Add LayerNorm support for Vivado

[rianbrooksflynn]

Description

This PR adds support for Layer Normalization using either Keras or PyTorch with the Vivado backend in io_parallel mode.

This implementation uses a lookup table for inverse square root; the inputs to the lookup table follow a logarithmic distribution for better accuracy.

Tests have been added for both Keras and Pytorch parsing.

Credit is due to @Ethan0Jiang and @LostEcho365 (Zhixing Jiang and Dennis Yin) for their Vivado implementation and Keras parsing support; my contributions were making a change to the inverse square root lookup table implementation, implementing PyTorch support, and adding unit tests. (Here's a link to their pre-print.) The original code authors have given permission for their code to be merged into hls4ml.

Linked issue: #1109

Type of change

• New feature (non-breaking change which adds functionality)
• A new research paper code implementation

Tests

Two unit tests added: test/pytest/test_layernorm.py and test/pytest/test_layernorm_pytorch.py

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.
• My changes generate no new warnings.
• 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.

[rianbrooksflynn]

I'm not sure why the diff is so broken, maybe something to do with the line endings?

[rianbrooksflynn]

Diff also broken here, maybe line endings issue

[JanFSchulte]

The pytest failure is the QKeras tests timing out. There's 299 tests being run in that batch, which I guess is too many. Is there a why to reshuffle the batches to avoid the timeout?

[rianbrooksflynn]

pre-commit.ci autofix