To further validate the model convergence performance, we list the perplexity (PPL) at 10w step (near 7 days) of GPT -Medium (12 layers, hidden size 1024, intermediate size 2048, GShard, Capacity factor 1.2) with different expert numbers on the openwebtext2 dataset. "ladder-like" distribution trend that the ranks within a node has a high preference to dispatch the data The detailed model configurations are listed in Table 2. Name Layers Gate Stage 1 Stage 2 Stage 3 Stage 4 Capacity factor Swin Transformer v1 12 GShard concat 4x4, 96-d, LN {win.sz. Figure 2: Speedup of T A-MoE over FastMoE on Swin Transformer Based Model. 2 References Swin transformer: Hierarchical vision transformer using shifted windows.

MoE dispatch pattern according to the network topology.

We collect and introduce diverse non-homophilous datasets from a variety of application areas that have up to 384x more nodes and 1398x more edges than prior datasets.

Deep Neural Networks (DNNs) are ubiquitous in today's computer vision landscape, despite involving considerable computational costs. The mainstream approaches for runtime acceleration consist in pruning connections ( unstructured pruning) or, better, filters ( structured pruning), both often requiring data to retrain the model.