The LLM Surgeon
This addresses the deployment challenges of large language models for users with limited computational resources, though it is incremental as it builds on existing pruning techniques.
The paper tackles the problem of deploying large language models under computational constraints by proposing a data-driven compression method that prunes 20%-30% of rows and columns from models like OPT and Llamav2-7B with negligible performance loss, achieving state-of-the-art results in unstructured and semi-structured pruning.
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.