gitee.com/mindspore/mindspore/pulls/11350))
-
-Previously the kernel size and pad mode attrs of pooling ops are named "ksize" and "padding", which is a little puzzling and inconsistent with convolution ops. So they are rename to "kernel_size" and "pad_mode".
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```python
->>> import mindspore.ops as ops
->>>
->>> avg_pool = ops.AvgPool(ksize=2, padding='same')
->>> max_pool = ops.MaxPool(ksize=2, padding='same')
->>> max_pool_with_argmax = ops.MaxPoolWithArgmax(ksize=2, padding='same')
-```
-
- |
-
-
-```python
->>> import mindspore.ops as ops
->>>
->>> avg_pool = ops.AvgPool(kernel_size=2, pad_mode='same')
->>> max_pool = ops.MaxPool(kernel_size=2, pad_mode='same')
->>> max_pool_with_argmax = ops.MaxPoolWithArgmax(kernel_size=2, pad_mode='same')
-```
-
- |
-
-
-
-##### `ops.TensorAdd`, change API name to `ops.Add` ([!11568](https://gitee.com/mindspore/mindspore/pulls/11568))
-
-The operator name TensorAdd is not standardized, it is changed to Add. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```python
->>> import mindspore.ops as ops
->>>
->>> add = ops.TensorAdd()
-```
-
- |
-
-
-```python
->>> import mindspore.ops as ops
->>>
->>> add = ops.Add()
-```
-
- |
-
-
-
-##### `ops.Gelu`, `ops.GeluGrad`, `ops.FastGelu`, `ops.FastGeluGrad`, change API name to `ops.GeLU`, `ops.GeLUGrad`, `ops.FastGeLU`, `ops.FastGeLUGrad` ([!11603](https://gitee.com/mindspore/mindspore/pulls/11603))
-
-Gelu, GeluGrad, FastGelu, and FastGeluGrad names are unified into ReLU naming rules, "lu" is changed to the uppercase "LU". The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```python
->>> import mindspore.ops as ops
->>>
->>> gelu = ops.Gelu()
->>> gelu_grad = ops.GeluGrad()
->>> fast_gelu = ops.FastGelu()
->>> fast_gelu_grad = ops.FastGeluGrad()
-```
-
- |
-
-
-```python
->>> import mindspore.ops as ops
->>>
->>> gelu = ops.GeLU()
->>> gelu_grad = ops.GeLUGrad()
->>> fast_gelu = ops.FastGeLU()
->>> fast_gelu_grad = ops.FastGeLUGrad()
-```
-
- |
-
-
-
-##### `ops.GatherV2`, change API name to `ops.Gather` ([!11713](https://gitee.com/mindspore/mindspore/pulls/11713))
-
-GatherV2 is changed to Gather. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```python
->>> import mindspore.ops as ops
->>>
->>> gather = ops.GatherV2()
-```
-
- |
-
-
-```python
->>> import mindspore.ops as ops
->>>
->>> gather = ops.Gather()
-```
-
- |
-
-
-
-##### `ops.Pack`、`ops.Unpack`, change API name to `ops.Stack`、`ops.Unstack` ([!11828](https://gitee.com/mindspore/mindspore/pulls/11828))
-
-Pack is changed to Stack, and Unpack is changed to Unstack. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```python
->>> import mindspore.ops as ops
->>>
->>> pack= ops.Pack()
->>> unpack= ops.Unpack()
-```
-
- |
-
-
-```python
->>> import mindspore.ops as ops
->>>
->>> stack= ops.Stack()
->>> unstack= ops.Unstack()
-```
-
- |
-
-
-
-##### `ops.ControlDepend`, add deprecated to ControlDepend ([!11844](https://gitee.com/mindspore/mindspore/pulls/11844))
-
-ControlDepend is deprecated and will be removed in a future version, use Depend instead.
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```pythonNote:
-Note:
- This operation does not work in `PYNATIVE_MODE`.
-```
-
- |
-
-
-```python
-Note:
- This operation does not work in `PYNATIVE_MODE`.
- `ControlDepend` is deprecated from version 1.1 and will be removed in a future version, use `Depend` instead.
