Block Parallelism For Efficient Distributed Long-Context Diffusion Language Model Training
Bridging the gap between block-parallelism and long-context training efficiency
Block diffusion language models (BDLMs) combine autoregressive dependencies across blocks with parallel denoising within blocks, but long-context training is constrained by distributed attention communication and activation memory. Conventional context parallelism (CP) shards the...
ArXiv CS.LG
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This research optimizes training efficiency for long-context diffusion models by bridging the gap between block-based parallelism and memory-intensive attention mechanisms. It represents a critical step toward scaling generative AI to process massive datasets without the prohibitive hardware costs associated with traditional distributed training.
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