DISTMM: Accelerating Distributed Multimodal Model Training

Multimodal model training takes multiple types of inputs to process with differently structured submodules, and aggregates outcomes from the submodules to learn the relationship among various types of inputs, e.g., correlating text to image for text-to-image generation. The differences of submodule architectures as well as their inputs lead to heterogeneity in terms of computation efficiency. Failing to account for such heterogeneity, existing distributed training systems treat all submodules as a monolithic entity and thus have sub-optimal performance. Moreover, the outcome aggregation phase introduces cross-sample dependencies with contrasting positive and negative sample pairs (i.e., contrastive loss). Such dependencies make the existing pipeline parallelism scheduling algorithms not applicable for multimodal training with contrastive loss.

To address the limitations of existing solutions, we propose DISTIMM. For a given multimodal model, DISTIMM exploits the heterogeneity among submodules, applying different distributed parallelism strategies for each submodule, e.g., using Tensor Parallelism for a computation-intensive submodule, and Data Parallelism for a submodule with a small number of parameters. DISTIMM balances the computation of parallelized submodules to reduce the computing resource idle time of waiting for the slowest submodule. DISTIMM further optimizes the locality of submodules by leveraging the heterogeneous bandwidth of interconnections among accelerators. To address the limitation of existing pipeline execution schedules, we propose a new pipeline execution primitive, called batch-sync instruction, and a corresponding schedule, called DISTIMM-Pipe. We build a prototype of DISTIMM and evaluate it with existing solutions on models with various sizes ranging from 1.1 billion to 26 billion parameters and observe 1.32-3.27 × speedup over Megatron-LM.