DDP communication hook is a generic interface to control how to communicate gradients across workers by overriding the vanilla allreduce in DistributedDataParallel. A few built-in communication hooks are provided, and users can easily apply any of these hooks to optimize communication. Besides, the hook interface can also support user-defined communication strategies for more advanced use cases.
Warning
DDP communication hook is experimental and subject to change.
Warning
DDP communication hooks can only support single process single device mode on NCCL backend.
To use a communication hook, the user just needs to let the DDP model register the hook before the training loop as below.
Default communication hooks are simple stateless hooks, so the input state
in register_comm_hook is either a process group or None.
.. automodule:: torch.distributed.algorithms.ddp_comm_hooks.default_hooks
:members:
PowerSGD (Vogels et al., NeurIPS 2019) is a gradient compression algorithm, which can provide very high compression rates and accelerate bandwidth-bound distributed training. This algorithm needs to maintain both some hyperparameters and the internal state. Therefore, PowerSGD communication hook is a stateful hook, and the user needs to provide a state object defined as below.
.. currentmodule:: torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook
.. autoclass:: PowerSGDState
Warning
PowerSGD typically requires extra memory of the same size as the model's gradients to enable error feedback, which can compensate for biased compressed communication and improve accuracy.
Warning
The current implementation may cause gradient overflow for FP16 input.
.. autofunction:: powerSGD_hook
.. autofunction:: batched_powerSGD_hook
Many thanks to PowerSGD paper author Thijs Vogels for the code review on PowerSGD communication hook, as well as the comparison experiments, which show that the performance of PowerSGD communication hook is on par with the implementation in the original paper.