Accurate Post-Training Pruning of Foundation Models via Multiple Removal Problem

Основные понятия

This research paper introduces a novel post-training pruning method for foundation models that outperforms existing techniques by directly formulating and solving the Multiple Removal Problem (MRP), enabling simultaneous pruning of multiple weights and achieving higher accuracy without retraining.

Аннотация

• Bibliographic Information: Zhao, P., Sun, F., Shen, X., Yu, P., Kong, Z., Wang, Y., & Lin, X. (2024). Pruning Foundation Models for High Accuracy without Retraining. arXiv preprint arXiv:2410.15567.
• Research Objective: This paper aims to address the challenge of deploying large language models (LLMs) due to their massive size and computational requirements by proposing a novel post-training pruning method that achieves high accuracy without retraining.
• Methodology: The authors formulate the post-training pruning problem as a Multiple Removal Problem (MRP), which considers the simultaneous pruning of multiple weights in LLMs. They derive an optimal solution for the MRP and design post-training pruning algorithms for both unstructured and semi-structured sparsity. The proposed method is evaluated on various LLM families, including transformer-based and Mamba-based LLMs, using perplexity and zero-shot accuracy as evaluation metrics.
• Key Findings: The proposed MRP-based pruning method consistently outperforms existing state-of-the-art (SOTA) baselines, including SparseGPT and Wanda, in terms of perplexity and zero-shot accuracy across various LLM architectures, model sizes, and datasets. The method achieves significant accuracy improvements, particularly under high sparsity settings.
• Main Conclusions: This research demonstrates that directly addressing the MRP in post-training pruning leads to superior accuracy compared to traditional Single Removal Problem (SRP)-based methods. The proposed algorithms provide an efficient and effective way to compress LLMs without sacrificing accuracy, facilitating their deployment in practical applications.
• Significance: This work significantly contributes to the field of model compression by introducing a novel MRP-based approach for post-training pruning of LLMs. The proposed method addresses the limitations of existing SRP-based techniques and paves the way for deploying highly accurate and efficient LLMs on resource-constrained devices.
• Limitations and Future Research: The computational complexity of the proposed method, although manageable on a single GPU, could be further optimized for larger models and datasets. Future research could explore the integration of the MRP-based pruning with other compression techniques, such as quantization, to achieve even higher compression ratios without compromising accuracy.

Статистика

The proposed method achieves 4.278 perplexity on WikiText2 compared to 5.698 from SparseGPT for LLaMA2-70B under 2:4 sparsity.
The average accuracy on zero-shot datasets for Mamba-790M with 50% sparsity is 51.095% using the proposed method, compared to 50.555% with SparseGPT.

Цитаты

"The traditional pruning techniques, which fine-tune or retrain models on full datasets for many epochs (i.e., pruning-aware training), are too expensive for LLMs in terms of data and GPU resources."
"Different from the SRP-based SparseGPT, we directly formulate the MRP for layer-wise LLM pruning to simultaneously prune multiple weights in LLMs."
"Our comprehensive experiments across various LLM families (based on transformers and Mamba), model sizes, and datasets demonstrate our superior performance compared with the optimization-based SparseGPT and other heuristic SOTA baselines."

Ключевые выводы из

Pruning Foundation Models for High Accuracy without Retraining

by Pu Zhao, Fei... в arxiv.org 10-22-2024

https://arxiv.org/pdf/2410.15567.pdf

Дополнительные вопросы

How does the proposed MRP-based pruning method impact the inference speed and memory footprint of LLMs compared to other compression techniques?

The paper primarily focuses on improving the accuracy of post-training pruning for LLMs, specifically targeting the limitations of SRP-based methods like SparseGPT. While it acknowledges the importance of inference speed and memory footprint reduction, it doesn't provide explicit benchmarks or comparisons against other compression techniques in these specific areas.
Here's a breakdown based on the information available:

Memory Footprint: MRP, similar to SparseGPT, utilizes a layer-wise compression strategy. This approach significantly reduces memory demands as it only requires loading one block at a time, making it feasible to use even a single GPU for large LLMs. Therefore, the memory footprint reduction is comparable to SparseGPT and significantly better than loading the entire model.
Inference Speed: The paper doesn't directly measure or compare inference speed. However, we can infer some potential impacts:

