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MindSpore Release Notes

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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 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.
[STABLE] Python multiprocessing optimization and make processes exit normally.
[STABLE] Support Dataset Autotune for tuning the speed of dataset pipeline automatically.
[BETA] Dataset Offload 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)
Deprecated usage: import mindspore.dataset.engine.datasets as ds. Use import mindspore.dataset as ds instead as recommended in mindspore doc.
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)
Deprecate mindspore.SparseTensor and use mindspore.COOTensor instead. (!28505)

MindSpore Lite

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.

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 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 to speed up data processing by heterogeneous computing.
[BETA] Support new feature Dataset Autotune 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)

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.

Delete `mindspore.Tensor`'s property `virtual_flag`(!26989)

Delete `mindspore.Parameter`'s property `is_init`(!26989)

Delete `mindspore.nn.ROC`'s interface `roc`(!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)

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.

`mindspore.train.callback` add new interface `SummaryLandscape` (!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.

Bug fixes

Executor

Fix process hanging while calling MPI_comm_create in asymmetric pipeline split scenario. (!28707)
Fix the execution error when the weights are shared between graph mode and PyNative mode.(!26635)
Fixed the probability coredump when free memory under PyNative mode.(!25472)

Dataset

Fix memory increase abnormally when running dataset for a long time. (!26237)
Fix saving MindRecord files with Chinese path on Windows. (!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, 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, 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:

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 repositorymodels

FrontEnd

[STABLE] Supportwhile andbreak,continue statements of training network inGRAPH_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 includefor statement.(!23669)

Executor

RunTask failed when parameter_broadcast is enabled in PyNative mode. (!23255)
An illegal memory access was encountered in the dynamic shape net on GPU.
Fix tune failed for DynamicRnn. (!21081)

Dataset

Optimize thread monitoring to solve the problem of running multiple multiprocessesing on Windwos. (!23232)
Fix bugs of Dataset tensor operations in lite mode. (!21999)
Fix memory increasing when using create_dict_iterator in for loop. (!22529)(!22529)

MindSpore Lite

Major Features and Improvements

Converter and runtime

Optimize TDNN-like streaming model by reusing the result of last inference.
Support dynamic filter Convolution.
Support serializing float32 weight into float16 weight for reducing size of model file.
Provide unified runtime API for developer reusing their code between cloud side and end side.
Now developer can configure built-in pass as custom passes.
Now user can specify format and shape of model inputs while converting model.
Support multiple devices inference, includeing CPU, NPU, GPU. User can set devices in mindspore::Context.
Support mixed precision inference. User can set inference precision by LoadConfig API.
Support custom operator registration and enable inference on third-party hardware.

ARM backend optimization

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.
Fix bugs of memory leak on NPU.

Post quantization

Weight quantization supports mixed bit quantization.
Full quantization supports data pre-processing.
Adjust the quantization parameters from the command line to the configuration file.

Training on Device

Unify lite external api with MindSpore.
Implement static memory allocator and common workspace for TOD，save memory 10-20%.
Provide getgradients and setgradients interface，get and set optimizer params interfaces to support MOE Model.
Support user specified output node when export IOD Model.
Support more text networks (tinybert,albert) and operators.

Codegen

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:

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）

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)

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:

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 now supports Ascend310 and Ascend910 environments with one single wheel package and provides an alternate binary package for Ascend310 specifically.
[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)

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.

1.2.1	1.3.0
`device_que(prefetch_size=None, send_epoch_end=True, create_data_info_queue=False)`	`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)

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

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)

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.

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)
Fix the exception of 'exceeding limit call depth' in compile graph process when using while expression with grad operation. (!18662)

Executor

Fix reallocate memory bug for communication op.(!14492)
Replace memcpy_async op with tensor_move op.(!15204)
Fix the build error when multiple python versions are installed in the environment. (!19165)
The warning when the te/topi/hccl version does not match is optimized, and fix the repeated warning. (!18704)
Fix the error in a cluster with more than 8 pcs in pynative mode. (!16376)
Fix graph ring problem in UB fusion.(!16109)
Fix AllGather op select problem when the shape is not divisible by 16. (!18878)

Dataset

Fix an out-of-memory error when ImagefolderDataset gets an illegal directory. (!16196)
Fix bugs of vision transformations in lite mode. (!14722,!14774,!15050)
Fix default numbers of parallel workers of MindData for those CPUs with fewer cores. (!15921)
Fix MindRecord writing failed probabilistically in multiprocessing. (!15242)

MindSpore Lite

Major Features and Improvements

Converter and runtime

Support Caffe model running on Hi3516D.
Support delegate mechanism to run your models(part or whole) on user specified executor.
Support control flow models.
Support cross-compiling for iOS, so that we can inference models on iOS devices.

x86 backend optimization

Optimize kernels for x86 using Advanced Vector Extensions(AVX).

ARM backend optimization

Optimize fp16 kernels.
Support arm32 fp16 instruction acceleration on ARMv8.2.

Cuda backend optimization

Support NV GPU backend base on delegate mechanism(use TensorRT as delegate).

OpenCL backend

Optimize the strategy of workgroup and blocksize to improve performance.
Support OpenCL dynamic infershape.
Support INT32 type ops.

Post quantization

Support fp32 training model converts to quantization training model.

Training on Device

Support fp32 training model export to quantization model after training process end.
Unify APIs and output package name of training and inference.
Simplify implementation of Train Session.
Optimize train and infer compile, reduce libmindspore-lite-train.so memory.
Training memory optimization: memory reduce 10-50% compare with r1.2.
Training performance optimization: for 1*1 special input shape Cov2DGradInput and SparseSoftmaxCrossEntropyWithLogits operator optimization, improved 10%-20%.
Support more networks(transformer, albert).

Codegen

Support deployment on HarmonyOS for device.

API Change

API Incompatible Change

C++ API

Unify LiteSession and TrainSession, Merge LiteSession And TrainSession.(!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.

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<tensor::MSTensor *> GetPredictions() const {
    std::vector<tensor::MSTensor *> outputs;
    return outputs;
 }
...

Add Export API for Training on device, obsolete SaveToFile API.(!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.

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)

When Training on the device, we may need to update the model featuremap and get model featuremap.particularly in MindSpore Federated Scenario.

virtual std::vector<tensor::MSTensor *> GetFeatureMaps() const {
    std::vector<tensor::MSTensor *> features;
    return features;
  }
  virtual int UpdateFeatureMaps(const std::vector<tensor::MSTensor *> &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:

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:

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:

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:

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.

public List<MSTensor> getFeaturesMap() {
         List<Long> ret = this.getFeaturesMap(this.sessionPtr);
                ArrayList<MSTensor> tensors = new ArrayList<MSTensor>();
                for (Long msTensorAddr : ret) {
                    MSTensor msTensor = new MSTensor(msTensorAddr);
                    tensors.add(msTensor);
                }
                return tensors;
   }
   public boolean updateFeatures(List<MSTensor> 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.

public boolean export(String modelFileName, int modelType, int quantizationType) {
        return this.export(this.sessionPtr, modelFileName, modelType, quantizationType);
    }

Align with update of C++ api in LiteSession class, add new java API to LiteSession.java Correspondingly.

public class LiteSession {
...
public static LiteSession createTrainSession(String modelName, final MSConfig config, boolean trainMode){...}
public boolean train() {...}
public boolean eval() {...}
...

Bug fixes

Fix the bug that the train session does not release memory cause of refcount bug.

Deprecations

Contributors

Thanks goes to these wonderful people:

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 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)

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

>>> 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

>>> 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)

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

1.1.1 1.2.0

>>> 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

>>> 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)

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)

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)

The FusedBatchNorm and FusedBatchNormEx interface has been deleted. Please use the batchnorm operator to replace it.

`MetaTensor` deleted (!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)

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

    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)

    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

@C.add_flags(has_effect=True)
def construct(self, *inputs):
    ...
    loss = self.network(*inputs)
    init = self.allo_status()
    self.clear_status(init)
    ...

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)

1.1.1 supports only the old ABI. Currently, both the new and the old are supported.

1.1.1 1.2.0

add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)

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)

The Context class is refactored. For details, see the API docs.

1.1.1 1.2.0

GlobalContext::SetGlobalDeviceTarget(kDeviceTypeAscend310);       // set device target is ascend310
GlobalContext::SetGlobalDeviceID(0);                              // set device id is 0
auto model_context = std::make_shared<ModelContext>();            // create a model context
ModelContext::SetInsertOpConfigPath(model_context, "./aipp.cfg")  // set aipp config file is ./aipp.cfg

auto model_context = std::make_shared<Context>();                 // create a model context
auto ascend310_info = std::make_shared<Ascend310DeviceInfo>();
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)

LoadModel is renamed Load. No exception is thrown new but the return status should be checked.

1.1.1 1.2.0

try {
  auto graph = Serialization::LoadModel(model_file_path, kMindIR);
} catch (...) { ... }

Graph graph;
auto ret = Serialization::Load(model_file_path, kMindIR, &graph);
if (ret != kSuccess) { ... }

Model ctor changes.(!13515)

Model uses a non-parameter ctor now, and arguments are passed in through Build.

1.1.1	1.2.0
`Model net(net_cell, model_context); auto ret = net.Build(); if (ret != kSuccess) { ... }`	`Model net; auto ret = net.Build(net_cell, model_context); if (ret != kSuccess) { ... }`

MSTensor::CreateTensor returns a native pointer now.(!13515)

MSTensor::CreateTensor and MSTensor::CreateRefTensor returns a native pointer now, need to be destroy by DestroyTensorPtr.

