Research from ETH Zurich and Microsoft introduces SliceGPT, a post-training sparsification scheme for large language models (LLMs). It reduces the embedding dimension, leading to faster inference without extra code optimization. The method utilizes computational invariance in transformer networks and has been shown to outperform SparseGPT, offering significant speedups across various models and tasks.
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Efficient Compression of Large Language Models through Sparsification
Large language models (LLMs) like GPT-4 require substantial computational power and memory, posing challenges for their efficient deployment. While sparsification methods have been developed to mitigate these resource demands, they often introduce new complexities. For example, these techniques may require extra data structures to support the sparse representations, complicating the system architecture. The potential speedups from sparsification are only partially realized due to limitations in current hardware architectures, which are typically optimized for dense computations.
Compression Methods and Breakthrough
LLM compression methods include sparsification, low-rank approximation, and structured pruning. Researchers at ETH Zurich and Microsoft Research have proposed SliceGPT, a post-training sparsification scheme that reduces the embedding dimension of the network by replacing each weight matrix with a smaller dense matrix. This breakthrough method efficiently cuts down up to 25% of model parameters, including embeddings, while preserving high task performance. It enables the models to run on fewer GPUs and achieve faster inference times without additional code optimization.
Research Approach and Validation
The research approach focuses on RMSNorm operations, which maintain transformation invariance, allowing for the application of orthogonal transformations without altering the model’s function. Networks with LayerNorm can be converted to RMSNorm by integrating LayerNorm’s linear components into adjacent blocks. Principal Component Analysis (PCA) is pivotal in this process and is used to identify and project signals onto their principal components at each layer. Minor components are then sliced off, reducing the network size without compromising performance. This technique, validated through experiments, has been shown to outperform SparseGPT, offering significant speedups across various models and tasks.
Practical Applications and Future Opportunities
SliceGPT demonstrates a breakthrough in compressing LLMs like LLAMA-2 70B, OPT 66B, and Phi-2, offering significant speedups across various models and tasks. It allows for structured pruning of LLMs, reducing the cost of inference and maintaining better performance than SparseGPT. Opportunities for improvement include exploring combined methods with SparseGPT, improving Q computation, and using complementary methods like quantization and structural pruning. Observing computational invariance in SliceGPT can contribute to future research in improving the efficiency of deep learning models and inspire new theoretical insights.
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