#59 Zhuohan Li - Alpa: Automated Model-Parallel Deep Learning

Zhuohan Li - Alpa: Automated Model-Parallel Deep Learning

Alpa (https://github.com/alpa-projects/alpa) automates model-parallel training of large deep learning models by generating execution plans that unify data, operator, and pipeline parallelism. Alpa distributes the training of large deep learning models by viewing parallelisms as two hierarchical levels: inter-operator and intra-operator parallelisms. Based on it, Alpa constructs a new hierarchical space for massive model-parallel execution plans. Alpa designs a number of compilation passes to automatically derive the optimal parallel execution plan in each independent parallelism level and implements an efficient runtime to orchestrate the two-level parallel execution on distributed compute devices. Alpa generates parallelization plans that match or outperform hand-tuned model-parallel training systems even on models they are designed for. Unlike specialized systems, Alpa also generalizes to models with heterogeneous architectures and models without manually-designed plans.