You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning

• URL: http://arxiv.org/abs/2501.15296v2
• Date: Wed, 19 Feb 2025 06:34:23 GMT
• Title: You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
• Authors: Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty,
• Abstract summary: PruneNet is a novel model compression method that reformulates model pruning as a policy learning process. It can compress the LLaMA-2-7B model in just 15 minutes, achieving over 80% retention of its zero-shot performance. On complex multitask language understanding tasks, PruneNet demonstrates its robustness by preserving up to 80% performance of the original model.
• Score: 20.62274005080048
• License:
• Abstract: The ever-increasing size of large language models (LLMs) presents significant challenges for deployment due to their heavy computational and memory requirements. Current model pruning techniques attempt to alleviate these issues by relying heavily on external calibration datasets to determine which parameters to prune or compress, thus limiting their flexibility and scalability across different compression ratios. Moreover, these methods often cause severe performance degradation, particularly in downstream tasks, when subjected to higher compression rates. In this paper, we propose PruneNet, a novel model compression method that addresses these limitations by reformulating model pruning as a policy learning process. PruneNet decouples the pruning process from the model architecture, eliminating the need for calibration datasets. It learns a stochastic pruning policy to assess parameter importance solely based on intrinsic model properties while preserving the spectral structure to minimize information loss. PruneNet can compress the LLaMA-2-7B model in just 15 minutes, achieving over 80% retention of its zero-shot performance with a 30% compression ratio, outperforming existing methods that retain only 75% performance. Furthermore, on complex multitask language understanding tasks, PruneNet demonstrates its robustness by preserving up to 80% performance of the original model, proving itself a superior alternative to conventional structured compression techniques.

Related papers

• Forget the Data and Fine-Tuning! Just Fold the Network to Compress [13.611551223875194]
  We introduce model folding, a novel data-free model compression technique that merges structurally similar neurons across layers. We show that model folding achieves comparable performance to data-driven compression techniques and outperforms recently proposed data-free methods. This approach is particularly effective for compressing large-scale models, making it suitable for deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-02-14T15:10:43Z)
• Choose Your Model Size: Any Compression by a Single Gradient Descent [9.074689052563878]
  We present Any Compression via Iterative Pruning (ACIP) ACIP is an algorithmic approach to determine a compression-performance trade-off from a single gradient descent run. We show that ACIP seamlessly complements common quantization-based compression techniques.
  arXiv  Detail & Related papers  (2025-02-03T18:40:58Z)
• Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models [11.93284417365518]
  We introduce COMP, a lightweight post-training structured pruning method that employs a hybrid-granularity pruning strategy. COMP improves performance by 6.13% on the LLaMA-2-7B model with a 20% pruning ratio compared to LLM-Pruner.
  arXiv  Detail & Related papers  (2025-01-25T16:03:58Z)
• Self-Data Distillation for Recovering Quality in Pruned Large Language Models [1.5665059604715017]
  One-shot pruning results in significant quality degradation, particularly in tasks requiring multi-step reasoning. To recover lost quality, supervised fine-tuning (SFT) is commonly applied, but it can lead to catastrophic forgetting. In this work, we utilize self-data distilled fine-tuning to address these challenges.
  arXiv  Detail & Related papers  (2024-10-13T19:53:40Z)
• LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy [59.1298692559785]
  Key-Value ( KV) cache is crucial component in serving transformer-based autoregressive large language models (LLMs) Existing approaches to mitigate this issue include: (1) efficient attention variants integrated in upcycling stages; (2) KV cache compression at test time; and (3) KV cache compression at test time. We propose a low-rank approximation of KV weight matrices, allowing plug-in integration with existing transformer-based LLMs without model retraining. Our method is designed to function without model tuning in upcycling stages or task-specific profiling in test stages.
  arXiv  Detail & Related papers  (2024-10-04T03:10:53Z)
• Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks [0.0]
  This work evaluates the compression techniques on ConvNeXt models in image classification tasks using the CIFAR-10 dataset. Results show significant reductions in model size, with up to 75% reduction achieved using structured pruning techniques. Dynamic quantization achieves a reduction of up to 95% in the number of parameters.
  arXiv  Detail & Related papers  (2024-09-02T11:48:19Z)
• Activations and Gradients Compression for Model-Parallel Training [85.99744701008802]
  We study how simultaneous compression of activations and gradients in model-parallel distributed training setup affects convergence. We find that gradients require milder compression rates than activations. Experiments also show that models trained with TopK perform well only when compression is also applied during inference.
  arXiv  Detail & Related papers  (2024-01-15T15:54:54Z)
• Learning Accurate Performance Predictors for Ultrafast Automated Model Compression [86.22294249097203]
  We propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost.
  arXiv  Detail & Related papers  (2023-04-13T10:52:49Z)
• CrAM: A Compression-Aware Minimizer [103.29159003723815]
  We propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way. CrAM produces dense models that can be more accurate than the standard SGD/Adam-based baselines, but which are stable under weight pruning. CrAM can produce sparse models which perform well for transfer learning, and it also works for semi-structured 2:4 pruning patterns supported by GPU hardware.
  arXiv  Detail & Related papers  (2022-07-28T16:13:28Z)
• What do Compressed Large Language Models Forget? Robustness Challenges in Model Compression [68.82486784654817]
  We study two popular model compression techniques including knowledge distillation and pruning. We show that compressed models are significantly less robust than their PLM counterparts on adversarial test sets. We develop a regularization strategy for model compression based on sample uncertainty.
  arXiv  Detail & Related papers  (2021-10-16T00:20:04Z)
• Training with Quantization Noise for Extreme Model Compression [57.51832088938618]
  We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training, where the weights are quantized during training and the gradients approximated with the Straight-Through Estimator. In this paper, we extend this approach to work beyond int8 fixed-point quantization with extreme compression methods.
  arXiv  Detail & Related papers  (2020-04-15T20:10:53Z)

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.