аналитика - Machine Learning - # Pruning Large Language Models

Effective Pruning of Large Language Models without Retraining

Q: How can the insights from Wanda's pruning metric be extended to other types of neural networks beyond language models

The insights from Wanda's pruning metric can be extended to other types of neural networks beyond language models by considering the unique characteristics of each network architecture. The key idea behind Wanda's pruning metric is to evaluate weight importance based on the product of weight magnitudes and input activation norms. This approach can be generalized to various neural network architectures by adapting the metric to suit the specific features of each network. For convolutional neural networks (CNNs), the pruning metric could be modified to incorporate spatial information, such as the location of weights within convolutional filters and the corresponding input feature maps. By considering the spatial relationships between weights and activations, a similar per-output weight comparison approach could be applied to identify and prune less important weights effectively. For recurrent neural networks (RNNs) and transformers, the pruning metric could be adjusted to account for the sequential nature of the data. By analyzing the dependencies between weights and input sequences, a tailored metric could be developed to capture the importance of weights in capturing temporal dependencies and long-range dependencies in the data. In summary, the insights from Wanda's pruning metric can be extended to other neural network architectures by customizing the metric to leverage the specific characteristics and requirements of each network type.

Q: What are the potential limitations of Wanda's per-output weight comparison approach, and how could it be further improved or generalized

The per-output weight comparison approach used in Wanda may have some potential limitations that could be addressed for further improvement or generalization: Limited Contextual Information: The per-output comparison may overlook interactions between weights across different outputs. Incorporating a broader context, such as considering interactions between weights in neighboring outputs or layers, could enhance the pruning process. Sensitivity to Calibration Data: The effectiveness of Wanda's approach relies on accurate calibration data for estimating input activation norms. Improvements in estimating these norms, such as using more diverse or representative calibration data, could enhance the robustness of the pruning metric. Scalability to Different Network Architectures: The per-output comparison approach may need to be adapted for different network architectures with varying structures and connectivity patterns. Generalizing the approach to accommodate diverse network designs could improve its applicability across a wider range of models. To address these limitations and further improve the per-output weight comparison approach, future research could explore techniques for incorporating more comprehensive contextual information, enhancing the robustness of calibration data estimation, and developing adaptable strategies for different network architectures.

Q: Given the fast pruning speed of Wanda, how could it be leveraged in the context of sparse training of large language models from scratch

Given the fast pruning speed of Wanda, it could be leveraged in the context of sparse training of large language models from scratch to expedite the training process and improve efficiency. Here are some ways Wanda's speed could be beneficial in sparse training scenarios: Real-time Pruning Updates: Wanda's fast pruning speed allows for real-time updates during training, enabling dynamic adjustments to the network structure based on performance metrics or resource constraints. This capability can facilitate adaptive pruning strategies that optimize model efficiency during training. Iterative Pruning: Wanda's efficiency in computing the pruning metric enables iterative pruning cycles during training, where the model is pruned at regular intervals without significant computational overhead. This iterative approach can gradually refine the network structure and improve performance over time. Sparse Model Initialization: Wanda's speed can be utilized to initialize sparse models efficiently, providing a starting point for training sparse networks from scratch. By quickly identifying and removing less important weights, Wanda can accelerate the process of creating sparse network architectures for training. Overall, leveraging Wanda's fast pruning speed in sparse training scenarios can streamline the model optimization process, reduce computational costs, and facilitate the development of efficient and effective sparse neural networks.

Основные понятия

A simple and effective pruning method, Wanda, can induce high sparsity in pretrained large language models without any retraining or weight update.

Аннотация

The paper introduces a novel pruning method, Wanda (Pruning by Weights and activations), for efficiently compressing large language models (LLMs).

Key highlights:

Wanda prunes weights based on the product of their magnitude and the corresponding input activation norm, on a per-output basis. This is motivated by the recent observation of emergent large magnitude features in LLMs.
Wanda requires no retraining or weight update, and the pruned LLM can be used as is. This is in contrast to existing pruning methods that often require computationally expensive retraining or weight reconstruction.
Wanda significantly outperforms the standard magnitude pruning baseline and performs competitively against the previous best LLM pruning method, SparseGPT, while being much faster to compute.
Wanda is more robust to the amount of calibration data used, compared to SparseGPT.
Fine-tuning the pruned LLMs, either with LoRA or full parameter updates, can further mitigate the performance gap to the original dense models.
The authors provide extensive experiments on the widely adopted LLaMA and LLaMA-2 model families, demonstrating the effectiveness of Wanda in pruning LLMs.

