From 0cb47457b792794fad2e698663cf4be954dcdedd Mon Sep 17 00:00:00 2001 From: skleee Date: Thu, 21 Dec 2023 17:08:04 +0900 Subject: [PATCH] GLEN first release --- .gitignore | 186 +++ LICENSE | 201 +++ README.md | 140 +- assets/evaluation.png | Bin 0 -> 877313 bytes assets/glen_poster.pdf | Bin 0 -> 1205562 bytes assets/glen_slide.pdf | Bin 0 -> 906265 bytes assets/model.png | Bin 0 -> 1119346 bytes data/.gitkeep | 0 examples/glen_phase1/evaluate_glen.py | 304 ++++ examples/glen_phase1/makeid_glen.py | 284 ++++ examples/glen_phase1/train_glen.py | 298 ++++ examples/glen_phase2/evaluate_glen.py | 322 +++++ examples/glen_phase2/makeid_glen.py | 265 ++++ examples/glen_phase2/train_glen.py | 330 +++++ requirements.txt | 14 + scripts/eval_inference_query_glen_arguana.sh | 18 + scripts/eval_inference_query_glen_marco.sh | 18 + scripts/eval_inference_query_glen_nfcorpus.sh | 18 + scripts/eval_inference_query_glen_nq.sh | 18 + scripts/eval_make_docid_glen_arguana.sh | 18 + scripts/eval_make_docid_glen_marco.sh | 18 + scripts/eval_make_docid_glen_nfcorpus.sh | 18 + scripts/eval_make_docid_glen_nq.sh | 18 + scripts/train_glen_p1_marco.sh | 68 + scripts/train_glen_p1_nq.sh | 68 + scripts/train_glen_p2_marco.sh | 35 + scripts/train_glen_p2_nq.sh | 35 + set_environment.sh | 28 + src/tevatron/__init__.py | 0 src/tevatron/arguments.py | 255 ++++ src/tevatron/datasets/__init__.py | 2 + src/tevatron/datasets/glen_p1_dataset.py | 743 ++++++++++ src/tevatron/datasets/glen_p2_dataset.py | 984 +++++++++++++ src/tevatron/loss.py | 199 +++ src/tevatron/main_utils.py | 53 + src/tevatron/metrics.py | 250 ++++ src/tevatron/modeling/__init__.py | 8 + .../modeling/encoder_decoder_seq2seq.py | 413 ++++++ src/tevatron/modeling/glen_phase1.py | 759 ++++++++++ src/tevatron/modeling/glen_phase2.py | 449 ++++++ src/tevatron/modeling/glen_t5.py | 330 +++++ src/tevatron/modeling/glen_t5_config.py | 126 ++ src/tevatron/modeling/glen_t5_modeling.py | 1271 +++++++++++++++++ src/tevatron/modeling/glen_t5_outputs.py | 145 ++ src/tevatron/trainer/__init__.py | 2 + src/tevatron/trainer/glen_p1_trainer.py | 419 ++++++ src/tevatron/trainer/glen_p2_trainer.py | 348 +++++ src/tevatron/tree.py | 91 ++ 48 files changed, 9567 insertions(+), 2 deletions(-) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 assets/evaluation.png create mode 100644 assets/glen_poster.pdf create mode 100644 assets/glen_slide.pdf create mode 100644 assets/model.png create mode 100644 data/.gitkeep create mode 100644 examples/glen_phase1/evaluate_glen.py create mode 100644 examples/glen_phase1/makeid_glen.py create mode 100644 examples/glen_phase1/train_glen.py create mode 100644 examples/glen_phase2/evaluate_glen.py create mode 100644 examples/glen_phase2/makeid_glen.py create mode 100644 examples/glen_phase2/train_glen.py create mode 100644 requirements.txt create mode 100644 scripts/eval_inference_query_glen_arguana.sh create mode 100644 scripts/eval_inference_query_glen_marco.sh create mode 100644 scripts/eval_inference_query_glen_nfcorpus.sh create mode 100644 scripts/eval_inference_query_glen_nq.sh create mode 100644 scripts/eval_make_docid_glen_arguana.sh create mode 100644 scripts/eval_make_docid_glen_marco.sh create mode 100644 scripts/eval_make_docid_glen_nfcorpus.sh create mode 100644 scripts/eval_make_docid_glen_nq.sh create mode 100644 scripts/train_glen_p1_marco.sh create mode 100644 scripts/train_glen_p1_nq.sh create mode 100644 scripts/train_glen_p2_marco.sh create mode 100644 scripts/train_glen_p2_nq.sh create mode 100644 set_environment.sh create mode 100644 src/tevatron/__init__.py create mode 100644 