  State-of-the-art language models are becoming increasingly large in an effort
to achieve the highest performance on large corpora of available textual data.
However, the sheer size of the Transformer architectures makes it difficult to
deploy models within computational, environmental or device-specific
constraints. We explore data-driven compression of existing pretrained models
as an alternative to training smaller models from scratch. To do so, we scale
Kronecker-factored curvature approximations of the target loss landscape to
large language models. In doing so, we can compute both the dynamic allocation
of structures that can be removed as well as updates of remaining weights that
account for the removal. We provide a general framework for unstructured,
semi-structured and structured pruning and improve upon weight updates to
capture more correlations between weights, while remaining computationally
efficient. Experimentally, our method can prune rows and columns from a range
of OPT models and Llamav2-7B by 20%-30%, with a negligible loss in performance,
and achieve state-of-the-art results in unstructured and semi-structured
pruning of large language models.

Machine Learning;Computation and Language