Pipeline Stash Balancing for Memory-Balanced Model Parallelism
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Solution Overview
Problem
Training large neural networks requires significant memory usage, particularly in pipelined systems where memory size grows quadratically, leading to inefficiencies due to memory imbalances across stages in model parallelism.
Innovation Solution
Implement stash balancing across stages by sharing and managing memory resources to ensure balanced memory usage, utilizing techniques such as assigning stages to devices based on bandwidth and optimizing memory allocation to minimize storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pipelining is used to process data through multiple stages, then processing throughput is improved, but memory size grows quadratically
Solution Approach 1:
The patent divides the neural network into multiple stages processed in parallel pipelines, where each stage handles a portion of the computation. This segmentation allows throughput improvement while managing memory requirements by localizing data storage to specific stages rather than requiring all data to be held in memory simultaneously across the entire network.
Solution Approach 2:
The patent introduces a new dimension of computation by processing multiple micro-batches through different stages simultaneously in a three-dimensional pipeline architecture (stages × micro-batches × time). This dimensional approach increases throughput without proportionally increasing memory requirements, as memory is reused across different dimensions of the computation.
2Duration of action of stationary object
If memory is allocated to all stages to enable continuous processing, then processing continuity is improved, but memory usage becomes unbalanced across stages
Solution Approach 1:
The patent applies local quality by allocating memory resources differently to different stages based on their specific needs. Earlier stages that require stashing intermediate activations for backpropagation receive larger memory allocations, while later stages receive smaller allocations. This non-uniform, localized memory allocation enables processing continuity while avoiding the waste and complexity of uniform memory distribution.
Solution Approach 2:
The patent dynamically adjusts memory allocation parameters across stages, varying the stash size and memory buffer dimensions based on the stage position in the pipeline. This parameter change approach allows each stage to have optimized memory characteristics matched to its computational role, maintaining continuity while reducing overall complexity compared to uniform allocation.
Data Source
AI summary
Embodiments of the present disclosure include systems and methods for training neural networks. In one embodiment, data for an artificial intelligence model is processed in a first plurality of stages and in a second plurality of stages. The first and second pluralities of stages form a pipeline. One or more of the first plurality of stages uses at least one memory associated with a corresponding one or more of the second plurality of stages to balance memory across the pipeline.