1.1.1	1.2.0
`auto tensor = MSTensor::CreateTensor(xxx, xxx, ...); auto name = tensor.Name();`	`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)

ops.matmul follows the API of numpy.matmul as closely as possible. As a function interface, 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

>>> 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)

>>> 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)
fix parameter naming conflict bug for CellList and SequentialCell. (!13260)

Executor

fix executor pending task not execute in some heterogeneous cases.(!13465)
add passes to support frontend IR unification, including following operations: SliceGrad(!11783), ApplyFtrl, ApplyMomentum, ApplyRMSProp, CenteredRMSProp(!11895), AvgPoolGrad(!12813), BatchNorm(!12115)

Dataset

Fix getter functions(e.g. GetDatasetSize) terminated abnormally when use python multi-processing. (!13571, !13823)
Fix unclear error log of data augmentation operators. (!12398, !12883, !13176)
Fix profiling performs abnormally when sink_size = False, as saving data is later than profiling analysis. (!13944)

MindSpore Lite

Major Features and Improvements

Converter and runtime

Support TensorFlow model in Converter except aware-training model.
Add fusion pattern for same horizontal operators in Converter.
Support Jar in x86_64 system for integrating into server with Java backend conveniently.
Provide unified runtime API for developer reusing their code between cloud side and end side.[BETA]
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

Add NLP dependent float16 operators(like lstm) to enhance inference performance.
Optimize operators: lstm, gru, depthwise.
Add 6 NPU operators(like FullConnection), and fix some bugs about buildIR failed.

OpenCL backend

Add new ops：add 10+ ops，total 72 ops；
Performance optimization：by memory layout optimize，block tiling，Performance improved by 30% compared to version 1.1 at Adreno GPU.
Initialization time optimization：initialization time improve 100% vs MSLITE Version1.1 by store kernel cache as binary.
Support Java call on Mali or Adreno GPU.

Post quantization

Support quantization of gather and lstm ops.
Support quantizatizing TF Lite models with sub-graph node.
Add quantiztion strategy to decide quantize ops or not，less accuracy loss and higher compression rate.

Training on Device

Virtual batching, use mini-batch to minic large batch in theorical with few RAM consumption.
Converter unify, do not compile tod and iod converter separately.
Performance optimization to BWD ops.
TrainLoop with Off-The-Shelf Functionality blocks, like LR scheduler, Loss Monitor, Ckpt Saver, Accuracy Monitor.
Integration of code with Minddata lite.
Support more networks (googlenet, densenet, shufflenetv2, nin, vgg) and operators.

Codegen

Support 79 ops for the ARM platform and all CMSIS ops for Arm Cortex-M Series.
Multiplatform support, including Android, IoT Devices.
Support offline model weight preprocessing while compiling.
Support offline memory reuse computing for minimum runtime buffer size.
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)

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

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)

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

/// \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<int> &shape, const void *data,
                                size_t data_len);

set_shape

/// \brief Set the shape of MSTensor.
virtual void set_shape(const std::vector<int> &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

/// \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)

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()

/// \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)

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

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;
}

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

Fix the bug that the array in kernel registrar is not initialized.
Fix segment fault caused by releasing of OpParameter in Crop kernel in mistake.
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)

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

>>> 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')

>>> 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)

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
`>>> import mindspore.ops as ops >>> >>> add = ops.TensorAdd()`	`>>> 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)

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

>>> import mindspore.ops as ops
>>>
>>> gelu = ops.Gelu()
>>> gelu_grad = ops.GeluGrad()
>>> fast_gelu = ops.FastGelu()
>>> fast_gelu_grad = ops.FastGeluGrad()

>>> 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)

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
`>>> import mindspore.ops as ops >>> >>> gather = ops.GatherV2()`	`>>> import mindspore.ops as ops >>> >>> gather = ops.Gather()`

`ops.Pack`、`ops.Unpack`, change API name to `ops.Stack`、`ops.Unstack` (!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
`>>> import mindspore.ops as ops >>> >>> pack= ops.Pack() >>> unpack= ops.Unpack()`	`>>> import mindspore.ops as ops >>> >>> stack= ops.Stack() >>> unstack= ops.Unstack()`

`ops.ControlDepend`, add deprecated to ControlDepend (!11844)

ControlDepend is deprecated and will be removed in a future version, use Depend instead.

1.1.0 1.1.1

Note:
    This operation does not work in `PYNATIVE_MODE`.

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), (!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

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``

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)

1.1.0	1.1.1
`namespace ms = mindspore::api;`	`namespace ms = mindspore;`

`Context` (!11574)

1.1.0	1.1.1
`ms::Context::Instance().SetDeviceTarget(ms::kDeviceTypeAscend310).SetDeviceID(0);`	`ms::GlobalContext::SetGlobalDeviceTarget(ms::kDeviceTypeAscend310); ms::GlobalContext::SetGlobalDeviceID(0);`

rename `Tensor` to `MSTensor` (!11574)

1.1.0	1.1.1
`ms::Tensor a;`	`ms::MSTensor a;`

`Model` move setting of model options from `Build` to ctor `Model` (!11574)

1.1.0	1.1.1
`ms::Model model(graph_cell); model.Build(model_options);`	`ms::Model model(graph_cell, model_context); model.Build();`

`Model` modify `GetInputsInfo`, `GetOutputsInfo` to `GetInputs`, `GetOutputs` (!11574)

1.1.0 1.1.1

std::vector<std::string> names;
std::vector<ms::DataType> types;
std::vector<std::vector<int64_t>> shapes;
std::vector<size_t> mem_sizes;
model.GetInputsInfo(&names, &types, &shapes, &mem_sizes);
std::cout << "Input 0 name: " << names[0] << std::endl;

auto inputs = model.GetInputs();
std::cout << "Input 0 name: " << inputs[0].Name() << std::endl;

`Model` modify `Predict` parameters type from `Buffer` to `MSTensor` (!11574)

1.1.0 1.1.1

std::vector<ms::Buffer> inputs;
std::vector<ms::Buffer> outputs;
model.Predict(inputs, &outputs);

std::vector<ms::MSTensor> inputs;
std::vector<ms::MSTensor> outputs;
model.Predict(inputs, &outputs);

Deprecations

Python API

`ops.SpaceToBatch`, `ops.BatchToSpace` are deprecated in favor of `ops.SpaceToBatchND`, `ops.BatchToSpaceND`(!11527)

The ops.SpaceToBatchND, ops.BatchToSpaceND are more general and have same behavior as ops.SpaceToBatch, ops.BatchToSpace when block_shape is a int.

`ops.DepthwiseConv2dNative` is deprecated in favor of `nn.Conv2D`(!11702)

The ops.DepthwiseConv2dNative is only supported by Ascend, it is recommended to directly use nn.Conv2D. If group is equal to in_ channels and out_channels, the 2D convolution layer is also a 2D depthwise convolution layer.

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, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jesse, 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, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, 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, 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, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa

Contributions of any kind are welcome!

MindSpore 1.1.0 Release Notes

MindSpore

Major Features and Improvements

NewModels

[STABLE] GNMT v2: similar to the model described in Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, which is mainly used for corpus translation, on WMT Englis-German dataset.(Ascend)
[STABLE] MaskRCNN: a conceptually simple, flexible, and general framework for object instance segmentation on COCO2017 dataset.(Ascend)
[STABLE] YOLOv4: a state-of-the-art detector which is faster and more accurate than all available alternative detectors on MS COCO dataset.(Ascend)
[STABLE] Openpose: proposes a bottom-up human attitude estimation algorithm using Part Affinity Fields on COCO2017 dataset.(Ascend)
[STABLE] CNN-CTC: proposes three major contributions to addresses scene text recognition (STR) on MJSynth and SynthText dataset.(Ascend)
[STABLE] CenterFace: a practical anchor-free face detection and alignment method for edge devices on WiderFace dataset.(Ascend)
[STABLE] ShuffleNetV2: a much faster and more accurate network than the previous networks on ImageNet 2012 dataset.(GPU)
[STABLE] EfficientNet-B0: a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient on ImageNet 2012 dataset.(GPU)
[BETA] SSD-GhostNet: based on an Ghost module structure which generate more features from cheap operations on Oxford-IIIT Pet dataset.(Ascend)
[BETA] DS-CNN: Depthwise separable convolutional neural network on Speech commands dataset.(Ascend)
[BETA] DeepPotentialH2O: A neural network model for molecular dynamics simulations. (Ascend)
[BETA] GOMO: A classical numerical method called GOMO for ocean simulation. (GPU)

FrontEnd

[STABLE] Refactor the MINDIR to support 310 inference(Ascend).
[STABLE] The execution backend of sparse operations in optimizer can be set through 'target'. (Ascend/GPU/CPU)
[STABLE] Support saving specified network to checkpoint and filtering parameters according to prefix when load checkpoint. (Ascend/GPU/CPU)
[STABLE] Allow users choose whether to load parameter into network strictly.(Ascend/GPU/CPU)
[STABLE] Before training, in graph mode, in order to have the same network initialization parameter values for all devices, broadcast the parameters on device 0 to other devices. (Ascend/GPU)
[STABLE] Support if by if of control flow subgraph. (Ascend/GPU)
[STABLE] Support the judgment that whether a tensor is in a list. (Ascend/GPU/CPU)
[STABLE] Support to get a value by using the corresponding key in a dictionary in the network; Support to get keys and values of a dictionary in the network. (Ascend/GPU/CPU)
[STABLE] Support Tensor in enumerate. (Ascend/GPU/CPU)
[STABLE] Support multilevel index assignment. (Ascend/GPU/CPU)
[STABLE] Support the 'expand_as','view','abs','mean' method of Tensor. (Ascend/GPU/CPU)
[STABLE] Support ResizeBilinear operation transfer ratio. (Ascend)
[STABLE] nn.Matmul supports matrix-vector product and batched matrix multiply. (Ascend/GPU)
[STABLE] nn.Dense supports input tensor whose dimension can be greater than 2. (Ascend/GPU)
[BETA] Support higher order differentiation for partial operators.(CPU/GPU/Ascend)
[STABLE] Support Tensor Augassign.(Ascend/GPU)
[BETA] Support 22 numpy native interfaces.