Unstructured Pruning: Unstructured pruning generally doesn't translate to direct speedups on conventional hardware unless dedicated sparse matrix multiplication libraries are employed.
Semi-structured Pruning (2:4 Sparsity): This type of sparsity, while offering less sparsity than unstructured pruning, has a higher potential for leveraging hardware acceleration, potentially leading to faster inference.
In summary: While MRP demonstrates superior accuracy in pruning, its impact on inference speed and memory footprint compared to other techniques is not directly quantified in the paper. It's expected to have a similar memory footprint reduction to SparseGPT due to the layer-wise approach. The impact on inference speed would depend on the sparsity structure and the hardware's ability to exploit it.

Could the performance gap between the MRP-based method and SRP-based methods be attributed to the specific LLM architectures and datasets used in the evaluation, or is it a more general phenomenon?

The paper provides evidence suggesting that the performance gap between MRP-based and SRP-based pruning methods is likely a more general phenomenon rather than being limited to specific architectures or datasets. Here's why:

Theoretical Foundation: The paper highlights the inherent limitations of SRP in addressing the multiple weight removal problem. SRP's reliance on the zero Jacobian assumption and sequential weight freezing introduces approximations that can lead to sub-optimal solutions. MRP, on the other hand, directly formulates and addresses the multiple removal problem, leading to a more accurate solution.
Extensive Evaluation: The authors validate their method on a diverse range of LLM families, including transformer-based (LLaMA2, OPT, BLOOM) and Mamba-based architectures. They also use various datasets for calibration (C4, LAMBADA) and evaluation (WikiText2, PTB, C4, zero-shot datasets). The consistent performance advantage of MRP across these variations suggests a broader applicability.
Addressing SRP Limitations: The paper demonstrates that MRP's ability to simultaneously consider multiple weight removals and avoid the freezing of unpruned weights directly addresses the shortcomings of SRP, leading to improved accuracy.
However, further research is necessary to definitively conclude its generalizability:

Exploring other domains: Applying MRP to other domains like computer vision or reinforcement learning would provide stronger evidence for its general applicability.
Analyzing different pruning rates: Investigating the performance gap across a wider range of sparsity levels would offer a more comprehensive understanding.

How can the insights from this research on MRP-based pruning be applied to other areas of machine learning beyond natural language processing, such as computer vision or reinforcement learning?

The insights from MRP-based pruning, while originating from NLP, hold significant potential for application in other machine learning areas like computer vision and reinforcement learning:
1. Computer Vision:

Convolutional Neural Networks (CNNs): Similar to LLMs, CNNs often have redundant parameters and can benefit from pruning. MRP's ability to handle multiple weight removals simultaneously and accurately compensate for them can be applied to various CNN layers, potentially leading to more efficient and accurate models for tasks like image classification, object detection, and segmentation.
Vision Transformers: With the increasing popularity of transformer architectures in computer vision, MRP's success in pruning LLMs directly translates to these models.
2. Reinforcement Learning:

Deep Reinforcement Learning (DRL): DRL agents often employ deep neural networks for policy and value function approximation. These networks can be large and complex, making them suitable candidates for pruning. MRP can be applied to compress these networks, potentially enabling more efficient deployment on resource-constrained devices like robots.
Multi-Agent Reinforcement Learning: In multi-agent scenarios, communication and coordination between agents are crucial. Pruning the communication channels or the agents' policy networks using MRP could lead to more efficient communication protocols and reduced computational overhead.
Key Considerations for Adaptation:

Domain-Specific Pruning Criteria: While the core principles of MRP remain relevant, the criteria for selecting weights to prune might need adjustments based on the specific domain. For instance, in computer vision, pruning filters with low activations or gradients might be more effective.
Hardware Constraints: The choice of sparsity structure should consider the target hardware's ability to efficiently process sparse architectures.
Exploration-Exploitation Trade-off: In reinforcement learning, pruning should be carefully balanced with the agent's need to explore the environment and learn effectively.
In conclusion: MRP's core principles of simultaneous multiple weight removal and accurate compensation offer valuable insights for model compression in various machine learning domains. Adapting the pruning criteria, considering hardware constraints, and understanding the specific challenges of each domain will be crucial for successful application.