GraphMend uses two Jaseci-based code transformations to eliminate dynamic-control-flow and side-effect graph breaks in PyTorch 2, reducing breaks to zero in six of eight Hugging Face models and yielding up to 75% latency reduction on RTX 3090 and A40 GPUs.
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FDA-Opt unifies and improves upon FedOpt and FDA for communication-efficient federated fine-tuning of language models on NLP tasks, outperforming optimized FedOpt baselines.
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GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2
GraphMend uses two Jaseci-based code transformations to eliminate dynamic-control-flow and side-effect graph breaks in PyTorch 2, reducing breaks to zero in six of eight Hugging Face models and yielding up to 75% latency reduction on RTX 3090 and A40 GPUs.
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Communication-Efficient Federated Fine-Tuning
FDA-Opt unifies and improves upon FedOpt and FDA for communication-efficient federated fine-tuning of language models on NLP tasks, outperforming optimized FedOpt baselines.