Auto Parallel

[STABLE] Support parallel optimizer with weight shard. (Ascend/GPU)
[STABLE] Support distributed operators: element-wise series, UnsortedSegmentSum, UnsortedSegmentMin, Split, BroadcastTo and Unique etc. (Ascend/GPU)
[STABLE] Support distributed model prediction. (Ascend/GPU)
[STABLE] Support auto mixed precision level "O2" in auto and semi auto parallel mode. (Ascend/GPU)
[STABLE] Add MultiFieldEmbeddingLookup high-level interface. (Ascend/GPU)

Executor

[STABLE] ResNet50 performance optimize. (GPU)
[STABLE] Support modelzoo net in PyNative mode(Ascend 29, GPU 23, CPU 2).(Ascend/GPU/CPU)
[STABLE] Support PyNative mode on CPU.(CPU)
[STABLE] Optimize performance in PyNative mode.(Ascend/GPU/CPU)
[STABLE] Support Safe Optimized Memory Allocation Solver (SOMAS) on Ascend to improve the memory-reuse, the batch size of Bert large model (128 sequence length) is increased from 160 to 208.(Ascend)
[BETA] Support second order differentiation in PyNative mode.(Ascend/GPU)
[DEMO] Add distributed trainning in PyNative mode.(Ascend/GPU)

MDP

[STABLE] Add new operators for Ascend and GPU: IGamma, LGamma, DiGamma;
[STABLE] Add new distributions for Ascend and GPU: LogNormal, and Logistic;
[BETA] Add new distributions for Ascend only: Gumbel, Cauchy, Gamma, Beta, and Poisson; Add Categorical distribution for GPU;
[STABLE] Add new bijectors for Ascend and GPU: GumbelCDF, Invert;
[STABLE] Add Bayesian layer realized by local reparameterization method for Ascend and GPU;
[STABLE] Add Anomaly Detection Toolbox based on VAE for Ascend and GPU.

DataSet

[STABLE] Support single node multi-p distributed cache data sharing
[STABLE] Support GPU profiling with data processing
[STABLE] Support YOLOV3 dynamic shape in sink mode with dataset
[STABLE] Support unique processing in the data processing pipeline
[STABLE] Python layer parameter verification error information unified

API Change

Backwards Incompatible Change

Python API

Delete shape and dtype of class Initializer (!7373)

Delete shape and dtype attributes of Initializer class.

Modify the return type of initializer (!7373)

Previously, the return type of initializer function may be string, number, instance of class Tensor or subclass of class Initializer.

After modification, initializer function will return instance of class MetaTensor, class Tensor or subclass of class Initializer.

Noted that the MetaTensor is forbidden to initialize parameters, so we recommend that use str, number or subclass of Initializer for parameters initialization rather than the initializer functions.

1.0.1 1.1.0

>>> import mindspore.nn as nn
>>> from mindspore.common import initializer
>>> from mindspore import dtype as mstype
>>>
>>> def conv3x3(in_channels, out_channels)
>>>   weight = initializer('XavierUniform', shape=(3, 2, 32, 32), dtype=mstype.float32)
>>>   return nn.Conv2d(in_channels, out_channels, weight_init=weight, has_bias=False, pad_mode="same")

>>> import mindspore.nn as nn
>>> from mindspore.common.initializer import XavierUniform
>>>
>>> #1) using string
>>> def conv3x3(in_channels, out_channels)
>>>   return nn.Conv2d(in_channels, out_channels, weight_init='XavierUniform', has_bias=False, pad_mode="same")
>>>
>>> #2) using subclass of class Initializer
>>> def conv3x3(in_channels, out_channels)
>>>   return nn.Conv2d(in_channels, out_channels, weight_init=XavierUniform(), has_bias=False, pad_mode="same")

Advantages: After modification, we can use the same instance of Initializer to initialize parameters of different shapes, which was not allowed before.

1.0.1 1.1.0

>>> import mindspore.nn as nn
>>> from mindspore.common import initializer
>>> from mindspore.common.initializer import XavierUniform
>>>
>>> weight_init_1 = XavierUniform(gain=1.1)
>>> conv1 = nn.Conv2d(3, 6, weight_init=weight_init_1)
>>> weight_init_2 = XavierUniform(gain=1.1)
>>> conv2 = nn.Conv2d(6, 10, weight_init=weight_init_2)

>>> import mindspore.nn as nn
>>> from mindspore.common import initializer
>>> from mindspore.common.initializer import XavierUniform
>>>
>>> weight_init = XavierUniform(gain=1.1)
>>> conv1 = nn.Conv2d(3, 6, weight_init=weight_init)
>>> conv2 = nn.Conv2d(6, 10, weight_init=weight_init)

Modify get_seed function (!7429)

Modify get_seed function implementation

Previously, if seed is not set, the value of seed is default, parameters initialized by the normal function are the same every time.

After modification, if seed is not set, the value of seed is generated randomly, the initialized parameters change according to the random seed.

If you want to fix the initial value of parameters, we suggest to set seed.

>>> from mindspore.common import set_seed
>>> set_seed(1)

`nn.LinSpace` (!9494) has been removed and modify `ops.LinSpace` (!8920)

The nn.LinSpace interface only support passing the value by args previously. For the convenience, we provided enhancive ops.LinSpace interface, which support passing the value by the inputs at the latest version. So there is no need for nn.LinSpace.

1.0.1 1.1.0

>>> from mindspore import nn
>>>
>>> start = 1
>>> stop = 10
>>> num = 5
>>> linspace = nn.LinSpace(start, stop, num)
>>> output = linspace()

>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore import ops
>>>
>>> linspace = ops.LinSpace()
>>> start = Tensor(1, mindspore.float32)
>>> stop = Tensor(10, mindspore.float32)
>>> num = 5
>>> output = linspace(start, stop, num)

Parts of `Optimizer` add target interface (!6760)

The usage of the sparse optimizer is changed.

The target interface is used to set the execution backend of the sparse operator.

The add_primitive_attr interface is no longer allowed.

The following optimizers add the target interface: Adam, FTRL, LazyAdam, ProximalAdagrad

1.0.1 1.1.0

>>> from mindspore.nn import Adam
>>>
>>> net = LeNet5()
>>> optimizer = Adam(filter(lambda x: x.requires_grad, net.get_parameters()))
>>> optimizer.sparse_opt.add_prim_attr("primitive_target", "CPU")

>>> from mindspore.nn import Adam
>>>
>>> net = LeNet5()
>>> optimizer = Adam(filter(lambda x: x.requires_grad, net.get_parameters()))
>>> optimizer.target = 'CPU'

`export` Modify the input parameters and export's file name (!7385， !9057)

Export the MindSpore prediction model to a file in the specified format.

The reference includes: net, *inputs, file_name, file_format, **kwargs.

Input parameters can be input according to specific export requirements.

Add the file name extension based on the format.

1.0.1 1.1.0

>>> from mindspore.train.quant import quant
>>>
>>> network = LeNetQuant()
>>> inputs = Tensor(np.ones([1, 1, 32, 32]), mindspore.float32)
>>> quant.export(network, inputs, file_name="lenet_quant.mindir", file_format='MINDIR')
lenet_quant.mindir

>>> from mindspore import export
>>>
>>> network = LeNetQuant()
>>> inputs = Tensor(np.ones([1, 1, 32, 32]), mindspore.float32)
>>> export(network, inputs, file_name="lenet_quant", file_format='MINDIR', quant_mode='AUTO')
lenet_quant.mindir

`Dense`, `Conv2dBnAct`, `DenseBnAct`, `DenseQuant` support setting the activation attribute as an instance of a class derived from `nn.Cell` or `Primtive` (!7581)

activation (Union[str, Cell, Primitive]): activate function applied to the output of the fully connected layer

1.0.1 1.1.0

>>> import mindspore.nn as nn
>>>
>>> dense = nn.Dense(1, 1, activation='relu')

>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>>
>>> dense = nn.Dense(1, 1, activation=nn.ReLU())
>>> dense = nn.Dense(1, 1, activation=ops.ReLU())

`tensor.dim()`, `tensor.size()` has been renamed to `tensor.ndim`, `tensor.size` (!10175)

Previously, tensor.size() and tensor.dim() were used for checking the total number of elements/dimensions in the tensor. However, from a user's perspective, tensor.size and tensor.ndim (methods -> properties) are better choices, since they follow the numpy naming convention.

1.0.1	1.1.0
`>>> from mindspore import Tensor >>> >>> Tensor((1,2,3)).size() >>> Tensor((1,2,3)).dim()`	`>>> from mindspore import Tensor >>> >>> Tensor((1,2,3)).size >>> Tensor((1,2,3)).ndim`

`EmbeddingLookup` add a config in the interface: sparse (!8202)

sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.

