Summary of Sdp4bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism For Llm Training, by Jinda Jia et al.
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
by Jinda Jia, Cong Xie, Hanlin Lu, Daoce Wang, Hao Feng, Chengming Zhang, Baixi Sun, Haibin Lin, Zhi Zhang, Xin Liu, Dingwen Tao
First submitted to arxiv on: 20 Oct 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 This research paper proposes a novel technique called SDP4Bit (Toward 4Bit Communication Quantization in Sharded Data Parallelism for LLM Training) to mitigate the communication overhead in distributed training of large language models. The proposed method reduces the communication of weights and gradients to nearly 4 bits through two novel techniques: quantization on weight differences, and two-level gradient smooth quantization. Additionally, SDP4Bit presents an algorithm-system co-design with runtime optimization to minimize the computation overhead of compression. The paper empirically evaluates the accuracy of SDP4Bit on the pre-training of GPT models with up to 6.7 billion parameters, demonstrating a negligible impact on training loss and achieving up to 4.08speedup in end-to-end throughput on a scale of 128 GPUs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper tries to make it faster and more efficient to train really big language models on many computers at the same time. They came up with a new way called SDP4Bit that helps reduce the amount of information that needs to be shared between these computers, making it go faster. The method uses two new ideas to make this happen: one for weight values and another for gradient calculations. They tested their approach on really big language models and found that it didn’t hurt performance but actually made it run about 4 times faster! |
Keywords
» Artificial intelligence » Gpt » Optimization » Quantization
