Accelerating Language Model Pre-training with Masked Structural Growth

핵심 개념

Masked Structural Growth (MSG) proposes a novel framework for progressive pre-training of language models, achieving up to 2.2x speedup while maintaining comparable or better downstream performances.

초록

Masked Structural Growth (MSG) introduces a method to accelerate large language model pre-training by progressively growing from a small Transformer structure to a large one. The approach involves determining optimal growth schedules and designing efficient growth operators. MSG outperforms existing methods by achieving significant speed-up ratios while maintaining performance levels. The study focuses on the impact of each growth dimension on training dynamics and explores the importance of function preservation in model expansion.
The authors propose a methodology called Masked Structural Growth (MSG) that involves masking mechanisms to ensure function preservation during model expansion. By gradually increasing mask values post-growth, MSG achieves strict function preservation and independence from specific weight initialization strategies. Experimental results demonstrate that MSG outperforms existing methods across diverse model configurations, providing insights into the role of function preservation in progressive pre-training.
Key points include:

Accelerating large language model pre-training is crucial but faces computational challenges.
Progressive growth from smaller to larger models is inspired by neurogenesis in the human brain.
Existing methods for progressive growth lack strict function preservation and rely heavily on weight initialization.
Masked Structural Growth (MSG) introduces a novel framework with masking mechanisms for function preservation.
MSG achieves state-of-the-art speed-up ratios while maintaining or improving downstream performances.

통계

Experiments show that MSG achieves up to 2.2x speedup in pre-training different types of language models while maintaining comparable or better downstream performances.
In Bert-large, the loss at 10k steps ranges from 3.23 to 3.36 with varying mask strength values.

인용구

"MSG offers growth operators for all possible dimensions with decent flexibility in schedule design."
"We propose Masked Structural Growth (MSG), including growth schedules involving all possible dimensions."

핵심 통찰 요약

Masked Structural Growth for 2x Faster Language Model Pre-training

by Yiqun Yao,Zh... 게시일 arxiv.org 03-11-2024

https://arxiv.org/pdf/2305.02869.pdf

더 깊은 질문

What are the potential drawbacks associated with existing operators for progressive growth

Existing operators for progressive growth, such as Net2Net and zeroizing methods, have potential drawbacks related to function preservation. These methods may not achieve strict function preservation in all cases, leading to disparities in model behavior after growth. For example, when applying Layer Normalization after expansion, existing operators like Net2Net may fail to preserve the function due to discrepancies in mean and variance calculations. Additionally, these operators often rely heavily on the initialization of new weights, which can limit improvements in training dynamics by imposing constraints on weight updates.

How does Masked Structural Growth address issues related to function preservation and weight initialization

Masked Structural Growth (MSG) addresses issues related to function preservation and weight initialization by introducing a novel framework that ensures strict function preservation during progressive pre-training of language models.

Function Preservation: MSG uses masking mechanisms to eliminate the effects of new structures post-growth initially and gradually increases their influence over time. This approach guarantees that the outputs of post-growth models mirror those of their precursors for any input.

Weight Initialization: Unlike existing operators that depend on specific weight initialization strategies, MSG supports arbitrary initialization of new weights. By decoupling the growth process from weight initialization constraints, MSG allows for more flexibility and potentially better training dynamics.

How can the concept of neurogenesis in human brains be further explored in relation to progressive training

The concept of neurogenesis in human brains can be further explored in relation to progressive training by drawing parallels between how neural networks grow through structural changes during pre-training and how neurons develop in the brain through neurogenesis processes.

Learning Dynamics: Just as neurogenesis contributes to learning and memory formation in humans by creating new neurons with unique connections over time, progressive training with structural growth could mimic this process by expanding model capacity gradually while preserving knowledge learned at each stage.

Adaptability: Neurogenesis enables adaptation to changing environments or tasks by generating new neurons with different functions. Similarly, incorporating principles inspired by neurogenesis into progressive training could enhance adaptability of language models when faced with diverse datasets or tasks.

Optimization Strategies: Studying how neural networks evolve structurally during pre-training akin to neurogenesis could lead to insights on optimizing growth schedules based on learning rates or task complexities analogous to biological mechanisms regulating neuron development based on environmental cues.
By exploring these connections between neural network growth during pre-training and neurogenesis processes observed in human brains, researchers can potentially uncover novel approaches for enhancing adaptive learning capabilities and improving efficiency in model training procedures.