LATTE: Low-Precision Approximate Attention with Head-wise Trainable Threshold for Efficient Transformer

With the rise of Transformer models in NLP and CV domain, Multi-Head Attention has been proven to be a game-changer. However, its expensive computation poses challenges to the model throughput and efficiency, especially for the long sequence tasks. Exploiting the sparsity in attention has been proven to be an effective way to reduce computation. Nevertheless, prior works do not consider the various distributions among different heads and lack a systematic method to determine the threshold. To address these challenges, we propose Low-Precision Approximate Attention with Head-wise Trainable Threshold for Efficient Transformer (LATTE). LATTE employs a headwise threshold-based filter with the low-precision dot product and computation reuse mechanism to reduce the computation of MHA. Moreover, the trainable threshold is introduced to provide a systematic method for adjusting the thresholds and enable end-to-end optimization. Experimental results indicate LATTE can smoothly adapt to both NLP and CV tasks, offering significant computation savings with only a minor compromise in performance. Also, the trainable threshold is shown to be essential for the leverage between the performance and the computation. As a result, LATTE filters up to 85.16% keys with only a 0.87% accuracy drop in the CV task and 89.91% keys with a 0.86 perplexity increase in the NLP task.

TSPTQ-ViT: Two-scaled post-training quantization for vision transformer

Vision transformers (ViTs) have achieved remarkable performance in various computer vision tasks. However, intensive memory and computation requirements impede ViTs from running on resource-constrained edge devices. Due to the non-normally distributed values after Softmax and GeLU, post-training quantization on ViTs results in severe accuracy degradation. Moreover, conventional methods fail to address the high channel-wise variance in LayerNorm. To reduce the quantization loss and improve classification accuracy, we propose a two-scaled post-training quantization scheme for vision transformer (TSPTQ-ViT). We design the value-aware two-scaled scaling factors (V-2SF) specialized for post-Softmax and post-GeLU values, which leverage the bit sparsity in non-normal distribution to save bit-widths. In addition, the outlier-aware two-scaled scaling factors (O-2SF) are introduced to LayerNorm, alleviating the dominant impacts from outlier values. Our experimental results show that the proposed methods reach near-lossless accuracy drops (<0.5%) on the ImageNet classification task under 8-bit fully quantized ViTs.