In-context Language Learning: Architectures and Algorithms [WIP]

This repo serves for the experiments for the paper:

Title: In-context Language Learning: Architectures and Algorithms

Authors : Ekin Akyürek, Bailin Wang, Yoon Kim, Jacob Andreas

Setup

conda create -n seq_icl python=3.11
pip install -r requirements.txt

Experiments

Experiments on DFA

To run the training,

python -m train experiment=dfa/lstm
python -m train experiment=dfa/retnet
python -m train experiment=dfa/gla
python -m train experiment=dfa/transformer+

Troubleshooting

• add export PATH=$PATH:/usr/local/sbin:/usr/sbin:/sbin so that ldconfig can work properly
• The MHA in simple_lm.py use num_heads, but in other modules we use n_heads. The name needs to be changed for consistency, but they're kept as is for now.
• you might need to set up conv1d following the command in this issue

git clone https://github.com/Dao-AILab/causal-conv1d.git
cd causal_conv1d
git checkout v1.0.2  # this is the highest compatible version allowed by Mamba
CAUSAL_CONV1D_FORCE_BUILD=TRUE pip install .

Acknowledgements

This repo is adapted from safari. Triton implementations are taken from linear rnn.