Up to 5x less communication and 3.4x faster training through 1-bit Adam

Adam is an effective and probably the most well-utilized optimizer for training many large-scale deep learning models. However, Adam is generally not compatible with communication-efficient optimization algorithms, and therefore the communication cost could become a bottleneck while scaling across distributed devices. We introduce a new algorithm - 1-bit Adam - and its efficient implementation in DeepSpeed. 1-bit Adam offers the same convergence as Adam, incurs up to 5x less communication that enables up to 3.5x higher throughput for BERT-Large pretraining and up to 2.7x higher throughput for SQuAD fine-tuning on bandwidth-limited clusters.

• Brief overview, see our [press release]({{ site.press_release_v3 }}).
• Detailed technology deep dive, see our blog post.
• Tutorial on how to reproduce our results, see our 1-bit Adam tutorial.
• The source code for 1-bit Adam can be found in the DeepSpeed repo. The implementation of 1-bit Adam is in onebit_adam.py and CUDA-Aware communication for 1-bit Adam is in custom_collectives.py. Example codes to try this feature can be found in the DeepSpeedExamples repo as shown in the tutorial.