Add support for parsing simple brevitas layers as part of pytorch models

[JanFSchulte]

This PR adds support for the parsing of simple brevitas layers (QuantLinear, QuantActivation, QuantConv1D, QuantConv2D) to the pytorch parser. More complex models will still have to go through QONNX, but simple cases can be handled easily within the pytorch parser itself. To this end, this PR adds a new quantizer which only propagates the desired precision to the hsl4ml model, as brevitas already provides the quantized tensors which we pick up directly.

Type of change

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• New feature (non-breaking change which adds functionality)

Tests

Tested locally with simple model and added pytests.

Checklist

• I have read the guidelines for contributing.
• I have commented my code, particularly in hard-to-understand areas.
• I have made corresponding changes to the documentation.
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• I have installed and run pre-commit on the files I edited or added.
• I have added tests that prove my fix is effective or that my feature works.

[JanFSchulte]

pre-commit.ci autofix

[jmitrevs]

Do we need to add brevitas to our test environment docker image? Or can we just add brevitas as a testing dependency? I think that's why the test failed.

[JanFSchulte]

pre-commit.ci autofix

[JanFSchulte]

Hey @jmitrevs I can't reproduce the oneAPI compilation error in the test_brevitas_parsing pytest locally for some reason. I'm suspecting it's genuine, but it's hard to tell because locally it compiles fine. I have oneAPI version 2025.0 installed. Could you see if you can reproduce this?

[JanFSchulte]

To summarize the current state of this: I think this PR contains what we can reasonably support currently and most things work somewhat well. Everything is limited to power-of-2 scales,but I think that's fine for now. The big issue that I still have is QuantLSTM. Brevitas completely reimplements RNNs and re-quantizes frequently internally. I tried to match that by setting the precision of the internally used variables more granular than we currently do, but the precision is horrible. I am tempted to remove it for the moment. Currently, you can't even export QuantLSTM to QONNX, so this is something that brevitas itself is still figuring out.

[JanFSchulte]

Removed QuantLSTM for now. QuantRNN is also not great, but at least usually gets results in the same ballpark. Otherwise I think this is ready for review.