Add typing annotations for torch.nn.quantized.dynamic.modules.rnn #43186

guilhermeleobas · 2020-08-18T02:44:32Z

Fixes #43185

xref: gh-43072

dr-ci · 2020-08-18T02:51:49Z

💊 CI failures summary and remediations

As of commit c5939ad (more details on the Dr. CI page):

2/2 failures possibly* introduced in this PR
- 2/2 non-CircleCI failure(s)

Extra GitHub checks: 2 failed

Failed: Codecov - codecov/project
Failed: GitHub Actions - flake8-py3

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This comment has been revised 71 times.

hameerabbasi · 2020-08-20T11:23:50Z

Probably rebase or merge viable/strict.

guilhermeleobas · 2020-08-21T22:55:17Z

It seems that the failures were introduced in this PR. I can't reproduce because it requires fbgemm:

Command to reproduce test failure:

$ pytest test/test_quantization.py -k 'test_quantized_rnn' -sv -rs

platform linux -- Python 3.8.4, pytest-5.4.3, py-1.9.0, pluggy-0.13.1 -- /home/guilhermel/miniconda3/envs/pytorch-cuda-dev/bin/python
cachedir: .pytest_cache
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/work/guilhermel/git/Quansight/pytorch/.hypothesis/examples')
rootdir: /work/guilhermel/git/Quansight/pytorch
plugins: hypothesis-5.20.3
collected 323 items / 321 deselected / 2 selected

test/test_quantization.py::TestPostTrainingDynamic::test_quantized_rnn SKIPPED
test/test_quantization.py::TestPostTrainingDynamic::test_quantized_rnn_cell SKIPPED

======================================================================================================== short test summary info ========================================================================================================
SKIPPED [1] test/quantization/test_quantize.py:737: Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs with instruction set support AVX2 or newer.
SKIPPED [1] test/quantization/test_quantize.py:778: Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs with instruction set support AVX2 or newer.

torch/nn/quantized/dynamic/modules/rnn.py

hameerabbasi · 2020-08-25T12:04:46Z

Perhaps merge viable/strict?

guilhermeleobas · 2020-08-26T16:46:36Z

It seems that the failures were introduced in this PR. I can't reproduce because it requires fbgemm:

Command to reproduce test failure:

$ pytest test/test_quantization.py -k 'test_quantized_rnn' -sv -rs

platform linux -- Python 3.8.4, pytest-5.4.3, py-1.9.0, pluggy-0.13.1 -- /home/guilhermel/miniconda3/envs/pytorch-cuda-dev/bin/python
cachedir: .pytest_cache
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/work/guilhermel/git/Quansight/pytorch/.hypothesis/examples')
rootdir: /work/guilhermel/git/Quansight/pytorch
plugins: hypothesis-5.20.3
collected 323 items / 321 deselected / 2 selected

test/test_quantization.py::TestPostTrainingDynamic::test_quantized_rnn SKIPPED
test/test_quantization.py::TestPostTrainingDynamic::test_quantized_rnn_cell SKIPPED

======================================================================================================== short test summary info ========================================================================================================
SKIPPED [1] test/quantization/test_quantize.py:737: Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs with instruction set support AVX2 or newer.
SKIPPED [1] test/quantization/test_quantize.py:778: Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs with instruction set support AVX2 or newer.

I don't think this test are related to the changes introduced in this pull request. I can reproduce the same error on master and viable/strict.

The test fails in the following CI machines

pytorch_linux_bionic_py3_8_gcc9_test
pytorch_linux_bionic_py3_6_clang9_test
pytorch_linux_xenial_py3_clang5_asan_test2
pytorch_linux_xenial_py3_6_gcc5_4_test
pytorch_windows_vs2019_py36_cuda10.1_test2
pytorch_linux_xenial_py3_6_gcc5_4_ge_config_simple_test
pytorch_linux_xenial_cuda10_2_cudnn7_py3_gcc7_test

rgommers · 2020-08-26T21:56:40Z

I don't think this test are related to the changes introduced in this pull request. I can reproduce the same error on master and viable/strict.

You mean in CI on this PR? https://ezyang.github.io/pytorch-ci-hud/build/pytorch-master looks very green right now. It's likely caused by this PR.

The 11 JIT failures in pytorch_linux_bionic_py3_6_clang9_test look unrelated, but would be nice to be sure.

rgommers · 2020-08-26T23:57:32Z

You mean in CI on this PR? https://ezyang.github.io/pytorch-ci-hud/build/pytorch-master looks very green right now. It's likely caused by this PR.

I rebuilt with fbgemm support and tested this branch - the two hypothesis tests pass for me locally.

rgommers · 2020-08-27T09:04:26Z

@guilhermeleobas other PRs don't have these failures, so some more digging is needed. For starters, can you squash all your commits and rebase on current master?

guilhermeleobas · 2020-08-27T20:44:53Z

The error was related to pytorch not parsing # type: ignore comments correctly. PR #43446 seems to fix that. I will rebase and run CI again.

codecov · 2020-08-28T19:44:57Z

Codecov Report

Merging #43186 into master will decrease coverage by 0.00%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master   #43186      +/-   ##
==========================================
- Coverage   69.34%   69.34%   -0.01%     
==========================================
  Files         378      378              
  Lines       46698    46697       -1     
==========================================
- Hits        32381    32380       -1     
  Misses      14317    14317

Impacted Files	Coverage Δ
torch/nn/quantized/dynamic/modules/rnn.py	`89.02% <100.00%> (-0.03%)`	⬇️

Continue to review full report at Codecov.

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rgommers

LGTM now, thanks @guilhermeleobas

rgommers · 2020-08-28T19:59:24Z

The two CI failures are unrelated.

guilhermeleobas · 2020-08-28T20:14:02Z

Thanks for the review, @rgommers and @hameerabbasi

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facebook-github-bot · 2020-09-01T18:14:58Z

@malfet merged this pull request in 63a0bb0.

guilhermeleobas added the module: typing Related to mypy type annotations label Aug 18, 2020

guilhermeleobas self-assigned this Aug 18, 2020

pytorchbot added the open source label Aug 18, 2020

mrshenli requested a review from raghuramank100 August 18, 2020 17:41

mrshenli added oncall: quantization Quantization support in PyTorch triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module labels Aug 18, 2020

hameerabbasi reviewed Aug 22, 2020

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torch/nn/quantized/dynamic/modules/rnn.py Outdated Show resolved Hide resolved

guilhermeleobas requested a review from rgommers August 26, 2020 16:47

guilhermeleobas marked this pull request as ready for review August 26, 2020 16:49

guilhermeleobas requested a review from apaszke as a code owner August 26, 2020 16:49

guilhermeleobas added 2 commits August 28, 2020 11:25

Add typing annotations for torch.nn.quantized.dynamic.modules.rnn

80b5138

add type:ignore

c5939ad

pytorch deleted a comment from codecov bot Aug 28, 2020

guilhermeleobas requested a review from hameerabbasi August 28, 2020 19:47

rgommers approved these changes Aug 28, 2020

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rgommers requested a review from malfet August 28, 2020 19:59

malfet approved these changes Sep 1, 2020

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facebook-github-bot reviewed Sep 1, 2020

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facebook-github-bot closed this in 63a0bb0 Sep 1, 2020

facebook-github-bot added the merged label Sep 1, 2020

guilhermeleobas deleted the quantized-rnn branch September 4, 2020 19:27

mruberry added the Merged label Oct 28, 2020

Add typing annotations for torch.nn.quantized.dynamic.modules.rnn #43186

Add typing annotations for torch.nn.quantized.dynamic.modules.rnn #43186

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