  Deep learning using large models have achieved great success in a wide range
of domains. However, training these models on billions of parameters is very
challenging in terms of the training speed, memory cost, and communication
efficiency, especially under the privacy-preserving regime with differential
privacy (DP). On the one hand, DP optimization has comparable efficiency to the
standard non-private optimization on a single GPU, but on multiple GPUs,
existing DP distributed learning (such as pipeline parallel) has suffered from
significantly worse efficiency. On the other hand, the Zero Redundancy
Optimizer (ZeRO) is a state-of-the-art solution to the standard distributed
learning, exhibiting excellent training efficiency on large models, but to work
compatibly with DP is technically complicated. In this work, we develop a new
systematic solution, DP-ZeRO, (I) to scale up the trainable DP model size, e.g.
to GPT-100B, (II) to obtain the same computation and communication efficiency
as the standard ZeRO, and (III) to enable mixed-precision DP training. Our
DP-ZeRO, like the standard ZeRO, has the potential to train models with
arbitrary size and is evaluated on the world's largest DP models in terms of
the number of trainable parameters.
