Distributed Neural Network Paths for Low-Communication Training
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Solution Overview
Problem
Large and complex neural networks require significant computational and communication resources during training, leading to inefficiencies and resource overcapacity issues, especially in distributed computing systems.
Innovation Solution
A distributed computing system that dynamically composes neural network components into different paths, allowing for a sparsely activated network that uses only a subset of parameters for inference, reducing communication needs and enabling training on heterogeneous, geographically dispersed devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If larger and more complicated neural networks are used to achieve higher accuracy, then model performance is improved, but computational and communication cost during training increases
Solution Approach 1:
The neural network is divided into multiple independent components that can be selectively activated. Each component processes a specific portion of the input data, and only the necessary components are activated for each inference task, reducing the overall computational burden while maintaining high accuracy through the combined power of all components.
Solution Approach 2:
Instead of activating the entire neural network for every inference, the system activates only the necessary subset of components required for the specific task. This partial activation approach reduces computational and communication costs while still achieving the required accuracy by selecting the most relevant components.
2Productivity
If conventional data parallelism or model parallelism algorithms are used for training large neural networks, then training capability is improved, but communication bandwidth requirements increase significantly
Solution Approach 1:
The training process is segmented into independent component updates. Each component maintains its own parameters and can be trained independently on local data, eliminating the need for frequent synchronization and high-bandwidth communication between devices. This segmentation allows distributed training with minimal communication overhead.
Solution Approach 2:
Each component performs self-training using local data without requiring constant coordination or data exchange with other components. The independent parameter updates allow each device to train autonomously, significantly reducing communication bandwidth requirements while maintaining overall training effectiveness.
3Stability of the object's composition
If synchronous parameter synchronization is performed after every inner optimization step, then model consistency is improved, but communication frequency and overhead increase
Solution Approach 1:
Instead of synchronizing parameters after every inner optimization step, the system performs synchronization only when necessary, such as after completing a full epoch or when component parameters diverge significantly. This partial synchronization approach maintains model consistency while dramatically reducing communication frequency and overhead.
Solution Approach 2:
Parameter synchronization is performed periodically rather than continuously. The system establishes a synchronization schedule that balances model consistency requirements with communication overhead minimization, allowing components to train independently between synchronization points.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, are described for training a neural network on a training dataset using a distributed computing system that includes a plurality of workers. The neural network includes a plurality of components. Each component includes a respective subset of the plurality of parameters of the neural network.