Summary of Accelerating Large Language Model Training with 4d Parallelism and Memory Consumption Estimator, by Kazuki Fujii et al.
Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator
by Kazuki Fujii, Kohei Watanabe, Rio Yokota
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 The paper investigates optimization strategies for training large language models (LLMs) on multiple GPUs. It explores four parallelization methods: Tensor Parallelism, Pipeline Parallelism, Data Parallelism, and Sequence/Context Parallelism. The authors develop precise formulas to estimate memory consumption by model parameters, gradients, optimizer states, and activations during 4D parallel training in the Llama architecture. They conduct 454 experiments on A100 and H100 GPUs, considering factors like temporary buffers and memory fragmentation. Results show that when estimated memory usage is below 80% of available GPU memory, out-of-memory errors do not occur. The paper presents a simple yet effective formula for identifying parallelization configurations prone to memory overflow, reducing the search space. Additionally, the analysis provides insights into optimal 4D parallelism configurations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps large language models train faster and more efficiently on multiple computers (GPUs). It looks at four ways to split up the model’s work across GPUs: different methods for handling calculations, data, and memory. The authors create formulas to predict how much memory each GPU will need during training. They test these formulas by running many experiments on different types of GPUs. The results show that if the predicted memory usage is below 80% of the available memory, the training won’t run out of space. This formula can help find the best way to split up the work across GPUs without running into memory problems. |
Keywords
* Artificial intelligence * Llama * Optimization
