Neural Network Weight Compression for Distributed Training
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Solution Overview
Problem
Distributed training of neural networks faces significant network traffic bottlenecks due to the large amount of data required for exchanging weights among processors, particularly in data-parallel convolutional neural network training, which can be expensive and prevent system scaling.
Innovation Solution
Implementing a method that sorts and compresses weights using lossless compression algorithms like ZIP, transmitting the sorted weights and sort order periodically, allowing nodes to rearrange parameters according to a prior sort order during subsequent iterations, thereby reducing overall communication overhead without loss of accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weights are transmitted in full precision without compression, then model accuracy is maintained, but network traffic increases significantly
Solution Approach 1:
The patent applies lossless compression algorithms to transform weight parameters from their original high-precision floating-point representation into a compressed format that maintains numerical accuracy while reducing data size. This parameter transformation enables significant network traffic reduction without sacrificing model accuracy, directly resolving the contradiction between precision and data quantity.
Solution Approach 2:
The patent creates compressed representations (copies) of the weight parameters that preserve the essential information needed for model accuracy. Instead of transmitting the full-precision original weights, the system transmits compressed copies that can be reconstructed at the receiving end, reducing network traffic while maintaining the functional equivalence of the parameters.
2Quantity of substance
If lossless compression is applied to reduce network traffic, then communication overhead decreases, but computational complexity increases
Solution Approach 1:
The patent performs compression operations on the weight parameters before transmission, preparing the data in advance to reduce the burden during the actual communication phase. By pre-compressing the weights, the system reduces real-time computational complexity during training iterations while maintaining the ability to achieve significant traffic reduction.
3Productivity
If data parallel training is implemented across multiple processors, then training speed increases, but weight exchange overhead increases
Solution Approach 1:
The patent transforms the weight parameters through lossless compression, changing their representation from full-precision floating-point format to a compressed format. This parameter change reduces the amount of data that needs to be exchanged between processors during data parallel training, thereby reducing communication overhead while maintaining training speed benefits.
Solution Approach 2:
The patent creates and transmits compressed copies of weight parameters between processors instead of the original full-size weights. This copying approach with compression enables multiple processors to maintain synchronized models while exchanging significantly less data, resolving the contradiction between training speed and communication volume.
Data Source
AI summary
Systems and methods may make exchanging data in a neural network (NN) during training more efficient. Exchanging weights among a number of processors training a NN across iterations may include sorting generated weights, compressing the sorted weights, and transmitting the compressed sorted weights. On each Kth iteration a sort order of the sorted weights may be created and transmitted. Exchanging weights among processors training a NN may include executing a forward pass to produce a set of loss values for processors, transmitting loss values to other processors, and at each of the processors, performing backpropagation on at least one layer of the NN using loss values received from other processors.