src/tevatron/arguments.py create mode 100644 src/tevatron/datasets/__init__.py create mode 100644 src/tevatron/datasets/glen_p1_dataset.py create mode 100644 src/tevatron/datasets/glen_p2_dataset.py create mode 100644 src/tevatron/loss.py create mode 100644 src/tevatron/main_utils.py create mode 100644 src/tevatron/metrics.py create mode 100644 src/tevatron/modeling/__init__.py create mode 100644 src/tevatron/modeling/encoder_decoder_seq2seq.py create mode 100644 src/tevatron/modeling/glen_phase1.py create mode 100644 src/tevatron/modeling/glen_phase2.py create mode 100644 src/tevatron/modeling/glen_t5.py create mode 100644 src/tevatron/modeling/glen_t5_config.py create mode 100644 src/tevatron/modeling/glen_t5_modeling.py create mode 100644 src/tevatron/modeling/glen_t5_outputs.py create mode 100644 src/tevatron/trainer/__init__.py create mode 100644 src/tevatron/trainer/glen_p1_trainer.py create mode 100644 src/tevatron/trainer/glen_p2_trainer.py create mode 100644 src/tevatron/tree.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ca0b398 --- /dev/null +++ b/.gitignore @@ -0,0 +1,186 @@ +.idea/ +__pycache__/ +*.egg-info/ +logs/* +GradCache/* +wandb/* +data/* +checkpoint/* +*.tsv + +# Created by https://www.toptal.com/developers/gitignore/api/python +# 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We also provide blog posts (Korean) at [here](https://dial.skku.edu/blog/2023_glen). Please refer to the paper for more details. + + +## Environment +We have confirmed that the results are reproduced successfully in `python==3.8.12`, `transformers==4.15.0`, `pytorch==1.10.0` with `cuda 12.0`. Please create a conda environment and install the required packages with the following scripts: +``` +sh set_environment.sh +conda activate glen +``` + +Optionally, you can also install [GradCache](https://github.com/luyug/GradCache) to gradient cache feature during training **ranking-based ID refinement** by: +``` +git clone https://github.com/luyug/GradCache +cd GradCache +pip install . +``` + +## Dataset +Datasets can be downloaded from: [NQ320k](https://o365skku-my.sharepoint.com/:f:/g/personal/sk1027_o365_skku_edu/Et5O6pBBRFhDszKxUd0S3wMB05986kg_bYi_uT8SRh80QQ?e=jAEowr), [MS MARCO Passage Ranking set](https://o365skku-my.sharepoint.com/:f:/g/personal/sk1027_o365_skku_edu/Evg4kq5x2OhOtxu4IMs_39sBcxYjY6EQLoqyKh5toM9bRg?e=jZW04M), [BEIR](https://o365skku-my.sharepoint.com/:f:/g/personal/sk1027_o365_skku_edu/EuSgMCJynLdCuKFG_UldBKMB953_RsbzA1jliw2ashEEGw?e=y57m5Y). +After downloading each folder, unzip it into the `data` folder. The structure of each folder is as follows. + +``` +data +├── beir +│ ├── arguana +│ └── nfcorpus +├── nq320k +└── msmarco +``` + +- For NQ320k, we follow the same data preprocessing as [NCI](https://github.com/solidsea98/Neural-Corpus-Indexer-NCI) and the setup in [GENRET](https://github.com/sunnweiwei/GenRet), splitting the test set into two subsets; *seen test* and *unseen test*. +- For MS MARCO passage ranking set, we use the official development set consisting of 6,980 queries with a **full corpus**, i.e., 8.8M passages. +- For BEIR, we assess the model on Arguana and NFCorpus and the code is based on [BEIR](https://github.com/beir-cellar/beir). +- Further details are described in the paper. + + + +## Training +The training process consists of two phases: **(1) Keyword-based ID assignment** and **(2) Ranking-based ID refinement**. In the `/examples` folder, we provide GLEN code for each phase: `glen_phase1`, `glen_phase2`. Please refer to `src/tevatron` for the trainer. +Run the scripts to train GLEN from the scratch for NQ320k or MS MARCO.
