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Low Rank Decomposition of matrix - splitting a large matrix into a product oftwo smaller matrix offers a means for compression that reduces the parametersof a model without sparsification, and hence delivering more speedup on modernhardware. Moreover, unlike quantization, the compressed linear layers remainfully differentiable and all the parameters trainable, while being able toleverage the existing highly efficient kernels over floating point matrices. Westudy the potential to compress Large Language Models (LLMs) for monolingualCode generation via Low Rank Decomposition (LoRD) and observe that ranks forthe linear layers in these models can be reduced by upto 39.58% with less than1% increase in perplexity. We then use Low Rank Decomposition (LoRD) tocompress StarCoder 16B to 13.2B parameter with no drop and to 12.3B withminimal drop in HumanEval Pass@1 score, in less than 10 minutes on a singleA100. The compressed models speeds up inference by up to 22.35% with just asingle line of change in code over huggingface's implementation with pytorchbackend. Low Rank Decomposition (LoRD) models remain compatible with state ofthe art near-lossless quantization method such as SpQR, which allows leveragingfurther compression gains of quantization. Lastly, QLoRA over Low RankDecomposition (LoRD) model further reduces memory requirements by as much as21.2% over vanilla QLoRA while offering similar gains from parameter efficientfine tuning. Our work shows Low Rank Decomposition (LoRD) as a promising newparadigm for LLM compression.

LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression