Improve expert parallelism placement

[wrmedford]

Changes two key behaviors:

1. Changes default expert parallelism placement logic to be round robin instead of overloading the last rank.
2. Changes expert parallelism placement logic to be group aware which should improve GPU utilization.

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[youkaichao]

@tlrmchlsmth can you help review?

[youkaichao]

why does it improve performance?

[wrmedford]

why does it improve performance?

Without group aware placement, there is nothing preventing all experts from the same group existing on the same rank. Because grouped topk does a group selection before expert selection, at least with DeepSeek-V3 models no experts in 4 of 8 groups are used at all in any given expert layer. In the worst case scenario where a group is entirely on one rank and the expert TopK only selects from that single group, that could mean that only one rank can process that layer.

This removes that worst case and should also reduce waiting both with the current implementation where inactive experts are zeroed followed by an All-Reduce, as well as after DeepEP is implemented as it should result in better resource utilization.

Technically you can still get "unlucky" and have all selected experts share the same rank (which is significantly more likely with fewer GPUs), but it should be significantly less likely than in the current implementation.