UniLM

We develop pre-trained models for natural language understanding (NLU) and generation (NLG) tasks

***** New October 1st, 2019: UniLM v1 release *****

UniLM v1 (September 30th, 2019): the code and pre-trained models for the NeurIPS 2019 paper entitled "Unified Language Model Pre-training for Natural Language Understanding and Generation". UniLM (v1) achieves the new SOTA results in NLG (especially sequence-to-sequence generation) tasks/benchmarks, including abstractive summarization (the Gigaword and CNN/DM dataset), question generation (the SQuAD QG dataset), etc.

UniLM v2: the new pre-training protocol and implementation scheme (coming soon).

Environment

Docker

The recommended way to run the code is using docker under Linux:

alias=`whoami | cut -d'.' -f2`; docker run -it --rm --runtime=nvidia --ipc=host --privileged -v /home/${alias}:/home/${alias} pytorch/pytorch:1.1.0-cuda10.0-cudnn7.5-devel bash

The docker is initialized by:

. .bashrc
apt-get update
apt-get install -y vim wget ssh

PWD_DIR=$(pwd)
cd $(mktemp -d)
git clone -q https://github.com/NVIDIA/apex.git
cd apex
git reset --hard 1603407bf49c7fc3da74fceb6a6c7b47fece2ef8
python setup.py install --user --cuda_ext --cpp_ext
cd $PWD_DIR

pip install --user tensorboardX six numpy tqdm path.py pandas scikit-learn lmdb pyarrow py-lz4framed methodtools py-rouge pyrouge nltk
python -c "import nltk; nltk.download('punkt')"
pip install -e git://github.com/Maluuba/nlg-eval.git#egg=nlg-eval

The mixed-precision training code requires the specific version of NVIDIA/apex, which only supports pytorch<1.2.0.

Install the repo as a package in the docker:

mkdir ~/code; cd ~/code
git clone https://github.com/microsoft/unilm.git
cd ~/code/unilm/src
pip install --user --editable .

Pre-trained Models

We release a large cased UniLM model pre-trained with Wikipedia and BookCorpus corpora. The model is trained by using the same model configuration and WordPiece vocabulary as BERT. The model parameters can be loaded as in the fine-tuning code.

The links to the pre-trained models:

UniLMv1-large-cased: 24-layer, 1024-hidden, 16-heads, 340M parameters

Fine-tuning

We provide instructions on how to fine-tune UniLM as a sequence-to-sequence model to support various downstream natural language generation tasks as follows. It is recommended to use 2 or 4 v100-32G GPU cards to fine-tune the model. Gradient accumulation (--gradient_accumulation_steps) can be enabled if there is an OOM error.

Abstractive Summarization - Gigaword (10K)

In the example, only 10K examples of the Gigaword training data are used to fine-tune UniLM. As shown in the following table, pre-training significantly improves performance for low-resource settings.

Model	ROUGE-1	ROUGE-2	ROUGE-L
Transformer	10.97	2.23	10.42
UniLM	34.21	15.28	31.54

The data can be downloaded from here.

# run fine-tuning
DATA_DIR=/{path_of_data}/gigaword
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_RECOVER_PATH=/{path_of_pre-trained_model}/unilmv1-large-cased.bin
export PYTORCH_PRETRAINED_BERT_CACHE=/{tmp_folder}/bert-cased-pretrained-cache
export CUDA_VISIBLE_DEVICES=0,1,2,3
python biunilm/run_seq2seq.py --do_train --fp16 --amp --num_workers 0 \
  --bert_model bert-large-cased --new_segment_ids --tokenized_input \
  --data_dir ${DATA_DIR} --src_file train.src.10k --tgt_file train.tgt.10k \
  --output_dir ${OUTPUT_DIR}/bert_save \
  --log_dir ${OUTPUT_DIR}/bert_log \
  --model_recover_path ${MODEL_RECOVER_PATH} \
  --max_seq_length 192 --max_position_embeddings 192 \
  --trunc_seg a --always_truncate_tail --max_len_b 64 \
  --mask_prob 0.7 --max_pred 64 \
  --train_batch_size 128 --gradient_accumulation_steps 1 \
  --learning_rate 0.00001 --warmup_proportion 0.1 --label_smoothing 0.1 \
  --num_train_epochs 30

We provide a fine-tuned checkpoint (downloaded from here) used for decoding. The inference and evaluation process is conducted as follows:

