Key Points

1. The paper addresses the challenge of training large-scale machine learning models, specifically Generative AI models, which can suffer from training instability manifested as loss spikes, interrupting and prolonging the training process.

2. The study focuses on the potential cause of training instability, namely numeric deviation between an optimization and its corresponding baseline, and develops a principled quantitative approach to understanding the effects of numeric deviation in training optimizations.

3. The research introduces a microbenchmark to perturb numeric precision in the given optimization and evaluates how numeric deviation translates to changes in model weights through a data-driven analysis based on Wasserstein distance, providing upper bounds on the impact of numeric deviation for a given optimization.

4. As a case study, the paper analyzes the state-of-the-art optimization technique Flash Attention, which is designed to accelerate the Attention bottleneck characteristic of Transformers. The technique introduces rescaling factors, potentially increasing numeric deviation, and the study quantifies the potential numeric deviation introduced by Flash Attention from its baseline.

5. The research finds that Flash Attention sees roughly an order of magnitude more numeric deviation compared to Baseline Attention at low numerical precision (BF16) during an isolated forward pass, and the impact of this numeric deviation on downstream model weights is evaluated using data-driven analysis based on the Wasserstein distance.

6. The paper presents detailed experiments and analyses on the impact of Flash Attention during the forward pass, quantifying the numeric deviation between Flash Attention and Baseline Attention, and demonstrating how different numerical precisions and sequence lengths impact the numeric deviation.

7. The study quantifies the model weight difference between a model trained with Flash Attention and Baseline Attention, finding that Flash Attention introduces roughly 2-5 times less model weight deviation as compared to low-precision training.

8. The research also incorporates broader research questions regarding training instability, system overhead, and sustainability, and opens up new inquiries into understanding how various other optimizations impact numeric deviation.

9. In conclusion, the paper develops a principled approach to understanding numeric deviation and contextualizing the impact of training optimizations on model weights, with the aim of encouraging further investigations and research in this challenging area of training instability in large-scale machine learning models.

Summary

Mitigating Training Instability with Principled Approach
The paper aims to mitigate training instability in large language models by addressing the potential cause of instability, numeric deviation in model training. It introduces a principled approach to understand the effects of numeric deviation and construct proxies to contextualize observations when downstream effects are challenging to quantify. The study presents a quantitative analysis of the widely-adopted Flash Attention optimization, which is designed to accelerate the attention bottleneck characteristic of Transformers. The research comprises two phases: developing a microbenchmark to perturb numeric precision and evaluating how numeric deviation translates to changes in model weights through a data-driven analysis based on Wasserstein distance.

Quantifying Numeric Deviation and Its Impact
The study quantifies the potential numeric deviation introduced by Flash Attention and provides upper bounds on how this deviation impacts model weights during training. The microbenchmark designed isolates the impact of numerical precision on numeric deviation and demonstrates that Flash Attention sees roughly an order of magnitude more numeric deviation compared to Baseline Attention at low numerical precision (BF16). Furthermore, a data-driven analysis based on the Wasserstein Distance metric contextualizes the observed numeric deviation and forms an upper bound for the impact on downstream model properties. The analysis shows that Flash Attention introduces roughly 2-5 times less model weight deviation as compared to low-precision training.

Impact of Numerical Precision on Attention Output and Weight Differences
The paper also examines how different numerical precisions impact the output matrix of the Attention calculation during the forward pass. It is found that the numerical precision significantly impacts the output of Flash Attention, causing it to deviate from the output of Baseline Attention. Additionally, the research employs a series of experiments to compare how weight differences change over the course of training under different scenarios, including different model initializations and low-precision training. The results suggest that although numeric deviation occurs with Flash Attention, it is bounded by random model initialization and low-precision training and introduces roughly 2-5 times less model weight deviation as compared to low-precision training.

In conclusion, the paper introduces a principled approach to understanding the effects of numeric deviation and develops proxies to put observations into context when downstream effects are challenging to measure. It also encourages future research to investigate additional training optimizations and their corresponding numeric deviation from the appropriate baseline, as well as broader research questions related to training instability, system overhead, and sustainability. The findings provide insights into the impact of training optimizations on numeric deviation and the broader research questions related to training instability.

Reference: https://arxiv.org/abs/2405.028...