I build tools to catch bugs in Deep-Learning (DL) toolchains, with the goal of improving user experience, safety, and performance. To assess the effectiveness of these techniques, the bugs caught by my testers are reported to the open-source projects (e.g., via GitHub Issues), and we seek validation from developers to confirm that the bugs are legitimate. The process of discovering and witnessing the resolution of these issues is highly gratifying, as it gives purpose to my research. Meanwhile, as the testers detect more and more bugs, it’s essential to report them accurately and effectively. Because we don’t want to waste developers’ time by inundating the issue tracker, which could eventually get us blocklisted, adversely affecting our reputation in academia and industry.
One year ago, I read John Regehr’s “Responsible and Effective Bugfinding” article1, which motivated me to think about how to improve bug reporting. While I still highly recommend reading it, in this article, I want to talk about my best practices in reporting bugs specifically to the DL library/compiler community (e.g., PyTorch, TensorFlow, TVM, etc.)
S1: Compile good reports
In addition to some cliche, such as reproducibility, using an issue template and pasting the log, there are more to consider:
- Minimization: Test-case reduction papers2 tell us that a small bug report is easier for diagnosis. This is very important: making bugs easier to investigate improves the chances of letting developers fix it timely.
- Mutation: Mutate your test-cases and observe if the bug still manifests themselves – giving developers more improves the chance of instant fixes.
- Cleaness: For example, to optimize Signal-to-Noise ratio, if some logs or environment stuffs are too long, we can use the HTML template below to make it foldable:
1 2 3 4 5 6 7 <details><summary><i>Click to expand.</i></summary> <div> Some very very long text. </div> </details>
which will look like:
Click to expand.
To conclude, a good bug report should be informative by providing rich context, while making it tidy by arranging them according to priority. This lowers the “bar” for getting contributors to start investigation. Notably, it is also possible to automate these process, with tools such as using delta debugging (for reduction).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 import torch p0 = torch.tensor([[4.9334, 5.5571]]) p1 = torch.tensor([[4.5627, 5.6945]]) # torch.rand(1) # no error # p0 = torch.rand(2) # ERROR def fn(): v7 = torch.cat([p0, p0], dim=0) # ERROR # v7 = torch.cat([p1, p1], dim=0) # ERROR # v7 = torch.cat([p1, p0], dim=0) # ERROR v1 = torch.mul(v7, v7) # v1: (5, 2) return v7, v1 ret_eager = fn() compiled = torch.compile(fn) ret_compiled = compiled() assert torch.allclose(ret_eager, ret_compiled), '\n'.join(map(str, ["", ret_eager, ret_compiled])) # ^^^ no error assert torch.allclose(ret_eager, ret_compiled), '\n'.join(map(str, ["", ret_eager, ret_compiled])) ''' ^^^ WRONG! Traceback (most recent call last): File "/home/colin/bug.py", line 23, in <module> assert torch.allclose(ret_eager, ret_compiled), '\n'.join(map(str, ["", ret_eager, ret_compiled])) AssertionError: tensor([[24.3384, 30.8814], [24.3384, 30.8814]]) tensor([[0., 0.], [0., 0.]]) '''
- Minimization: this test-case is already minimized;
- L5~6: some shapes are wrong, while others are not;
- L10~11: test if it is related to alias analysis, turning out to be not；
- L20~21: test which of the outputs are wrong, turning out to be the second only.
- L12: annotate the shape of the tensor (so that developers do not need to compute it manually);
- L23~32: print error messages (also remember to shorten the path!).
