OLLA (Optimizing the Lifetime and Location of Arrays) enables training larger deep neural networks on existing hardware. It accomplishes this with a few techniques:

• Operator order optimization — reodering tensor operators to reduce peak memory usage
• Fragmentation reduction — dynamic memory profiling and scheduling to better-utilize memory.

Our approach is described in detail on the OLLA arXiv paper. See citing below to attribute the work.

Installing OLLA in your Python environment is simple:

git clone https://github.com/facebookresearch/olla
pip install . [--extra-index-url <url>]

Note:

• The above install will attempt to install torch, torchaudio, torchvision, and torchtext based on default distributions. To install for your CUDA version/OS, see the PyTorch Getting Started documentation, appending the --extra-index-url flag and value to the above install command as needed.
• OLLA is tested with Gurobi 9.1.1; use your own license or version as needed.

To run benchmarks:

python benchmarks.py

Run all unit tests with:

python -m unittest discover -s tests --pattern "*_test.py"

Run unit tests that are skipped with by setting RUN_SKIPPED=1 in the environment before the command.

If you use OLLA, please use the below BibTex for citing:

@article{steiner2022olla,
  title={OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks},
  author={Steiner, Benoit and Elhoushi, Mostafa and Kahn, Jacob, and Hegarty, James},
  doi = {10.48550/arXiv.2210.12924},
  year={2022},
}