This is the official repo for the paper SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers

Parts of our codes are modified from DiffusionLM and minimaldiffusion repos.

Before running our code, you may setting the environments using the following lines.

conda create -n seqdiffuseq python=3.8
conda install mpi4py
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0
pip install -r requirements.txt

For the non-translation tasks, we follows DiffuSeq for the dataset settings.

For IWSLT14 and WMT14, we follow the data preprocessing from fairseq, we also provide the processed datasets in this links. (Update 04/13/2023: Sorry for missing WMT14 data, I just uploaded it. Download from here)

To run the code, we use iwslt14 en-de as an illustrative example:

1. Prepare the data of iwslt14 under ./data/iwslt14/ directory;
2. Learning the BPE tokenizer by

python ./tokenizer_utils.py train-byte-level iwslt14 10000 

3. To train with the following line:

mkdir ckpts
bash ./train_scripts/iwslt_en_de.sh 0 de en
#(for en to de translation) bash ./train_scripts/iwslt_en_de.sh 0 en de

You may modify the scripts in ./train_scripts for your own training settings.

After training accomplish, you can run the following line for inference:

bash ./inference_scrpts/iwslt_inf.sh path-to-ckpts/ema_0.9999_280000.pt path-to-save-results path-to-ckpts/alpha_cumprod_step_260000.npy

The ema_0.9999_280000.pt file is the model weights and alpha_cumprod_step_260000.npy is the saved noise schedule. You have to use the most recent .npy schedule file saved before .pt model weight file.

Note that for all the training experiments, we all set the maximum training steps and warmups to 1000000 and 10000. For different datasets, it is needless to stop training until maximum training steps. IWSLT14 use checkpoint around 300000 training steps, WMT15 around 500000 train steps and non-translation task around 100000 train steps.

You can change the hyperparameter setting for your own experiments, maybe increasing the training batches or modify the training schedule will bring some improvements.

If you find our work and codes interesting and useful, please cite:

@article{Yuan2022SeqDiffuSeqTD,
  title={SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers},
  author={Hongyi Yuan and Zheng Yuan and Chuanqi Tan and Fei Huang and Songfang Huang},
  journal={ArXiv},
  year={2022},
  volume={abs/2212.10325}
}