Distributed Neural Network Training with Gradient Clipping
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Solution Overview
Problem
Current neural network training methods face challenges in efficiently performing distributed training due to the need for extensive communication between processors for gradient exchange, which increases training time and can lead to unstable gradient updates.
Innovation Solution
The proposed solution involves a distributed training apparatus and method where each processor performs forward and backward operations, determines local gradients, and applies gradient clipping based on thresholds, allowing for simultaneous gradient determination and transmission, thereby reducing communication overhead and stabilizing updates through parallel processing using GPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If processors perform distributed training with extensive gradient exchange, then training accuracy can be maintained, but training time increases and communication overhead increases
Solution Approach 1:
The system performs gradient clipping as a preliminary action before gradient exchange between processors. Each processor clips its local gradients to satisfy the Lipschitz condition before transmitting them to other processors, preventing the need for extensive communication rounds and reducing overall training time while maintaining convergence accuracy.
Solution Approach 2:
The gradient computation and clipping process is segmented and distributed across multiple processors. Each processor independently computes and clips its local gradients, then exchanges only the clipped gradients with other processors. This segmentation reduces communication overhead while maintaining the reliability of distributed training.
2Stability of the object's composition
If processors exchange gradients frequently for stable updates, then parameter update stability improves, but communication overhead increases
Solution Approach 1:
Gradient clipping is applied as a preliminary action locally at each processor before gradient exchange. By ensuring gradients satisfy the Lipschitz condition beforehand, the system achieves stable parameter updates without requiring frequent communication rounds, thus reducing communication overhead while maintaining update stability.
Solution Approach 2:
Each processor independently performs gradient clipping on its own local gradients before exchange. This self-service approach ensures that each processor contributes stable, clipped gradients to the distributed training process without requiring complex coordination or validation from other processors, reducing communication overhead.
3Stability of the object's composition
If gradient clipping is performed for all layers, then gradient stability improves, but computational overhead increases
Solution Approach 1:
The system applies gradient clipping selectively based on local conditions at each processor and layer. Gradient clipping is performed on local gradients that exceed the Lipschitz bound, rather than uniformly applying it to all layers. This localized approach maintains gradient stability where needed while reducing unnecessary computational overhead in layers that already satisfy the Lipschitz condition.
Data Source
AI summary
Disclosed are a training method and apparatus for distributed training of a neural network, the training apparatus including processors configured to perform distributed training, wherein each of the processors is further configured to perform a forward direction operation for layers of the neural network, determine a loss of the neural network based on the forward direction operation, determine a local gradient for each layer of the neural network by performing a backward direction operation for the layers of the neural network based on the loss, determine whether to perform gradient clipping for a local gradient determined for a previous layer, in response to determining a local gradient for a current layer through the backward direction operation, determine an aggregated gradient based on the backward direction operation and the gradient clipping performed by each of the processors, and update parameters of the neural network based on the aggregated gradient.