How to Implement a Zero Redundancy Optimizer ZeRO for large model training

Question

Can i know How to Implement a Zero Redundancy Optimizer (ZeRO) for large model training.

Answer 1

You can implement a Zero Redundancy Optimizer (ZeRO) for large model training by partitioning optimizer states across data-parallel processes to minimize memory use.

Here is the code snippet below:

In the above code we are using the following key points:

• ZeroRedundancyOptimizer from PyTorch’s distributed library.

• DDP (DistributedDataParallel) for synchronized training.

• Efficient memory usage by sharding optimizer states across GPUs.

Hence, ZeRO allows scaling of massive models efficiently by optimizing memory and computational distribution across GPUs.