Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference (2312.10193v2)

Abstract: While transformer models have been highly successful, they are computationally inefficient. We observe that for each layer, the full width of the layer may be needed only for a small subset of tokens inside a batch and that the "effective" width needed to process a token can vary from layer to layer. Motivated by this observation, we introduce the Adaptive Computation Module (ACM), a generic module that dynamically adapts its computational load to match the estimated difficulty of the input on a per-token basis. An ACM consists of a sequence of learners that progressively refine the output of their preceding counterparts. An additional gating mechanism determines the optimal number of learners to execute for each token. We also propose a distillation technique to replace any pre-trained model with an "ACMized" variant. Our evaluation of transformer models in computer vision and speech recognition demonstrates that substituting layers with ACMs significantly reduces inference costs without degrading the downstream accuracy for a wide interval of user-defined budgets.

• The paper presents Adaptive Computation Modules (ACMs) that dynamically adjust per-token computation to enhance efficiency.
• ACMs use a gating mechanism with progressive learners to tailor computation, preserving model accuracy across varying budgets.
• Empirical evaluations on ImageNet-1k and Wav2Vec demonstrate a superior performance-efficiency trade-off compared to traditional methods.

Adaptive Computation Modules: Granular Conditional Computation for Efficient Inference

Adaptive computation has emerged as a pivotal concept in enhancing the efficiency of deep learning models, particularly in domains that demand low-latency or low-power consumption. Traditional transformer models, while powerful, often incur substantial computational costs, which are not always justified by the representational demands of all input tokens. The paper at hand introduces the Adaptive Computation Module (ACM), an innovative approach designed to dynamically tailor computational load on a per-token basis, addressing inefficiencies in transformer-based networks.

Overview of Adaptive Computation Modules

ACMs are built on the observation that the full computational capability of each layer in a transformer is not ubiquitously required for every input token. Specifically, ACMs consist of a series of "learners" that progressively refine output representations, with a gating mechanism determining the necessary number of learners for each token. This granular level of computation adaptation contrasts with existing techniques such as quantization or static sparsification, which apply global reductions and can degrade model accuracy.

The ACM methodology includes a distillation process wherein a pre-trained model is converted into an "ACMized" variant. This process is designed to retain the original model's accuracy across varying computational budgets and is inherently parallelizable, making it suitable for integration with existing neural architectures.

Experimental Evaluation and Results

The ACM approach was evaluated on well-established datasets across computer vision and speech recognition domains. Specifically, the authors tested ACMs on the ImageNet-1k dataset using Vision Transforms (ViTs) and on Wav2Vec networks for speech recognition. The results demonstrated that ACMs can significantly reduce inference costs without sacrificing downstream task accuracy, achieving a better performance-efficiency trade-off than existing methods like Mixture-of-Experts (MoE), Early Exiting, and Token Dropping.

For the ViT models in computer vision, ACM-based models achieved a Pareto frontier, offering superior performance across various computational budgets. In the speech recognition domain, ACMs outperformed MoE-based models consistently across metrics such as Word Error Rate (WER), confirming the efficacy of ACMs in bandwidth-intensive tasks.

Theoretical and Practical Implications

The introduction of ACMs presents several theoretical and practical implications. Theoretically, ACMs underscore the principle of conditional computation, not only in temporal domains but also spatially across different tokens. This aligns with broader trends in adaptive neural processing, suggesting further research could explore hybrid models combining ACMs with temporal adaptive techniques.

Practically, ACMs offer a pathway to reduce carbon emissions associated with deep learning by minimizing unnecessary computations, enhancing the sustainability of AI deployments. Additionally, the modularity of ACMs facilitates their incorporation into varied architectures, prompting potential developments in model agnostic plug-and-play strategies.

Future Prospects

While ACMs present a promising advancement in efficient inference, challenges remain. Future work may investigate the integration of ACMs with other efficiency-oriented strategies, such as network pruning or low-rank adaptations, to further reduce computational overhead. Additionally, custom implementations optimized for contemporary GPU architectures could unlock even greater accelerations.

In conclusion, Adaptive Computation Modules represent a meaningful stride towards more efficient AI models. By leveraging conditional computation at a granular level, ACMs align model complexity with input demands, setting a foundation for more resource-efficient and sustainable AI practices.