Summary of Acco: Accumulate While You Communicate, Hiding Communications in Distributed Llm Training, by Adel Nabli (mlia et al.
ACCO: Accumulate while you Communicate, Hiding Communications in Distributed LLM Training
by Adel Nabli, Louis Fournier, Pierre Erbacher, Louis Serrano, Eugene Belilovsky, Edouard Oyallon
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Training Large Language Models (LLMs) relies heavily on distributed implementations, employing multiple GPUs to compute stochastic gradients on model replicas in parallel. However, synchronizing gradients in data parallel settings induces a communication overhead increasing with the number of distributed workers, which can impede the efficiency gains of parallelization. To address this challenge, optimization algorithms reducing inter-worker communication have emerged, such as local optimization methods used in Federated Learning. The proposed algorithm is designed to overcome memory costs while maintaining scalability, allowing for the sharing of optimizer states across workers and overlapping gradient computations with communications to conceal communication costs. This novel approach eliminates the need for warmup steps and aligns with the training dynamics of standard distributed optimization, converging faster in terms of wall-clock time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it more efficient to train large language models on many computers at the same time. When you do this, there’s a problem called communication overhead that slows things down. To fix this, the authors developed a new way to train the model that reduces the communication overhead and makes training faster. This method allows for sharing information between the different computers and overlaps certain tasks so that they happen simultaneously. The result is a more efficient and scalable way to train large language models. |
Keywords
» Artificial intelligence » Federated learning » Optimization
