Neural Network Distributed Training Mode Switching
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Solution Overview
Problem
Existing distributed training methods for neural networks face inefficiencies due to imbalances in computation and communication times, leading to suboptimal resource utilization and prolonged training times.
Innovation Solution
A processor-implemented method that measures time data for sub-operations during neural network training, determines computation and communication times, and dynamically selects a training mode to balance these times, thereby optimizing the distributed training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed training is performed using multiple processing modules, then training speed is improved, but imbalance between computation time and communication time causes resource utilization to deteriorate
Solution Approach 1:
The patent implements dynamic training mode switching that adjusts the training strategy based on real-time measurement of computation and communication time ratios. The system transitions between different training modes (e.g., changing gradient synchronization frequency, adjusting batch sizes, modifying parallelism strategies) to maintain optimal resource utilization while preserving training speed improvements from distributed computing.
Solution Approach 2:
The system changes operational parameters such as gradient accumulation steps, synchronization intervals, and batch processing sizes based on the measured time ratio. When computation time dominates, the system increases batch sizes or reduces synchronization frequency; when communication time dominates, it increases synchronization frequency or reduces batch sizes, thereby optimizing resource utilization without sacrificing training speed.
2Productivity
If distributed training is performed using multiple processing modules, then training speed is improved, but total training time is prolonged due to time imbalance
Solution Approach 1:
The patent employs dynamic adaptation of training parameters based on measured computation and communication time ratios. By continuously monitoring the time balance and adjusting training mode accordingly, the system prevents idle waiting periods that would otherwise extend total training time, while maintaining the speed benefits of distributed processing.
Solution Approach 2:
The system measures the computation and communication time ratios during training and uses this feedback to dynamically adjust the training mode. This closed-loop control ensures that the training process adapts to actual performance characteristics, preventing both underutilization and excessive synchronization that would prolong total training time.
3Ease of manufacture
If fixed training mode is used, then implementation simplicity is improved, but adaptability to different computation-communication scenarios deteriorates
Solution Approach 1:
The patent implements a dynamic training mode selection mechanism that automatically adapts to different computation-communication scenarios. The system measures the time ratio and selects from multiple predefined training modes (such as different gradient synchronization strategies, batch processing configurations, or parallelism levels), providing both ease of implementation through predefined modes and adaptability through automatic selection.
Solution Approach 2:
The training system autonomously monitors its own performance characteristics and self-adjusts the training mode without external intervention. By measuring computation and communication times and automatically selecting the appropriate training mode, the system achieves adaptability to different scenarios while maintaining implementation simplicity through automated decision-making.
Data Source
AI summary
A processor-implemented method includes, while training a neural network (NN) using a current training mode selected from a plurality of training modes for training of the NN, measuring time data of a plurality of sub-operations for the training of the NN; based on the time data, determining a computation time to perform computation operations among the plurality of sub-operations and a communication time to perform communication operations among the plurality of sub-operations; based on a comparison result of the computation time and the communication time, selecting a next training mode from the plurality of training modes; and training the NN based on the next training mode.