ONNXPruner: ONNX-Based General Model Pruning Adapter
Dongdong Ren Wenbin Li Tianyu Ding Lei Wang Qi Fan Jing Huo Hongbing Pan Yang Gao
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.