1.0.1 1.1.0

>>> from mindspore.nn import EmbeddingLookup
>>>
>>> input_indices = Tensor(np.array([[1, 0], [3, 2]]), mindspore.int32)
>>> result = EmbeddingLookup(4,2)(input_indices)
>>> print(result.shape)
(2, 2, 2)

>>> from mindspore.nn import EmbeddingLookup
>>>
>>> input_indices = Tensor(np.array([[1, 0], [3, 2]]), mindspore.int32)
>>> result = EmbeddingLookup(4,2)(input_indices, sparse=False)
>>> print(result.shape)
(2, 2, 2)

`nn.probability.bijector` change types of attributes from (int, float) to (float, list, numpy.ndarray, Tensor) (!8191)

Attributes Type change: (int, float) -> (float, list, numpy.ndarray, Tensor). Int type is not supported anymore. Parameters of all bijectors should be type float, list, numpy.ndarray or Tensor.

1.0.1 1.1.0

>>> import mindspore.nn.probability.bijector as msb
>>>
>>> power = 2
>>> bijector = msb.PowerTransform(power=power)

>>> import mindspore.nn.probability.bijector as msb
>>>
>>> power = 2.0
>>> bijector = msb.PowerTransform(power=power)

`nn.probability.bijector.GumbelCDF` remove a attribute in the interface: dtype (!8191)

dtype is removed from GumbelCDF and is no longer an argument of the class.

1.0.1 1.1.0

>>> import mindspore.nn.probability.bijector as msb
>>> from mindspore import dtype as mstype
>>>
>>> bijector = msb.GumbelCDF(loc=0.0, scale=1.0, dtype=mstype.float32)

>>> import mindspore.nn.probability.bijector as msb
>>>
>>> bijector = msb.GumbelCDF(loc=0.0, scale=1.0)

`nn.layer.combined.Conv2dBnAct`, `nn.layer.combined.DenseBnAct` move from nn.layer.quant to nn.layer.combined (!8187)

Previously Conv2dBnAct and DenseBnAct are in nn.layer.quant, since they are not quant cells, now they are moved to nn.layer.combined. If you import Conv2dBnAct, DenseBnAct from mindspore.nn, then your code doesn't need any change.

1.0.1	1.1.0
`>>> from mindspore.nn.layer.quant import Conv2dBnAct, DenseBnAct`	`>>> from mindspore.nn import Conv2dBnAct, DenseBnAct`

`nn.layer.conv.Conv2D`, `nn.layer.quant.Conv2dBnFoldQuant`, `nn.layer.quant.Conv2dBnWithoutFoldQuant` change weight shape when group > 1 in Ascend platform (!9723)

In Ascend platform, if group > 1, the weight shape of Conv2D change from [in_channels//group, out_channels, kernel_size, kernel_size] to [out_channels, in_channels//group, kernel_size, kernel_size]. Previously, checkpoints of the networks are used, which use Conv2D with group > 1, such as MobileNet, can not be directly used now, need to transpose the first and second axis of the weight.

Bug fixes

FrontEnd

[STABLE] Fix the problem of the cse optimization in the situation of control flow. (Ascend/GPU)

Auto Parallel

[STABLE] Resolve the restriction: input and output layouts of Reshape are restricted in tensor redistribution. (Ascend/GPU)
[STABLE] Resolve the restriction: output strategy should be data parallel in model evaluation. (Ascend/GPU)

Executor

[STABLE] Fix fusion operator compilation cache. (Ascend)
[STABLE] Fix compilation error of dynamic shape operator. (Ascend)
[STABLE] Fix bug of pynative cannot insert transdata of node output when node should be spilted in the backend opt.(Ascend)
[STABLE] Fix the bug of TensorMove and memcpy_async merge to one after backend cse pass (Ascend)

DataSet

[STABLE] Fix cache server hang on RequestFreeTag. (Ascend/GPU/CPU)
[STABLE] Fix hung when use pyfunc multi-processing. (Ascend/GPU/CPU)
[STABLE] Fix add multiple parent nodes to tree node cause core dump. (Ascend/GPU/CPU)

MindSpore Lite

Major Features and Improvements

Converter and runtime

Support dynamic shape in MindSpore Lite Converter.
Optimize sub-graph mechanism by dynamically splitting the entire graph into multiple subgraphs based on the operator supported, backend hardware and user configuration.
Support TensorList and TensorList operators such as TensorListFromTensor, TensorListGetItem and so on.
Support BatchMatMul fusion and LSTM fusion in MindSpore Lite Converter.
Support converting model and run inference on Windows operator system.
Support Model(.ms) visualization on Netron.
Support Tensorflow model in MindSpore Lite Converter
Add 86 converter parsers.
Convert aware training model without user's awareness
Support scalar tensor in MindSpore Lite Converter and Runtime
Support NPU backend on HUAWEI Kirin SoC.[BETA]
Merge timeprofiler into benchmark

CPU backend optimization

Add 50+ new operators, including new Op type(like Adder, Gru).
Enhanced performance on armv8.2 supported platform. For example, utilizing sdot instruction more efficiently.
Optimize all operators(fp32, fp16, int8) by implementing multi-thread, SIMD tech as much as possible. Model inference time can reduce at least 20% after these optimizations.
Extending to support operators for x86_64 platform based on SSE/AVX instruction set.

OpenCL backend

Add new ops: add 10+ ops, total 58 ops;
Performance optimization: by memory layout optimize, Winograd Convolution select strategyoptimize, SIMT local size optimize, local cache optimize, GPU performance improvement up to 20+% vs MSLITE Version1.0
Add Online Graph optimzation: by fusion Convolution/Matmul/Fullconnection and add/mul/pad/reshape, improve performance up to 50+% for some networks;
Add auto tuning: by online tuning in the graph compilation phase, optimize performance up to 10%;
Add weight quant: support weight quant
Add opencl kernel binary cache: improve Initialization time .

Post quantization

MindSpore Lite supports both weight quantization and full quantization. Currently, Weights can be quantized into 1 ~ 16 bits according to user configuration. In internal testing, quantization of networks, such as classification, detection, segmentation and transformer are well supported. To ensure high accuracy of quantized models, MindSpore Lite uses a pipeline quantization method. In the first phase, the weight and activation value are quantized using linear quantization methods, such as MIN-MAX. In the second phase, the quantization error is analyzed, and uses statistical methods to compensate loss caused by fp32 quantization to a fixed point such as Int8 to quantized models. The features of Post-training quantization are:

perchannel asymmetric quantization for weights, such as MAX_MIN and KMEANS
Perlayer symmetric quantization for activation, such as KL and MAX_MIN.
perlayer asymmetrical quantization for activation, such as, RemoveOutlier.
accuracy loss compensation, such as BiasCorrection

mobilenet_v2	ACC (ImageNet)
FP32	71.56%
A8W8	71.16%
A8W8(without BiasCorrection)	70.74%
A8W7	71.06%
A7W7	70.78%

The above table uses the mobilenet_v2 model from TF official website. Using MindSpore Lite quantization, the precision of A8W8 (8-bit activation value quantization and 8-bit weight quantization) decreases from 0.82% to 0.4% after accuracy loss compensation, for 7-bit quantization, the precision loss is still no more than 1%.

Training on Device

Within MindSpore 1.1 release, the MindSpore Lite provides the following Training-on-Device (ToD) capabilities:

Learning from scratch and Transfer Learning strategies are supported
MindSpore based models can be converted and used in training on the device. (Third-party models such as TensorFlow and PyTorch for now cannot be directly imported to the framework)
Grad operations are supported for more than 30 operators such as Dense layers, Convolutions and Batch Normalizations. Momentum, SGD, and ADAM optimizers are supported.
Supports networks such as LeNet, Alexnet, Resnet, MobileNetV1/V2/V3, and EffectiveNet, and provides complete model loading, conversion, and Python training scripts on the device side.

The MindSpore Lite ToD framework is already in use in the newest Huawei Smart TV, providing a unique and personalized user experience as a family entertainment center.

API Change

API Incompatible Change

C++ API

[Modify] Context now support multi-context configuration.(Context.h)
[Modify] Callback is move from lite_session.h into ms_tensor.h.
[Modify] GetInputsByName in lite_session.h is changed into GetInputsByTensorName
[Add] add static LiteSession CreateSession(const charmodel_buf, size_t size, const lite::Context *context) in lite_session.h
[Add] add GetErrorInfo interface returning error message in errorcode.h
[Delete] Remove model_generated.h, ops_generated.h and headers of FlatBuffers library from interfaces

Java API

[Add] Implement JNI layer and add Java api for CPU and GPU backend

Deprecations

C++ API

Deprecate Interface GetOutputsByNodeName

Bug fixes

[BUGFIX] Fix the bug in sub-graph segmentation
[BUGFIX] Fix the bug in Tensor getitem in which the ellipsis matches the wrong dim-size.
[BUGFIX] Fix the bug that activation modification after defining Dense will not take effect.

Contributors

Thanks goes to these wonderful people:

zhouyifengCode, huqi, JulyAi, damon0626, chenbo116, rmdyh, davidmc, gray0v0, doitH, Gogery, zymaa, xinyunfan

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, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jesse, 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, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, 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, 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, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa

Contributions of any kind are welcome!

