Search

The accuracy of AlphaFold2, a frontier end-to-end structure prediction system, is already close to that of the experimental determination techniques. Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to train AlphaFold2 from scratch. Efficient AlphaFold2 training could accelerate the development of life science. In this paper, we propose a Parallel Evoformer and Branch Parallelism to speed up the training of AlphaFold2. We conduct sufficient experiments on UniFold implemented in PyTorch and HelixFold implemented in PaddlePaddle, and Branch Parallelism can improve the training performance by 38.67% and 36.93%, respectively. We also demonstrate that the accuracy of Parallel Evoformer could be on par with AlphaFold2 on the CASP14 and CAMEO datasets. The source code is available on https://github.com/PaddlePaddle/PaddleFleetX, arXiv admin note: substantial text overlap with arXiv:2207.05477

Accurate protein structure prediction can significantly accelerate the development of life science. The accuracy of AlphaFold2, a frontier end-to-end structure prediction system, is already close to that of the experimental determination techniques. Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to implement the training and inference of AlphaFold2 from scratch. The cost of running the original AlphaFold2 is expensive for most individuals and institutions. Therefore, reducing this cost could accelerate the development of life science. We implement AlphaFold2 using PaddlePaddle, namely HelixFold, to improve training and inference speed and reduce memory consumption. The performance is improved by operator fusion, tensor fusion, and hybrid parallelism computation, while the memory is optimized through Recompute, BFloat16, and memory read/write in-place. Compared with the original AlphaFold2 (implemented with Jax) and OpenFold (implemented with PyTorch), HelixFold needs only 7.5 days to complete the full end-to-end training and only 5.3 days when using hybrid parallelism, while both AlphaFold2 and OpenFold take about 11 days. HelixFold saves 1x training time. We verified that HelixFold's accuracy could be on par with AlphaFold2 on the CASP14 and CAMEO datasets. HelixFold's code is available on GitHub for free download: https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein/forecast.