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• [OP] Add a new arange_like operator to contrib (#15400)
• PDF operators for each distribution for which we have a random sampler (plus also the PDF of the Dirichlet). Supports probabilities and log-probabilities, as well as gradients. (#14617)
• Group Normalization (#14959)
• Add RROIAlign (#16017)
• Add fast implementation of LARS (#16122)
• Round and sign straight-through-estimators C operators. (#16373)
• New ops for RCNN + old ops improvements for RCNN (#16215)
• Comparison ops implemented using mshadow (#16414)
• Add mask target generator operator for Mask-RCNN (#16268)
• Move MRCNNMaskTarget op to contrib (#16486)
• Mxnet allclose (#14443)
• Aggregated adamw update (#16398)
• Make mrcnn_mask_target arg mask_size a 2d tuple (#16567)
• Dgl ops 2 (#16416)
• Lamb optimizer update (#16715)
• [OP] changing data type of 't' to int in lamb_update_phase1 (#16903)
• Multi Precision Lamb Update operator (#16885)

MKL-DNN v1.0 update

MKL-DNN v1.0 release improved its robustness and simplified the execution model compared to v0.x, while keeps improving its performance with Intel(R) DL Boost and the upcoming BFloat16 supports, and enabled more kernels for those popular and frontier models. Lots of PRs are completed to adopt the minor API changes as below. Please refer to https://github.com/apache/incubator-mxnet/projects/16 for more details.

• [mkldnn-v1.0] Initiate the transition to MKL-DNN v1.0 (#15706)
• [mkldnn-v1.0] Update MKL-DNN to v1.0.2 (#16012)
• [mkldnn-v1.0] Enable base code with new APIs. (#16064)
• [mkldnn-v1.0] Add MKL-DNN Convolution (#16141)
• [mkldnn-v1.0] Add MKL-DNN activation (#16195)
• [mkldnn-v1.0] Add MKL-DNN BN (#16199)
• [mkldnn-v1.0] Add MKL-DNN Transpose (#16250)
• [mkldnn-v1.0] Add MKL-DNN softmax (#16246)
• [mkldnn-v1.0] Add MKL-DNN FC (#16221)
• [mkldnn-v1.0] Add MKL-DNN deconv (#16259)
• [mkldnn-v1.0] Add MKL-DNN Pooling (#16272)
• [mkldnn-v1.0] Add MKL-DNN reshape&flatten&expand_dims (#16258)
• [mkldnn-v1.0] Add MKL-DNN int8 activation&pooling&flatten (#16425)
• [mkldnn-1.0] int8 conv quantize dequantize requantize (#16283)
• [mkldnn-1.0] mkldnn int8 elemwise_add (#16454)
• [mkldnn-v1.0] Fused RNN Op (#16420)
• [mkldnn-v1.0] Add MKL-DNN int8 fc (#16457)
• [mkldnn-v1.0] Update enabling flag for MKL dropout (#16433)
• [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0 (#16466)
• [mkldnn-v1.0] Add MKL-DNN slice (#16484)
• [mkldnn-1.0] add mkldnn subgraph fc (#16468)
• [mkldnn-v1.0]enable mkldnn concat (#16507)
• [mkldnn-v1.0] Enable mkldnn cpp-test, copy op, concat op (#16503)
• [mkldnn-v1.0] Enable mkldnn test, copy op, concat op
• [mkldnn-1.0] add skipped case for mkldnn_v1.0 (#16470)
• [mkldnn-1.0]enable mkldnn elemwise_sum (#16521)
• [mkldnn-v1.0] Enable more checks for MXNET_USE_MKLDNN (#16520)
• [mkldnn-v1.0]Minor fix for leakyrelu compile flag (#16519)
• [mkldnn-v1.0] Skip flaky test for unidirectional rnn_relu (#16545)
• [mkldnn-v1.0]set fc weight layout as mkldnn v0.2x did (#16593)
• [mkldnn-v1.0] Upgrade to MKL-DNN v1.0.4 patch release (#16592)
• [mkldnn-1.0]Rebase to master (#16648)
• [mkldnn-v1.0]rebase with master (#16649)
• Revert "[mkldnn-1.0]Rebase to master (#16648)"
• [mkldnn-v1.0] Minor fix of mkldnn-v1.0 transition (#16644)
• [mkldnn-v1.0]rm int8 sum workaround (#16623)
• [mkldnn-v1.0] Must reorder and emplace weights for inference primitives (#16682)

Feature improvements

Automatic Mixed Precision

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