MindSpore 1.0.0 Release Notes

Major Features and Improvements

MindSpore Training and Inference Framework

Ascend 910

New models
- DenseNet121: a dense convolutional neural network, which connects each layer to every other layer in a feed-forward fashion for object recognition on ImageNet dataset.
- UNet2D-Medical: Unet Medical model for 2D image segmentation, Convolutional Networks for Biomedical Image Segmentation on ISBI Challenge database.
Frontend and user interface
- Second-Order Optimization
  - Enable second-order optimization for Bert on Ascend 910, which can achieve a masked lm accuracy of 71.3% in 800 seconds using 8 Ascend 910 (Bert-Large @MLPerf v0.7 dataset).
- New GNN model BGCF
  - Bayesian Graph Convolutional Filtering network which naturally incorporate the uncertainty in the user-item interaction graph shows excellent recommendation performance on Amazon-Beauty dataset.
- Add append interface for SequentialCell.
- Add a level auto for AMP.
Executor and performance optimization
- Support quantitative network (Resnet50 & YoloV3 & MobileNetV2).
- Project ease of use optimization: project compilation time optimization, CMakelist regularization, cudnn, cuda independent compilation and installation independent.
Data processing, augmentation, and save format
- Support GeneratorDataset return string type

Other Hardware Support

GPU platform
- Enable second-order optimization for resnet50 on GPU, which achieve 30% improvement on training time compared to SGD with Momentum (Resnet50 @ImageNet).

User interfaces change log

Remove global object GradOperation in Autodiff(!5011)
Remove useless attribute 'name' in Autodiff(!5172)
Rectification distributed init(!5350)
Move the setting of ParalleMode from train.parallel_utils to context(!5351)
Modification of save_checkpoint(!5482)
Wrap numpy random seed into an api(!5634)
Delete enable_fused_layernorm in some modelzoo scripts(!5665)
Move 'multi-subgraphs' interface to internal(!5696)
Rename mirror_mean to gradient_mean(!5700)
Remove default value of 'group' of DepthWiseConv2d(!5865)
Modify interface for function and remove duplicated def(!5958)
Unify Conv2d and DepthwiseConv2d(!5916)
Modification of SoftmaxCrossEntropyWithLogits(!5502)
Change API set_strategy() to shard()(!5991)
Move batch_size from bert_cfg_cfg to cfg(!6233)
Remove unused parameters from SummaryRecord init(!5548)
remove sens parameter of TrainOneStepWithLossScaleCell(!5753)
optimize the TrainOneStepCell for user's define(!6159)
delete seed0 and seed1 of nn.Dropout(!5735)
delete DataWrapper(!6101)
LSTM API optimization(!6374)
Merge P\C\F of ops(!5645)
delete SoftmaxCrossEntropyExpand interface(!6607)
Adjust GroupNorm interface(!6329)
Modify init interface to internal interface(!6651)
Log optimization(!5842)
Remove useless API dataset.set_dataset_size（!5806)
Some of Dataset API add usage parameter（!5605)
Change the import path, such as from mindspore.dataset.transforms.vision to mindspore.dataset.vision.transforms（!5384)
Rename ImageFolderDatasetV2 to ImageFolderDataset（!5384)
Dataset.map parameter optimization（!5384)
Add new api dataset.get_col_names（!5384)
Add new api dataset.get_col_names（!5384)
Remove useless API MindRecord finish（!5580)

MindSpore Lite

Converter
- Add 6 TFLite op, 7 Caffe op, 1 ONNX op.
- Add support for Windows.
- Support parallel inference of multiple sessions to adapt to more scenarios
- Support 8bits only weight-quantization, most main-stream models has small accuracy loss (less than 0.5%) when compared to non-qunantized fp32 model.
CPU & GPU
- Add 20 CPU ops，include FP32, int8/uint8, FP16 and int32 ops.
- Add supporting FP16 for GPU, add 14 GPU ops include FP32/FP16.
- Add Buffer/Image2D transform op for GPU
- Performance optimization for CPU ops focus on ARM32.
- Performance optimization for GPU Convolution using winograd.
Tool & example
- Add object detection Android Demo.

Bugfixes

Models
- fix the constant folding problem in multiply.(!6092)
- move batch_size from bert_net_cfg to cfg in bert scripts.(!6233)
- modify the checkpoint file path.(!6137)
Python API
- fix semi auto parallel parameter of reshape has another user(!5722)
- raise ValueError when call hook function in graph mode(!5831)
Executor
- fix pynative mode to build temporary nn objects.（!6189)
- fix the accuracy problem of multiple inputs of multi-card communication operator broadcast.(!6522)
- fix the problem that the sample distribution interface categorical does not support graph mode.(!5772)
- fix the random seed failure problem of the polynomial downsampling distribution operator.(!5948)
- fix unnecessary address binding issues in GPU heterogeneous scenarios.(!6232)
GPU platform
- fix for kernel resource leak(!5315)
- fix for insufficient memory for continuous unit test running(!5617)
- fix for the memory leak in the sparse slicer(!5578)
Data processing
- fix hang when use pyfunc(!6346)
- fix GPU device queue does not release GIL during resource clean up(!5964)
- fix hang if scripte exit unnormally(!6441)
Third party
- Sqlite : Update sqlite to 3.32.2 to handle CVE-2020-11656, CVE-2020-13871, CVE-2020-11655, CVE-2020-9327, CVE-2020-13630, CVE-2020-15358, CVE-2020-13631, CVE-2020-13632, CVE-2020-13434, CVE-2020-13435, and CVE-2020-15358.
- Libjpeg-turbo : Update libjpeg-turbo to 2.0.4 to handle CVE-2020-13790.

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, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, danish, Danish, dayschan, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, gongdaguo, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huzhifeng, hwjiaorui, Jesse, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, r1chardf1d0, riemann_penn, root, 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, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, 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, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, Zichun, Zirui, Ziyan, zjun, ZPaC

Contributions of any kind are welcome!

MindSpore 0.7.0-beta Release Notes

Major Features and Improvements

MindSpore Training and Inference Framework

Ascend 910

New models
- TinyBert: a smaller and faster version of BERT using transformer distillation for natural language understanding on GLUE benchmark.
- SE-ResNet50: add Squeeze-and-Excitation blocks(SE-Blocks) to the resnet50 network to improve channel interdependencies for image classification on ImageNet 2012 dataset.
- Inception V3: the third version of Inception convolutional architectures for image classification on ImageNet 2012 dataset.
Frontend and user interface
- Embedding operator high-level packaging to support segmented by field for Wide&Deep.
- Load multi-node checkpoint into single-process to support host-device hybrid inference.
- Support Concat/Tile/Strideslice distributed operators.
- Support cumulative gradient and batch training split.
- Support variable parameter input for Cell object.
- Parameter mixed calculation optimization for pynative mode.
- Deep Probabilistic Programming
  - Support statistical distributions classes used to generate stochastic tensors.
  - Support probabilistic inference algorithms.
  - Support BNN layers used to construct BNN in Graph mode.
  - Support interfaces for the transformation between BNN and DNN in Graph mode.
  - Support uncertainty estimation to estimate epistemic uncertainty and aleatoric uncertainty.
- User interfaces change log
  - change base class of parameter(!3473)
  - change binary to mindir(!4258)
  - change export from geir to air(!4269)
  - Init parameter data by default(!3967)
  - change IndexedSlices to RowTensor(!4031)
  - Must set or change parallel mode before any Initializer created(!4801)
Executor and performance optimization
- MindSpore graph compilation process performance improved by 20%.
- Decoupling C++ and Python modules to achieve separate compilation of core modules.
Data processing, augmentation, and save format
- Support automatic data augmentation
- Support GNN distributed cache in single node
- Support ConcatDataset using distributed sampler

Other Hardware Support

GPU platform
- New model supported: VGG16, ResNet101, DeepFM.
- Support some distributed operators in ResNet50 and Wide&Deep.
- Support automatic parallel for Wide&Deep.
- Support function funcsi (such as switch-case).
- Support distributed training with parameter server.
- Support GPU operator profiling.
- Performance optimization of the distributed training with allreduce.
- Performance optimization of the mixed precision training.
- Performance optimization of the pynative mode.
- Performance optimization of the convolution operator, batch normalization operator.
CPU platform
- Support MobileNetV2 Re-Training: Re-train the network with different class number.

MindSpore Lite

Converter
- Support third-party models, including TFLite/Caffe/ONNX.
- Add 93 TFLite op.
- Add 24 Caffe op.
- Add 62 ONNX op.
- Add 11 optimized passes, include fusion/const fold.
- Support aware-training and Post-training quantization.
CPU
- Add 100+ops，support fp32, int8/uint8, FP16 ops
- Support fast convolution algorithms: Sliding Window, Img2col + Gemm, Strassen, Winograd
- Support assembly/neon instruction.
- Support CPU fp16 and sdot on ARM v8.2+.
GPU
- Add 20+ ops for OpenCL.
- Support image2D/buffer format.
- Optimize online initialization time.
- add optimized convolution1X1/3X3/depthwise/convolution_transposed for OpenCL.
Tool & example
- Add benchmark and TimeProfile tools.
- Add image classification Android Demo.

Bugfixes

Models
- normalize the readme file(!5410)
- fix a sink_size bug for transformer(!5393)
- fix bool type optional for resnet50(!5363)
Python API
- improve interface 'bool' for tensor(!4000)
- fix GPU-ResizeNearestNeighbor(!3760)
- fix topK multi dimension grad func(!3711)
- fix scatterop error msg(!3699)
- fix bug of cast dtype when using mix_presion in pynative mode(!3730)
Executor
- fix etsnet train error when UnsegmentSum's first input shape is (1,) (!4573)
- fix bug of result error in while control flow because of unsupporting for value reference (!4103)
- fix bug of the output tensor does not carry device data type (!3774)
- fix bug of avoiding multi attr value are eliminated in pynative mode (!4225)
- fix bug of AssignAdd unable to work normally in multi-cases (!5171)
GPU platform
- improve the environment variable checking for nvcc compiler path (!5140)
- fix bug of error in cast operator conversion from fp16 to fp32 (!4147)
- fix bug of the array out of bound in case of make_tuple operator (!5219)
Data processing and Pro
- fix GeneratorDataset time out(!3624)
- fix concat operator get_dataset_size error(!4701)
- fixing python validator for Repeat Op(!4366)
Third party
- Sqlite : Update sqlite to 3.32.2 to handle CVE-2020-11656, CVE-2020-13871, CVE-2020-11655, CVE-2020-9327, CVE-2020-13630, CVE-2020-15358, CVE-2020-13631, CVE-2020-13632, CVE-2020-13434, CVE-2020-13435, and CVE-2020-15358.
- Libjpeg-turbo : Update libjpeg-turbo to 2.0.4 to handle CVE-2020-13790.

