Large language models (LLMs) have revolutionized various applications across industries by providing advanced natural language processing capabilities. These models’ ability to generate, understand, and interpret human language has opened new avenues for technological advancements. However, their significant computational, memory, and energy demands hinder LLMs’ deployment and operational efficiency, especially during the inference phase. The challenge stems from the extensive number of parameters within these models, necessitating considerable data storage and manipulation resources.
Researchers have turned to quantization to tackle these issues. This process reduces the precision of the model’s parameters to achieve lower memory consumption and faster computation times. However, a persistent challenge in this process is the presence of outliers within the data. These outliers can drastically affect the model’s accuracy when significantly reduced precision.Â
QuaRot is a breakthrough approach by researchers from ETH Zurich, EPFL, Microsoft Research, IST Austria, and NeuralMagic. It offers a promising solution by applying a novel quantization scheme based on rotations to mitigate the effects of outliers. It’s an innovative technique that employs randomized Hadamard transformations and leverages computational invariance, a principle ensuring that these transformations do not alter the final output of the model. This method allows for a comprehensive 4-bit quantization encompassing all model components, including weights, activations, and the key-value (KV) cache. By doing so, QuaRot significantly diminishes the model’s computational and memory requirements.
The efficacy of QuaRot is underscored by its performance on the LLAMA 2-70B model. The method achieved remarkable outcomes, demonstrating that a quantized model could retain up to 99% of its zero-shot performance capabilities post-quantization. The approach enabled up to 2.16 times speedup during the prefill phase of inference, a stage traditionally known for being compute-bound. It also facilitated a substantial reduction in memory usage, achieving up to 3.39 times savings during the decoding stage, a phase typically memory-bound. These improvements are pivotal, as they reduce operational costs and energy consumption associated with running such advanced models.
By enabling end-to-end 4-bit inference without significant performance loss, the method allows for the broader adoption and deployment of LLMs across various devices, including those with limited computational resources. This access to advanced language models holds the potential to drive innovation and expand the applicability of LLMs in sectors where computational resources are a limiting factor.
In conclusion, QuaRot marks a significant leap forward in optimizing large language models. QuaRot successfully addresses the longstanding challenge of efficiently quantizing LLMs while maintaining high accuracy through its innovative use of randomized Hadamard transformations and computational invariance. The method’s ability to significantly reduce memory usage and computational demands is evidenced by its LLAMA 2-70B model performance.
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The post Researchers from ETH Zurich, EPFL, and Microsoft Introduce QuaRot: A Machine Learning Method that Enables 4-bit Inference of LLMs by Removing the Outlier Features appeared first on MarkTechPost.
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