# run decoding
DATA_DIR=/{path_of_data}/gigaword
MODEL_RECOVER_PATH=/{path_of_fine-tuned_model}/ggw10k_model.bin
EVAL_SPLIT=test
export PYTORCH_PRETRAINED_BERT_CACHE=/{tmp_folder}/bert-cased-pretrained-cache
# run decoding
python biunilm/decode_seq2seq.py --fp16 --amp --bert_model bert-large-cased --new_segment_ids --mode s2s --need_score_traces \
  --input_file ${DATA_DIR}/${EVAL_SPLIT}.src --split ${EVAL_SPLIT} --tokenized_input \
  --model_recover_path ${MODEL_RECOVER_PATH} \
  --max_seq_length 192 --max_tgt_length 32 \
  --batch_size 64 --beam_size 5 --length_penalty 0 \
  --forbid_duplicate_ngrams --forbid_ignore_word "."
# run evaluation
python gigaword/eval.py --pred ${MODEL_RECOVER_PATH}.${EVAL_SPLIT} \
  --gold ${DATA_DIR}/org_data/${EVAL_SPLIT}.tgt.txt --perl

The program eval.py generates a post-processed output file ${MODEL_RECOVER_PATH}.${EVAL_SPLIT}.post (downloaded from here).

Abstractive Summarization - Gigaword

The training set of Gigaword contains 3.8M examples for headline generation.

Model	ROUGE-1	ROUGE-2	ROUGE-L
OpenNMT	36.73	17.86	33.68
Re3Sum (Cao et al., 2018)	37.04	19.03	34.46
MASS (Song et al., 2019)	38.73	19.71	35.96
BertShare (Rothe et al., 2019)	38.13	19.81	35.62
UniLM	38.90	20.05	36.00

The data can be downloaded from here.

# run fine-tuning
DATA_DIR=/{path_of_data}/gigaword
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_RECOVER_PATH=/{path_of_pre-trained_model}/unilmv1-large-cased.bin
export PYTORCH_PRETRAINED_BERT_CACHE=/{tmp_folder}/bert-cased-pretrained-cache
export CUDA_VISIBLE_DEVICES=0,1,2,3
python biunilm/run_seq2seq.py --do_train --fp16 --amp --num_workers 0 \
  --bert_model bert-large-cased --new_segment_ids --tokenized_input \
  --data_dir ${DATA_DIR} \
  --output_dir ${OUTPUT_DIR}/bert_save \
  --log_dir ${OUTPUT_DIR}/bert_log \
  --model_recover_path ${MODEL_RECOVER_PATH} \
  --max_seq_length 192 --max_position_embeddings 192 \
  --trunc_seg a --always_truncate_tail --max_len_a 0 --max_len_b 64 \
  --mask_prob 0.7 --max_pred 48 \
  --train_batch_size 128 --gradient_accumulation_steps 1 \
  --learning_rate 0.00003 --warmup_proportion 0.1 --label_smoothing 0.1 \
  --num_train_epochs 30

The size of full training data (3.8M) is quite large. We can stop the fine-tuning procedure after 10 epochs.

We provide a fine-tuned checkpoint (downloaded from here) used for decoding. The inference and evaluation process is conducted as follows:

DATA_DIR=/{path_of_data}/gigaword
MODEL_RECOVER_PATH=/{path_of_fine-tuned_model}/ggw38m_model.bin
EVAL_SPLIT=test
export PYTORCH_PRETRAINED_BERT_CACHE=/{tmp_folder}/bert-cased-pretrained-cache
# run decoding
python biunilm/decode_seq2seq.py --fp16 --amp --bert_model bert-large-cased --new_segment_ids --mode s2s --need_score_traces \
  --input_file ${DATA_DIR}/${EVAL_SPLIT}.src --split ${EVAL_SPLIT} --tokenized_input \
  --model_recover_path ${MODEL_RECOVER_PATH} \
  --max_seq_length 192 --max_tgt_length 32 \
  --batch_size 64 --beam_size 5 --length_penalty 0 \
  --forbid_duplicate_ngrams --forbid_ignore_word "."
# apply length penalty
python biunilm/gen_seq_from_trace.py --bert_model bert-large-cased --alpha 0.6 \
  --input ${MODEL_RECOVER_PATH}.${EVAL_SPLIT}
# run evaluation
python gigaword/eval.py --pred ${MODEL_RECOVER_PATH}.${EVAL_SPLIT}.alp0.6 \
  --gold ${DATA_DIR}/org_data/${EVAL_SPLIT}.tgt.txt --perl

The program eval.py generates a post-processed output file ${MODEL_RECOVER_PATH}.${EVAL_SPLIT}.alp0.6.post (downloaded from here).

