ColossalAI provides three main parallel training strategies that can optimize the training efficiency of large-scale models at different levels:
- data parallelism: Divide the dataset into subsets and assign them to different GPUs to train the same model in parallel. This is the most common basic parallelization strategy.
- tensor parallelism: Divide the parameter tensor of the model for parallel computation on multiple GPUs.ColossalAI supports multiple tensor parallel schemes in 1D, 2D, 2.5D, and 3D.
- parallel assembly lines: Divide the model into multiple stages, each executed by a specific GPU, creating a factory assembly line-like training process.
In addition, ColossalAI supports the use of a combination of these parallelization strategies, allowing the selection of the optimal parallelization scheme based on the model and hardware configuration. This flexibility enables it to effectively handle the training needs of various types of large-scale AI models.
This answer comes from the articleColossalAI: Providing Efficient Large-Scale AI Model Training SolutionsThe
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