metadata

license: mit

LongMem

Official implementation of our paper "Augmenting Language Models with Long-Term Memory".

Please cite our paper if you find this repository interesting or helpful:

@article{LongMem,
  title={Augmenting Language Models with Long-Term Memory},
  author={Wang, Weizhi and Dong, Li and Cheng, Hao and Liu, Xiaodong and Yan, Xifeng and Gao, Jianfeng and Wei, Furu},
  journal={arXiv preprint arXiv:2306.07174},
  year={2023}
}

Environment Setup

torch: Please follow torch official installation guide. We recommend torch>=1.8.0. Please select the torch-gpu version which is consistent with your cuda driver version.
Faiss-GPU: For Nvidia V100 GPUs, simply install via pip install faiss-gpu. For Nvidia A100, A6000 GPUs, please run conda install faiss-gpu cudatoolkit=11.0 -c pytorch. The A100 GPU is not officially supported by faiss-gpu, sometimes it will lead to errors, you can refer to this git issue of faiss for help.
fairseq: pip install --editable ./fairseq Then the revised fairseq and dependency packages will be installed. We strongly recommend you to use python 3.8 for stability.
other packages: pip install -r requirements.txt

Project Structure

Pre-trained LLM Class (L24, E1024, Alibi positional embedding): fairseq/fairseq/models/newgpt.py
Transformer Decoder with SideNetwork (L12, E1024): fairseq/fairseq/models/sidenet/transformer_decoder_sidenet.py
Transformer Language Model with SideNetwork Class: fairseq/fairseq/models/transformer_lm_sidenet.py
Memory Bank and Retrieval: fairseq/fairseq/modules/dynamic_memory_with_chunk.py
Joint Attention for Memory Fusion: fairseq/fairseq/modules/joint_multihead_attention_sum.py

Memory-Augmented Adaptation Training

Data collection and Preprocessing

Please download the Pile from official release. Each sub-dataset in the Pile is organized as various jsonline splits. You can refer to preprocess/filter_shard_tnlg.py fpr how we sample the training set and binalize following standard fairseq preprocessing process.

Memory-Augmented Adaptation Training:

bash train_scripts/train_longmem.sh

Evaluation

Please firstly download the checkpoints for pre-trained GPT2-medium model and LongMem model to checkpoints/.

Memory-Augmented In-Context Learning

# Evaluate gpt2 baseline
python eval_scripts/eval_longmem_icl.py --path /path/to/gpt2_pretrained_model
# Evaluate LongMem model
python eval_scripts/eval_longmem_icl.py --path /path/to/longmem_model --pretrained-model-path /path/to/gpt2_pretrained_model

Credits

LongMem is developed based on fairseq. Thanks to the team from eleuther.ai who constructed the largest high-quality corpora, the Pile.