derive PyTorchIEPipeline from pie_core.AnnotationPipeline

[ArneBinder]

This is the last non-breaking piece of the pie-core refactor. See this for context.

requires:

• Fix test_annotation_pipeline fails with half-precision-model=True #477
• pie-core 0.2.1 because of Add tests for AnnotationPipeline pie-core#80

[ArneBinder]

Strangely, the tests succeed for me locally. May this be because of different PyTorch versions? Locally, I used the setup from poetry.lock (poetry sync --with dev), but I think the tests install just from the pyproject.toml? But if this is really the case, why are the scores different?

EDIT: I was able to reproduce the behavior locally. I deleted the poetry.lock file and re-created one from the pyproject.toml (poetry lock) and installed from that (poetry sync --with dev). Important for the poetry commands: I'm using poetry version 2.1.3!

[RainbowRivey]

I cannot reproduce this environment from scratch.
I do:

conda create -n pie python=3.9 poetry=2.1.3 pip 
conda activate pie   
poetry install --with dev

->

[expand for full log]

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[🔴] × poetry install --with dev                                                                                                                                                                                              (pie) 
Removing the project's plugin cache because it is outdated
Ensuring that the Poetry plugins required by the project are available...
The following Poetry plugins are required by the project but are not installed in Poetry's environment:
  - poetry-plugin-export (>=1.8)
Installing Poetry plugins only for the current project...
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Updating dependencies
Resolving dependencies... (0.0s)

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UPD: Seems to be a Conda issue
UPD2: FIXED, poetry-plugin-export manually installed seems to solve the issue, poetry install works now 🤷

[RainbowRivey]

From what I've noticed:
All tests failing are half-precision-model=True
They also fail right on the edge of tolerance:

  comparison failed
  Obtained: 0.408203125
  Expected: 0.398 ± 1.0e-02

-> (0.398 + 0.01 = 0.408) != 0.408203125

Where does the expected precision come from?
Also, shouldn't these tests be deterministic, e.g. via pytorch_lightning.seed_everything() or something similar?

[ArneBinder]

Where does the expected precision come from?

You mean the 1.0e-02? I think that was just for convenience to not write very long score numbers (I think Chris wrote these tests very long ago). Thinking about it now,1.0e-02 seems to be very much too relaxed...

shouldn't these tests be deterministic?

But they are deterministic, aren't they? And what does determinism have to do with the issue at all? I thought you already identified switching half-precision-model as the culprit? The question is, imo, why does this occur only now? Btw how was the behavior when we did the experiments on drugprot?

[codecov]

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[RainbowRivey]

Minor typo: pie-core 0.2.1 in description
Else good 👍