Gradient Compression with Sparse Mapping for Faster Distributed Training
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Solution Overview
Problem
Distributed training of neural networks is hindered by substantial network latency during gradient exchange between worker nodes, which diminishes the training time reduction achieved by distributing the training process across multiple computing systems.
Innovation Solution
Implementing gradient compression techniques by clipping gradients below a threshold and generating a mapping for non-clipped data elements, allowing for efficient transmission and reconstruction of compressed data between worker nodes in a distributed system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If gradient compression is implemented by clipping gradients below a threshold, then gradient exchange throughput requirements are reduced and compression rates improve, but data precision and training accuracy may be affected
Solution Approach 1:
The patent changes the parameter representation by clipping gradient values below a threshold to zero, transforming the gradient data from continuous to sparse format. This parameter change reduces the amount of data that needs to be exchanged while maintaining the essential information needed for training convergence.
Solution Approach 2:
The patent extracts only the significant gradient values (those above the threshold) for transmission, leaving out the insignificant values (below threshold) by setting them to zero. This extraction principle reduces communication overhead by focusing only on the important data elements that contribute meaningfully to the weight updates.
2Productivity
If distributed training is implemented across multiple worker nodes, then training time is reduced through parallel processing, but network latency during gradient exchange increases
Solution Approach 1:
The patent extracts and transmits only the non-zero gradient values along with their positions, removing the need to transmit redundant zero values. This significantly reduces the communication volume between worker nodes, thereby reducing network latency and allowing distributed training to scale more effectively.
Solution Approach 2:
The patent changes the communication parameter from transmitting full gradient vectors to transmitting sparse gradient representations with position metadata. This parameter change reduces the communication burden on the network while maintaining the essential information needed for synchronized training across distributed workers.
3Reliability
If full precision gradient data is transmitted between worker nodes, then training accuracy is maintained, but communication bandwidth requirements and hardware complexity increase
Solution Approach 1:
The patent extracts only the meaningful gradient information (non-zero values) and transmits them with position indicators, removing unnecessary zero values from the communication stream. This reduces the hardware requirements for data transmission and processing while preserving the essential information needed for accurate training.
Solution Approach 2:
The patent creates a compressed representation of the gradient data that copies only the essential information (non-zero values and their positions) rather than copying the entire gradient vector. This compressed copy reduces communication bandwidth requirements while maintaining training accuracy.
Data Source
AI summary
Techniques for exchanging compressed gradient data within a distributed system are disclosed. A set of gradients are computed at a first worker node of the distributed system using a neural network model and a set of weights associated with the neural network model. Each of the set of gradients having a value less than a threshold is clipped, resulting in non-clipped data elements and clipped data elements. A mapping indicating which of the set of gradients correspond to non-clipped data elements and which of the set of gradients correspond to clipped data elements is generated. Compressed data is generated based on the non-clipped data elements. The mapping and the compressed data are transmitted from the first worker node to a second worker node of the distributed system.