Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees

• URL: http://arxiv.org/abs/2206.01299v1
• Date: Thu, 2 Jun 2022 20:49:12 GMT
• Title: Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees
• Authors: Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Re, Ce Zhang
• Abstract summary: We show that AC-SGD can be combined with state-of-the-art gradient compression algorithms to enable "end-to-end compression" AC-SGD provides up to 4.3X end-to-end speed-up in slower networks, without sacrificing model quality.
• Score: 33.38465345409054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Communication compression is a crucial technique for modern distributed learning systems to alleviate their communication bottlenecks over slower networks. Despite recent intensive studies of gradient compression for data parallel-style training, compressing the activations for models trained with pipeline parallelism is still an open problem. In this paper, we propose AC-SGD, a novel activation compression algorithm for communication-efficient pipeline parallelism training over slow networks. Different from previous efforts in activation compression, instead of compressing activation values directly, AC-SGD compresses the changes of the activations. This allows us to show, to the best of our knowledge for the first time, that one can still achieve $O(1/\sqrt{T})$ convergence rate for non-convex objectives under activation compression, without making assumptions on gradient unbiasedness that do not hold for deep learning models with non-linear activation functions.We then show that AC-SGD can be optimized and implemented efficiently, without additional end-to-end runtime overhead.We evaluated AC-SGD to fine-tune language models with up to 1.5 billion parameters, compressing activations to 2-4 bits.AC-SGD provides up to 4.3X end-to-end speed-up in slower networks, without sacrificing model quality. Moreover, we also show that AC-SGD can be combined with state-of-the-art gradient compression algorithms to enable "end-to-end communication compression: All communications between machines, including model gradients, forward activations, and backward gradients are compressed into lower precision.This provides up to 4.9X end-to-end speed-up, without sacrificing model quality.

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