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apache / mxnet / ab222114afb234bce6ebffb99b09e2b16d2ddcec
commitab222114afb234bce6ebffb99b09e2b16d2ddcec[log] [tgz]
authorPaweł Głomski <pawel.glomski@intel.com>Wed Sep 29 17:41:36 2021 +0200
committerGitHub <noreply@github.com>Wed Sep 29 11:41:36 2021 -0400
tree6a8cab0c35449370ad20c1a08f65a37b55bb34b8
parent80d72b534d4bd07d2db929c5c8dbb6181bdf62ea [diff]
[BACKPORT][BUGFIX][FEATURE] Add oneDNN 1D and 3D deconvolution support and fix bias (#20292)

* [v1.x][BUGFIX] Implement oneDNN deconvolution primitives to deconvolution 2D (#20107)

* Use mkldnn deconvolution primitive in deconvolution

* Apply clang-format

* Refactor deconvolution version 1

* Refactor deconvolution version 2 and use permute_axes in IOLogicalSwapDesc

* Refactor deconvolution version 3

* Enable Deconvolution2D test

* Fix sanity

* Fix windows builds

* Fix deconvolution with bias test

* [v1.x][FEATURE] Add MKLDNN Deconvolution 1D and 3D support (#20137)

* Use MXNET_USE_ONEDNN

* Fix test

* Apply formatter

* Add native support for 3D deconvolution

* Remove outdated check

* Replace math.prod with np.prod

* Check convolution layout only when it has value

* Remove outdated check

* Change tests

* Increase default workspace size to mach convolution

* Fix deconv workspace size

* Increase default deconv workspace size in python API

* Disable 3D tests for GPU

* Add deconv arguments checks

* Remove next_impl calls until it is fixed

* Share workspace

* Fix documentation

* Add test_deconv_dilation

* Fix check

* Fix include order
13 files changed
tree: 6a8cab0c35449370ad20c1a08f65a37b55bb34b8
  1. .github/
  2. 3rdparty/
  3. benchmark/
  4. cd/
  5. ci/
  6. cmake/
  7. config/
  8. contrib/
  9. cpp-package/
  10. docker/
  11. docs/
  12. example/
  13. include/
  14. licenses/
  15. plugin/
  16. python/
  17. src/
  18. tests/
  19. tools/
  20. .asf.yaml
  21. .clang-tidy
  22. .codecov.yml
  23. .gitattributes
  24. .gitignore
  25. .gitmodules
  26. .mxnet_root
  27. CMakeLists.txt
  28. CODE_OF_CONDUCT.md
  29. CODEOWNERS
  30. conftest.py
  31. CONTRIBUTORS.md
  32. DISCLAIMER
  33. doap.rdf
  34. KEYS
  35. LICENSE
  36. MKLDNN_README.md
  37. NEWS.md
  38. NOTICE
  39. pytest.ini
  40. rat-excludes
  41. README.md
  42. readthedocs.yml
  43. SECURITY.md
  44. snap.python
README.md

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Apache MXNet (incubating) for Deep Learning

GitHub release (latest SemVer) GitHub stars GitHub forks GitHub contributors GitHub issues good first issue GitHub pull requests by-label GitHub license Twitter Twitter Follow

Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scalable to many GPUs and machines.

MXNet is more than a deep learning project. It is a community on a mission of democratizing AI. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

Licensed under an Apache-2.0 license.

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v1.xCentOS CPU Build Status CentOS GPU Build Status Clang Build Status
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Unix CPU Build Status Unix GPU Build Status Website Build Status
Windows CPU Build Status Windows GPU Build Status Documentation Status

Features

  • NumPy-like programming interface, and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start in deep learning.
  • Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.
  • Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects such as TVM, TensorRT, OpenVINO.
  • Scales up to multi GPUs and distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.
  • Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.
  • Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, and Julia.
  • Cloud-friendly and directly compatible with AWS and Azure.

Contents

What's New

Ecosystem News

Stay Connected

ChannelPurpose
Follow MXNet Development on GithubSee what's going on in the MXNet project.
MXNet Confluence Wiki for Developers MXNet developer wiki for information related to project development, maintained by contributors and developers. To request write access, send an email to send request to the dev list .
dev@mxnet.apache.org mailing listThe “dev list”. Discussions about the development of MXNet. To subscribe, send an email to dev-subscribe@mxnet.apache.org .
discuss.mxnet.io Asking & answering MXNet usage questions.
Apache Slack #mxnet Channel Connect with MXNet and other Apache developers. To join the MXNet slack channel send request to the dev list .
Follow MXNet on Social MediaGet updates about new features and events.

Social Media

Keep connected with the latest MXNet news and updates.

History

MXNet emerged from a collaboration by the authors of cxxnet, minerva, and purine2. The project reflects what we have learned from the past projects. MXNet combines aspects of each of these projects to achieve flexibility, speed, and memory efficiency.

Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015