The Unreasonable Ineffectiveness of the Deeper Layers

We empirically study a simple layer-pruning strategy for popular families of openweight pretrained LLMs, finding minimal degradation of performance on different question-answering benchmarks until after a large fraction (up to half) of the layers are removed. To prune these models, we identify the optimal block of layers to prune by considering similarity across layers; then, to “heal” the damage, we perform a small amount of finetuning. In particular, we use parameter-efficient finetuning (PEFT) methods, specifically quantization and Low Rank Adapters (QLoRA), such that each of our experiments can be performed on a single A100 GPU. From a practical perspective, these results suggest that layer pruning methods can complement other PEFT strategies to further reduce computational resources of finetuning on the one hand, and can improve the memory and latency of inference on the other hand. From a scientific perspective, the robustness of these LLMs to the deletion of layers implies either that current pretraining methods are not properly leveraging the parameters in the deeper layers of the network or that the shallow layers play a critical role in storing knowledge.

Introduction. Over the last few years, large language models (LLMs) have evolved from mere research artifacts [1] into useful products [2]. To a large extent this evolution can be attributed to a dramatic increase in scale of the resources used for training [3, 4]. Since these models will likely see most of their total lifetime FLOPs in inference mode after training completes, the pretraining of LLMs requires not only considerations for efficient, i.e. compute-optimal, training [5, 6], but also requires inference awareness [7, 8]. What about models that have already been trained? Beyond the training considerations indicated by neural scaling laws, there are also numerous post-training techniques for reducing the cost and time of finetuning and then inferencing LLMs.

Discussion / Conclusion. Beginning with the release of the open-weight LLaMA family [84], the open-source machine-learning community has rallied around the philosophy of making LLMs accessible to everyone. This has engendered many innovations around efficiency, such as LoRA [13] and quantization (with LoRA) [19], allowing large (near-)state-of-the-art 70B models to be finetuned on only single 80GB A100 GPUs. In conjunction with these other tools, our work enables further efficiency gains via a simpleto-implement layer-pruning technique. In particular, the released version of Llama-2-70B spans 140 GB of memory and consumes approximately 3 × 1010 FLOPs per token.