Rate-Distortion Optimisation Achieves Improved Accuracy with Transformer Inference

Researchers are tackling the considerable computational demands of running large transformer models, which excel at numerous tasks but require substantial resources during inference. Anderson de Andrade, Alon Harell, and Ivan V. Bajić, all from Simon Fraser University, present a novel rate-distortion framework for lossy compression, learning efficient encodings that balance data compression with maintained accuracy. This work is significant because it not only achieves substantial savings in computational cost but also improves accuracy over existing compression methods on language benchmarks. By extending information-theoretic concepts, the team characterise rate-distortion performance and establish bounds, offering a more explainable and unified understanding of representation coding in these powerful models.

Transformer inference via learned rate-distortion compression improves efficiency

Scientists have developed a new rate-distortion framework for lossy compression, significantly improving the efficiency of transformer models during inference. This research addresses the substantial computational and memory demands associated with running these powerful models, proposing a method to partition processing across multiple devices. The team achieved this by learning compact encodings that explicitly balance bitrate and accuracy, effectively compressing the intermediate representations generated during inference. Experiments conducted on language benchmarks demonstrate that this novel codec achieves considerable savings, and in some instances, even surpasses the performance of more complex baseline compression methods.

The study meticulously characterizes and analyses the rate-distortion performance of transformers, offering a unified perspective for understanding representation coding. Researchers extended established information-theoretic concepts to define a ‘V-entropy gap’ , the difference between rate and entropy, and derived rigorous bounds for this gap. Furthermore, they developed probably approximately correct (PAC)-style bounds to estimate this gap, providing a theoretical foundation for their compression approach. Empirical validation across diverse architectures and tasks confirms that the observed rates are indeed governed by these derived bounds, enhancing the explainability of the proposed formulation.

This work establishes that the rate-distortion performance of transformers is not simply correlated with the entropy of the coded representation, a common expectation in learnable coding. Instead, the team’s empirical results reveal a decrease in performance as processing progresses, a behaviour explained through the lens of ‘usable information’ theory. They define and provide bounds for the V-entropy gap, and a generalization error bound linked to the Rademacher complexity of the target representation and the Lipschitz constant of the entropy model. These bounds were empirically estimated, validating the observed phenomena and providing insights into the limitations of entropy model complexity.

The research demonstrates that the V-entropy gap, and its associated generalization error, can increase with the complexity of the target representation, unlike its entropy which typically decreases in deeper layers. This finding highlights a crucial trade-off between model complexity and compression efficiency. Finally, the proposed method has significant implications for distributed inference, enabling tasks like deploying a transformer module on a mobile device, compressing its intermediate representation, and transmitting it to a cloud server for completion, paving the way for more efficient and scalable machine learning applications.

Transformer Compression via Rate-Distortion Balancing optimizes performance

Researchers developed a novel rate-distortion framework to compress intermediate representations within transformer architectures, addressing the computational demands of inference. The study pioneered a lossy compression codec that explicitly balances bitrate against accuracy, enabling efficient partitioning of inference across multiple devices. Experiments utilising language benchmarks demonstrated that this codec achieves substantial savings while, in some instances, improving accuracy compared to more complex baseline methods. This performance gain stems from the codec’s ability to learn compact encodings tailored to the specific trade-off between compression rate and acceptable distortion.

To characterise and analyse the rate-distortion performance of transformers, the team formulated a unified lens for understanding representation coding. This involved extending information-theoretic concepts to define the ‘V-entropy gap’, representing the difference between rate and entropy, and subsequently deriving bounds for this gap. Scientists then developed probably approximately correct (PAC)-style bounds to estimate the V-entropy gap, providing a rigorous mathematical foundation for their compression approach. Empirical validation across diverse architectures and tasks confirmed that observed rates align with these theoretically derived bounds, enhancing the explainability of the formulation.

