Introduction to Photon: A Revolutionary Approach
The landscape of training large language models (LLMs) is undergoing a significant transformation with the introduction of Photon. Traditionally, training LLMs required massive data centers equipped with high-bandwidth connections, which made the process expensive and limited collaboration. Current distributed training methods struggle to work effectively across low-bandwidth internet connections. Photon addresses these challenges by enabling federated training of LLMs across geographically distributed GPUs connected via regular internet.
The Original Problem
Training LLMs has always been resource-intensive, necessitating the use of large data centers with high-bandwidth connections. This not only incurs high costs but also limits the potential for global collaboration. Existing distributed training methods fail to perform efficiently over low-bandwidth internet connections, creating a bottleneck in the democratization of AI training.
The Solution: Photon
Photon leverages cross-silo Federated Learning to minimize communication overhead by 64x-512x compared to standard methods. It implements adaptive local parallelism to optimize training based on each client’s connectivity and exploits small batch sizes with high learning rates for better model generalization. The system is built on a three-component architecture: Aggregator (central server), LLM Client (local training), and Data Sources.
Key Insights from Photon
Photon has demonstrated that Federated Learning can match or even exceed the performance of centralized training for LLMs. Small batch sizes with high learning rates are particularly effective in federated settings. Moreover, the frequency of communication can be drastically reduced without sacrificing model quality, allowing data to remain at its source while enabling global collaboration.
Results and Performance
Photon successfully trained 7B parameter models with 16.9% lower perplexity than centralized training. It also achieved a 64x-512x reduction in communication while improving wall-time performance by 35%. Additionally, Photon converged twice as fast as previous methods like DiLoCo and achieved 20% higher throughput than centralized distributed training.
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