EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training

• URL: http://arxiv.org/abs/2511.10333v1
• Date: Fri, 14 Nov 2025 01:45:49 GMT
• Title: EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training
• Authors: Qingao Yi, Jiaang Duan, Hanwen Hu, Qin Hua, Haiyan Zhao, Shiyou Qian, Dingyu Yang, Jian Cao, Jinghua Tang, Yinghao Yu, Chenzhi Liao, Kangjin Wang, Liping Zhang,
• Abstract summary: Training large language models (LLMs) poses significant challenges regarding computational resources and memory capacity.<n>Existing approaches primarily rely on static gradient compression to enhance communication efficiency.<n>We propose an entropy-driven dynamic gradient compression framework called EDGC.
• Score: 11.898292264147427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training large language models (LLMs) poses significant challenges regarding computational resources and memory capacity. Although distributed training techniques help mitigate these issues, they still suffer from considerable communication overhead. Existing approaches primarily rely on static gradient compression to enhance communication efficiency; however, these methods neglect the dynamic nature of evolving gradients during training, leading to performance degradation. Accelerating LLM training via compression without sacrificing performance remains a challenge. In this paper, we propose an entropy-driven dynamic gradient compression framework called EDGC. The core concept is to adjust the compression rate during LLM training based on the evolving trends of gradient entropy, taking into account both compression efficiency and error. EDGC consists of three key components.First, it employs a down-sampling method to efficiently estimate gradient entropy, reducing computation overhead. Second, it establishes a theoretical model linking compression rate with gradient entropy, enabling more informed compression decisions. Lastly, a window-based adjustment mechanism dynamically adapts the compression rate across pipeline stages, improving communication efficiency and maintaining model performance. We implemented EDGC on a 32-NVIDIA-V100 cluster and a 64-NVIDIA-H100 cluster to train GPT2-2.5B and GPT2-12.1B, respectively. The results show that EDGC significantly reduces communication latency and training time by up to 46.45% and 16.13% while preserving LLM accuracy.

Related papers

• Adacc: An Adaptive Framework Unifying Compression and Activation Recomputation for LLM Training [40.371351103295765]
  Training large language models (LLMs) is often constrained by GPU memory limitations.<n>Adacc is the first adaptive memory optimization framework that unifies activation recomputation and data compression.<n>Adacc improves training throughput by 1.01x to 1.37x compared to state-of-the-art frameworks.
  arXiv  Detail & Related papers  (2025-08-01T17:39:25Z)
• TAGC: Optimizing Gradient Communication in Distributed Transformer Training [0.0]
  Transformer-Aware Gradient Compression (TAGC) is an optimized gradient compression algorithm for transformer-based models.<n>We show that TAGC accelerates training by up to 15% compared to the standard Fully Sharded Data Parallel (FSDP) approach.
  arXiv  Detail & Related papers  (2025-04-08T03:33:39Z)
• Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models [56.00251589760559]
  Large language models (LLMs) can act as gradient priors in a zero-shot setting.<n>We introduce LM-GC, a novel method that integrates LLMs with arithmetic coding.<n>Experiments indicate that LM-GC surpasses existing state-of-the-art lossless compression methods.
  arXiv  Detail & Related papers  (2024-09-26T13:38:33Z)
• Accelerating Large Language Model Training with Hybrid GPU-based Compression [3.204387803072905]
  MPI libraries have been proven to reduce message size significantly and leverage interconnect bandwidth. We investigate the efficacy of compression-assisted MPI collectives under the context of distributed Large Language Model (LLM) training.
  arXiv  Detail & Related papers  (2024-09-04T04:05:30Z)
• MoDeGPT: Modular Decomposition for Large Language Model Compression [59.361006801465344]
  This paper introduces textbfModular bfDecomposition (MoDeGPT), a novel structured compression framework.<n>MoDeGPT partitions the Transformer block into modules comprised of matrix pairs and reduces the hidden dimensions.<n>Our experiments show MoDeGPT, without backward propagation, matches or surpasses previous structured compression methods.
  arXiv  Detail & Related papers  (2024-08-19T01:30:14Z)
• LoCo: Low-Bit Communication Adaptor for Large-scale Model Training [63.040522637816906]
  Low-bit communication often degrades training quality due to compression information loss.<n>We propose Low-bit Communication Adaptor (LoCo), which compensates local local GPU nodes before, without compromising quality.<n> Experimental results show that across moving large-scale training model frameworks like Megatron-LM and PyTorchs FSDP, LoCo significantly improves compression communication efficiency.
  arXiv  Detail & Related papers  (2024-07-05T13:01:36Z)
• Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees [53.950234267704]
  We introduce Global-QSGD, an All-reduce gradient-compatible quantization method.<n>We show that it accelerates distributed training by up to 3.51% over baseline quantization methods.
  arXiv  Detail & Related papers  (2023-05-29T21:32:15Z)
• L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient and Accurate Deep Learning [24.712888488317816]
  We provide a framework for adapting the degree of compression across the model's layers dynamically during training. Our framework, called L-GreCo, is based on an adaptive algorithm, which automatically picks the optimal compression parameters for model layers.
  arXiv  Detail & Related papers  (2022-10-31T14:37:41Z)
• Compressed Communication for Distributed Training: Adaptive Methods and System [13.244482588437972]
  Communication overhead severely hinders the scalability of distributed machine learning systems. Recently, there has been a growing interest in using gradient compression to reduce the communication overhead. In this paper, we first introduce a novel adaptive gradient method with gradient compression.
  arXiv  Detail & Related papers  (2021-05-17T13:41:47Z)
• An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems [77.88178159830905]
  Sparsity-Inducing Distribution-based Compression (SIDCo) is a threshold-based sparsification scheme that enjoys similar threshold estimation quality to deep gradient compression (DGC) Our evaluation shows SIDCo speeds up training by up to 41:7%, 7:6%, and 1:9% compared to the no-compression baseline, Topk, and DGC compressors, respectively.
  arXiv  Detail & Related papers  (2021-01-26T13:06:00Z)
• Sparse Communication for Training Deep Networks [56.441077560085475]
  Synchronous gradient descent (SGD) is the most common method used for distributed training of deep learning models. In this algorithm, each worker shares its local gradients with others and updates the parameters using the average gradients of all workers. We study several compression schemes and identify how three key parameters affect the performance.
  arXiv  Detail & Related papers  (2020-09-19T17:28:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.