使用 PyTorch Fully Sharded Data Parallel (FSDP) 加速超大型模型訓練
Original: Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel
As AI model scale has grown exponentially, training large models with billions of parameters has become the norm — but this also presents…
Hugging Face 宣布在其 Accelerate 庫中整合 PyTorch FSDP（完全分片數據並行）技術。FSDP 透過將模型參數、梯度和優化器狀態分片到多個 GPU 上，解決了單一 GPU 記憶體不足（OOM）的問題。這項技術讓開發者與研究人員能夠以更低的硬體門檻，高效訓練和微調擁有數十億甚至數百億參數的超大型語言模型。
As AI model scale has grown exponentially, training large models with billions of parameters has become the norm — but this also presents enormous hardware challenges. Traditional DDP (Distributed Data Parallel) replicates the full model on each GPU, which easily leads to out-of-memory (OOM) errors when dealing with ultra-large models.
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