Skip to content
This repository has been archived by the owner on Nov 17, 2023. It is now read-only.

Refactor NumPy-compatible elemwise broadcast operators #16827

Merged
merged 1 commit into from
Nov 16, 2019
Merged

Refactor NumPy-compatible elemwise broadcast operators #16827

merged 1 commit into from
Nov 16, 2019
+728 −431

Conversation

haojin2
Copy link
Contributor

@haojin2 haojin2 commented Nov 15, 2019

Description

Adding bitwise_xor on the side.

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

  • The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant JIRA issue created (except PRs with tiny changes)
  • Changes are complete (i.e. I finished coding on this PR)
  • All changes have test coverage:
  • Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
  • Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
  • Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
  • Code is well-documented:
  • For user-facing API changes, API doc string has been updated.
  • For new C++ functions in header files, their functionalities and arguments are documented.
  • For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
  • Check the API doc at https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
  • To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Changes

  • Refactor and split files
  • bitwise_xor operator
  • Unit tests

Comments

bitwise_xor kernel was contributed by @gyshi.

@haojin2 haojin2 added the Numpy label Nov 15, 2019
@haojin2 haojin2 requested a review from szha as a code owner November 15, 2019 08:56
@haojin2 haojin2 self-assigned this Nov 15, 2019
reminisce
reminisce reviewed Nov 15, 2019
View reviewed changes
python/mxnet/ndarray/numpy/_op.py Outdated Show resolved Hide resolved
tests/python/unittest/test_numpy_op.py Show resolved Hide resolved
src/operator/tensor/elemwise_binary_op.h Outdated Show resolved Hide resolved
@haojin2 haojin2 force-pushed the refactor_binary branch 2 times, most recently from 3070e53 to b2fd0ac Compare November 15, 2019 22:56
@reminisce reminisce merged commit 3256131 into apache:master Nov 16, 2019
@haojin2 haojin2 mentioned this pull request Nov 21, 2019
Merged
@haojin2
Copy link
Contributor Author

haojin2 commented Nov 21, 2019

@ptrendx

@haojin2 haojin2 added the R1.6.0 label Nov 21, 2019
ptrendx pushed a commit to ptrendx/mxnet that referenced this pull request Nov 25, 2019
@haojin2 @ptrendx
refactor and reduce float types for some functions, also add bitwise_…
c576d97 
…xor (apache#16827)
@ptrendx ptrendx mentioned this pull request Nov 25, 2019
Merged
ptrendx added a commit that referenced this pull request Nov 26, 2019
@ptrendx
Backport of #16827, #16791 and #16888 to 1.6 branch (#16901)
4c41afd 
* refactor and reduce float types for some functions, also add bitwise_xor (#16827)

* Mixed precison binary op backward (use in) for numpy (#16791)

* mixed precison binary op backward

* reduce unix cpu runtime

* Add evaluation_loss to the estimator base class. (#16888)

* Add evaluation_loss to the estimator base class.

* Update the base estimator class to support the separate evaluation loss.

* Add evaluation loss to the base estimator class.

* Add unittest for evaluation loss in the test_evaluation function

* Update estimator.py

* Update estimator.py
Sign up for free to subscribe to this conversation on GitHub. Already have an account? Sign in.
Reviewers

@reminisce reminisce reminisce approved these changes

@szha szha Awaiting requested review from szha szha is a code owner

Assignees

@haojin2 haojin2

Labels
Numpy R1.6.0
Projects
None yet
Milestone
No milestone
Development

Successfully merging this pull request may close these issues.
2 participants
@haojin2 @reminisce