Methods › General › Data Parallel Methods › ALQ and AMQ
Gradient Quantization with Adaptive Levels/Multiplier
ALQ and AMQ
Introduced by Fartash Faghri et al. in Adaptive Gradient Quantization for Data-Parallel SGD
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Many communication-efficient variants of SGD use gradient quantization schemes. These schemes are often heuristic and fixed over the course of training. We empirically observe that the statistics of gradients of deep models change during the training. Motivated by this observation, we introduce two adaptive quantization schemes, ALQ and AMQ. In both schemes, processors update their compression schemes in parallel by efficiently computing sufficient statistics of a parametric distribution. We improve the validation accuracy by almost 2% on CIFAR-10 and 1% on ImageNet in challenging low-cost communication setups. Our adaptive methods are also significantly more robust to the choice of hyperparameters.
Papers archive 2025-07-28
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Adaptive Gradient Quantization for Data-Parallel SGD 23 Oct 2020 · 1 repository · arXiv:2010.12460Syntology ran 2 of 2 samples · 0 unverified
Tasks archive 2025-07-28
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| Task | Papers |
|---|---|
| Quantization | 1 |
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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