Efficient Task-Agnostic Pruning of Vision-Language Models for Transferable Performance

Core Concepts

The core message of this work is to propose a novel pruning framework, Multimodal Flow Pruning (MULTIFLOW), that can prune a vision-language model once and maintain its transferability to multiple unknown downstream tasks, without the need for task-specific knowledge.

Abstract

This paper introduces the concept of Task-Agnostic Vision-Language Pruning (TA-VLP), which aims to prune a vision-language model (VLM) once and obtain a sparse model that can be transferred to multiple unknown tasks when fine-tuned. This is in contrast to existing VLM pruning methods that require task-specific knowledge and need to prune the model from scratch for each new task.
The key highlights of the paper are:

Formalization of the TA-VLP problem, which is challenging as it requires preserving the transferable representations encoded in the pretrained VLM without any task-specific priors or feedback during pruning.

Proposal of Multimodal Flow Pruning (MULTIFLOW), a gradient-free pruning framework for TA-VLP. MULTIFLOW models each layer as a bipartite graph, where the importance of a parameter is expressed in terms of its magnitude and the saliency of the neurons it connects. It also exploits multimodal priors to guide the distribution of each layer and avoid biases.

Extensive benchmarking of 8 pruning algorithms, including MULTIFLOW, on two VLM architectures (BLIP and XVLM), three vision-language tasks (Image-Text Retrieval, Image Captioning, Visual Question Answering), and three pruning ratios. The results show that MULTIFLOW outperforms recent sophisticated, combinatorial competitors in the vast majority of the cases.

Additional analyses that reveal the different "prunabilities" of VLMs and vision-language tasks, and the importance of considering potential biases in activation patterns among layers and modalities for VLM pruning, especially in high-sparsity regimes.

Stats

"Large-scale vision-language models (VLMs) show remarkable transfer learning capabilities but come with a huge number of parameters, hindering deployment in memory-constrained devices."
"Existing VLM pruning methods perform task-specific pruning, requiring re-pruning the model from scratch if the downstream task changes."
"The proposed Task-Agnostic Vision-Language Pruning (TA-VLP) aims to prune a VLM once and obtain a sparse model transferable to multiple unknown tasks when fine-tuned."

Quotes

"While existing pruning methods use task-specific knowledge, hence requiring pruning the dense model from scratch for different tasks, we propose to shift the perspective and formalize TA-VLP, which only requires pruning once."
"Pretraining uses a generic objective, such as vision-language alignment, which applied to large-scale data enables learning generic and transferable representations. These representations depend on the network parameters and on how the (multimodal) activations propagate through the network."
"Intuitively, if we assume the pretrained model to be transferable, its pruned counterpart should preserve the learned activation patterns."

Key Insights Distilled From

MULTIFLOW

by Matteo Farin... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05621.pdf

Deeper Inquiries

How can the proposed TA-VLP framework be extended to other modalities beyond vision and language, such as audio or video

The proposed TA-VLP framework can be extended to other modalities beyond vision and language by adapting the principles of multimodal flow pruning to suit the specific characteristics of the new modalities. For example:

Audio: For audio modalities, the saliency of nodes could be based on features extracted from audio signals, such as spectrogram data or MFCC coefficients. The importance of parameters could be determined by how well they capture relevant audio features for the task at hand.
Video: Extending to video modalities would involve considering temporal information in addition to spatial features. The information flow could be modeled across frames, and the saliency of nodes could be based on motion vectors or optical flow information.

By customizing the saliency criteria and information flow modeling for each new modality, the TA-VLP framework can be effectively applied to a wide range of multimodal tasks beyond just vision and language.

What are the potential limitations of the MULTIFLOW approach, and how could it be further improved to handle more challenging pruning scenarios

Potential Limitations of MULTIFLOW:

Task-Specific Performance: MULTIFLOW may not perform optimally for all types of downstream tasks, especially those with highly specialized requirements that deviate significantly from the pretraining objectives.
Scalability: As the complexity of the model and tasks increases, the computational demands of MULTIFLOW may become prohibitive, especially at extreme sparsity levels.
Generalization: The effectiveness of MULTIFLOW may vary across different VLM architectures and datasets, limiting its generalizability.

Improvements for Handling Challenging Pruning Scenarios:

Dynamic Adaptation: Implementing a mechanism for the algorithm to dynamically adjust its pruning strategy based on task-specific feedback during fine-tuning could enhance its adaptability.
Ensemble Methods: Combining MULTIFLOW with ensemble pruning techniques could help mitigate the limitations of individual methods and improve overall performance.
Transfer Learning: Leveraging transfer learning techniques to transfer knowledge from successful pruning scenarios to new, challenging tasks could enhance the robustness of MULTIFLOW in handling diverse pruning scenarios.

Given the observed differences in "prunability" across VLMs and vision-language tasks, can we develop methods to automatically predict the optimal pruning strategy for a given model and task

Given the observed differences in "prunability" across VLMs and vision-language tasks, developing methods to automatically predict the optimal pruning strategy for a given model and task is a promising direction for future research. Here are some approaches that could be explored:

Meta-Learning: Utilizing meta-learning techniques to learn the optimal pruning strategy across a diverse set of VLMs and tasks. This would involve training a meta-learner to adaptively select the most effective pruning method based on the specific characteristics of the model and task.
Reinforcement Learning: Applying reinforcement learning algorithms to automatically discover the best pruning strategy through trial and error. The agent could learn to navigate the space of pruning methods and parameters to maximize performance on a given task.
Automated Machine Learning (AutoML): Integrating pruning strategies into AutoML frameworks to automatically search for the most effective pruning configuration for a given VLM and task. This could involve hyperparameter optimization and neural architecture search techniques tailored for pruning.

By developing automated methods to predict the optimal pruning strategy, we can streamline the process of model compression and make it more accessible for a wide range of applications and scenarios.