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@kshitij12345 kshitij12345 commented Jun 13, 2020

Reference #38349

TODO:

  • Add tests
  • Add docs (pending add to docs.rst)

@kshitij12345 kshitij12345 changed the title Add torch.count_nonzero [WIP] Add torch.count_nonzero Jun 13, 2020
@kshitij12345 kshitij12345 marked this pull request as draft June 13, 2020 15:22
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dr-ci bot commented Jun 13, 2020

💊 CI failures summary and remediations

As of commit 61a9937 (more details on the Dr. CI page):


None of the CI failures appear to be your fault 💚



🚧 1 fixed upstream failure:

These were probably caused by upstream breakages that were already fixed.

Please rebase on the viable/strict branch (expand for instructions)

Since your merge base is older than viable/strict, run these commands:

git fetch https://github.com/pytorch/pytorch viable/strict
git rebase FETCH_HEAD

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This comment has been revised 46 times.

@kshitij12345 kshitij12345 marked this pull request as ready for review June 14, 2020 15:07
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@mruberry

Please review

@kshitij12345 kshitij12345 changed the title [WIP] Add torch.count_nonzero Add torch.count_nonzero Jun 14, 2020
@mruberry mruberry self-requested a review June 15, 2020 07:35
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Hey @kshitij12345, thanks for the PR! I added some comments.

Summary + followup checklist:

  • Suggestion to simplify test in test_torch.py
  • Minor doc improvements
  • Method variants
  • Add a "not_implemented" entry to tools/autograd/derivatives.yaml
  • Update docs/source/name_inference.rst
  • Update docs/source/tensors.rst and docs/source/torch.rst
  • Update torch/_torch_docs.py

Overall this is really good, it just needs a few updates.

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@mruberry
Thanks for the great review with summary!

I believe have addressed the comments. Please review.

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A few more test changes (sorry about that, I made a note to make self.compare_with_numpy easier to use!) and I suggest you remove the name variant to keep this PR more focused, otherwise you'll need to add tests for it, and that could be a separate PR if you wanted.

Also I made a note on the name_inference.rst doc.

* scale the randomly generated values to have bigger range for intergral.
* pass the input data as tolist(), this will force compare_with_numpy,
  to obey the passed device and dtype.
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#40064 clarifies the compare_with_numpy behavior, thank you for revealing an issue with it. You will have to rebase after it goes in, unfortunately, but the code change it requires is very small. We really want to avoid those tolist() calls.

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@mruberry

PTAL:)

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@mruberry

Would be great if you can have another look at this. I am planning to use the _generate_input and _test_reduction_function_with_numpy for few tests for nansum.

Thank You.

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Excellent work. This looks really good, @kshitij12345!

Should the test also consider complex dtypes?

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@mruberry

Have added complex dtypes to the test as well.
I guess that should cover all the bases.

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@mruberry

Have added complex dtypes to the test as well.
I guess that should cover all the bases.

Thanks kshitij12345! We just need to wait for the tests again since there was a merge conflict with master.

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Another merge conflict, unfortunately. We'll have to wait for the tests again.

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@mruberry has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator.

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@mruberry Gentle ping :)

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@mruberry Gentle ping :)

Thanks for the ping. I'm fighting with our internal landing system, unfortunately.

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Oh. Sure. Thank You! :)

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4 participants