README.md

LLM Performance Predictors are good initializers for Architecture Search

This repository contains the code and the data used in the LLM-PP work. This repository builds on Hardware Aware Transformer (HAT)'s repository.

Data (data/)

1. Train from scratch Architectures (30)
2. PP Prompt Instruction templates
3. LLM PP Data Generator Input

LLM-PP Data Generator

To generate LLM-PP GPT-4 predictions for WMT'14 En-De, run this:

python gpt_scorer.py --source_dir data/train-test_seedarchs --dest_dir /tmp/gpt_scorer_outputs --template_dir data/instruction_templates --experiment_name nov15_exp --datasets wmt14ende --prompt_template_f gpt_scorer_concentrate_statement --openai_models gpt-4

LLM-Distill-PP Trainer

To train LLM-Distill-PP model, run this:

python bleu_predictor.py --gpt_scorer_outputs /tmp/gpt_scorer_outputs --testset_outputs data/train-test_seedarchs --task wmt14ende --teacher_model gpt-4 --save-file /tmp/model.ckpt

Hybrid-search

To execute hybrid-search NAS algorithm, run this:

CUDA_VISIBLE_DEVICES=0 python evo_search.py --configs=configs/wmt14.en-de/supertransformer/space0.yml --evo-configs=configs/wmt14.en-de/evo_search/wmt14ende_titanxp.yml --evo-iter 30 --population-size 125 --parent-size 25 --mutation-size 50 --crossover-size 50 --mutation-prob 0.3 --latency-constraint 150 --bleu-ckpt-path  /tmp/model.ckpt --bleu-predictor-start-idx 0 --bleu-predictor-end-idx 14

Citation

If you use this code, please cite:

@inproceedings{jawahar2024llmpp,
      title={LLM Performance Predictors are good initializers for Architecture Search}, 
      author={Ganesh Jawahar and Muhammad Abdul-Mageed and Laks V. S. Lakshmanan and Dujian Ding},
      year={2024},
      booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
}

License

This repository is MIT-licensed.