Dtensor Distributed Training

Splitting AI training across GPUs can produce plausible outputs while calculating the wrong updates to the model. Runway illustrates how this happens when software confuses duplicated data with partial results that must be added together. PyTorch’s DTensor records whether each array is duplicated, split or awaiting combination, then uses that information to coordinate the calculations. The engineering account also describes a cost: checking these arrangements and moving data between them can slow training. Runway’s approach combines DTensor’s bookkeeping with carefully optimized operations in the busiest parts of its training system.