HiRED: Attention-Guided Token Dropping for Efficient Inference of
  High-Resolution Vision-Language Models in Resource-Constrained Environments
Authors: name: Kazi Hasan Ibn Arif, name: JinYi Yoon, name: Dimitrios S. Nikolopoulos, name: Hans Vandierendonck, name: Deepu John, name: Bo Ji

High-resolution Vision-Language Models (VLMs) have been widely used in
multimodal tasks to enhance accuracy by preserving detailed image information.
However, these models often generate excessive visual tokens due to encoding
multiple partitions of the input image. Processing these excessive visual
tokens is computationally challenging, especially in resource-constrained
environments with commodity GPUs. To support high-resolution images while
meeting resource constraints, we propose High-Resolution Early Dropping
(HiRED), a token-dropping scheme that operates within a fixed token budget
before the Large Language Model (LLM) stage. HiRED can be integrated with
existing high-resolution VLMs in a plug-and-play manner, as it requires no
additional training while still maintaining superior accuracy. We strategically
use the vision encoder's attention in the initial layers to assess the visual
content of each image partition and allocate the token budget accordingly.
Then, using the attention in the final layer, we select the most important
visual tokens from each partition within the allocated budget, dropping the
rest. Empirically, when applied to LLaVA-Next-7B on NVIDIA TESLA P40 GPU, HiRED
with a 20% token budget increases token generation throughput by 4.7, reduces
first-token generation latency by 15 seconds, and saves 2.3 GB of GPU memory
for a single inference.