Official Codebase for DiffuSeq: Sequence to Sequence Text Generation With Diffusion Models and DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models.

The diffusion process of our conditional diffusion language model DiffuSeq.

The diffusion process of accelerated DiffuSeq.

• We add soft learned absorbing state. By using absorbing states, we can remove the clamp operation. In other words, we can consider the absorbing state as a landmark in the embedding space.
• We add discrete noise, which can further bridge the gap between the continous and discrete text space.
• We use DPM-solver++ to speed up sampling.

Our enhanced version effectively accelerates the training convergence by 4x and generates samples of similar quality 800x faster, rendering it significantly closer to practical application.

The code is based on PyTorch and HuggingFace transformers.

pip install -r requirements.txt 

cd scripts
bash train.sh

Arguments explanation:

• --dataset: the name of datasets, just for notation
• --data_dir: the path to the saved datasets folder, containing train.jsonl,test.jsonl,valid.jsonl
• --seq_len: the max length of sequence $z$ ($x\oplus y$)
• --resume_checkpoint: if not none, restore this checkpoint and continue training
• --vocab: the tokenizer is initialized using bert or load your own preprocessed vocab dictionary (e.g. using BPE)

It will take 2 more days to train a DiffuSeq model on 4 NVIDIA A100 80G GPUs for QG and QQP, and the training steps should be increased accordingly along with the size of the training set. To reproduce the results of Table 1 in our paper, we suggest the following configuration for each dataset when training.

• --learned_mean_embed: set whether to use the learned soft absorbing state.
• --denoise: set whether to add discrete noise
• --use_fp16: set whether to use mixed precision training
• --denoise_rate: set the denoise rate, with 0.5 as the default

It only take around 11 hours to train a model on 2 NVIDIA A100 80G GPUs for QQP.

We customize the implementation of DPM-Solver++ to DiffuSeq to accelerate its sampling speed.

cd scripts
bash run_decode_solver.sh

Please add the citation if our paper or code helps you.

@inproceedings{gong2022diffuseq,
  author = {Gong, Shansan and Li, Mukai and Feng, Jiangtao and Wu, Zhiyong and Kong, Lingpeng},
  booktitle = {International Conference on Learning Representations, ICLR},
  title = {{DiffuSeq}: Sequence to Sequence Text Generation with Diffusion Models},
  year = 2023
}

@article{gong2023diffuseqv2,
  title={DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models},
  author={Gong, Shansan and Li, Mukai and Feng, Jiangtao and Wu, Zhiyong and Kong, Lingpeng},
  journal={arXiv preprint arXiv:2310.05793},
  year={2023}
}