Mats Leon Richter

Simple and Scalable Strategies to Continually Pre-train Large Language Models

Quentin Gregory Anthony

Timothee LESORT

Eugene Belilovsky

Irina Rish

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start the process over again once new data becomes ava… (voir plus)ilable. A much more efficient solution is to continually pre-train these models, saving significant compute compared to re-training. However, the distribution shift induced by new data typically results in degraded performance on previous data or poor adaptation to the new data. In this work, we show that a simple and scalable combination of learning rate (LR) re-warming, LR re-decaying, and replay of previous data is sufficient to match the performance of fully re-training from scratch on all available data, as measured by the final loss and the average score on several language model (LM) evaluation benchmarks. Specifically, we show this for a weak but realistic distribution shift between two commonly used LLM pre-training datasets (English

2024-03-13

ArXiv (prépublication)

Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

Pablo Pernias

Dominic Rampas

Marc Aubreville

2024-01-16

ICLR.cc/2024/Conference (présentation orale)

openreview.net

Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

Pablo Pernias

Dominic Rampas

Marc Aubreville

2024-01-01

International Conference on Learning Representations (publié)

openreview.net

Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

Pablo Pernias

Dominic Rampas

Marc Aubreville

2024-01-01

International Conference on Learning Representations (publié)

dblp.uni-trier.de

Continual Pre-Training of Large Language Models: How to (re)warm your model?

Quentin Gregory Anthony

Eugene Belilovsky

Irina Rish

Timothee LESORT

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to restart the process over again once new data becomes a… (voir plus)vailable. A much cheaper and more efficient solution would be to enable the continual pre-training of these models, i.e. updating pre-trained models with new data instead of re-training them from scratch. However, the distribution shift induced by novel data typically results in degraded performance on past data. Taking a step towards efficient continual pre-training, in this work, we examine the effect of different warm-up strategies. Our hypothesis is that the learning rate must be re-increased to improve compute efficiency when training on a new dataset. We study the warmup phase of models pre-trained on the Pile (upstream data, 300B tokens) as we continue to pre-train on SlimPajama (downstream data, 297B tokens), following a linear warmup and cosine decay schedule. We conduct all experiments on the Pythia 410M language model architecture and evaluate performance through validation perplexity. We experiment with different pre-training checkpoints, various maximum learning rates, and various warmup lengths. Our results show that while rewarming models first increases the loss on upstream and downstream data, in the longer run it improves the downstream performance, outperforming models trained from scratch

2023-06-20

ICML.cc/2023/Workshop/ES-FoMO (poster)

openreview.net

Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

Pablo Pernias

Dominic Rampas

Marc Aubreville

We introduce W\"urstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-eff… (voir plus)ectiveness for large-scale text-to-image diffusion models. A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed but extremely compact semantic image representation used to guide the diffusion process. This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results. Our approach also improves the quality of text-conditioned image generation based on our user preference study. The training requirements of our approach consists of 24,602 A100-GPU hours - compared to Stable Diffusion 2.1's 200,000 GPU hours. Our approach also requires less training data to achieve these results. Furthermore, our compact latent representations allows us to perform inference over twice as fast, slashing the usual costs and carbon footprint of a state-of-the-art (SOTA) diffusion model significantly, without compromising the end performance. In a broader comparison against SOTA models our approach is substantially more efficient and compares favorably in terms of image quality. We believe that this work motivates more emphasis on the prioritization of both performance and computational accessibility.

2023-06-01

ArXiv (prépublication)

Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance

Minimal changes to neural architectures (e.g. changing a single hyperparameter in a key layer), can lead to significant gains in predictive … (voir plus)performance in Convolutional Neural Networks (CNNs). In this work, we present a new approach to receptive field analysis that can yield these types of theoretical and empirical performance gains across twenty well-known CNN architectures examined in our experiments. By further developing and formalizing the analysis of receptive field expansion in convolutional neural networks, we can predict unproductive layers in an automated manner before ever training a model. This allows us to optimize the parameter-efficiency of a given architecture at low cost. Our method is computationally simple and can be done in an automated manner or even manually with minimal effort for most common architectures. We demonstrate the effectiveness of this approach by increasing parameter efficiency across past and current top-performing CNN-architectures. Specifically, our approach is able to improve ImageNet1K performance across a wide range of well-known, state-of-the-art (SOTA) model classes, including: VGG Nets, MobileNetV1, MobileNetV3, NASNet A (mobile), MnasNet, EfficientNet, and ConvNeXt - leading to a new SOTA result for each model class.

2022-11-26

ArXiv (prépublication)