Gradient Aggregation Architecture for Low-Overhead Distributed Training
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Solution Overview
Problem
Distributed training of deep neural networks faces significant communication costs due to the large size of weights and gradients, which leads to substantial communication overhead and bottlenecks in existing hierarchical worker-aggregator systems.
Innovation Solution
Embedding data compression accelerators in network interface devices and implementing a gradient-centric, aggregator-free training algorithm that leverages lossy compression of floating-point gradients, allowing gradients to be communicated in all directions and aggregated gradually by worker nodes, thereby reducing the need for designated aggregator nodes and balancing the load among nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed training uses hierarchical worker-aggregator nodes to communicate gradients and weights, then training can be performed across multiple computing devices, but communication overhead becomes substantial due to large weight sizes (hundreds of megabytes)
Solution Approach 1:
The patent extracts the compression function from the main training algorithm and places it in the network interface device. This separates the compression task from the training computation, allowing gradients to be compressed during transmission without affecting the training workflow. The compression accelerator is embedded in the network interface device, extracting the communication bottleneck from the critical training path.
Solution Approach 2:
The patent introduces a compression accelerator as an intermediary component between the computing devices and the network. This intermediary performs lossy compression on gradient values before transmission and decompression upon receipt, mediating the communication between nodes. The intermediary handles the precision-loss tradeoff automatically, allowing efficient communication without burdening the training algorithm.
2Quantity of substance
If general-purpose compression techniques are used for gradient communication, then communication size is reduced, but hardware complexity and latency overhead increase substantially
Solution Approach 1:
The patent changes the precision parameter of gradient values by applying lossy compression that reduces floating-point precision. Instead of using full-precision gradients, the system uses reduced-precision representations that achieve 4-9x compression ratios. This parameter change (from full precision to reduced precision) enables significant data size reduction while maintaining training accuracy within acceptable bounds.
Solution Approach 2:
The patent employs simple, lightweight compression algorithms that can be implemented with minimal hardware resources. The compression accelerator uses straightforward techniques like gradient truncation and quantization rather than complex compression schemes. These simple algorithms achieve adequate compression ratios with minimal hardware complexity and low latency overhead.
3Productivity
If lossy compression is applied to gradient values, then communication efficiency is improved, but precision loss may affect training accuracy
Solution Approach 1:
The patent applies partial compression to gradient values by selectively reducing precision based on gradient magnitude. Small gradients are compressed more aggressively while larger gradients retain more precision. This partial action approach ensures that critical gradient information is preserved while achieving overall compression. The system performs excessive compression on non-critical data (small gradients) while maintaining adequate precision on important data.
Solution Approach 2:
The system incorporates feedback mechanisms to monitor training accuracy and adjust compression parameters accordingly. By observing the impact of compression on training convergence and final accuracy, the system can dynamically tune the compression level. This feedback loop ensures that compression efficiency is optimized while maintaining training accuracy within acceptable bounds.
Data Source
AI summary
A distributed network includes a first group of computing devices. Each computing device is to be coupled to two neighbor computing devices of the first group of computing device and is to: (i) aggregate gradient values received from a first neighbor computing device with local gradient values to generate a partial aggregate of gradient values that are to train a neural network model; (ii) transfer the partial aggregate of gradient values to a second neighbor computing device; and repeat (i) and (ii) until a first aggregate of gradient values from the first group of computing devices is buffered at a first computing device of the first group of computing devices. The first computing device is to transfer the first aggregate of gradient values to a second group of computing devices of the distributed network for further aggregation.