WebbThe most straight-forward to prune is to take a trained model and prune it once; also called one-shot pruning. In Learning both Weights and Connections for Efficient Neural Networks Song Han et. al show that this is surprisingly effective, but also leaves a lot of potential sparsity untapped. WebbTo address this, we propose a fast post-training pruning framework for Transformers that does not require any retraining. Given a resource constraint and a sample dataset, our framework automatically prunes the Transformer model using structured sparsity methods. To retain high accuracy without retraining, we introduce three novel …
Supervised Robustness-preserving Data-free Neural Network Pruning
Webb8 apr. 2024 · Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning. Shanglin Zhou, Mikhail A. Bragin, Lynn Pepin, Deniz Gurevin, Fei Miao, Caiwen Ding. Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline significantly … WebbThe actual pruning rates are much higher than these presented in the paper since we do not count the next-layer channel removal (For example, if 50 filters are removed in the … cleaning a bathroom fan
Rethinking Network Pruning -- under the Pre-train and Fine-tune …
WebbDeep networks are very sensitive to such pruning strategies, thus pre-training and retraining are required to guarantee performance, which is not biologically plausible. Some developmental plasticity-inspired pruning methods prune neurons or synapses adaptively through a biologically reasonable dynamic strategy, helping to effectively prevent … Webb8 jan. 2024 · To achieve a high Winograd-domain weight sparsity without changing network structures, we propose a new pruning method, spatial-Winograd pruning. As the first step, spatial-domain weights are pruned in a structured way, which efficiently transfers the spatial-domain sparsity into the Winograd domain and avoids Winograd-domain retraining. WebbWhile some one-shot pruning methods also exist, which compress the model without retraining, they are unfortunately too computationally-expensive to be applied to models with billions of parameters. Thus, to date, there is virtually no work on accurate pruning of GPT3-scale models. Overview. cleaning a baseball helmet