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Efficient Adaptive Transformer: An Empirical Study and Reproducible Framework

Jan Miller

TL;DR

Efficient Adaptive Transformer (EAT) presents a unified, input-adaptive framework that combines progressive token pruning, sparse attention, and dynamic early exiting to reduce inference latency on easy inputs while preserving performance on harder ones. The authors provide a fully open, reproducible benchmarking pipeline across SST-2, QQP, and MNLI, including ablations and accuracy–latency frontiers that bridge BERT-base and DistilBERT. Theoretical analysis demonstrates a shift from quadratic to near-linear compute with respect to sequence length under pruning and early exits, contingent on retention and exit probabilities. Empirical results show a tunable frontier controlled by exit thresholds, with calibration and reliability analyses to stabilize performance across seeds. The work contributes a practical framework for adaptive computation in NLP and offers insights for latency-sensitive deployments, including cybersecurity pipelines, while acknowledging limitations in shallow architectures and the need for deeper model studies.

Abstract

The Efficient Adaptive Transformer (EAT) framework unifies three adaptive efficiency techniques - progressive token pruning, sparse attention, and dynamic early exiting - into a single, reproducible architecture for input-adaptive inference. EAT provides an open-source benchmarking pipeline that automates data processing, timing, and ablation across GLUE tasks (SST-2, QQP, MNLI). Although this empirical study finds that combining these mechanisms can increase latency in shallow six-layer models, it demonstrates that EAT achieves slightly higher accuracy than the optimized DistilBERT baseline on SST-2, illustrating the potential of dynamic computation for latency-sensitive NLP. The main contribution is the open, end-to-end reproducible framework - complete with scripts, CSV logging, and analysis utilities - intended to serve as a community tool for further research on adaptive transformers.

Efficient Adaptive Transformer: An Empirical Study and Reproducible Framework

TL;DR

Efficient Adaptive Transformer (EAT) presents a unified, input-adaptive framework that combines progressive token pruning, sparse attention, and dynamic early exiting to reduce inference latency on easy inputs while preserving performance on harder ones. The authors provide a fully open, reproducible benchmarking pipeline across SST-2, QQP, and MNLI, including ablations and accuracy–latency frontiers that bridge BERT-base and DistilBERT. Theoretical analysis demonstrates a shift from quadratic to near-linear compute with respect to sequence length under pruning and early exits, contingent on retention and exit probabilities. Empirical results show a tunable frontier controlled by exit thresholds, with calibration and reliability analyses to stabilize performance across seeds. The work contributes a practical framework for adaptive computation in NLP and offers insights for latency-sensitive deployments, including cybersecurity pipelines, while acknowledging limitations in shallow architectures and the need for deeper model studies.

Abstract

The Efficient Adaptive Transformer (EAT) framework unifies three adaptive efficiency techniques - progressive token pruning, sparse attention, and dynamic early exiting - into a single, reproducible architecture for input-adaptive inference. EAT provides an open-source benchmarking pipeline that automates data processing, timing, and ablation across GLUE tasks (SST-2, QQP, MNLI). Although this empirical study finds that combining these mechanisms can increase latency in shallow six-layer models, it demonstrates that EAT achieves slightly higher accuracy than the optimized DistilBERT baseline on SST-2, illustrating the potential of dynamic computation for latency-sensitive NLP. The main contribution is the open, end-to-end reproducible framework - complete with scripts, CSV logging, and analysis utilities - intended to serve as a community tool for further research on adaptive transformers.
Paper Structure (33 sections, 1 theorem, 6 equations, 6 figures, 4 tables, 2 algorithms)

This paper contains 33 sections, 1 theorem, 6 equations, 6 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Contributions.
  3. Background
  4. Transformer encoder.
  5. Model compression.
  6. Token pruning.
  7. Sparse attention.
  8. Early exits.
  9. Method: Efficient Adaptive Transformer
  10. Progressive token pruning
  11. Sparse attention
  12. Early exits and confidence
  13. Training Objectives and Schedule
  14. Schedule.
  15. Theoretical Considerations
  16. ...and 18 more sections

Key Result

Proposition 1

Assume (i) $\mathbb{E}[T_\ell] = r_\ell T$ with $r_\ell\in(0,1]$ non-increasing (progressive pruning), (ii) $\bar{r}=\frac{1}{L}\sum_\ell r_\ell \ll 1$, (iii) $\bar{p}=\frac{1}{L}\sum_\ell \Pr(L'\ge \ell) < 1$ (early exits), and (iv) fixed $k\ll T$. Then for sufficiently large $T$, Thus EAT replaces quadratic dependence on $T$ with linear dependence on $T$ (up to constants), scaled by $\bar{r}$ a

Figures (6)

  • Figure 1: EAT overview: prune low-importance tokens across depth; use sparse attention per layer; early exit if prediction is confident/stable.
  • Figure 2: Token pruning progression in EAT: scheduled vs. observed retention.
  • Figure 3: Ablation on SST-2: normalized compute and accuracy deltas across variants, generated directly from logged results.
  • Figure 4: Accuracy--latency frontiers generated by plot_frontiers.py. EAT (orange, labeled by $\tau$) bridges BERT (black) and DistilBERT (blue), enabling task-specific operating points between static and dynamic inference.
  • Figure 5: Final token retention as a function of input sequence length.
  • ...and 1 more figures

Theorems & Definitions (1)

  • Proposition 1