pytorch-fsdp2

To train large models exceeding single-GPU memory, you can use PyTorch FSDP2 to distribute model parameters, gradients, and optimizer states across multiple GPUs and nodes, reducing peak memory usage per device.

DTensor integration in PyTorch distributed training provides more flexible and inspectable parameter sharding. It works with FSDP2 to manage correct initialization and distribute sharding configurations across a DeviceMesh.

The best way to save and load distributed checkpointing states for FSDP2 is by integrating PyTorch's Distributed Checkpoint (DCP), which ensures robust saving and loading of distributed optimizer states and sharded parameters.

Yes, you can use FSDP2 to optimize optimizer state distribution for multi-billion parameter LLMs. It manages correct initialization and parameter sharding via DTensor to enable distributed training of models that cannot fit on a single GPU.

Yes, you need a DeviceMesh to enable advanced sharding configurations with fully_shard in PyTorch. FSDP2 leverages DeviceMesh to optimize distributed training performance and manage parameter sharding across multiple nodes.