HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models in Resource-Constrained Environments
- URL: http://arxiv.org/abs/2408.10945v1
- Date: Tue, 20 Aug 2024 15:34:27 GMT
- Title: HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models in Resource-Constrained Environments
- Authors: Kazi Hasan Ibn Arif, JinYi Yoon, Dimitrios S. Nikolopoulos, Hans Vandierendonck, Deepu John, Bo Ji,
- Abstract summary: High-Resolution Early Dropping (HiRED) is 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 Vision-Language Models in a plug-and-play manner.
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.
- Score: 10.50453920206006
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient [52.96232442322824]
Collaborative Decoding (CoDe) is a novel efficient decoding strategy tailored for the Visual Auto-Regressive ( VAR) framework.
CoDe capitalizes on two critical observations: the substantially reduced parameter demands at larger scales and the exclusive generation patterns across different scales.
CoDe achieves a 1.7x speedup, slashes memory usage by around 50%, and preserves image quality with only a negligible FID increase from 1.95 to 1.98.
arXiv Detail & Related papers (2024-11-26T15:13:15Z) - Inference Optimal VLMs Need Only One Visual Token but Larger Models [54.01228554126122]
Vision Language Models (VLMs) have demonstrated strong capabilities across various visual understanding and reasoning tasks.
VLMs are often constrained by high latency during inference due to substantial compute required to process the large number of input tokens.
We take some initial steps towards building approaches tailored for high token compression settings.
arXiv Detail & Related papers (2024-11-05T18:54:21Z) - SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference [45.11612407862277]
In vision-language models (VLMs), visual tokens usually bear a significant amount of computational overhead despite sparsity of information in them when compared to text tokens.
We propose a text-guided training-free token optimization mechanism dubbed SparseVLM that eliminates the need of extra parameters or fine-tuning costs.
arXiv Detail & Related papers (2024-10-06T09:18:04Z) - VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation [66.00245701441547]
We introduce a novel approach to reduce vision compute by leveraging redundant vision tokens "skipping layers" rather than decreasing the number of vision tokens.
Our method, VideoLLM-MoD, is inspired by mixture-of-depths LLMs and addresses the challenge of numerous vision tokens in long-term or streaming video.
arXiv Detail & Related papers (2024-08-29T17:21:58Z) - LookupViT: Compressing visual information to a limited number of tokens [36.83826969693139]
Vision Transformers (ViT) have emerged as the de-facto choice for numerous industry grade vision solutions.
But their inference cost can be prohibitive for many settings, as they compute self-attention in each layer which suffers from complexity in the number of tokens.
In this work, we introduce LookupViT, that exploits this information sparsity to reduce ViT inference cost.
arXiv Detail & Related papers (2024-07-17T17:22:43Z) - ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models [77.59651787115546]
High-resolution Large Multimodal Models (LMMs) encounter the challenges of excessive visual tokens and quadratic visual complexity.
We propose ConvLLaVA, which employs ConvNeXt, a hierarchical backbone, as the visual encoder of LMM.
ConvLLaVA compresses high-resolution images into information-rich visual features, effectively preventing the generation of excessive visual tokens.
arXiv Detail & Related papers (2024-05-24T17:34:15Z) - LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation [37.72775203647514]
This paper proposes to use learnable meta tokens to formulate sparse tokens, which effectively learn key information and improve inference speed.
By employing Dual Cross-Attention (DCA) in the early stages with dense visual tokens, we obtain the hierarchical architecture LeMeViT with various sizes.
Experimental results in classification and dense prediction tasks show that LeMeViT has a significant $1.7 times$ speedup, fewer parameters, and competitive performance compared to the baseline models.
arXiv Detail & Related papers (2024-05-16T03:26:06Z) - An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models [65.37846460916042]
We find out that the attention computation over visual tokens is of extreme inefficiency in the deep layers of popular LVLMs.
We introduce FastV, a versatile plug-and-play method designed to optimize computational efficiency.
arXiv Detail & Related papers (2024-03-11T14:35:32Z) - Vision Transformer with Super Token Sampling [93.70963123497327]
Vision transformer has achieved impressive performance for many vision tasks.
It may suffer from high redundancy in capturing local features for shallow layers.
Super tokens attempt to provide a semantically meaningful tessellation of visual content.
arXiv Detail & Related papers (2022-11-21T03:48:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.