Contributors

Thanks goes to these wonderful people:

Adel, Alexey, andy, andy_wangrui, anthonyaje, anzhengqi, askmiao, avakh, baihuawei, bingyaweng, BowenK, buxue, caifubi, CaoJian, caozhou, Cathy, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chentingting, chenzomi, chenzupeng, chujinjin, cjh9368, Corleone, cristoval, danish, dengyutao, eric, Eric, ervinzhang, etone-chan, fangzehua, fary86, fuzhiye, gengdongjie, genglishuai, Giancarlo, gongdaguo, gukecai, guohongzilong, GuoMengHao, hangq, hanhaocheng, hanhuifeng2020, hanjun996, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, hongxing, huangdongrun, huanghui, huangxinjing, islam_amin, Jesse, jianghui58, jiangzhiwen, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, kai00, kingfo, kpy, kswang, laiyongqiang, leilei_snow, leopz, Li, liangzelang, lianliguang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, lingyunli63, linqingke, lirongzhen1, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuzhongkai, Lixia, lixian, liyong, lizhenyu, looop5, luoyang, lvchangquan, lvliang, lvwenyuan, lyvette, mahdi, Mahdi, mamba_ni, maning202007, Margaret_wangrui, mayang, meixiaowei, meng_chunyang, ms_yan, nhussain, panbingao, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, pengyongrong, Pengyongrong, qianlong, qujianwei, root, shenwei41, shibeiji, simson, songhonglei413, Su, sunsuodong, suteng, tao_yunhao, TFbunny, tinazhang, tom__chen, tony_liu2, tronzhang, VectorSL, wandongdong, wangdongxu, wanghua, wangmin, wangshaocong, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, wuyongkang, xiefangqi, xuanyue, Xun, xutianchun, xuyongfei, yanghaitao, yangjie159, YangLuo, yangruoqi713, yangyongjie, yangzhenzhang, yankai, yao_yf, yelihua, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zhangxuetong, zhaizhiqiang, Zhang, zhangxinfeng3, zhangxuetong, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaoting, zhaozhenlong, zhengjun10, zhongligeng, zhoufeng, zhousiyi, zhouyaqiang, zhouyuanshen, Zichun, Zirui, zjun, zongha, ZPaC, lijiaqi, liangchenghui, wangminggui

Contributions of any kind are welcome!

MindSpore 0.6.0-beta Release Notes

Major Features and Improvements

Ascend 910 Training and Inference Framework

New models
- There are official, research and community under modelzoo.
  - Official is maintained with the newest APIs by MindSpore team, MaskRCNN are added.
  - Research is uploaded by researchers for official review, and APIs may not be updated in time.
  - Community reprints the relevant links of partner research results.
- Hub added on the same level as modelzoo, synchronous storage of materials needed for official hub web pages which will be launched soon.
- Support pre-trained models, few lines of code can be used to download and load pre-trained models, supporting inference or transfer learning.
Frontend and user interface
- Supports user side operator compilation and graph execution error rendering.
- Uniform definition dynamic learning rate behavior in optimizers.
- Support IndexSlice in sparse expression.
- Support use parent construct method during construct.
- Support asynchronous execution save checkpoint file.
- Support implicit type conversion in pynative mode.
- User interfaces change log
  - unform learning rate behavior in optimizers(!2755)
  - rename operator of sparse optimizer(!3217)
  - move profiler module from mindinsight to mindspore(!3075)
  - VOCDataset output change to multi-columns(!3093)
  - GetDatasize feature(!3212)
  - dataset: modify config api(!2936)
Executor and performance optimization
- Decouple C++ and python, so make the architecture more extensible.
- Parameter Server for distributed deep learning supported.
- Serving：a flexible service deployment framework for deep learning models.
- Memory reuse is enhanced, and the batch size of Bert large model is increased from 96 to 160 on a single server.
Data processing, augmentation, and save format
- Support MindRecord save operator after date processing
- Support automatic fusion operator, such as decode/resize/crop
- Support CSV dataset loading

Other Hardware Support

GPU platform
- New model supported: ResNext50, WarpCTC and GoogLeNet.
- Support hyperparametric search and data enhanced automl on GPU.
- Support Resnet50 automatic parallel in GPU backend.

Bugfixes

Models
- Improved the performance and accuracy on ResNet50(!3456)
- Fixed the performance test case of bert(!3486)
Python API
- Fix assign used in while loop(!2720)
- Revert optimize the graph output of all nop node.(!2857)
- Print tensor as numpy.(!2859)
- Support weight decay for sparse optimizer(!2668)
- Fix BatchToSpaceND(!2741)
- Fixing type check mistakes of InplaceAdd and Inplace Sub ops(!2744)
- Change order param only equal to group param(!2748)
Executor
- The performance of graph with control flow is optimized(!2931)
- Fix bug of wrong number of tuple layers(!3390)
- Fix cpu multi graph memory exception(!3631)
- Enable data sync when calling operator without defining a cell(!3081)
- Fix argmaxwith value error in pynative mode on GPU(!3082)
- Fix precision error with fp16 input on pynative mode(!3196)
Data processing
- Fix bug of RandomColor and RandomSharpness default parameter checking (!2833)
- Fix process hung when training and eval (!3469)
Third party
- Sqlite : Update sqlite to 3.32.2 to handle CVE-2020-11656, CVE-2020-13871, CVE-2020-11655, CVE-2020-9327, CVE-2020-13630, CVE-2020-15358, CVE-2020-13631, CVE-2020-13632, CVE-2020-13434, CVE-2020-13435, and CVE-2020-15358.
- Libjpeg-turbo : Update libjpeg-turbo to 2.0.4 to handle CVE-2020-13790.

Contributors

Thanks goes to these wonderful people:

Alexey Shevlyakov, avakh, baihuawei, BowenK, buxue, caifubi, caojian05, Cathy Wong, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chentingting, chenzomi, chujinjin, Danish Farid, dayschan, dengwentao, dinghao, etone-chan, fangzehua, fary86, geekun, Giancarlo Colmenares, gong chen, gukecai, guohongzilong, hangangqiang, heleiwang, hesham, He Wei, hexia, hongxing, huangdongrun, huanghui, islam_amin, Jamie Nisbet, Jesse Lee, jiangjinsheng, jiangzhiwen, jinyaohui, jjfeing, jojobugfree, Jonathan Yan, jonyguo, Junhan Hu, Kang, kingfo, kouzhenzhong, kpy, kswang, laiyongqiang, leopz, liangzelang, lichenever, lihongkang, Li Hongzhang, lilei, limingqi107, lirongzhen1, liubuyu, liuchongming74, liuwenhao4, liuxiao, Lixia Chen, liyanliu, liyong, lizhenyu, lvliang, Mahdi, Margaret_wangrui, meixiaowei, ms_yan, nhussain, ougongchang, panfengfeng, panyifeng, peilinwang, Peilin Wang, pkuliuliu, qianlong, rick_sanchez, shibeiji, Shida He, shijianning, simson, sunsuodong, suteng, Tinazhang, Tron Zhang, unknown, VectorSL, wandongdong, wangcong, wangdongxu, wangdongxu6, wanghua, wangnan39, Wei Luning, wenchunjiang, wenkai, wilfChen, WilliamLian, wukesong, Xian Weizhao, Xiaoda Zhang, xiefangqi, xulei2020, xunxue, xutianchun, Yang, yanghaitao, yanghaitao1, yanghaoran, yangjie, yangjie159, YangLuo, Yanjun Peng, yankai, yanzhenxiang2020, yao_yf, Yi Huaijie, yoonlee666, yuchaojie, yujianfeng, zhangzhongpeng, zhangdengcheng, Zhang Qinghua, zhangyinxia, zhangz0911gm, zhaojichen, zhaoting, zhaozhenlong, zhoufeng, zhouneng, zhousiyi, Zirui Wu, Ziyan, zjun, ZPaC, lihongzhang, wangdongxu

Contributions of any kind are welcome!

MindSpore 0.5.2-beta Release Notes

Major Features and Improvements

Ascend 910 Training and Inference Framework

New models
- DenseNet121: a convolution based neural network for the task of image classification on ImageNet 2012 dataset.

