indsigt - Machine Learning - # Transformer Model Pruning

SEVEN: Pruning Transformer Model by Reserving Sentinels

Q: How does the introduction of noise through Symbolic Descent impact the filtering of short-term sentinel weights

Symbolic Descent (SD) introduces noise to filter out short-term sentinel weights in the pruning process. This noise plays a crucial role in evaluating the importance of weights by dynamically assessing their gradients during iterative pruning. Specifically, SD corrects stochastic gradients using Polyak averaging and correction coefficients to determine which weights should be preserved or removed. The noise introduced through SD helps in distinguishing between temporary sentinel weights (TSW) with high sensitivity but large gradient noise and sentinel weights (SW) with stable gradients over time. By filtering out short-term SW based on the impact of stochastic gradient noise, SEVEN tends to preserve more stable SW while removing TSW that exhibit significant fluctuations in their gradients.

Q: What implications does the dynamic nature of training have on the judgment of redundant weights

The dynamic nature of training has a significant impact on the judgment of redundant weights, especially in complex models like Transformers. Due to this dynamism, the model's gradient behavior becomes more intricate and sensitive to changes during training iterations. In the context of pruning methods like SEVEN, where noisy batch gradient sequences are evaluated for weight importance scores, this dynamic nature influences how redundant weights are identified and retained or pruned. The fluctuating gradients across different batches can affect the decision-making process regarding which weights should be preserved based on their stability and sensitivity to noise.

Q: How can the findings from this study be applied to optimize other machine learning models beyond Transformers

The findings from this study can be applied beyond Transformers to optimize other machine learning models by considering the impact of stochastic gradient noise on weight pruning strategies. Understanding how noisy gradients influence weight importance scores can help improve pruning methods for various neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning models. By incorporating techniques like Symbolic Descent and Polyak averaging to evaluate gradient variations during iterative pruning, researchers can develop more effective pruning algorithms that prioritize preserving stable weights while removing those susceptible to noise across different types of machine learning tasks and datasets.

Kernekoncepter

SEVEN introduces a pruning method that favors weights with consistently high sensitivity, achieving significant improvements in various tasks.

Resumé

The content discusses the challenges of pruning Transformer models due to their dynamic gradients and introduces SEVEN, a method that preserves weights with low gradient noise. Extensive experiments validate SEVEN's effectiveness across different domains.

Abstract:

Large-scale Transformer models show exceptional performance but face limitations on mobile devices due to their size.
Common pruning methods retain weights with large gradient noise, leading to suboptimal performance.
Symbolic Descent is used to evaluate noisy batch gradient sequences and introduce SEVEN for preserving weights with low noise.
Experiments demonstrate SEVEN's effectiveness in natural language, question-answering, and image classification tasks.

Introduction:

Pre-trained Transformer models offer powerful language representation capabilities but come with increased computational costs.
Pruning methods like SNIP and GraSP are ineffective for Transformers due to dynamic gradients.
Gradient-based pruning methods exhibit limitations at moderate sparsity levels in Transformers.

Method:

Examines variations in model gradients during training process.
Introduces SEVEN method inspired by Symbolic Descent to preserve weights with low gradient noise.
Utilizes dynamic assessment of importance scores of weights during iterative pruning.

Experiments:

Extensive experiments conducted on various benchmark tasks validate SEVEN's superiority over existing methods.
SEVEN consistently outperforms state-of-the-art methods across different datasets and sparsity levels.

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arxiv.org

Statistik

"Large-scale Transformer models have demonstrated outstanding performance across various tasks."
"Extensive experiments on various TM in natural language, question-answering, and image classification domains are conducted."

Citater

"The results demonstrate significant improvements of SEVEN in multiple pruning scenarios and across different sparsity levels."
"SEVEN exhibits robust performance under various fine-tuning strategies."

Vigtigste indsigter udtrukket fra

SEVEN

by Jinying Xiao... kl. arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12688.pdf

Dybere Forespørgsler

How does the introduction of noise through Symbolic Descent impact the filtering of short-term sentinel weights

Symbolic Descent (SD) introduces noise to filter out short-term sentinel weights in the pruning process. This noise plays a crucial role in evaluating the importance of weights by dynamically assessing their gradients during iterative pruning. Specifically, SD corrects stochastic gradients using Polyak averaging and correction coefficients to determine which weights should be preserved or removed. The noise introduced through SD helps in distinguishing between temporary sentinel weights (TSW) with high sensitivity but large gradient noise and sentinel weights (SW) with stable gradients over time. By filtering out short-term SW based on the impact of stochastic gradient noise, SEVEN tends to preserve more stable SW while removing TSW that exhibit significant fluctuations in their gradients.

What implications does the dynamic nature of training have on the judgment of redundant weights

The dynamic nature of training has a significant impact on the judgment of redundant weights, especially in complex models like Transformers. Due to this dynamism, the model's gradient behavior becomes more intricate and sensitive to changes during training iterations. In the context of pruning methods like SEVEN, where noisy batch gradient sequences are evaluated for weight importance scores, this dynamic nature influences how redundant weights are identified and retained or pruned. The fluctuating gradients across different batches can affect the decision-making process regarding which weights should be preserved based on their stability and sensitivity to noise.

How can the findings from this study be applied to optimize other machine learning models beyond Transformers

The findings from this study can be applied beyond Transformers to optimize other machine learning models by considering the impact of stochastic gradient noise on weight pruning strategies. Understanding how noisy gradients influence weight importance scores can help improve pruning methods for various neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning models. By incorporating techniques like Symbolic Descent and Polyak averaging to evaluate gradient variations during iterative pruning, researchers can develop more effective pruning algorithms that prioritize preserving stable weights while removing those susceptible to noise across different types of machine learning tasks and datasets.