• New features
  • Dynamic graph and custom operator support
  • OpPerf utility enabled in the binary distribution
  • MKL-DNN 
    • MKL-DNN as the default CPU backend in binary distribution
    • Branding change to DNNL
    • Support bfloat16 datatype
  • New operators
• Feature improvements
  • Numpy compatible interface(experimental)
  • Large tensor support
  • MKL-DNN enhancement
  • TensorRT integration
  • Quantization
  • Profiler
  • ONNX
  • New models
  • Operator improvements
  • Bug fixes
• Front end API
  • Gluon
  • Symbol
• Language Bindings
  • Python
  • C/C++
  • R
  • Clojure
  • Julia
  • Perl
  • Scala
• Performance improvements
• Example and tutorials
• Website and documentation
• CI/CD
• License
• Miscellaneous changes
• How to build MXNet
  • List of submodules used by Apache MXNet (Incubating) and when they were updated last

New features

MXNet Extensions: custom operators, partitioning, and graph passes

Adds support for extending MXNet with custom operators, partitioning strategies, and graph passes. All implemented in a library easily compiled separately from the MXNet codebase, and dynamically loaded at runtime into any prebuilt installation of MXNet. 

• fix for number of inputs/outputs for backward custom ops (#17069)
• Enhancements for custom subgraph op (#17194)
• Disable flaky test_custom_op_fork (#17481)
• fix custom op makefile (#17516)
• Update CustomOp doc with changes for GPU support (#17486)
• [WIP] MXNet Extensions enhancements (#17885) (#18128)
• Dynamic subgraph property (#17034)
• Dynamic subgraph property doc (#17585)
• [1.7] Backport MXNet Extension PRs (#17623, #17569, #17762) #18063 (#18069)

OpPerf utility enabled in the binary distribution

• [OpPerf] Add Neural network loss ops (#17482)
• [OpPerf] Fixes the issue when you pass NDArray to run_perf_test (#17508)
• [OpPerf] Fix markdown for native profile and add profile param in function desc (#17494)
• [OpPerf] Add Indexing ops (#16253)
• [OpPerf] Implement remaining random sampling ops (#17502)
• [OpPerf] Implement remaining GEMM ops (#17501)
• [OpPerf] Implement all linalg ops (#17528)
• [OpPerf] Fixed native output ordering, added warmup & runs command line args (#17571)
• [OpPerf] Add norm, cast ops, remaining optimizer ops (#17542)
• [Large Tensor] Fixed Embedding op (#17599)
• [OpPerf] Fixed Python profiler bug (#17642)

MKL-DNN

MKL-DNN as the default CPU backend in binary distribution

Branding change to DNNL

• Upgrade MKL-DNN dependency to v1.1 (#16823)

Support bfloat16 datatype

• Add bfloat16 floating-point format support based on AMP  (#17265)

New operators

• [New Op] Add deformable conv v2 (#16341)
• Add MXNet Ops for fast multihead attention (#16408)
• Support boolean elemwise/broadcast binary add, multiply and true_divide (#16728)
• add gammaln, erf, erfinv (#16811)
• add aligned roi introduced in Detectron2 (#16619)
• Implement atleast_1d/2d/3d (#17099)
• Interleaved MHA for CPU path (#17138)
• Lamb optimizer update (#16715)
• Quantized Embedding (#16691)
• Add gelu fuse ops (#18082) (#18092)

Feature improvements

Numpy compatible interface(experimental)

