Large models have been widely used in the field of neural language processing, information retrieving, etc. With the development of the large models, not only is the parameter scale increased, but the model architecture has also become more complex. For example, the multi-modal transformer-based model mainly has concurrent branches, which we denoted as the concurrent branch model (CBM). Many CBMs have enlarged to tens of billions of parameters, and require distributed resources to train this kind of model. Existing distributed training systems cannot fully handle this type of model architecture because there are interactions between branches. Inspired by the unbalanced resource usage of pipeline parallelism, we prefer to organize different branches with a fine-grained bidirectional pipeline schedule of communication and computation. However, improper coordination between branches leads to idle time for computation and low training efficiency. In this paper, we present Flexpipe, a pipeline engine for c3oncurrent-branch models. We first introduce a branch-aware pipeline parallelism (BAPP) to make full use of the concurrent characteristic of the model architecture. Then, based on a multi-branch pipeline simulator, we propose an adaptive interaction coordinator, which facilitates the low-overhead branch interactions during the distributed model training. We evaluate our approach on popular concurrent branch models combined with modern training systems. Compared with the Chimera, the experiential results show that our method improves the end-to-end training throughput by 20% on average.

As the computational demands driven by large model technologies continue to grow rapidly, leveraging GPU hardware to expedite parallel training processes has emerged as a commonly-used strategy. When computational resources within a single cluster are insufficient for large-model training, the hybrid utilization of heterogeneous acceleration hardware has emerged as a promising technical solution. The utilization of heterogeneous acceleration hardware and scheduling of diverse cloud resources have become a focal point of considerable interest. However, these computing resources are often geographically distributed. Due to the lack of awareness of heterogeneous devices and network topologies, existing parallel training frameworks struggle to leverage mixed GPU resources across constrained networks effectively. To boost the computing capability of the connected heterogeneous clusters, we propose HGTrainer, an optimizer designed to plan heterogeneous parallel strategies across distributed clusters for large model training. HGTrainer can adaptively saturate heterogeneous clusters because of the expanded tunable parallelism space for heterogeneous accelerators, with the awareness of relatively lower inter-cluster bandwidth. To achieve this goal, we formulate the model partitioning problem among heterogeneous hardware and introduce a hierarchical searching algorithm to solve the optimization problem. Besides, a mixed-precision pipeline method is used to reduce the cost of inter-cluster communications. We evaluate HGTrainer on heterogeneous connected clusters with popular large language models. The experimental result shows that HGTrainer effectively improves 1.49× training throughput on average for the mixed heterogeneous cluster compared with the state-of-the-art Metis.