Abstractive Summarization - CNN / Daily Mail

Model	ROUGE-1	ROUGE-2	ROUGE-L
PGNet (See et al., 2017)	39.53	17.28	36.38
Bottom-Up (Gehrmann et al., 2018)	41.22	18.68	38.34
GPT-2 TL;DR: (Radford et al., 2019)	29.34	8.27	26.58
MASS (Song et al., 2019)	42.12	19.50	39.01
BertShare (Rothe et al., 2019)	39.25	18.09	36.45
BertSumAbs (Liu and Lapata, 2019)	41.72	19.39	38.76
UniLM	43.08	20.43	40.34

The data can be downloaded from here.

# run fine-tuning
DATA_DIR=/{path_of_data}/cnn_dailymail
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_RECOVER_PATH=/{path_of_pre-trained_model}/unilmv1-large-cased.bin
export PYTORCH_PRETRAINED_BERT_CACHE=/{tmp_folder}/bert-cased-pretrained-cache
export CUDA_VISIBLE_DEVICES=0,1,2,3
python biunilm/run_seq2seq.py --do_train --fp16 --amp --num_workers 0 \
  --bert_model bert-large-cased --new_segment_ids --tokenized_input \
  --data_dir ${DATA_DIR} \
  --output_dir ${OUTPUT_DIR}/bert_save \
  --log_dir ${OUTPUT_DIR}/bert_log \
  --model_recover_path ${MODEL_RECOVER_PATH} \
  --max_seq_length 768 --max_position_embeddings 768 \
  --trunc_seg a --always_truncate_tail \
  --max_len_a 568 --max_len_b 200 \
  --mask_prob 0.7 --max_pred 140 \
  --train_batch_size 48 --gradient_accumulation_steps 2 \
  --learning_rate 0.00003 --warmup_proportion 0.1 --label_smoothing 0.1 \
  --num_train_epochs 30

We provide a fine-tuned checkpoint (downloaded from here) used for decoding. The inference and evaluation process is conducted as follows:

DATA_DIR=/{path_of_data}/cnn_dailymail
MODEL_RECOVER_PATH=/{path_of_fine-tuned_model}/cnndm_model.bin
EVAL_SPLIT=test
export PYTORCH_PRETRAINED_BERT_CACHE=/{tmp_folder}/bert-cased-pretrained-cache
# run decoding
python biunilm/decode_seq2seq.py --fp16 --amp --bert_model bert-large-cased --new_segment_ids --mode s2s --need_score_traces \
  --input_file ${DATA_DIR}/${EVAL_SPLIT}.src --split ${EVAL_SPLIT} --tokenized_input \
  --model_recover_path ${MODEL_RECOVER_PATH} \
  --max_seq_length 768 --max_tgt_length 128 \
  --batch_size 64 --beam_size 5 --length_penalty 0 \
  --forbid_duplicate_ngrams --forbid_ignore_word ".|[X_SEP]"
# apply length penalty
python biunilm/gen_seq_from_trace.py --bert_model bert-large-cased --alpha 1.0 \
  --input ${MODEL_RECOVER_PATH}.${EVAL_SPLIT}
# run evaluation
python cnndm/eval.py --pred ${MODEL_RECOVER_PATH}.${EVAL_SPLIT}.alp1.0 \
  --gold ${DATA_DIR}/org_data/${EVAL_SPLIT}.summary --trunc_len 70 --perl

The program eval.py generates a post-processed output file ${MODEL_RECOVER_PATH}.${EVAL_SPLIT}.alp1.0.post (downloaded from here).

Question Generation - SQuAD

We present the results following the same data split and evaluation scripts as in (Du et al., 2017).

Model	BLEU-4	METEOR	ROUGE-L
(Du and Cardie, 2018)	15.16	19.12	-
(Zhang and Bansal, 2019)	18.37	22.65	46.68
UniLM	22.78	25.49	51.57

We also report the results following the data split as in (Zhao et al., 2018), which uses the reversed dev-test setup.

Model	BLEU-4	METEOR	ROUGE-L
(Zhao et al., 2018)	16.38	20.25	44.48
(Zhang and Bansal, 2019)	20.76	24.20	48.91
UniLM	24.32	26.10	52.69

Note: If we directly use the tokenized references provided by Du et al. (2017), the results are (22.17 BLEU-4 / 25.47 METEOR / 51.53 ROUGE-L) on the raw data split, and (23.69 BLEU-4 / 26.08 METEOR / 52.70 ROUGE-L) in the reversed dev-test setup.

Our processed data can be downloaded from here.

