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.
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).
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).
.. autoclass:: torch.distributed.pipeline.sync.Pipe :members: forward
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.
.. autofunction:: torch.distributed.pipeline.sync.skip.skippable.skippable
.. autoclass:: torch.distributed.pipeline.sync.skip.skippable.stash
.. autoclass:: torch.distributed.pipeline.sync.skip.skippable.pop
.. autofunction:: torch.distributed.pipeline.sync.skip.skippable.verify_skippables
The following tutorials give a good overview of how to use the :class:`~torch.distributed.pipeline.sync.Pipe` API to train your models with the rest of the components that PyTorch provides:
- Training Transformer models using Pipeline Parallelism
- Training Transformer models using Distributed Data Parallel and Pipeline Parallelism
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.