Visualizing 6D Mesh Parallelism in Deep Learning Training
This article delves into the complexities of 6D mesh parallelism in deep learning model training. Using a series of visualizations, the author meticulously explains the communication mechanisms of various parallel strategies—data parallelism, fully sharded data parallelism, tensor parallelism, context parallelism, expert parallelism, and pipeline parallelism—during the model's forward and backward passes. The author uses a simple attention layer model to illustrate the implementation details of each parallel approach, highlighting their interactions and potential challenges, such as the conflict between pipeline parallelism and fully sharded data parallelism. The article concludes by discussing mesh ordering, combining different parallel strategies, and practical considerations.
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