BEExformer: A Fast Inferencing Transformer Architecture via Binarization with Multiple Early Exits (2412.05225v1)

Abstract: LLMs based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and processing requirements make deployment on devices with constrained resources extremely difficult. Among various efficiency considerations, model binarization and Early Exit (EE) are common effective solutions. However, binarization may lead to performance loss due to reduced precision affecting gradient estimation and parameter updates. Besides, the present early-exit mechanisms are still in the nascent stages of research. To ameliorate these issues, we propose Binarized Early Exit Transformer (BEExformer), the first-ever selective learning transformer architecture to combine early exit with binarization for textual inference. It improves the binarization process through a differentiable second-order approximation to the impulse function. This enables gradient computation concerning both the sign as well as the magnitude of the weights. In contrast to absolute threshold-based EE, the proposed EE mechanism hinges on fractional reduction in entropy among intermediate transformer blocks with soft-routing loss estimation. While binarization results in 18.44 times reduction in model size, early exit reduces the FLOPs during inference by 54.85% and even improves accuracy by 5.98% through resolving the "overthinking" problem inherent in deep networks. Moreover, the proposed BEExformer simplifies training by not requiring knowledge distillation from a full-precision LLM. Extensive evaluation on the GLUE dataset and comparison with the SOTA works showcase its pareto-optimal performance-efficiency trade-off.

• The paper introduces BEExformer, a transformer architecture that enhances inference speed and efficiency by combining binarization with a novel entropy-based early exit mechanism.
• Evaluation on the GLUE benchmark shows BEExformer achieves a 54.85% FLOPs reduction with less than a 6% accuracy drop compared to full-precision models.
• This approach enables deployment of powerful transformer models on resource-constrained devices, bridging the gap between state-of-the-art NLP and practical applications like edge computing.

Analysis of BEExformer: Enhancements in Transformer Efficiency through Binarization and Early Exits

This paper introduces BEExformer, a novel transformer architecture designed specifically for textual inference tasks. The haLLMark of this model lies in its ability to combine two distinct approaches to enhance efficiency: binarization and early exits (EE). This solution addresses the computational challenges faced by large transformer models, making them more applicable for deployment in resource-constrained environments.

Core Contributions

The BEExformer model provides several key contributions:

1. Binarization Technique: The paper utilizes a piecewise polynomial approximation to the impulse function for transformer models, framed through Binarization-Aware Training (BAT). This ensures that gradients reflect both the magnitude and the sign of the weights, a novel application of such techniques to transformers. This binarization reduces memory requirements significantly compared to full-precision models, which is advantageous for using edge devices.
2. Enhanced Early Exit Mechanism: Traditional EE mechanisms rely on absolute threshold values which can be suboptimal across varying inputs. BEExformer introduces a new EE criterion based on fractional reductions in entropy of the logits between subsequent transformer blocks. This allows the model to dynamically optimize computational efficiency by exiting early when sufficient confidence is achieved, addressing the "overthinking" problem where deeper networks may introduce unnecessary complexity to correct predictions.
3. Selective Learn-Forget Network Integration: Within each transformer block, the integration of a binarized version of the Selective Learn-Forget Network (SLFN) enhances inference precision by filtering out insignificant information, leading to a refined comprehension of inputs.

Evaluation and Results

The performance of BEExformer was tested against the GLUE benchmark’s varied data-set tasks, such as SST-2, CoLA, MRPC, and RTE. The results highlight its efficiency:

• BEExformer attained a 54.85% reduction in FLOPs, evidencing substantial computational savings.
• Despite these efficiency gains, BEExformer exhibited less than a 6% performance drop in accuracy when compared to full-precision baselines, indicating remarkable preservation of performance.

These outcomes demonstrate a Pareto-optimal trade-off between performance and resource efficiency, positioning BEExformer as a significant step towards practical, large-scale deployment of transformers in constrained environments.

Practical and Theoretical Implications

Practically, the BEExformer model strengthens the potential for deploying high-performing NLP models on devices where computational resources are limited, such as in edge computing scenarios. This could transform the utility of transformer models in domains like mobile applications, where such constraints are most prevalent.

Theoretically, the work enlarges the frontier of knowledge on transformer efficiency with its innovative approach in combining binarization and entropy-based early exits. It encourages future exploration into:

• Extending these methodologies to other types of generative models.
• Investigating further binarization techniques that could ameliorate the usual trade-offs between computation precision and performance.
• Exploring adaptations for tasks outside traditional inference, like generation, possibly transforming text generation models.

Conclusion and Future Directions

The BEExformer not only proposes a new paradigm for efficient transformer architectures but also paves the way for more adaptable model architectures through dynamic computation management. Future work may involve expanding BEExformer capabilities beyond inference, addressing generative tasks or predicting real-time device constraints—further amplifying its practicality and efficiency in various AI landscapes.