# run fine-tuning
DATA_DIR=/{path_of_data}/qg/train
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_RECOVER_PATH=/{path_of_pre-trained_model}/unilmv1-large-cased.bin
export PYTORCH_PRETRAINED_BERT_CACHE=/{tmp_folder}/bert-cased-pretrained-cache
export CUDA_VISIBLE_DEVICES=0,1,2,3
python biunilm/run_seq2seq.py --do_train --num_workers 0 \
  --bert_model bert-large-cased --new_segment_ids --tokenized_input \
  --data_dir ${DATA_DIR} --src_file train.pa.tok.txt --tgt_file train.q.tok.txt \
  --output_dir ${OUTPUT_DIR}/bert_save \
  --log_dir ${OUTPUT_DIR}/bert_log \
  --model_recover_path ${MODEL_RECOVER_PATH} \
  --max_seq_length 512 --max_position_embeddings 512 \
  --mask_prob 0.7 --max_pred 48 \
  --train_batch_size 32 --gradient_accumulation_steps 2 \
  --learning_rate 0.00002 --warmup_proportion 0.1 --label_smoothing 0.1 \
  --num_train_epochs 10

We provide a fine-tuned checkpoint (downloaded from here) used for decoding. The inference and evaluation process is conducted as follows:

DATA_DIR=/{path_of_data}/qg/test
MODEL_RECOVER_PATH=/{path_of_fine-tuned_model}/qg_model.bin
EVAL_SPLIT=test
export PYTORCH_PRETRAINED_BERT_CACHE=/{tmp_folder}/bert-cased-pretrained-cache
# run decoding
python biunilm/decode_seq2seq.py --bert_model bert-large-cased --new_segment_ids --mode s2s \
  --input_file ${DATA_DIR}/$test.pa.tok.txt --split ${EVAL_SPLIT} --tokenized_input \
  --model_recover_path ${MODEL_RECOVER_PATH} \
  --max_seq_length 512 --max_tgt_length 48 \
  --batch_size 16 --beam_size 1 --length_penalty 0
# run evaluation using our tokenized data as reference
python qg/eval_on_unilm_tokenized_ref.py --out_file qg/output/qg.test.output.txt
# run evaluation using tokenized data of Du et al. (2017) as reference
python qg/eval.py --out_file qg/output/qg.test.output.txt

The files qg/eval_on_unilm_tokenized_ref.py and qg/eval.py are in Python 2.*, because they are dependent on the evaluation scripts of Du et al., (2017). The output files can be downloaded from here. Notice that our model predictions are cased, while the gold outputs provided by Du et al., (2017) are uncased. So the predicted results need to be converted to lowercase before computing the evaluation metrics.

FAQ

Install ROUGE-1.5.5
- If we would like to use the Perl script of ROUGE, it can be installed by following instruction-1 and instruction-2. The ROUGE-1.5.5 package (written in Perl) can be downloaded from here. We can also use the Python-version evaluation script by removing the flag --perl when running eval.py. Notice that there would be slight number difference between them due to the implementation details.

Citation

If you find UniLM useful in your work, you can cite the following paper:

@inproceedings{unilm,
    title={Unified Language Model Pre-training for Natural Language Understanding and Generation},
    author={Dong, Li and Yang, Nan and Wang, Wenhui and Wei, Furu and Liu, Xiaodong and Wang, Yu and Gao, Jianfeng and Zhou, Ming and Hon, Hsiao-Wuen},
    year={2019},
    booktitle = "33rd Conference on Neural Information Processing Systems (NeurIPS 2019)"
}

Related Projects/Codebase

Vision-Language Pre-training: https://github.com/LuoweiZhou/VLP
MT-DNN: https://github.com/namisan/mt-dnn
Response Generation Pre-training: https://github.com/microsoft/DialoGPT

Acknowledgments

Our code is based on pytorch-transformers v0.4.0. We thank the authors for their wonderful open-source efforts.

License

This project is licensed under the license found in the LICENSE file in the root directory of this source tree. Portions of the source code are based on the pytorch-transformers v0.4.0 project.

Microsoft Open Source Code of Conduct

Contact Information

For help or issues using UniLM, please submit a GitHub issue.

For personal communication related to UniLM, please contact Li Dong (lidong1@microsoft.com), Furu Wei (fuwei@microsoft.com).

yangapku/unilm

UniLM

Environment

Docker

Pre-trained Models

Fine-tuning

Abstractive Summarization - Gigaword (10K)

Abstractive Summarization - Gigaword

Abstractive Summarization - CNN / Daily Mail

Question Generation - SQuAD

FAQ

Citation

Related Projects/Codebase

Acknowledgments

License

Contact Information