Add support for Layer Normalization

[rianbrooksflynn]

I've got a branch adding support for Layer Normalization using either Keras or PyTorch with the Vivado backend in io_parallel mode, and I'd like to submit a pull request.

The 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 parsing, 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.

While I haven't run this on an actual board, below I have some latency / resource usage estimations from Vitis HLS 2023.2.

keras_layernorm_report.txt pytorch_layernorm_report.txt

I believe that transformer architecture is a widely requested feature for hls4ml, and Layer Normalization is a key step in that direction.

[rianbrooksflynn]

PR up here: https://github.com/fastmachinelearning/hls4ml/pull/1110

[The-Padi]

Small question @rianbrooksflynn : When you say

input size is not currently supported by hls4ml, only dim3 is supported

does this take into account the "None" that is automatically added at the start of the list ? Because my input shape is currently (5, 125, 96) but on line 129 of pytorch_to_hls.py there is this :

# first element needs to 'None' as placeholder for the batch size, insert it if not present
input_shapes = [[None] + list(shape) if shape[0] is not None else list(shape) for shape in input_shapes]

So my list is now [None, 20, 125, 96] when it arrives at your len check

[rianbrooksflynn]

Hi @The-Padi - thanks for your question.

Yes, the "dim3" limit for the input size is counting the "None" at the start as the batch size placeholder. Perhaps this wording is unclear; do you have any suggestions for a better error message? Maybe something like "input size is not currently supported by hls4ml, only dim3 (including 'None' first dimension) is supported"