Fine-tuning Llama 2 70B using PyTorch FSDP — memory management challenges

AI Impact Summary

Fine-tuning Llama 2 70B using PyTorch FSDP presents significant technical challenges, primarily around memory management. The process requires leveraging Fully Sharded Data Parallelism (FSDP) to distribute the model across multiple GPUs, but the initial loading of the 70B model necessitates a massive amount of CPU RAM (around 2TB) – far exceeding the capacity of a single node. This leads to out-of-memory errors and process terminations. Addressing this involves utilizing SHARDED_STATE_DICT to enable faster checkpoint saving and resuming, alongside Flash Attention V2 and gradient checkpointing to reduce VRAM usage and accelerate training, ultimately mitigating compute costs.