Learning to love mesh-oriented sharding (blog.ezyang.com)

A recent discussion highlights the differences in sharding strategies between PyTorch and JAX, focusing on their implications for AI/ML frameworks. PyTorch employs a mesh-dim oriented approach, allowing each dimension in a device mesh to specify corresponding sharding methods, while JAX adopts a tensor-dim oriented perspective, indicating mesh dimensions for each tensor dimension. This fundamental difference creates a divergence in extensibility, with PyTorch's sharding being inherently more open for user-defined customizations, despite some limitations in support and convenience. The significance of these distinctions lies in their impact on distributed computing and tensor manipulation within deep learning applications. JAX's closed system simplifies operations by limiting extensibility, which can prevent complex errors, but also restricts user flexibility. Conversely, PyTorch's mesh-oriented design can accommodate more varied and complex sharding methods, allowing for greater expressivity in cases like uneven sharding during model training. This adaptability is crucial as modern AI applications increasingly require advanced distribution strategies, particularly as models grow in complexity and scale.