• [NumPy] NumPy support for linalg.inv (#16730)
• add numpy op nan_to_num (#16717)
• [Numpy] Add sampling method for bernoulli (#16638)
• Fix numpy-compatible mean output type for integer inputs (#16792)
• [Numpy] Fix collect_params().zero_grad() in gluon numpy interface (#16716)
• [Numpy][Operator] 'where' Implementation in MXNet (#16829)
• [Numpy] Random.normal() with backward (#16330)
• Add OP diag [numpy] (#16786)
• Mixed precison binary op backward (use in) for numpy (#16791)
• add numpy op diagflat [numpy] (#16813)
• add op bitwise_or [numpy] (#16801)
• [Numpy] Implementation npx.{sample}_n (#16876)
• [Numpy] Add NumPy support for np.linalg.det and np.linalg.slogdet (#16800)
• Op Unravel_index PR [Numpy] (#16862)
• [Numpy] Fix imperative basic indexing in numpy (#16902)
• [Numpy] Basic indexing in symbolic interface of DeepNumpy (#16621)
• [Numpy] add op full_like, c++ impl, fix zeros_like, ones_like type inference (#16804)
• [Numpy] Implement numpy operator 'average' (#16720)
• [Bugfix] [Numpy] Add `kAddTo` and kNullOp to Transpose (#16979)
• set rtol = 1e-2 and atol = 1e-4 when dtype == np.float32 in test_numpy_op.py:test_np_linalg_solve (#17025)
• Op_Diagonal [Numpy] (#16989)
• numpy bincount (#16965)
• [numpy] add op bitwise_not (#16947)
• [Numpy ]Modify np.random.shuffle to enable inplace by default (#17133)
• [numpy] fix argsort typo (#17150)
• [numpy] add op round (#17175)
• [numpy]Add op delete (#17023)
• [numpy] add op flipud, fliplr (#17192)
• [CI] Re-enable testing with numpy 1.18 (#17200)
• [Numpy] Add broadcast_to scalar case (#17233)
• [Numpy] Random.gamma() implemented (#16152)
• [Numpy] add row_stack (=vstack) (#17171)
• [Numpy] Add infra for performing constraint check (#17272)
• porting numpy-compatible hstack to master and add dstack for interoperability (#17030)
• adding asnumpy() to output of gather(implicitly called) to fix gather test in large vector and tensor tests (#17290)
• [numpy] add op random.exponential (#17280)
• [NumPy] Add NumPy support for norm (#17014)
• [numpy]add op random.lognormal  (#17415)
• Add numpy random weibull operator (#17505)
• [numpy] Add np.random.pareto and np.random.power (#17517)
• [Numpy] Add sort op (#17393)
• [numpy]implement exponential backward (#17401)
• [Numpy] Where operator scalar version (#17249)
• [numpy] add op matmul (#16990)
• [numpy]add op random.logistic, random.gumbel (#17302)
• [numpy][Do Not Review]add op insert (#16865)
• [numpy] add op random.rayleigh (#17541)
• [numpy] add fallback ops (#17609)
• [numpy] add op pad (#17328)
• [numpy] add op fabs, sometrue, round_ (#17619)
• Add arange_like to npx (#16883)
• try to move shape_array to npx (#16897)
• support np.argsort (#16949)
• np.broadcast_to extension (#17358)
• support bitwise_and (#16861)
• fix np.argmax/argmin output data type (#17476)
• add op random.beta (#17390)
• add op isnan isinf (#17535)
• array_split pr (#17032)
• Mixed data type binary ops (#16699)
• randn implemented (#17141)
• refactor and reduce float types for some functions, also add bitwise_xor (#16827)
• any/all (#17087)
• amax (#17176)
• fix format (#17100)
• add op empty_like, add nan_to_num to dispatch (#17169)
• handle array_like fill_value for np.full; add unit test coverage (#17245)
• add np.amin (#17538)
• add npx.gather_nd (#17477)
• add np.random.chisquare (#17524)
• add polyval (#17416)
• add isposinf isneginf isfinite (#17563)
• Support broadcast assign for `npi_boolean_mask_assign_tensor` (#17131)
• Implement Weibull backward (#17590)
• support np.dsplit, fix some error msgs and corner cases for hsplit and vsplit, add interoperability tests for h/v/dsplit (#17478)
• add np.product (#17489)
• Implement np.random.pareto backward (#17607)
• add np.ediff1d (#17624)
• more support for boolean indexing and assign (#18352)
• Fix einsum gradient (#18482)
• [v1.7.x] Backport PRs of numpy features (#18653)
• [v1.7.x] backport mixed type binary ops to v1.7.x (#18649)
• revise activations (#18700)