Bugfixes

Models
- VGG16,Alexnet,GoogleNet,optimize network for better performance. (!5539)
- YOLOV3, fix yolov3_darknet53 dataset bug. (!5658)

Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

MindSpore 0.5.0-beta Release Notes

Major Features and Improvements

Ascend 910 Training and Inference Framework

New models
- ResNext50: a simple, highly modularized network architecture using aggregated resdiual transformations for image classification on ImageNet 2012 dataset.
- MASS: a pre-training method for sequence to sequence based language generation tasks on Text Summarization and Conversational Response Generation using News Crawls 2007-2017 dataset, Gigaword corpus and Cornell movie dialog corpus.
- Transformer: a neural network architecture for language understanding on WMT 2014 English-German dataset.
- GCN：Graph Convolutional Networks for the task of classification of nodes in a graph on Cora and Citeseer datasets.
- GAT：an attention-based graph neural network for node classification on Cora and CiteSeer dataset.
Frontend and user interface
- Support tensor value and assignment of mixed tensor index in graph mode.
- Support tensor comparison, len operator, constexpr syntax, value and assignment of tensor index in pynative mode.
- Support converting MindSpore IR to pb format for infer model.
- Support print operator to write data directly on the hard disk.
- Add the double recursive programming solution for very high speed parallel strategy search in automatic parallel.
- User interfaces change log
  - Allow the learning rate of AdamWeightDecayDynamicLR and Lamb to be 0(!1826)
  - Restricting the entire network input parameter is Tensor(!1967)
  - Turn shape and dtype into attributes instead of interfaces(!1919)
  - Delete multitypefungraph(!2116)
  - Refactor the callback module in an encapsulated way, use _CallbackManager instead of_build_callbacks(!2236)
  - Delete EmbeddingLookup(!2163)
  - Checkpoint add model_type(!2517)
Executor and performance optimization
- Heterogeneous execution on CPU and Ascend devices supported, and is verified in Wide&Deep model.
- Quantitative training of MobileNetV2, Lenet and Resnet50 on Ascend-910 are supported.
- Support new fusion architecture, which can do fusion optimization across graphs and kernels to improve execution speed.
Data processing, augmentation, and save format
- Support data processing pipeline performance profiling.
- Support public dataset loading, such as CLUE and Coco.
- Support more text processing, such as more tokenizers and vocab data.
- Support MindRecord padded data.

Other Hardware Support

GPU platform
- New model supported: Bert / Wide&Deep.
- Support setting max device memory.
CPU platform
- New model supported: LSTM.

Bugfixes

Models
- Bert, Move Bert from example to model_zoo, optimize network for better performance. (!1902)
- VGG16, Move VGG16 from example to model_zoo, optimize network for better accuracy. (!2645)
- Alexnet, modify parameter setting to improve accuracy (!1364)
- Wide&Deep, Move Wide&Deep from example to model_zoo, optimize network for better performance. (!2221)
Python API
- Fix bug in auto cast(!1766)
- Fix bug of register_backward_hook(!2148)
- Fix bug of tuple args in pynative mode(!1878)
- Fix bug of checking numbers of arguments and graph parameters(!1701)
Executor
- Fix bug of loading input data repeatedly in pynative mode(!1966)
- Fix bug of list cannot be used as input in pynative mode(!1765)
- Fix bug of kernel select (!2103)
- Fix bug of pattern matching for batchnorm fusion in the case of auto mix precision.(!1851)
- Fix bug of generate hccl's kernel info.(!2393)
GPU platform
- Fix bug of summary feature invalid(!2173)
Data processing
- Fix bug of Cifar dataset reading(!2096)
- Fix bug of C++ behavior in RandomCropAndResize(!2026)
- Fix the bug of mindrecord shuffle(!2420)
Third party
- Sqlite : Update sqlite to 3.32.2 to handle CVE-2020-11656, CVE-2020-13871, CVE-2020-11655, CVE-2020-9327, CVE-2020-13630, CVE-2020-15358, CVE-2020-13631, CVE-2020-13632, CVE-2020-13434, CVE-2020-13435, and CVE-2020-15358.

Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

MindSpore 0.3.1-alpha Release Notes

Major Features and Improvements

Ascend 910 Training and Inference Framework

Frontend and User Interface
- Independent model init interface.
Data processing, augmentation, and save format
- Support sample padding for minddataset.

Bugfixes

Python API
- Fix bugs in the lars optimizer(!1894)
Data processing
- Fix accuracy problem of RandomCropDecodeResize (!2340)

Release 0.3.0-alpha

Major Features and Improvements

Ascend 910 Training and Inference Framework

New models
- DeepFM: a factorization-machine based neural network for CTR prediction on Criteo dataset.
- DeepLabV3: significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2007 semantic image segmentation benchmark.
- Faster-RCNN: towards real-time object detection with region proposal networks on COCO 2017 dataset.
- SSD: a single stage object detection methods on COCO 2017 dataset.
- GoogLeNet: a deep convolutional neural network architecture codenamed Inception V1 for classification and detection on CIFAR-10 dataset.
- Wide&Deep: jointly trained wide linear models and deep neural networks for recommender systems on Criteo dataset.
Frontend and User Interface
- Complete numpy advanced indexing method. Supports value and assignment through tensor index.
- Some optimizers support separating parameter groups. Different parameter groups can set different learning_rate and weight_decay.
- Support setting submodule's logging level independently, e.g. you can set logging level of module A to warning and set logging level of module B to info.
- Support weights to be compiled according to shape to solve the problem of large memory overhead.
- Add some operators implement and grammar support in pynative mode. To be consistent with graph mode.
- User interfaces change log
  - Learning rate and weight decay making group params(!637)
  - Support weights to be compiled according to shape(!1015)
  - delete some context param(!1100)
  - ImageSummary/ScalarSummary/TensorSummary/HistogramSummary(!1329)(!1425)
Executor and Performance Optimization
- Support doing evaluation while in training process, so that the accuracy of training can be easily obtained.
- Enable second-order optimization for resnet50, which can achieve 75.9% accuracy in 45 epochs (Resnet50 @ImageNet).
- Optimize pynative implementation and improve it's execution performance.
- Optimize summary record implementation and improve its performance.
Data processing, augmentation, and save format
- Support simple text processing, such as tokenizer/buildvocab/lookup.
- Support padding batch.
- Support split or concat dataset.
- Support MindDataset reading from file list.

Other Hardware Support

GPU platform
- New models supported: MobileNetV2, MobileNetV3.
- Support mixed precision training.
- Support device memory swapping.

Bugfixes

Python API
- An exception to the broadcast input data type check(!712)
- Fix issues assignsub return value 0(!1036)
- Fix issue Conv2dBackpropInput bprop should return 3 instead of 2 items(!1001)
- Fix sens shape error of TrainOneStepWithLossScaleCell(!1050)
- Fix BatchNormGrad operator(!1344)
Executor
- Fix dropout，topK and addn errors in PyNative mode (!1285, !1138, !1033).
- Fix memory leaks after execution in PyNatvie mode (!1201).
- Fix HCCL failure in some special scenes (!1204, !1252).
- Fix SSD network when Select failed, can't find kernel info(!1449).
- Fix Topk operator selection strategy bug between aicore and aicpu(!1367).
- Fix input memory size of 'assign' op unequal in control sink mode when assigning a data from one child graph to another child graph(!802).
- Fix allreduce ir inconsistency(!989).
GPU platform
- Fix summary for gradient collection (!1364)
- Fix the slice operator (!1489)
Data processing
- Fix memory problems of GeneratorDataset of sub-process (!907)
- Fix getting data timeout when training the cifar10 dataset under the lenet(!1391)

Contributors

Thanks goes to these wonderful people:

Alexey Shevlyakov, Amir Lashkari, anthony, baihuawei, biffex, buxue, caifubi, candanzg, caojian05, Cathy Wong, changzherui, chenfei, chengxianbin, chenhaozhe, chenzomi, chujinjin, cristoval, dengwentao, eric, etone-chan, fary86, gaojing, gengdongjie, gongchen, guohongzilong, guozhijian, heleiwang, hesham, He Wei, Hoai Linh Tran, hongxing, huangdongrun, huanghui, Jamie Nisbet, Jesse Lee, jiangjinsheng, jiangzhiwen, jinyaohui, jjfeing, jonwe, jonyguo, Junhan Hu, Kang, kingfo, kswang, laiyongqiang, leopz, lichenever, lihongkang, limingqi107, liubuyu, liuliyan2, liuwenhao4, liuxiao, liuxiao, liyong, lizhenyu, lvliang, Margaret_wangrui, meixiaowei, ms_yan, Nat Sutyanyong, ougongchang, panfengfeng, panyifeng, Peilin Wang, peixu_ren, qianlong, rick_sanchez, seatea, sheng, shijianning, simson, sunsuodong, Tinazhang, VectorSL, wandongdong, wangcong, wanghua, wangnan39, Wei Luning, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuxuejian, Xiaoda Zhang, xiefangqi, xulei2020, Yang, yangjie159, yangruoqi713, yangyongjie, yangzhenzhang, Yanjun Peng, yanzhenxiang2020, yao_yf, Yi Huaijie, yoonlee666, yujianfeng, YuJianfeng, yvetteliu, zhangdengcheng, Zhang Qinghua, zhangz0911gm, zhaojichen, zhaoting, zhaozhenlong, zhoufeng, zhouneng, zhousiyi, zhouyuanshen, Zirui Wu, Ziyan, zjun, ZPaC, lihongzhang

Contributions of any kind are welcome!

