ZeRO++: Extremely Efficient Collective Communication for Giant Model
  Training
Guanhua Wang Heyang Qin Sam Ade Jacobs Connor Holmes Samyam Rajbhandari Olatunji Ruwase Feng Yan Lei Yang Yuxiong He
Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large
language models on massive GPUs clusters due to its ease of use, efficiency,
and good scalability. However, when training on low-bandwidth clusters, or at
scale which forces batch size per GPU to be small, ZeRO's effective throughput
is limited because of high communication volume from gathering weights in
forward pass, backward pass, and averaging gradients. This paper introduces
three communication volume reduction techniques, which we collectively refer to
as ZeRO++, targeting each of the communication collectives in ZeRO. First is
block-quantization based all-gather. Second is data remapping that trades-off
communication for more memory. Third is a novel all-to-all based quantized
gradient averaging paradigm as replacement of reduce-scatter collective, which
preserves accuracy despite communicating low precision data. Collectively,
ZeRO++ reduces communication volume of ZeRO by 4x, enabling up to 2.16x better
throughput at 384 GPU scale.