-```
-
- |
-
-
-
-##### `ops.Depend`, add operator description and use case ([!11815](https://gitee.com/mindspore/mindspore/pulls/11815)), ([!11879](https://gitee.com/mindspore/mindspore/pulls/11879))
-
-Since the ControlDepend operator will be deprecated from version 1.2, it is recommended to use the Depend operator instead.
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```python
-Depend is used for processing side-effect operations.
-
-Inputs:
- - **value** (Tensor) - the real value to return for depend operator.
- - **expr** (Expression) - the expression to execute with no outputs.
-
-Outputs:
- Tensor, the value passed by last operator.
-
-Supported Platforms:
- ``Ascend`` ``GPU`` ``CPU``
-```
-
- |
-
-
-```python
-Depend is used for processing dependency operations.
-
-In some side-effect scenarios, we need to ensure the execution order of operators.
-In order to ensure that operator A is executed before operator B, it is recommended
-to insert the Depend operator between operators A and B.
-
-Previously, the ControlDepend operator was used to control the execution order.
-Since the ControlDepend operator will be deprecated from version 1.2, it is
-recommended to use the Depend operator instead. The replacement method is as follows::
-
- a = A(x) ---> a = A(x)
- b = B(y) ---> y = Depend(y, a)
- ControlDepend(a, b) ---> b = B(y)
-
-Inputs:
- - **value** (Tensor) - the real value to return for depend operator.
- - **expr** (Expression) - the expression to execute with no outputs.
-
-Outputs:
- Tensor, the value passed by last operator.
-
-Supported Platforms:
- ``Ascend`` ``GPU`` ``CPU``
-
-Examples:
- >>> import numpy as np
- >>> import mindspore
- >>> import mindspore.nn as nn
- >>> import mindspore.ops.operations as P
- >>> from mindspore import Tensor
- >>> class Net(nn.Cell):
- ... def __init__(self):
- ... super(Net, self).__init__()
- ... self.softmax = P.Softmax()
- ... self.depend = P.Depend()
- ...
- ... def construct(self, x, y):
- ... mul = x - y
- ... y = self.depend(y, mul)
- ... ret = self.softmax(y)
- ... return ret
- ...
- >>> x = Tensor(np.ones([4, 5]), dtype=mindspore.float32)
- >>> y = Tensor(np.ones([4, 5]), dtype=mindspore.float32)
- >>> net = Net()
- >>> output = net(x, y)
- >>> print(output)
- [[0.2 0.2 0.2 0.2 0.2]
- [0.2 0.2 0.2 0.2 0.2]
- [0.2 0.2 0.2 0.2 0.2]
- [0.2 0.2 0.2 0.2 0.2]]
-```
-
- |
-
-
-
-#### C++ API
-
-##### change namespace from `mindspore::api` to `mindspore` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```c++
-namespace ms = mindspore::api;
-```
-
- |
-
-
-```c++
-namespace ms = mindspore;
-```
-
- |
-
-
-
-##### `Context` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```c++
-ms::Context::Instance().SetDeviceTarget(ms::kDeviceTypeAscend310).SetDeviceID(0);
-```
-
- |
-
-
-```c++
-ms::GlobalContext::SetGlobalDeviceTarget(ms::kDeviceTypeAscend310);
-ms::GlobalContext::SetGlobalDeviceID(0);
-```
-
- |
-
-
-
-##### rename `Tensor` to `MSTensor` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))
-
-
-
-| 1.1.0 | 1.1.1 |
-
-
-|
-
-```c++
-ms::Tensor a;
-```
-
- |
-
-
-```c++
-ms::MSTensor a;
-```
-
- |
-
-
-
-##### `Model` move setting of model options from `Build` to ctor `Model` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))
+ |
+
+
@@ -10,22 +10,21 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
### MindSpore Lite features
-1. Cooperative work with MindSpore training
- - Provides training, optimization, and deployment.
+1. Terminal and Cloud one-stop inference deployment
+ - Provide end-to-end processes for model transformation optimization, deployment, and inference.
- The unified IR realizes the device-cloud AI application integration.
2. Lightweight
- Provides model compress, which could help to improve performance as well.
- - Provides the ultra-lightweight reasoning solution MindSpore Micro to meet the deployment requirements in extreme environments such as smart watches and headphones.