MindSpore 0.2.0-alpha Release Notes

Major Features and Improvements

Ascend 910 Training and Inference Framework

New models
- MobileNetV2: Inverted Residuals and Linear Bottlenecks.
- ResNet101: Deep Residual Learning for Image Recognition.
Frontend and User Interface
- Support for all python comparison operators.
- Support for math operators **,//,%. Support for other python operators like and/or/not/is/is not/ in/ not in.
- Support for the gradients of function with variable arguments.
- Support for tensor indexing assignment for certain indexing type.
- Support for dynamic learning rate.
- User interfaces change log
  - DepthwiseConv2dNative, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropInput(!424)
  - ReLU6, ReLU6Grad(!224)
  - GeneratorDataset(!183)
  - VOCDataset(!477)
  - MindDataset, PKSampler(!514)
  - map(!506)
  - Conv(!226)
  - Adam(!253)
  - _set_fusion_strategy_by_idx,_set_fusion_strategy_by_size(!189)
  - CheckpointConfig(!122)
  - Constant(!54)
Executor and Performance Optimization
- Support parallel execution of data prefetching and forward/backward computing.
- Support parallel execution of gradient aggregation and forward/backward computing in distributed training scenarios.
- Support operator fusion optimization.
- Optimize compilation process and improve the performance.
Data processing, augmentation, and save format
- Support multi-process of GeneratorDataset/PyFunc for high performance
- Support variable batchsize
- Support new Dataset operators, such as filter,skip,take,TextLineDataset

Other Hardware Support

GPU platform
- Use dynamic memory pool by default on GPU.
- Support parallel execution of computation and communication.
- Support continuous address allocation by memory pool.
CPU platform
- Support for windows 10 OS.

Bugfixes

Models
- Fix mixed precision bug for VGG16 model (!629).
Python API
- Fix ControlDepend operator bugs on CPU and GPU (!396).
- Fix ArgMinWithValue operator bugs (!338).
- Fix Dense operator bugs on PyNative mode (!276).
- Fix MatMul operator bugs on PyNative mode (!288).
Executor
- Fix operator selection bugs and make it general (!300).
- Fix memory reuse bug for GetNext op (!291).
GPU platform
- Fix memory allocation in multi-graph scenarios (!444).
- Fix bias_add_grad under fp16 precision (!598).
- Fix support for fp16 kernels on nvidia 1080Ti(!571).
- Fix parsing of tuple type parameters (!316).
Data processing
- Fix TypeErrors about can't pickle mindspore._c_dataengine.DEPipeline objects(!434).
- Add TFRecord file verification(!406).

Contributors

Thanks goes to these wonderful people:

Alexey_Shevlyakov, Cathy, Chong, Hoai, Jonathan, Junhan, JunhanHu, Peilin, SanjayChan, StrawNoBerry, VectorSL, Wei, WeibiaoYu, Xiaoda, Yanjun, YuJianfeng, ZPaC, Zhang, ZhangQinghua, ZiruiWu, amongo, anthonyaje, anzhengqi, biffex, caifubi, candanzg, caojian05, casgj, cathwong, ch-l, chang, changzherui, chenfei, chengang, chenhaozhe, chenjianping, chentingting, chenzomi, chujinjin, dengwentao, dinghao, fanglei, fary86, flywind, gaojing, geekun, gengdongjie, ghzl, gong, gongchen, gukecai, guohongzilong, guozhijian, gziyan, h.farahat, hesham, huangdongrun, huanghui, jiangzhiwen, jinyaohui, jjfeing, jojobugfree, jonathan_yan, jonyguo, jzw, kingfo, kisnwang, laiyongqiang, leonwanghui, lianliguang, lichen, lichenever, limingqi107, liubuyu, liuxiao, liyong, liyong126, lizhenyu, lupengcheng, lvliang, maoweiyong, ms_yan, mxm, ougongchang, panfengfeng, panyifeng, pengyanjun, penn, qianlong, seatea, simson, suteng, thlinh, vlne-v1, wangchengke, wanghua, wangnan39, wangqiuliang, wenchunjiang, wenkai, wukesong, xiefangqi, xulei, yanghaitao, yanghaoran, yangjie159, yangzhenzhang, yankai10, yanzhenxiang2020, yao_yf, yoonlee666, zhangbuxue, zhangz0911gm, zhangzheng, zhaojichen, zhaoting, zhaozhenlong, zhongligeng, zhoufeng, zhousiyi, zjun, zyli2020, yuhuijun, limingqi107, lizhenyu, chenweifeng.

Contributions of any kind are welcome!

MindSpore 0.1.0-alpha Release Notes

Main Features

Ascend 910 Training and Inference Framework

Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.5 or EulerOS 2.8
Python version: 3.7.5
Preset models
- ResNet-50: residual structure-based convolutional neural network (CNN) for image classification, which is widely used.
- AlexNet: classic CNN for image classification, achieving historical results in ImageNet LSVRC-2012.
- LeNet: classic CNN for image classification, which was proposed by Yann LeCun.
- VGG16: classic CNN for image classification, which was proposed by Oxford Visual Geometry Group.
- YoloV3: real-time object detection network.
- NEZHA: BERT-based Chinese pre-training network produced by Huawei Noah's Ark Laboratory.
Execution modes
- Graph mode: provides graph optimization methods such as memory overcommitment, IR fusion, and buffer fusion to achieve optimal execution performance.
- PyNative mode: single-step execution mode, facilitating process debugging.
Debugging capability and methods
- Save CheckPoints and Summary data during training.
- Support asynchronous printing.
- Dump the computing data.
- Support profiling analysis of the execution process performance.
Distributed execution
- Support AllReduce, AllGather, and BroadCast collective communication.
- AllReduce data parallel: Each device obtains different training data, which accelerates the overall training process.
- Collective communication-based layerwise parallel: Models are divided and allocated to different devices to solve the problem of insufficient memory for large model processing and improve the training speed.
- Automatic parallel mode: The better data and model parallel mode can be predicted based on the cost model. It is recommended that this mode be used on ResNet series networks.
Automatic differentiation
- Implement automatic differentiation based on Source to Source.
- Support distributed scenarios and automatic insertion of reverse communication operators.
Data processing, augmentation, and save format
- Load common datasets such as ImageNet, MNIST, CIFAR-10, and CIFAR-100.
- Support common data loading pipeline operations, such as shuffle, repeat, batch, map, and sampler.
- Provide basic operator libraries to cover common CV scenarios.
- Support users to customize Python data augmentation operators through the Pyfunc mechanism.
- Support the access of user-defined datasets through the GeneratorDataset mechanism.
- Provide the MindSpore data format, data aggregation and storage, random access example, data partition, efficient parallel read, user-defined index, and dataset search.
- Convert user datasets to the MindSpore data format.
- After data processing and augmentation, provide training applications in feed and graph modes.
FP32/16 mixed precision computation, supporting automatic and manual configuration
Provide common operators such as nn, math, and array, which can be customized.

Inference Deployment

Deploy models in MindSpore format on the Ascend 310 platform for inference.
Save models in ONNX format.
Support saving models in LITE format and running models based on the lightweight inference framework.
- Recommended OS: Android 4.3 or later
- Supported network type: LeNet
- Provide the generalization operators generated by TVM and operators generated after specific networks are tuned.

Other Hardware Support

GPU platform training
- Recommended OS: Ubuntu 16.04
- CUDA version: 9.2 or 10.1
- CuDNN version: 7.6 or later
- Python version: 3.7.5
- NCCL version: 2.4.8-1
- OpenMPI version: 3.1.5
- Supported models: AlexNet, LeNet, and LSTM
- Supported datasets: MNIST and CIFAR-10
- Support data parallel.
CPU platform training
- Recommended OS: Ubuntu 16.04
- Python version: 3.7.5
- Supported model: LeNet
- Supported dataset: MNIST
- Provide only the stand-alone operation version.

173 KiB Raw Blame History Unescape Escape

MindSpore Release Notes

MindSpore 1.7.0 Release Notes

Major Features and Improvements

OS

FrontEnd

PyNative

Auto Parallel

Executor

DataSet

API Change

Backwards Incompatible Change

Python API

MindSpore Lite

Major Features and Improvements

Post quantization

Contributors

MindSpore 1.6.0

MindSpore 1.6.0 Release Notes

Major Features and Improvements

OS

FrontEnd

PyNative

Auto Parallel

Executor

DataSet

GraphKernel Fusion

Federated Learning

Debug

API Change

Backwards Incompatible Change

Python API

mindspore.dataset.MindDataset interface changes input parameter dataset_file(!27542)

Delete mindspore.Tensor's property virtual_flag(!26989)

Delete mindspore.Parameter's property is_init(!26989)

Delete mindspore.nn.ROC's interface roc(!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)

mindspore.train.callback add new interface SummaryLandscape (!26229)

Bug fixes

Executor

Dataset

MindSpore Lite

Major Features and Improvements

Converter and runtime

x86 backend optimization

ARM backend optimization

Post quantization

Training on Device

Contributors

MindSpore 1.5.2

MindSpore 1.5.2 Release Notes

Bug fixes

Contributors

MindSpore 1.5.1

MindSpore 1.5.1 Release Notes

Bug fixes

Contributors

MindSpore 1.5.0

MindSpore 1.5.0 Release Notes

Major Features and Improvements

NewModels

FrontEnd

Auto Parallel

Executor

DataSet

Federated Learning

Running Data Recorder

GraphKernel Fusion

API Change

Backwards Incompatible Change

Python API

New Recomputation Configuration for AutoParallel and SemiAutoParallel Scenarios

Bug fixes

FrontEnd

Executor

173 KiB

Raw Blame History

`mindspore.dataset.MindDataset` interface changes input parameter dataset_file(!27542)

Delete `mindspore.Tensor`'s property `virtual_flag`(!26989)

Delete `mindspore.Parameter`'s property `is_init`(!26989)

Delete `mindspore.nn.ROC`'s interface `roc`(!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")`

`mindspore.train.callback.SummaryCollector` interface's parameter `collect_specified_data` add new operations `collect_landscape` (!26229)

`mindspore.train.callback` add new interface `SummaryLandscape` (!26229)

`mindspore.dataset.Dataset.device_que` interface removes unused parameter `prefetch_size`(!18973)

`mindspore.nn.optim.thor` interface changes to lowercase `thor` and adds two parameters `enable_clip_grad` and `frequency`(!17212)