MARD1NO / powersgd

Practical low-rank gradient compression for distributed optimization:

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Practical Low-Rank Gradient Compression for Distributed Optimization


Abstract: We study gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well or fail to achieve the target test accuracy. We propose a new low-rank gradient compressor based on power iteration that can i) compress gradients rapidly, ii) efficiently aggregate the compressed gradients using all-reduce, and iii) achieve test performance on par with SGD. The proposed algorithm is the only method evaluated that achieves consistent wall-clock speedups when benchmarked against regular SGD with an optimized communication backend. We demonstrate reduced training times for convolutional networks as well as LSTMs on common datasets.

Reference implementation

This is a reference implementation for the PowerSGD algorithm.


pip install git+


+ from powersgd import PowerSGD, Config, optimizer_step

  model = torchvision.models.resnet50(pretrained=True)
  params = list(model.parameters())
  optimizer = torch.optim.SGD(params, lr=0.1)

+ powersgd = PowerSGD(params, config=Config(
+     rank=1,  # lower rank => more aggressive compression
+     min_compression_rate=10,  # don't compress gradients with less compression
+     num_iters_per_step=2,  #   # lower number => more aggressive compression
+     start_compressing_after_num_steps=0,
+ ))

  for each batch:
      loss = ...
-     optimizer.zero_grad()
-     optimizer.step()
+     optimizer_step(optimizer, powersgd)

PyTorch implementation

PyTorch features an implementation of PowerSGD as a communucation hook for DistributedDataParallel models. Because of the integration with DDP, the code is more involved than the code in this repository.

Research code

Research code for the experiments in the PowerSGD paper is located under paper-code.

Selected follow-up work

  • (Cho et al., 2019) concurrently developed an algorithm that is fundamentally very similar to PowerSGD.
  • (Ramesh et al., 2021 - DALL-E) share valuable recommendations in using PowerSGD for large-scale transformer training.
  • (Agarwal et al., 2020) share insights into adaptive compression with PowerSGD.
  • (Vogels et al., 2020) adapt PowerSGD to work in a decentralized setting (with sparse connectivity between workers.)
  • (Wang, 2021) introduces a variation to PowerSGD and describes his experience with PowerSGD on large language models.
  • (Please submit a PR if you want your work to be included here.)


If you use this code, please cite the following paper

  author = {Vogels, Thijs and Karimireddy, Sai Praneeth and Jaggi, Martin},
  title = "{{PowerSGD}: Practical Low-Rank Gradient Compression for Distributed Optimization}",
  booktitle = {NeurIPS 2019 - Advances in Neural Information Processing Systems},
  year = 2019,
  url = {}


Practical low-rank gradient compression for distributed optimization:

License:MIT License


Language:Python 96.0%Language:Shell 4.0%