+ - Provides the ultra-lightweight reasoning solution MindSpore Lite Micro to meet the deployment requirements in extreme environments such as smart watches and headphones.
3. High-performance
- - The built-in high-performance kernel computing library NNACL supports multiple convolution optimization algorithms such as Slide window, im2col+gemm, winograde, etc.
+ - The built-in high-performance kernel computing library NNACL supports high-performance inference for dedicated chips such as CPU, NNRt, and Ascend, maximizing hardware computing power while minimizing inference latency and power consumption.
- Assembly code to improve performance of kernel operators. Supports CPU, GPU, and NPU.
+
4. Versatility
- - Supports IOS, Android.
- - Supports Lite OS.
- - Supports mobile device, smart screen, pad, and IOT devices.
- - Supports third party models such as TFLite, CAFFE and ONNX.
+ - Support deployment of multiple hardware such as server-side Ascend and CPU.
+ - Supports HarmonyOS and Android mobile operating systems.
## MindSpore Lite AI deployment procedure
@@ -33,7 +32,7 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
Select a new model or use an existing model for incremental training using labeled data. When designing a model for mobile device, it is necessary to consider the model size, accuracy and calculation amount.
- The MindSpore team provides a series of pre-training models used for image classification, object detection. You can use these pre-trained models in your application.
+ The MindSpore Lite team provides a series of pre-training models used for image classification, object detection. You can use these pre-trained models in your application.
The pre-trained model provided by MindSpore: [Image Classification](https://download.mindspore.cn/model_zoo/official/lite/). More models will be provided in the feature.
@@ -43,7 +42,7 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
If you use MindSpore or a third-party model, you need to use [MindSpore Lite Model Converter Tool](https://www.mindspore.cn/lite/docs/en/master/converter/converter_tool.html) to convert the model into MindSpore Lite model. The MindSpore Lite model converter tool provides the converter of TensorFlow Lite, Caffe, ONNX to MindSpore Lite model, fusion and quantization could be introduced during convert procedure.
- MindSpore also provides a tool to convert models running on IoT devices .
+ MindSpore Lite also provides a tool to convert models running on IoT devices .
3. Model deployment
@@ -53,16 +52,4 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
Load the model and perform inference. [Inference](https://www.mindspore.cn/lite/docs/en/master/infer/runtime_cpp.html) is the process of running input data through the model to get output.
- MindSpore provides pre-trained model that can be deployed on mobile device [example](https://www.mindspore.cn/lite/examples/en).
-
-## MindSpore Lite benchmark test result
-
-We test a couple of networks on HUAWEI Mate40 (Hisilicon Kirin9000e) mobile phone, and get the test results below for your reference.
-
-| NetWork | Thread Number | Average Run Time(ms) |
-| ------------------- | ------------- | -------------------- |
-| basic_squeezenet | 4 | 6.415 |
-| inception_v3 | 4 | 36.767 |
-| mobilenet_v1_10_224 | 4 | 4.936 |
-| mobilenet_v2_10_224 | 4 | 3.644 |
-| resnet_v2_50 | 4 | 25.071 |
+ MindSpore Lite provides pre-trained model that can be deployed on mobile device [example](https://www.mindspore.cn/lite/examples/en).
diff --git a/README_CN.md b/README_CN.md
index 15b305475a0c345a3ec07cfe14aa3bc4ac08c8fb..32fd121d49d090483df773742d9697e49aafdb3a 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -3,7 +3,7 @@
## MindSpore Lite介绍
-MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推理框架，用于满足越来越多的端测AI应用需求。MindSpore Lite聚焦AI技术在端侧设备上的部署和运行，已经在华为HMS和智能终端的图像分类、目标识别、人脸识别、文字识别等应用中广泛使用，未来MindSpore Lite将与MindSpore AI社区一起，致力于丰富AI软硬件应用生态。
+MindSpore Lite面向不同硬件设备提供轻量化AI推理加速能力，使能智能应用，为开发者提供端到端的解决方案，为算法工程师和数据科学家提供开发友好、运行高效、部署灵活的体验，帮助人工智能软硬件应用生态繁荣发展，未来MindSpore Lite将与MindSpore AI社区一起，致力于丰富AI软硬件应用生态。