Search Results for author:
Found 6 papers, 4 papers with code
Improving Automatic Parallel Training via Balanced Memory Workload Optimization
Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-scale deep learning (DL) models.
FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement
We first present an empirical analysis on the problems and opportunities of training MoE models, which motivates us to overcome the routing imbalance and fluctuation problems by a dynamic expert management and device placement mechanism.
Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent
Recent years have witnessed the unprecedented achievements of large-scale pre-trained models, especially the Transformer models.
Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism
Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) models.
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
Then it diversifies the experts and continues to train the MoE with a novel Dense-to-Sparse gate (DTS-Gate).
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework
Embedding models have been an effective learning paradigm for high-dimensional data.
