Activation Outliers in Transformer Quantization: Reproduction, Statistical Analysis, and Deployment Tradeoffs

TL;DR

This paper investigates activation outliers in transformer quantization, reproduces known issues, analyzes their statistical properties, and evaluates mitigation strategies, emphasizing the importance of channel-aware approaches for maintaining accuracy.

Contribution

It provides a reproducible analysis of activation outliers in transformer PTQ, introduces statistical insights, and compares mitigation methods with deployment considerations.

Findings

Heavy-tailed activation distributions intensify with depth.

Mixed precision PTQ nearly restores baseline accuracy.

Channel-aware quantization improves robustness over scalar clipping.

Abstract

Post-training quantization (PTQ) of transformers is known to suffer from severe accuracy degradation due to structured activation outliers, as originally analyzed by Bondarenko et al. (EMNLP 2021) in work associated with Qualcomm AI Research. This paper provides a reproducible empirical reproduction and systems-level extension of that phenomenon in BERT-base fine-tuned on QNLI. When global W8A8 quantization is applied, validation accuracy drops sharply from 89.66% (FP32) to 54.33%, a decrease of 35.33 points. Statistical analysis of FP32 activations shows strongly heavy-tailed behavior that intensifies with model depth: kurtosis reaches 271 in the final layers and approximately 55% of activation energy is concentrated in the top 1% of channels. We evaluate several mitigation strategies. Mixed precision PTQ restores accuracy close to the FP32 baseline (89.42%). Per-embedding-group (PEG) quantization shows strong sensitivity to grouping structure, improving accuracy from 66.12% with three groups to 86.18% with four groups. In contrast, percentile-based calibration, even at thresholds between 99.0 and 99.99, fails to recover accuracy (about 50.54%), indicating that large activation channels encode structured signal rather than rare noise. Deployment profiling on an RTX 3050 GPU shows minimal differences in latency and memory usage across methods (median latency about 58-59 ms; VRAM usage about 484-486 MB), highlighting the importance of hardware-aware evaluation. Overall, the results show that PTQ failure in transformers is primarily driven by structured channel dominance amplified through residual connections. Effective mitigation therefore requires channel-aware precision allocation rather than scalar clipping alone.