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Статистика

LLaMA-7B with unstructured 50% sparsity has a perplexity of 17.29 using magnitude pruning, compared to 7.26 using Wanda.
Structured 2:4 sparsity on LLaMA-65B can achieve a 1.6x speedup for matrix multiplication in linear layers.
Fine-tuning the 50% sparse LLaMA-7B with full parameter updates can recover the zero-shot accuracy from 54.21% to 58.15%, close to the original dense model at 59.99%.

Цитаты

"As their size increases, Large Languages Models (LLMs) are natural candidates for network pruning methods: approaches that drop a subset of network weights while striving to preserve performance."
"Wanda significantly outperforms the established baseline of magnitude pruning and performs competitively against recent method involving intensive weight update."
"Wanda can be executed in a single forward pass, and requires minimal memory overhead."

Ключевые выводы из

A Simple and Effective Pruning Approach for Large Language Models

by Mingjie Sun,... в arxiv.org 05-07-2024

https://arxiv.org/pdf/2306.11695.pdf

A Simple and Effective Pruning Approach for Large Language Models

Дополнительные вопросы

How can the insights from Wanda's pruning metric be extended to other types of neural networks beyond language models

The insights from Wanda's pruning metric can be extended to other types of neural networks beyond language models by considering the unique characteristics of each network architecture. The key idea behind Wanda's pruning metric is to evaluate weight importance based on the product of weight magnitudes and input activation norms. This approach can be generalized to various neural network architectures by adapting the metric to suit the specific features of each network.
For convolutional neural networks (CNNs), the pruning metric could be modified to incorporate spatial information, such as the location of weights within convolutional filters and the corresponding input feature maps. By considering the spatial relationships between weights and activations, a similar per-output weight comparison approach could be applied to identify and prune less important weights effectively.
For recurrent neural networks (RNNs) and transformers, the pruning metric could be adjusted to account for the sequential nature of the data. By analyzing the dependencies between weights and input sequences, a tailored metric could be developed to capture the importance of weights in capturing temporal dependencies and long-range dependencies in the data.
In summary, the insights from Wanda's pruning metric can be extended to other neural network architectures by customizing the metric to leverage the specific characteristics and requirements of each network type.

What are the potential limitations of Wanda's per-output weight comparison approach, and how could it be further improved or generalized

The per-output weight comparison approach used in Wanda may have some potential limitations that could be addressed for further improvement or generalization:

Limited Contextual Information: The per-output comparison may overlook interactions between weights across different outputs. Incorporating a broader context, such as considering interactions between weights in neighboring outputs or layers, could enhance the pruning process.

Sensitivity to Calibration Data: The effectiveness of Wanda's approach relies on accurate calibration data for estimating input activation norms. Improvements in estimating these norms, such as using more diverse or representative calibration data, could enhance the robustness of the pruning metric.

Scalability to Different Network Architectures: The per-output comparison approach may need to be adapted for different network architectures with varying structures and connectivity patterns. Generalizing the approach to accommodate diverse network designs could improve its applicability across a wider range of models.

To address these limitations and further improve the per-output weight comparison approach, future research could explore techniques for incorporating more comprehensive contextual information, enhancing the robustness of calibration data estimation, and developing adaptable strategies for different network architectures.

Given the fast pruning speed of Wanda, how could it be leveraged in the context of sparse training of large language models from scratch

Given the fast pruning speed of Wanda, it could be leveraged in the context of sparse training of large language models from scratch to expedite the training process and improve efficiency. Here are some ways Wanda's speed could be beneficial in sparse training scenarios:

Real-time Pruning Updates: Wanda's fast pruning speed allows for real-time updates during training, enabling dynamic adjustments to the network structure based on performance metrics or resource constraints. This capability can facilitate adaptive pruning strategies that optimize model efficiency during training.

Iterative Pruning: Wanda's efficiency in computing the pruning metric enables iterative pruning cycles during training, where the model is pruned at regular intervals without significant computational overhead. This iterative approach can gradually refine the network structure and improve performance over time.

Sparse Model Initialization: Wanda's speed can be utilized to initialize sparse models efficiently, providing a starting point for training sparse networks from scratch. By quickly identifying and removing less important weights, Wanda can accelerate the process of creating sparse network architectures for training.

Overall, leveraging Wanda's fast pruning speed in sparse training scenarios can streamline the model optimization process, reduce computational costs, and facilitate the development of efficient and effective sparse neural networks.