ONNXPruner: ONNX-Based General Model Pruning Adapter
This work addresses a practical problem for developers and researchers in deep learning by providing a tool to streamline pruning across frameworks and hardware, though it is incremental in building on existing pruning methods.
The paper tackles the challenge of applying model pruning algorithms across different models and platforms by proposing ONNXPruner, a versatile adapter for ONNX format models that uses node association trees and tree-level evaluation, resulting in confirmed strong adaptability and increased efficacy in experiments.
Recent advancements in model pruning have focused on developing new algorithms and improving upon benchmarks. However, the practical application of these algorithms across various models and platforms remains a significant challenge. To address this challenge, we propose ONNXPruner, a versatile pruning adapter designed for the ONNX format models. ONNXPruner streamlines the adaptation process across diverse deep learning frameworks and hardware platforms. A novel aspect of ONNXPruner is its use of node association trees, which automatically adapt to various model architectures. These trees clarify the structural relationships between nodes, guiding the pruning process, particularly highlighting the impact on interconnected nodes. Furthermore, we introduce a tree-level evaluation method. By leveraging node association trees, this method allows for a comprehensive analysis beyond traditional single-node evaluations, enhancing pruning performance without the need for extra operations. Experiments across multiple models and datasets confirm ONNXPruner's strong adaptability and increased efficacy. Our work aims to advance the practical application of model pruning.