Large tensor support

• [Large Tensor] Add support to Random Sample & Pdf ops (#17445)
• [Large Tensor] Add LT support for NN optimizers and 1 activation function (#17444)
• [Large Tensor] Fixed SoftmaxActivation op (#17634)
• [Large Tensor] Fixed col2im op (#17622)
• [Large Tensor] Fixed Spatial Transformer op (#17617)
• [Large Tensor] Fix ravel_multi_index op (#17644)
• Sparse int64 Large tensor support (#16898)
• Re-Enabling Large Tensor Nightly on GPU (#16164)
• enabling build stage gpu_int64 to enable large tensor nightly runs (#17546)

MKL-DNN enhancement

• MKLDNN FC : Add error info when mkldnn fc bias dimension is wrong (#16692)
• [MKLDNN] support mkldnn gelu (#16710)
• [MKLDNN] Fix int8 convolution/fc bias overflow (#16734)
• [MKLDNN] use dim_t instead of int in slice/transpose operators (#16737)
• Mkldnn fullyConnect bwd bug fix (#16890)
• Revert Mkldnn fullyConnect bwd bug fix (#16890) (#16907)
• [MKLDNN] Use MKLDNNRun (#16772)
• [MKLDNN] mkldnn RNN operator enhancement (#17075)
• [MKLDNN] enable MaxPooling with full pooling convention (#16860)
• update mkldnn to v1.1.2 (#17165)
• improve mkldnn doc (#17198)
• [MKLDNN] Fix _copyto  (#17173)
• [MKLDNN] Support channel wise quantization for FullyConnected (#17187)
• fixed seed for mkldnn test (#17386)
• add mkldnn softmax backward  (#17170)
• cmake: copy dnnl headers to include/mkldnn (#17647)
• [mkldnn]Mkldnn bn opt backport from master to 1.7x (#18009)
• [v1.x] Update 3rdparty/mkldnn remote URL and pin to v1.3 (#17972) (#18033)
• [v1.x] backport #17900 [MKLDNN] support using any format in pooling backward (#18067)
• Static link MKL-DNN library (#16731)
• Add large tensor nightly tests for MKL-DNN operators (#16184)
•  [MKL-DNN] Enable and Optimization for s8 eltwise_add (#16931)
• [MKL-DNN] Enhance Quantization Method (#17161)
• Static Build and CD for mxnet-cu102/mxnet-cu102mkl (#17074)
• MKL-DNN RNN backward path enhancement (#17183)
• cmake: check USE_OPENMP and pass proper MKL-DNN build flags (#17356)
• update mkl to 2020.0 (#17355)
• Enable MKL-DNN by default in pip packages (#16899)
• Enable MKL-DNN FullyConnected backward (#17318)
• Softmax primitive cache and in-place computation (#17152)
• boolean_mask_assign with start_axis (#16886)
• use identity_with_cast (#16913)
• change error tolerance for bf16 bn (#18110)
• [v1.x] Backport #17689 and #17884 to v1.x branch (#18064)
• refactor codes and add an option to skip/check weight's version to reduce overhead (#17707) (#18039)
• [v1.x] Backport #17702 and #17872 to v1.x branch (#18038)

TensorRT integration

• Update TensorRT tutorial to build-from-source. (#14860)
• Minor fix, use RAII for TensorRT builder and network object (#17189)

Quantization

• Add silent option to quantization script (#17094)

Profiler

• Implemented final two binary ops, added default params for functionality (#17407)
• Implement remaining nn_activation ops in opperf (#17475)
• Implement all miscellaneous ops (#17511)
• Implement remaining nn_basic ops in opperf (#17456)

ONNX

• Fix memory leak reported by ASAN in NNVM to ONNX conversion (#15516)
• ONNX export: Gather (#15995)
• ONNX export: Slice op - Handle None value for ends (#14942)

New models

• [Model] Implement Neural Collaborative Filtering with MXNet (#16689)
• Further optimization for NCF model (#17148)
• HMM Model (#17120)

