An engineering team is conducting two parallel, distributed training runs of the same large model. Both runs use identical hardware, software, datasets, and initial parameters. The only difference is that Run A uses 8 compute nodes and Run B uses 16 compute nodes. After several hundred steps, the team observes that the model weights in Run A and Run B, while very similar, are not bit-for-bit identical. Which of the following is the most fundamental and likely cause for this divergence?
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
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An engineering team is conducting two parallel, distributed training runs of the same large model. Both runs use identical hardware, software, datasets, and initial parameters. The only difference is that Run A uses 8 compute nodes and Run B uses 16 compute nodes. After several hundred steps, the team observes that the model weights in Run A and Run B, while very similar, are not bit-for-bit identical. Which of the following is the most fundamental and likely cause for this divergence?
Diagnosing Training Reproducibility Issues
Evaluating the Practical Impact of Floating-Point Non-Associativity