!29126 updata release md r1.6
Merge pull request !29126 from jjfeing/updata_release
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RELEASE.md
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RELEASE.md
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## MindSpore 1.6.0 Release Notes
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### Major Features and Improvements
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#### OS
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* [STABLE] Support macOS with CPU(X86)
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* [BETA] Supoport macOS with CPU(M1)
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#### FrontEnd
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* [STABLE] Support JIT Fallback feature in Graph mode.
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* [STABLE] Support compile cache feature in Graph mode.
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* [STABLE] Add new optimizers, including ASGD and Rprop.
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* [STABLE] Add new initializers, including Identity, Orthogonal, Dirac, Sparse and VarianceScaling.
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* [STABLE] Support resuming training when an exception occurs in the process.
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* [STABLE] Change `mindspore.nn.LSTMCell` from single-layer LSTM to single-cell LSTM.
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* [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).
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#### PyNative
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* [STABLE] Support heterogeneous feature in PyNative mode.
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* [STABLE] Optimize memory allocation in PyNative mode.
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#### Auto Parallel
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- [STABLE] Support configuring the output shard strategy of the MatMul distributed operator.
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- [STABLE] Support multi-instances parallel.
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- [STABLE] Support activation slice communication and calculation overlap in Transformer.
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- [STABLE] Support heterogeneous parallel tensor swap.
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- [STABLE] Add implementations of distributed operator of ResizeNearestNeighbor.
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- [STABLE] Add a communication operator named NeighborExchangeV2 that supports data exchange between adjacent 8 rank ids.
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- [STABLE] Pipeline parallel support GPU platform.
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- [STABLE] Add cell-level data parallel interface.
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- [STABLE] Support gradient AllReduce fusion according to the amount of data.
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- [STABLE] Support a sharding strategy search algorithm called sharding propagation.
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#### Executor
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* [STABLE] Support multigraph sink and subgraph sink of MindRT.
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* [STABLE] Support memory swap to break the device memory size limit on Ascend platform.
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* [STABLE] Support dynamic deployment of distributed training cluster(GPU).
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* [BETA] Support automatic failover of parameter server.
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#### DataSet
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* [STABLE] Support overwrite feature in MindRecord.
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* [STABLE] Log improvement and more friendly to users.
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* [BETA] Support new feature [Dataset Offload](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_dataset_offload.html) to speed up data processing by heterogeneous computing.
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* [BETA] Support new feature [Dataset Autotune](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_dataset_autotune.html) to adjust parallelism of dataset pipeline automatically.
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#### GraphKernel Fusion
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* [STABLE] Support kernel fusion and generation for CPU backend.
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#### Federated Learning
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* [STABLE] FL-Client framework and model decoupling.
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* [BETA] Support Cross-silo federated learning framework.
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#### Debug
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* [STABLE] Support dump in cell level(Ascend).
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* [STABLE] Support dump Tensor statistics(Ascend/GPU).
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* [STABLE] Support displaying corresponding code lines for fusion nodes.
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* [STABLE] Support passing dump flag in Ascend backend in order to dump correct operators after fusion transformation.
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### API Change
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#### Backwards Incompatible Change
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##### Python API
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###### `mindspore.dataset.MindDataset` interface changes input parameter dataset_file([!27542](https://gitee.com/mindspore/mindspore/pulls/27542))
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`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/api/zh-CN/master/api_python/dataset/mindspore.dataset.MindDataset.html#mindspore.dataset.MindDataset).
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###### Delete `mindspore.Tensor`'s property `virtual_flag`([!26989](https://gitee.com/mindspore/mindspore/pulls/26989))
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###### Delete `mindspore.Parameter`'s property `is_init`([!26989](https://gitee.com/mindspore/mindspore/pulls/26989))
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###### Delete `mindspore.nn.ROC`'s interface `roc`([!25713](https://gitee.com/mindspore/mindspore/pulls/25713))
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###### The `shard()` interface of primitive is changed from `shard(strategy)` to `shard(in_strategy=None, out_strategy=None)`
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###### The `set_auto_parallel_context()` interface of context is changed from
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###### `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")`
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#### Collect Data and Create Landscape
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##### Python API
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###### `mindspore.train.callback.SummaryCollector` interface's parameter `collect_specified_data` add new operations `collect_landscape` ([!26229](https://gitee.com/mindspore/mindspore/pulls/26229))
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`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/api/zh-CN/master/api_python/mindspore.train.html#mindspore.train.callback.SummaryCollector).
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###### `mindspore.train.callback` add new interface `SummaryLandscape` ([!26229](https://gitee.com/mindspore/mindspore/pulls/26229))
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`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/api/zh-CN/master/api_python/mindspore.train.html#mindspore.train.callback.SummaryLandscape).
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### Bug fixes
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#### Executor
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* Fix process hanging while calling MPI_comm_create in asymmetric pipeline split scenario. ([!28707](https://gitee.com/mindspore/mindspore/pulls/28707))
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* Fix the execution error when the weights are shared between graph mode and PyNative mode.([!26635](https://gitee.com/mindspore/mindspore/pulls/26635))
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* Fixed the probability coredump when free memory under PyNative mode.([!25472](https://gitee.com/mindspore/mindspore/pulls/25472))
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#### Dataset
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* Fix memory increase abnormally when running dataset for a long time. ([!26237](https://gitee.com/mindspore/mindspore/pulls/26237))
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* Fix saving MindRecord files with Chinese path on Windows. ([!28378](https://gitee.com/mindspore/mindspore/pulls/28378))
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## MindSpore Lite
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### Major Features and Improvements
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#### Converter and runtime
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1. Add more fusion patterns in the converter tool to improve runtime performance.
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2. Support inference on Ascend310.
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3. Support take OpenGL texture as input and output of inference.
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4. Refactor the JAVA API.
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* [STABLE] Add more fusion patterns in the converter tool to improve runtime performance.
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* [STABLE] Support take OpenGL texture as input and output of inference.
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* [STABLE] Refactor the JAVA API.
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* [BETA] Support inference on Ascend310.
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#### x86 backend optimization
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1. Optimize kernels for x86 using Advanced Vector Extensions(AVX512).
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* [STABLE] Optimize kernels for x86 using Advanced Vector Extensions(AVX512).
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#### ARM backend optimization
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1. Support heterogeneous parallel inference, including splitting operators, constructing heterogeneous subgraphs, and heterogeneous parallel scheduling between CPUs and GPUs.
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2. Add more FP16 operators.
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* [STABLE] Support heterogeneous parallel inference, including splitting operators, constructing heterogeneous subgraphs, and heterogeneous parallel scheduling between CPUs and GPUs.
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* [STABLE] Add more FP16 operators.
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#### Post quantization
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1. Post quantization supports debugging.
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2. Full quantization supports choosing non-quantized nodes.
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3. Mixed bit quantization supports auto-tune.
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* [STABLE] Post quantization supports debugging.
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* [STABLE] Full quantization supports choosing non-quantized nodes.
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* [STABLE] Mixed bit quantization supports auto-tune.
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#### Training on Device
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1. Support user-defined algorithm models to access the federated learning framework.
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* [STABLE] Support user-defined algorithm models to access the federated learning framework.
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### Contributors
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Thanks goes to these wonderful people:
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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.
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Contributions of any kind are welcome!
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# MindSpore 1.5.2
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