Operator improvements

• Faster GPU NMS operator (#16542)
• [MXNET-1421] Added (CuDNN)BatchNorm operator to the list of mirrored operators (#16022)
• dynamic custom operator support (#15921)
• Multi Precision Lamb Update operator (#16885)
• Add im2col and col2im operator (#16502)
• Quantized Elemwise Mul Operator (#17147)
• Enhancements for MXTensor for custom operators (#17204)
• Enabling large tensor support for binary broadcast operators (#16755)
• Fix operators lying about their number of inputs (#17049)
• [WIP] Fallback mechanism for mx.np operators (#16923)
• Dynamic custom operator GPU support (#17270)
• Fix flaky - test_operator_gpu.test_np_insert (#17620)
• MXNet FFI for Operator Imperative Invocation (#17510)
• [MXNET-978] Higher Order Gradient Support `logp1`, `expm1`, `square`. (#15416)
• [MXNET-978] Higher Order Gradient Support `arcsin`, `arccos`. (#15515)
• [MXNET-978] Higher Order Gradient Support `rsqrt`, `rcbrt`. (#15476)
• gather_nd: check bound and wrap negative indices (#17208)
• Remove dilation restriction for conv3d (#17491)
• Fix storage type infer of softmax backward (#17576)
• Fix and optimize handling of vectorized memory accesses (#17767) (#18113)
• Cherry-pick of #17995 and #17937 to 1.x branch (#18041)
• No tensor cores for fp32 interleaved attention, remove div by 8 restriction (#17994) (#18085)
• GPU gemms true fp16 (#17466) (#18023)
• Add support for boolean inputs to FusedOp (#16796)

Bug fixes

• [BUG FIX] Always preserve batch dimension in batches returned from dataloader (#16233)
• Fix SliceChannel Type inference (#16748)
• change _generate_op_module_signature get_module_file open with encoding=utf-8,it fix some encode error in Chinese windows system. (#16738)
• Fix rtrue_divide grad (#16769)
• fix inv test flakiness using random matrices generated by SVD (#16782)
• [MXNET-1426] Fix the wrong result of sum, mean, argmin, argmax when inputs contain inf or nan (#16234)
• Fix (#16781)
• fix expand_dims fall back when input's ndim is 0 (#16837)
• [fix] missing input log higher order. (#15331)
• Fix IndentationError in setup.py (#16857)
• Fix a few np issues (#16849)
• Fix InferAttr/InferShapeAttr not calling inference for all nodes in a graph (#16836)
• fix for enable model parallelism for non-fp32 data (#16683)
• Fix NDArrayIter iteration bug when last_batch_handle='pad' (#16166)
• Fix crashing on Windows in ObjectPool ~ctor (#16941)
• Fix NDArrayIter cant pad when size is large (#17001)
• fix axis=-1 bug (#17016)
• Fix CUDNN detection for CMake build (#17019)
• Fix omp assert issue (#17039)
• mshadow: fix vector access (#17021)
• [BUGFIX] Fix race condition in kvstore.pushpull (#17007)
• [BUGFIX] Fix trainer param order (#17068)
• [BugFix] fix filter channel calculation in ModulatedDeformableConvV2 (#17070)
• Fix reshape interoperability test (#17155)
• fix norm sparse fallback (#17149)
• fix py27 quantization (#17153)
• fix int8 add ut (#17166)
• Fix and clean up Ubuntu build from source instructions (#17229)
• fix lstm layer with projection save params (#17266)
• Fix rendering of ubuntu_setup.md codeblocks (#17294)
• Fix #17267, add expected and got datatype for concat error msgs (#17271)
• [BUGFIX] fix model zoo parallel download (#17372)
• fix use int8, uint8, int32, int64 (#17188)
• [Fix] Add ctx to the original ndarray and revise the usage of context to ctx (#16819)
• Fix ndarray indexing bug (#16895)
• fix requantize flaky test (#16709)
• Initial checkin (#16856)
• Fix flakey test_ndarray.py:test_reduce (#17312)
• fix flaky test: boolean index and fix bugs (#17222)
• Fix IOT Devices section of Get Started page (#17326)
• add logic for no batch size while getting data arrays from executors (#17772) (#18122)
• Fix reverse shape inference in LayerNorm (#17683)
• fix full and full_like when input is boolean (#17668)
• Fix MBCC inference (#17660)
• Additional fix for vector access. (#17230)
• Cherrypick Fix nightly large_vector test caused by incorrect with_seed path (#18178) (#18220)
• [1.7] Pass args fix3 (#18237)
• fixing batch_norm and layer_norm for large tensors (#17805) (#18261)
• [1.7.x] Backport of LSTM and GRU fix (#17898) and RNN op (#17632) (#18316)
• [v1.7.x] backport #18500 - [Bug Fixed] Fix batch norm when grad_req is `add` (#18517)
• Fix the monitor_callback invalid issue during calibration with variable input shapes (#18632) (#18703)

