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High-resolution Vision-Language Models (VLMs) are widely used in multimodal tasks to enhance accuracy by preserving detailed image information. However, these models often generate an excessive number of visual tokens due to the need to encode multiple partitions of a high-resolution image input. Processing such a large number of visual tokens poses significant computational challenges, particularly for resource-constrained commodity GPUs. To address this challenge, we propose High-Resolution Early Dropping (HiRED), a plug-and-play token-dropping method designed to operate within a fixed token budget. HiRED leverages the attention of CLS token in the vision transformer (ViT) to assess the visual content of the image partitions and allocate an optimal token budget for each partition accordingly. The most informative visual tokens from each partition within the allocated budget are then selected and passed to the subsequent Large Language Model (LLM). We showed that HiRED achieves superior accuracy and performance, compared to existing token-dropping methods. Empirically, HiRED-20% (i.e., a 20% token budget) on LLaVA-Next-7B achieves a 4.7x increase in token generation throughput, reduces response latency by 78%, and saves 14% of GPU memory for single inference on an NVIDIA TESLA P40 (24 GB). For larger batch sizes (e.g., 4), HiRED-20% prevents out-of-memory errors by cutting memory usage by 30%, while preserving throughput and latency benefits.
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IRET: Incremental Resolution Enhancing Transformer
In our research paper, we introduce a revolutionary approach to designing energy-aware dynamically prunable Vision Trans- formers for use in edge applications. Our solution denoted as Incremental Resolution Enhancing Transformer (IRET), works by the sequential sampling of the input image. However, in our case, the embedding size of input tokens is considerably smaller than prior-art solutions. This embedding is used in the first few layers of the IRET vision transformer until a reliable attention matrix is formed. Then the attention matrix is used to sample additional information using a learnable 2D lifting scheme only for important tokens and IRET drops the tokens receiving low attention scores. Hence, as the model pays more attention to a subset of tokens for its task, its focus and resolu- tion also increase. This incremental attention-guided sampling of input and dropping of unattended tokens allow IRET to sig- nificantly prune its computation tree on demand. By controlling the threshold for dropping unattended tokens and increasing the focus of attended ones, we can train a model that dynami- cally trades off complexity for accuracy. This is especially useful for edge devices, where accuracy and complexity could be dy- namically traded based on factors such as battery life, reliability, etc.
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- PAR ID:
- 10554695
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400706059
- Page Range / eLocation ID:
- 620 to 625
- Subject(s) / Keyword(s):
- Vision Transformer, Token Dropping, Attention, Focus
- Format(s):
- Medium: X
- Location:
- Clearwater FL USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3649476.3660380
