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HelixFold: An Efficient and Improved Implementation of AlphaFold 2 using PaddlePaddle

AlphaFold2 is an accurate protein structure prediction pipeline. HelixFold provides an efficient and improved implementation of the complete training and inference pipelines of AlphaFold2 in GPU and DCU. Compared with the computational performance of AlphaFold2 reported in the paper and OpenFold implemented through PyTorch, HelixFold reduces the training time from about 11 days to 7.5 days. Training HelixFold from scratch can achieve competitive accuracy with AlphaFold2.

Instruction

The detailed instructions on running HelixFold in GPU and DCU for training and inference are provided in the following links:

Technical Highlights for Efficient Implementation

  • Branch Parallelism and Hybrid Parallelism HelixFold proposes Branch Parallelism (BP) to split the calculation branch across multiple devices in order to accelerate computation during the initial training phase. The training cost is further reduced by training with Hybrid Parallelism, combining BP with Dynamic Axial Parallelism (DAP) and Data Parallelism (DP).

  • Operator Fusion and Tensor Fusion to Reduce the Cost of Scheduling Scheduling a huge number of operators is one of the bottlenecks for the training. To reduce the cost of scheduling, Fused Gated Self-Attention is utilized to combine multiple blocks into an operator, and thousands of tensors are fused into only a few tensors.

  • Multi-dimensional Memory Optimization Multiple techniques, including Recompute, BFloat16, In-place memory, and Subbatch (Chunking), are exploited to reduce the memory required for training.

Please refer to paper for more technical details.

Online Service

For those who want to try out our model without any installation, we also provide an online interface PaddleHelix HelixFold Forecast through web service.

Copyright

HelixFold code is licensed under the Apache 2.0 License, which is same as AlphaFold. However, we use the AlphaFold parameters pretrained by DeepMind, which are made available for non-commercial use only under the terms of the CC BY-NC 4.0 license.

Reference

[1] Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold[J]. Nature, 2021, 596(7873): 583-589.

Citation

If you use the code or data in this repos, please cite:

@article{wang2022helixfold,
  title={HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle},
  author={Wang, Guoxia and Fang, Xiaomin and Wu, Zhihua and Liu, Yiqun and Xue, Yang and Xiang, Yingfei and Yu, Dianhai and Wang, Fan and Ma, Yanjun},
  journal={arXiv preprint arXiv:2207.05477},
  year={2022}
}

@article{wang2022efficient_alphafold2,
  title={Efficient AlphaFold2 Training using Parallel Evoformer and Branch Parallelism},
  author={Wang, Guoxia and Wu, Zhihua and Fang, Xiaomin and Xiang, Yingfei and Liu, Yiqun and Yu, Dianhai and Ma, Yanjun},
  journal={arXiv preprint arXiv:2211.00235},
  year={2022}
}