pytorch-fsdp

Parameter sharding in PyTorch distributed training reduces GPU memory by partitioning model parameters across multiple GPUs instead of replicating them. This allows training large models that exceed single GPU memory limits by gathering shards only when needed for forward or backward computation.

To implement fully_shard on a transformer model, wrap the model layers with the fully_shard API call. This shards parameters across the device mesh, automatically handling gradient synchronization and parameter gathering during the forward and backward passes to optimize memory.

Yes, PyTorch FSDP supports CPU offloading and mixed precision. You can configure CPU offloading to move parameters to host memory, while mixed precision uses reduced floating point formats to further decrease memory consumption and accelerate distributed communication across GPUs.

The transition from FSDP1 to FSDP2 in PyTorch introduces per-parameter sharding and device mesh based architecture. FSDP2 offers finer-grained memory management, improved collective communication scheduling, and better integration with PyTorch native distributed training features compared to the original FSDP1 implementation.

Yes, you can use PyTorch FSDP for training large language models across multiple nodes. FSDP configures device meshes and schedules collective communications to shard transformer parameters efficiently, enabling distributed training of massive models that exceed the memory capacity of a single GPU.