The research employed an auto-regressive entropy model, based on transformers, to generate a secondary representation termed a ‘hyper-prior’, which was also coded. Benchmarking against other entropy models focused on language models, revealing that the proposed method’s rate-distortion performance often surpasses existing techniques. The team harnessed the data processing inequality to understand that the entropy of output representations typically decreases with each subsequent module. Interestingly, experiments showed that rate did not consistently correlate with the entropy of the coded representation, instead decreasing as input processing advanced.

To explain this counterintuitive behaviour, the study integrated the theory of usable information, accounting for the modelling power and computational constraints of the entropy model. Researchers defined and derived bounds for the V-entropy gap, alongside a generalization error bound expressed in terms of Rademacher complexity and the Lipschitz constant of the entropy model. Empirical estimation of these bounds substantiated observations, demonstrating that rate often exceeds entropy due to entropy model complexity limitations and that increased model complexity does not always guarantee improved performance, mirroring the bias-variance trade-off.

Rate-distortion codec boosts language model efficiency by reducing

Scientists have developed a novel rate-distortion framework for lossy compression, achieving substantial savings in computational resources while, in some instances, improving accuracy on language benchmarks. Experiments demonstrate that the proposed codec outperforms more complex baseline methods in efficiently partitioning inference processes across multiple devices. The research introduces a principled approach to trading off bitrate against accuracy, learning compact encodings for intermediate representations. Measurements confirm a significant reduction in computational demands during inference, a critical advancement for large language models.

The team characterized and analyzed the rate-distortion performance, offering a unified understanding of representation coding. Results show that the formulation extends information-theoretic concepts, defining the gap between rate and entropy and deriving associated bounds. Scientists further developed probably approximately correct (PAC)-style bounds to estimate this gap, empirically demonstrating that rates are driven by these bounds across different tasks. Data shows that for various tasks, the observed rates align with the theoretical bounds, enhancing the explainability of the proposed formulation.

Experiments revealed an unexpected behaviour in transformer-based models: rate-distortion performance decreases as input is further processed. This contrasts with expectations that rate should correlate with the entropy of the coded representation. Researchers explain this through the theory of usable information, accounting for modeling power and computational constraints of the entropy model. They defined and provided bounds for the V-entropy gap, the difference between rate and entropy, and a generalization error bound based on Rademacher complexity and the Lipschitz constant of the entropy model.

Measurements confirm that the bounds on the V-entropy gap explain why the rate often exceeds the entropy of the target representation due to limitations in entropy model complexity. Moreover, the bounds of its generalization error explain why increasing the complexity of the entropy model does not always improve performance, mirroring the bias-variance trade-off. Finally, the study shows that the V-entropy gap and its generalization error can increase with the complexity of the target representation, unlike its entropy, which can increase in deeper layers. The work establishes a lower bound on the achievable entropy, formalized by the concept of V-entropy, which can be estimated with guarantees if the complexity of the model is bounded.

V-entropy gap bounds explain model compression techniques

Scientists have developed a rate-distortion framework for lossy compression, learning compact encodings that balance bitrate and accuracy during inference. This new codec achieves substantial savings and, in some instances, improved accuracy compared to more complex baseline methods on language benchmarks. The research extends information-theoretic concepts to define a ‘V-entropy gap’ , the difference between rate and entropy, and establishes bounds for this gap using probably approximately correct (PAC) style bounds. Empirical results demonstrate that the rates of different models are consistent with these theoretical bounds, enhancing the explainability of the formulation.

The proposed entropy model outperforms complex baselines by at least 10.7%, and task performance in early layers is up to 17.8% better than unconstrained models. Researchers found that for transformer models, rate-distortion performance decreases with depth, correlating with increases in Rademacher complexity and the covariance determinant of the target representation. The authors acknowledge that increasing the complexity of the entropy model to reduce the V-entropy gap can be offset by a rise in generalization error, potentially explaining the superior performance of their simpler method. Future work will focus on developing solutions to address this trade-off and further refine the compression techniques.