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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)

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)

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)

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)

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

name

commit-id

last updated in MXNet

last update in module

dlpack

3efc489

Jan 20, 2020

Feb 16, 2020

dmlc-core

b3a4c71

Dec 10, 2019

Apr 25, 2020

googletest

eb9225c

Jan 14, 2019

Apr 16, 2020

mkldnn

07579e6

Mar 31, 2020

Apr 24, 2020

nvidia_cub

c3cceac

Feb 16, 2018

Jul 17, 2019

onnx-tensorrt

f4745fc

Jul 12, 2019

Apr 23, 2020

openmp

b76842e

Jul 18, 2019

Oct 15, 2019

ps-lite

f601054

Jan 24, 2020

Feb 28, 2020

tvm

9bd2c7b

Jan 23, 2020

Apr 26, 2020




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