  Training large neural network (NN) models requires extensive memory
resources, and Activation Compressed Training (ACT) is a promising approach to
reduce training memory footprint. This paper presents GACT, an ACT framework to
support a broad range of machine learning tasks for generic NN architectures
with limited domain knowledge. By analyzing a linearized version of ACT's
approximate gradient, we prove the convergence of GACT without prior knowledge
on operator type or model architecture. To make training stable, we propose an
algorithm that decides the compression ratio for each tensor by estimating its
impact on the gradient at run time. We implement GACT as a PyTorch library that
readily applies to any NN architecture. GACT reduces the activation memory for
convolutional NNs, transformers, and graph NNs by up to 8.1x, enabling training
with a 4.2x to 24.7x larger batch size, with negligible accuracy loss. We
implement GACT as a PyTorch library at
https://github.com/LiuXiaoxuanPKU/GACT-ICML.
