Quickly generate PyTorch/Brevitas Scaled Dot-Product Attention dummy operators for exploring QONNX and FINN graph transformations
Install the dependencies listed in the requirements.txt, for example via pip
(these are mostly for debugging and orchestrating the builds, FINN comes with
its own dependencies bundled in a docker image):
pip install -r requirements.txt
Clone and checkout the feature branch of FINN combining all necessary additions and modifications to FINN related to the attention feature and remember the path you cloned into:
git clone https://github.com/iksnagreb/[email protected]/merge/attention
Add the attention-hlslib dependency manually to your FINN installation (note that this is a private repository for now, you may have to request access):
cd <path-to-finn>/deps/
git clone https://github.com/iksnagreb/attention-hlslib.git
The whole export, build and evaluation pipeline is managed by
dvc and is executed as follows (see
dvc.yaml and params.yaml for configuration options and stage dependencies):
FINN=<path-to-finn> dvc repro
It is possible to specify all FINN related environment variables (e.g. the
FINN_HOST_BUILD_DIR) as usual. It might be necessary to pull existing model
and build artifacts into the local dvc cache first, though dvc should try to
reproduce these if not available:
dvc pull
All output artifacts, i.e., the exported model ONNX files, verification inputs,
FINN build logs, reports and the generated bitstream and driver script can be
found in the build directory.