Front end API

• Fix the problem in printing feature in c++ API examples : feature_extract (#15686)
• updating MXNet version to 1.6.0 in base.h for C APIs (#16905)
• [API] unified API for custom kvstores (#17010)
• fix parameter names in the estimator api (#17051)
• adding docs for 64bit C APIs of large tensor (#17309)
• Add API docs to INT64 APIs (#16617)

Gluon

• [Quantization] Enhance gluon quantization API (#16695)
• [Gluon] Improve estimator usability and fix logging logic (#16810)
• Fix test_gluon.py:test_sync_batchnorm when number of GPUS > 4 (#16834)
• [Gluon] Update contrib.Estimator LoggingHandler to support logging per batch interval (#16922)
• Include eval_net the validation model in the gluon estimator api (#16957)
• Fix Gluon Estimator nightly test (#17042)
• [MXNET-1431] Multiple channel support in Gluon PReLU (#16262)
• Fix gluon.Trainer regression if no kvstore is used with sparse gradients (#17199)
• refactor gluon.utils.split_data() following np.array_split() (#17123)
• Add RandomApply in gluon's transforms (#17242)
• Partitioning Gluon HybridBlocks (#15969)
• Random rotation (#16794)
• bump up atol for gradient check (#16843)
• Extend estimator.evaluate() to support event handlers (#16971)
• [MXNET-1438] Adding SDML loss function (#17298)

Symbol

• Add unoptimized symbol to executor for sharing (#16798)
• Enforces NDArray type in get_symbol (#16871)
• Fix #17164 symbolblock with BatchNorm inside during cast to fp16 (#17212)
• autograd video and image link fixes and removing symbol tutorials (#17227)
• Fix CosineEmbeddingLoss in when symbol API is used (#17308)
• Fix Horovod build error due to missing exported symbols (#17348)
• Update symbol.py (#17408)
• update symbol to json (#16948)

Language Bindings

Python

• Python 2 compatibility fix in base.py
• adding stacktrace in Jenkinsfile_utils.groovy to inspect Python2 failure cause in CI (#17065)
• Fix image display in python autograd tutorial (#17243)
• Fix Python 3 compatibility in example/speech_recognition (#17354)
• Stop testing Python 2 on CI (#15990)
• Docs: Python tutorials doc fixes (#17435)
• pin python dependencies (#17556)
• Python 2 cleanup (#17583)

C/C++

• Simplify C++ flags (#17413)

R

• fix R docs (#16733)
• [R package] Make R package compilation support opencv 4.0 (#16934)
• Support R-package with cmake build and fix installation instructions (#17228)
• Fix R-package/src/Makevars for OpenCV 4 (#17404)
• Fix typo in Install the MXNet Package for R (#17340)

Clojure

Julia

• [MXNET-1440] julia: porting `current_context` (#17142)
• julia: porting `context.empty_cache` (#17172)
• pin Markdown version to 3.1 in Julia doc build (#17549)

Perl

• [Perl] - ndarray operator overloading enhancements (#16779)
• MXNET-1447 [Perl] Runtime features and large tensor support. (#17610)

Scala

• Fix scala publish & nvidia-docker cublas issue (#16968)
• Fix publishing scala gpu with cpu instance (#16987)
• swap wget to curl in Scala scripts (#17041)
• [Scala/Java] Remove unnecessary data slicing (#17544)
• quantile_scalar (#17572)
• Fix get_started scala gpu (#17434)
• Fix MBCC & scala publish pipeline (#17643)
• Bump up additional scala 1.x branch to 1.7.0 (#17765)

Performance improvements

• Build.py improvement (#16976)
• Improvements to config.cmake (#17639)
• [Done] BilinearResize2D optimized (#16292)
• Speed fused_op compilation by caching ptx and jit-compiled functions (#16783)
• Improve the speed of the pointwise fusion graph pass (#17114)
• broadcast_axis optimization (#17091)
• Optimize AddTakeGrad Tensor Sum (#17906) (#18045)

