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Thanks goes to these wonderful people:
archer2049, caifubi, chenfei_mindspore, gaoshuanglong, Greatpan, guozhijian, huoxinyou, Kxiong, lanzhineng, lijunbin, liubuyu, liuchuting, luochao60, lyqlola, nomindcarry, TuDouNi, xiaotianci, xupan, yangshuo, yefeng, YingtongHu, yuchaojie, zhoufeng, ZPaC, 刘勇琪, 吕昱峰, 王禹程, 于振华.
Contributions of any kind are welcome!
Thanks goes to these wonderful people:
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
Contributions of any kind are welcome!
mindspore.amp.LossScaler, mindspore.amp.DynamicLossScaler, mindspore.amp.StaticLossScaler, mindspore.amp.auto_mixed_precision and mindspore.amp.all_finite.nn.AdaptiveAvgPool3d.ops.adaptive_avg_pool3d.ops.addcdiv.ops.addcmul.ops.approximate_equal.ops.atanh.ops.bessel_i0.ops.bessel_i0e.ops.bessel_i1.ops.bessel_i1e.ops.bessel_j0.ops.bessel_j1.ops.bessel_k0.ops.bessel_k0e.ops.bessel_k1.ops.bessel_k1e.ops.bessel_y0.ops.bessel_y1.ops.bias_add.ops.bitwise_and.ops.bitwise_or.ops.bitwise_xor.ops.grid_sample.ops.inplace_update.ops.isclose.ops.isnan.ops.lerp.ops.random_poisson.ops.reverse_sequence.ops.scatter_mul.ops.scatter_nd_max.ops.scatter_nd_min.ops.SparseToDense.ops.square.ops.standard_laplace.ops.std.ops.trunc.ops.unsorted_segment_sum.ops.xdivy.ops.xlogy.ops.poisson and use ops.random_poisson instead.ops.SparseApplyAdagrad and use ops.SparseApplyAdagradV2 instead.[BUGFIX] The logic of the auto mixed precision (amp) O2 level is revised. In addition to the BatchNorm1d and BatchNorm2d operators, the other two operators BatchNorm3d and LayerNorm are added. The four operators still use the float32 data type when calculating.
[BUGFIX] Fix the problem that when processing string type data, if output_numpy=True is specified when calling the create_dict_iterator or create_tuple_iterator interface, the obtained data will be of type numpy.bytes_. After this fixing, these interfaces will directly return numpy.str_ type data, and users do not need to perform string decoding operations on it. Likewise, when performing user defined processing functions, the received data will also be of type numpy.str_ directly, matching the original source data type.
Thanks goes to these wonderful people:
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, liyanliu, lizhenyu, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, panfengfeng, panyifeng, Payne, peixu_ren, Pengyongrong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanyuan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
Contributions of any kind are welcome!
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.Model.fit API, add mindspore.callback.EarlyStopping and mindspore.callback.ReduceLROnPlateau in Callback.mindspore.dataset.vision.c_transforms.SoftDvppDecodeRandomCropResizeJpeg and mindspore.dataset.vision.c_transforms.SoftDvppDecodeResizeJpeg interfaces.on_train_epoch_end method in LossMonitor, which implements printing metric information in the epoch level when it is used in mindspore.Model.fit.filter_prefix of mindspore.load_checkpoint interface: empty string ("") is no longer supported, and the matching rules are changed from strong matching to fuzzy matching.Thanks goes to these wonderful people:
AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.
Contributions of any kind are welcome!
mindspore.numpy.rand(), mindspore.numpy.randn(), mindspore.numpy.randint(), and mindspore.ops.arange().mindspore.train.callback.History in Callback.mindspore.ms_class class decorator.__getitem__/__next__ methods of GeneratorDataset return a single NumPy object.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.import mindspore.dataset.engine.datasets as ds. Use import mindspore.dataset as ds instead as recommended in mindspore doc.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:Support user-defined classes by ms_class decorators)mindspore.SparseTensor and use mindspore.COOTensor instead. (!28505:Change SparseTensor to COOTensor)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!
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.nn.LSTMCell from single-layer LSTM to single-cell LSTM.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).mindspore.dataset.MindDataset interface changes input parameter dataset_file(!27542:dataset: change dataset_file into dataset_files for MindDataset)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.
mindspore.Tensor's property virtual_flag(!26989:change tensor.virtual_flag and parameter.is_init interfaces to inner api)mindspore.Parameter's property is_init(!26989:change tensor.virtual_flag and parameter.is_init interfaces to inner api)mindspore.nn.ROC's interface roc(!25713:ROC.roc change to inner api)shard() interface of primitive is changed from shard(strategy) to shard(in_strategy=None, out_strategy=None)
set_auto_parallel_context() interface of context is changed fromset_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:add loss landscape visualization function)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:add loss landscape visualization function)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.
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!
jjfeing
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!
jjfeing
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!
jjfeing
model_zoo has been seperated to an individual repositorymodels
while andbreak,continue statements of training network inGRAPH_MODE.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.
for statement.(!23669:Fix inline pass problem in switch. )
jjfeing
model_zoo has been seperated to an individual repositorymodels
while andbreak,continue statements of training network inGRAPH_MODE.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.
for statement.(!23669:Fix inline pass problem in switch. )
jjfeing
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. |
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!
run_check to check whether MindSpore is working properly or not.set_indexes to select the inputs of update in the specified order._Loss to an external API LossBase as the base class of losses.Gather.mindspore.dataset.Dataset.device_que interface removes unused parameter prefetch_size(!18973:Delete unused param in device_que)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 |
|
|
mindspore.nn.optim.thor interface changes to lowercase thor and adds two parameters enable_clip_grad and frequency(!17212:clearn codechekc for thor)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 |
|
|
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. |
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;
}
...
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;
}
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; }
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 ...
}
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.
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);
}
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() {...}
...
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!
isinstance.(Ascend/GPU/CPU)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.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:Add March Numpy interfaces to mindspore)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 |
|
|
out and where(!12726:Add March Numpy interfaces to mindspore)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 |
|
|
ops.MakeRefKey into an internal interface (!12010:Convert MakeRefKey to an internal interface)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:unify mindir for different backend: the output num of optimizer ops, the backward of concat)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:IR operators of GPU and CPU are unified as batchnorm)The FusedBatchNorm and FusedBatchNormEx interface has been deleted. Please use the batchnorm operator to replace it.
MetaTensor deleted (!10325:modify MetaTensor and Tensor)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:remove control_depend from py file)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 |
|
|
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 |
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1.1.1 supports only the old ABI. Currently, both the new and the old are supported.
| 1.1.1 | 1.2.0 |
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The Context class is refactored. For details, see the API docs.
| 1.1.1 | 1.2.0 |
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LoadModel is renamed Load. No exception is thrown new but the return status should be checked.
| 1.1.1 | 1.2.0 |
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Model uses a non-parameter ctor now, and arguments are passed in through Build.
| 1.1.1 | 1.2.0 |
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MSTensor::CreateTensor and MSTensor::CreateRefTensor returns a native pointer now, need to be destroy by DestroyTensorPtr.
| 1.1.1 | 1.2.0 |
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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.nn.MatMul is now deprecated in favor of ops.matmul (!12817:numpy-native deprecate nn.MatMul)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 |
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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 |
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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 |
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| set_shape |
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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 |
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DimensionSize() in ms_tensor.h.(!13515:cpp api modify)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() |
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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 |
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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!
