How can pipeline parallelism be implemented to train larger models across multiple machines

Question

Can you explain, using code, how parallelism can be implemented to train large models across multiple machines?

Answer 1

Pipeline parallelism can be implemented by splitting a large model into sequential layers (stages) across multiple devices (like GPUs or machines). Each device only processes a subset of the model's layers, and data is passed sequentially through each stage, similar to an assembly line. 

This allows the training of larger models by distributing the model’s memory load across multiple machines while maintaining a continuous data flow.

You can refer to the code implementation in PyTorch using (torch.distributed.pipeline.sync.Pipe):

The code above splits a model into two stages across two devices, allowing training with pipeline parallelism by passing data sequentially through each device's stages. Hence, it supports large models across multiple machines.