Smart parameter sharding refers to advanced strategies for partitioning model parameters across distributed devices, going beyond simple uniform sharding. The goal is to optimize the placement of parameter shards based on various factors such as communication patterns, computational load, memory constraints, and hardware topology.
For example, in a hybrid parallelism setup, parameters involved in frequent tensor parallel communications (which require high bandwidth) might be sharded across GPUs within a node with fast NVLink connections, while parameters involved in less frequent data parallel communications could be sharded across nodes.
Smart sharding might also consider the access frequency of different parameters or layers, placing more frequently accessed shards on faster memory or closer to the compute units that need them. Effective smart parameter sharding can lead to significant improvements in training throughput and memory efficiency by minimizing data movement and balancing workloads.