+ +### NQ320k +``` +# (1) Keyword-based ID assignment +sh scripts/train_glen_p1_nq.sh +``` + +``` +# (2) Ranking-based ID refinement +sh scripts/train_glen_p2_nq.sh +``` + +### MS MARCO +``` +# (1) Keyword-based ID assignment +sh scripts/train_glen_p1_marco.sh +``` + +``` +# (2) Ranking-based ID refinement +sh scripts/train_glen_p2_marco.sh +``` + +You can directly download our trained checkpoints for each stage from the following link: [NQ320k](https://o365skku-my.sharepoint.com/:f:/g/personal/sk1027_o365_skku_edu/Eld_v5cO-s1ArCz-gkmjLbYBCD2f2xhgbALd6M4hoQnq5A?e=VC82mB), [MS MARCO](https://o365skku-my.sharepoint.com/:f:/g/personal/sk1027_o365_skku_edu/EnC1iEC3rApIjDcnVdVPN7ABTq2J8qYxEdw1ITfaDzR8uQ?e=vNhfoc) + + +## Evaluation +The evaluation process consists of two stages: **(1) Document processing via making document identifiers** and **(2) Query processing via inference**. + +![GLEN](assets/evaluation.png) +Run the scripts to evalute GLEN for each dataset.
+ +### NQ320k +``` +sh scripts/eval_make_docid_glen_nq.sh +sh scripts/eval_inference_query_glen_nq.sh +``` + +### MS MARCO +``` +sh scripts/eval_make_docid_glen_marco.sh +sh scripts/eval_inference_query_glen_marco.sh +``` + +### BEIR +``` +# Arguana +sh scripts/eval_make_docid_glen_arguana.sh +sh scripts/eval_inference_query_glen_arguana.sh +``` + +``` +# NFCorpus +sh scripts/eval_make_docid_glen_nfcorpus.sh +sh scripts/eval_inference_query_glen_nfcorpus.sh +``` + +## Acknowledgement +Our code is mainly based on [Tevatron](https://github.com/texttron/tevatron). Also, we learned a lot from [NCI](https://github.com/solidsea98/Neural-Corpus-Indexer-NCI), [Transformers](https://github.com/huggingface/transformers), and [BEIR](https://github.com/beir-cellar/beir). We appreciate all the authors for sharing their codes. + + +## Citation +If you find this work useful for your research, please cite our paper: +``` +@inproceedings{lee-etal-2023-glen, + title = "{GLEN}: Generative Retrieval via Lexical Index Learning", + author = "Lee, Sunkyung and + Choi, Minjin and + Lee, Jongwuk", + editor = "Bouamor, Houda and + Pino, Juan and + Bali, Kalika", + booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", + month = dec, + year = "2023", + address = "Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.emnlp-main.477", + doi = "10.18653/v1/2023.emnlp-main.477", + pages = "7693--7704", +} +``` + +## Contacts +For any questions, please contact the following authors via email or feel free to open an issue 😊 +- Sunkyung Lee sk1027@skku.edu +- Minjin Choi zxcvxd@skku.edu \ No newline at end of file diff --git a/assets/evaluation.png b/assets/evaluation.png 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