Example and tutorials

•  Add CustomOp tutorial doc (#17241)
• Correct the grammar in 1-ndarray tutorial (#17513)

Website and documentation

• Website edits (#17050)
• [Website 2.0] Nightly Build for v1.x (#17956)
• [docs] Fix runtime feature detection documentation (#16746)
• Adding user guidelines for using MXNet built with Large Tensor Support (#16894)
• fix typo and doc (#16921)
• large tensor faq doc fix (#16953)
• [DOC] Add a few tips for running horovod (#17235)
• Update NOTICE to fix copyright years (#17330)
• [DOC] Fix tutorial link, and better error msg (#17057)
• doc fix for argmax & argmin (#17604)

CI/CD

• support mixed-precision true_divide (#16711)
• Try to fix CI (#16908)
• mixed precision for power (#16859)
• Fix desired precision for test_ndarray.py:test_reduce (#16992)
• [reproducibility] multi_sum_sq review, AtomicAdd removal (#17002)
• fix precision problem in linalg_solve, linalg_tensorinv, linalg_cholesky op test (#16981)
• grouping large array tests based on type and updating nightly CI function (#17305)
• [LICENSE] fix cpp predcit license (#17377)
• [CI] Fix static build pipeline (#17474)
• skipping tests that cannot fit in nightly CI machine corrected imports (#17450)
• Update Windows CI scripts to use syntax compatible with Win 2019 server powershell. (#17526)
• Fix Non-ASCII character in docstring (#17600)
• [CI] Follow redirects when downloading apache-maven-3.3.9-bin.tar.gz (#17608)
• [CI] Upgrade sphinx and autodocsumm (#17594)
• Reduce load on CI due to excessive log flood (#17629)
• Enable users to specify BLAS (#17648)
• [CI] Add AMI id to instance info on builds (#17649)
• [v1.7.x] Backport staggered CI builds (#17999 & #18119) (#18142)
• [v1.7.x] Backport #17177 to 1.7.x (Fix incorrect calculation results when the C locale is set to a locale that uses commas as the decimal separator) (#18147)
• Fix formatting and typos in CD README.md (#16703)
• [CD] dynamic libmxet pipeline fix + small fixes (#16966)
• [CD] enable s3 publish for nightly builds in cd (#17112)
• [CD] fix CD pipeline (#17259)
• [CD] update publish path (#17453)
• fix CD and remove leftover from #15990 (#17551)
• Fix nightly build (#16773)
• Update pypi_publish.py to disable nighlty build upload to Pypi (#17082)
• [v1.7.x] update jetson dockerfile to support CUDA 10.0 (#18339)
• Remove manually created symbolic link to ninja-build (#18437) (#18456)
• Increase staggered build timeout to 180 min (#18568) (#18585)

License

• Don't relicense FindCUDAToolkit.cmake (#17334)
• fix license and copyright issues (#17364)
• Update ps-lite LICENSE (#17351)
• remove unused file with license issue (#17371)
• Update LICENSE for fonts (#17365)
• license np_einsum file under bsd (#17367)
• Update Apache License for mshadow (#18109) (#18134)
• Julia: remove downloading of the non-ASF binary build (#18489) (#18502)
• Add missing license header for md files (#18541)
• [v1.7.x]License checker enhancement (#18478)

Miscellaneous changes

• Link fixes4 (#16764)
• Refactoring names for mxnet version of nnvm to avoid conflicting with the original tvm/nnvm. (#15303)
• minor typo fix (#17008)
• Add micro averaging strategy to pearsonr metric (#16878)
• introduce  gradient update handler to the  base estimator (#16900)
• fix latency calculation and print issue (#17217)
• add inference benchmark script (#16978)
• change the wording and log level to be more in line with the general use (#16626)
• Updated logos. (#16719)
• Pinning rvm version to satisfy Jekyll build (#18016)
• Workaround gnu_tls handshake error on Ubuntu 14.04 Nvidia Docker (#18044)

How to build MXNet

Please follow the instructions at https://mxnet.incubator.apache.org/get_started

List of submodules used by Apache MXNet (Incubating) and when they were updated last