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OpInfo: nn.functional.conv2d by kshitij12345 · Pull Request #63517 · pytorch/pytorch · GitHub
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@kshitij12345 kshitij12345 commented Aug 18, 2021

Reference: #54261

Reference: pytorch/functorch#78

Mostly inspired from #62882

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facebook-github-bot commented Aug 18, 2021

🔗 Helpful links

💊 CI failures summary and remediations

As of commit 78287a1 (more details on the Dr. CI page):


None of the CI failures appear to be your fault 💚



❄️ 1 failure tentatively classified as flaky

but reruns have not yet been triggered to confirm:

See CircleCI build pytorch_linux_xenial_py3_clang7_onnx_build (1/1)

Step: "Build" (full log | diagnosis details | 🔁 rerun) ❄️

fatal: Could not read from remote repository.
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remote: Count
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remote: Total 1624 (delta 831), reused 1026 (delta 448), pack-reused 0        
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Receiving objects: 100% (1624/1624), 2.50 MiB | 15.09 MiB/s, done.
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Resolving deltas: 100% (831/831), completed with 123 local objects.
From ssh://github.com/01org/tbb
 * branch            a51a90bc609bb73db8ea13841b5cf7aa4344d4a9 -> FETCH_HEAD
remote: Total 0 (delta 0), reused 0 (delta 0), pack-reused 0        
kex_exchange_identification: Connection closed by remote host

fatal: Could not read from remote repository.

Please make sure you have the correct access rights
and the repository exists.
Fetched in submodule path 'third_party/tensorpipe', but it did not contain 1cd0ac3e4ce5144ee4ea2545741182c76fba6cf2. Direct fetching of that commit failed.


Exited with code exit status 1


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@kshitij12345 kshitij12345 marked this pull request as ready for review August 19, 2021 07:14
@kshitij12345 kshitij12345 requested a review from zou3519 August 19, 2021 07:14
@mruberry mruberry added the triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module label Aug 19, 2021
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Gentle Ping @zou3519

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Gentle ping @zou3519 :)

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zou3519 commented Aug 30, 2021

Thanks for the ping and sorry for the delay! Will review later today

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This looks great! I added some suggestions for more test cases. Because conv2d is one of PyTorch's most important ops (and also very complicated in terms of what it can call in the backend), we should try to add more test cases especially following existing test cases in common_nn.py

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codecov bot commented Sep 3, 2021

Codecov Report

Merging #63517 (e2434e2) into master (0f1bccb) will increase coverage by 0.08%.
The diff coverage is 100.00%.

❗ Current head e2434e2 differs from pull request most recent head 78287a1. Consider uploading reports for the commit 78287a1 to get more accurate results

@@            Coverage Diff             @@
##           master   #63517      +/-   ##
==========================================
+ Coverage   66.39%   66.48%   +0.08%     
==========================================
  Files         725      725              
  Lines       93461    93473      +12     
==========================================
+ Hits        62055    62141      +86     
+ Misses      31406    31332      -74     

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Some minor comments, but otherwise, this LGTM

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@zou3519 has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator.

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@zou3519 merged this pull request in 873255c.

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suo commented Sep 17, 2021

Sorry, this appears to have broken trunk so I am reverting.

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suo commented Sep 17, 2021

You can view the related failures here: https://hud.pytorch.org/commit/873255c6d95342d144e9d1b633c16410844b934e

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This pull request has been reverted by ecfc784.

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zou3519 commented Sep 17, 2021

@kshitij12345 could you send a new PR for this please?

The error message implies that the backward is non-deterministic, but I'm not sure if that is true (need to think about it for a little bit...)

 File "/opt/conda/lib/python3.6/site-packages/torch/autograd/gradcheck.py", line 556, in _get_analytical_vJu_backward_mode
    fast_mode=True, v=v)
  File "/opt/conda/lib/python3.6/site-packages/torch/autograd/gradcheck.py", line 548, in _check_analytical_jacobian_attributes
    FAILED_NONDET_MSG)
torch.autograd.gradcheck.GradcheckError: Backward is not reentrant, i.e., running backward with same input and grad_output multiple times gives different values, although analytical gradient matches numerical gradient.The tolerance for nondeterminism was 0.0.

NOTE: If your op relies on non-deterministic operations i.e., it is listed here:
https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html
this failure might be expected.

If you are adding a new operator, please file an issue and then use one of the
workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
If the test
- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
  with `nondet_tol=<tol>` as a keyword argument.
- is OpInfo-based (e.g., in test_ops.py), then modify the OpInfo for the test
  to have `gradcheck_nondet_tol=<tol>`.
- is a Module test (e.g., in common_nn.py), then modify the corresponding
  module_test entry to have `gradcheck_nondet_tol=<tol>`

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zou3519 commented Sep 17, 2021

Yes, convolution is deterministic so we want to add a nondet_tol for it: https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html

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@zou3519 @suo Thanks for the ping :)

facebook-github-bot pushed a commit that referenced this pull request Sep 21, 2021
Summary:
Reland : #63517
Reference: #54261

Reference: pytorch/functorch#78

Pull Request resolved: #65233

Reviewed By: malfet

Differential Revision: D31025538

Pulled By: zou3519

fbshipit-source-id: b1cd38c22f4cb8eedd3f958e02dd7410dcbb8d8d
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