Explain the concept of model parallelism and how it facilitates scaling Transformers.

Model parallelism is a technique used to train very large neural networks, such as Transformers, that are too large to fit on a single device's memory. It involves splitting the model across multiple devices (e.g., GPUs or TPUs), with each device responsible for storing and processing a portion of the model. This allows the training of models with a much larger number of parameters than would be possible with a single device. In the context of Transformers, model parallelism can be implemented in several ways. One approach is to split the layers of the Transformer across....