# Pipeline Parallelism¶

Pipeline parallelism was original introduced in the Gpipe paper and is an efficient technique to train large models on multiple GPUs.

Warning

Pipeline Parallelism is experimental and subject to change.

## Model Parallelism using multiple GPUs¶

Typically for large models which don’t fit on a single GPU, model parallelism is employed where certain parts of the model are placed on different GPUs. Although, if this is done naively for sequential models, the training process suffers from GPU under utilization since only one GPU is active at one time as shown in the figure below:

The figure represents a model with 4 layers placed on 4 different GPUs (vertical axis). The horizontal axis represents training this model through time demonstrating that only 1 GPU is utilized at a time (image source).

## Pipelined Execution¶

To alleviate this problem, pipeline parallelism splits the input minibatch into multiple microbatches and pipelines the execution of these microbatches across multiple GPUs. This is outlined in the figure below:

The figure represents a model with 4 layers placed on 4 different GPUs (vertical axis). The horizontal axis represents training this model through time demonstrating that the GPUs are utilized much more efficiently. However, there still exists a bubble (as demonstrated in the figure) where certain GPUs are not utilized. (image source).

## Pipe APIs in PyTorch¶

### Skip connections¶

Certain models like ResNeXt are not completely sequential and have skip connections between layers. Naively implementing as part of pipeline parallelism would imply that we need to copy outputs for certain layers through multiple GPUs till we eventually reach the GPU where the layer for the skip connection resides. To avoid this copy overhead, we provide APIs below to stash and pop Tensors in different layers of the model.

## Tutorials¶

The following tutorials give a good overview of how to use the Pipe API to train your models with the rest of the components that PyTorch provides:

## Acknowledgements¶

The implementation for pipeline parallelism is based on fairscale’s pipe implementation and torchgpipe. We would like to thank both teams for their contributions and guidance towards bringing pipeline parallelism into PyTorch.