From my personal taste, it could be made even better:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 import torch from numpy import testing i0 = torch.ones((1, 2)) # -> (1, 2) i1 = torch.ones((1, 2)) # -> (1, 2) # ❌ Using shape = (1, 2) or (2,) # ✔️ Using shape = (1,) def fn(v0, v1): v2 = torch.cat([v0, v1], dim=0) # -> (2, 2) ❌ ERROR v3 = torch.mul(v2, v2) # -> (5, 2) return v2, v3 ret_eager = fn(i0, i1) ret_compiled = torch.compile(fn)(i0, i1) # ❌ fn(i0, i0) or fn(i1, i1) testing.assert_allclose(ret_eager.numpy(), ret_compiled.numpy()) # 👆 ✔️ 1st output is correct. 👇 ❌ 2nd output is wrong! testing.assert_allclose(ret_eager.numpy(), ret_compiled.numpy()) # NOTE: Output is undeterministic 🤪 # Mismatched elements: 4 / 4 (100%) # Max absolute difference: 6.0096755 # Max relative difference: 0.85734004 # x: array([[1., 1.], # [1., 1.]], dtype=float32) # y: array([[6.684686, 0. ], # [7.009676, 0. ]], dtype=float32)
This includes (i) using some emojis and squeezing some lines for readability; (ii) using
np.testing.assert_allclose to render the output differences4; and (iii) use
torch.ones instead of some hard-coded values to indicate that this bug is not related to the values of the tensors.
S2: Understanding “customer’s” requirement
IMHO, the most important “number” for evaluating bug finding is the number of bugs fixed5. Because (i) it measures the real-world impact / consequence of the technique; and (ii) the metric is very well defined (other ones may have various interpretations). For example, papers in the hall of fame, such as EMI and YarpGen, have a fixing rate of around 60~70%. To learn from those award-winning work, we want to make our testers to focus on bugs that are likely to fit developers’ interest. According to my experience in DL communities6, there are a few patterns of reports I found appealing to the developers:
- Emerging components: The DL systems are ever emerging. For example, both PT2 and TF3 are targeting better support of compilation and distributed computing. Reports on these topics are likely to get more attention and are more important in terms of real-world impact (reports on them speed up the stablization of such components so they can be used out of fear more timely).
- Easy-to-fix bugs: Some bugs can get immediately fixed when the developer found it easy to fix. For example, some crash bugs from invalid API usages (due to lack of input checking), get fixed quickly as the fixes can just be adding more checkers for prevent such ill-formed conditions.
Lastest versions. When finding bugs, we should run the fuzzer on the latest versions. Many packages have some channels for distributing nightly packages:
1 2 pip install --pre tf-nightly --upgrade pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu --upgrade
When reporting bugs, we should also check if they are reproducible on the latest versions. It is not recommended IMHO to intentially report out-dated bugs just for letting the developers confirm these bugs so that the number of “confirmed” bugs in the paper looks “better”, given that it is distractive & unhelpful to the communities (so we should work on new bugs! or alternatively evaluate over a offline dataset of old bugs without bothering the community – many maintainers are super busy and some of them are even contributing “for free”).
Responsible. More importantly, since the preference of communities can vary, it is important to get feedbacks after some reports, via either communication or “inference” (say have any of your first N reports been fixed?). If such feedbacks are negative, then something is wrong and we should carefully re-evaluate our testing strategy before feeding more undesired reports (Prof. Regehr’s article1 also mentioned this point). It is also a way to show our respect to the community – responsible bug reporting “lives longer” in the community. It is possible that fuzzing bugs are not prioritized for not being user-facing or just the lack of “manpower” in the community.
Patch it if you can. Oftentimes developers in DL communities are very busy and may not have time to fix non-user-facing bugs. In such cases, reporters are often asked “any PRs are welcome” (or “patches are welcome”). Sometimes, such bugs might not be that hard to fix: according to my experience in TVM, some bugs, say the integer mismatches, can be easily fixed in a few minutes as they just need a casting7. This point is also suggested in Regehr’s article1.
Disclaimer: I am not a security researcher, it might not be applicable to security bugs. ↩
Based on my experience (200+ bugs reported) in recent year on PyTorch, TensorFlow, TensorRT, TVM, etc. ↩