tests: Fix flaky test for NLLB-MoE
Author: connor-henderson
Pull request: yes

# What does this PR do?

Fixes #22464 (and added some light docs edits I happened to notice)

From my comment in the issue:
Looked into this and I think the flakiness is caused by the natural variability in the sparse MoE layers. Specifically that when they calculate which experts to use in the gating logic, they’re computing probabilities imperfectly for two different sets of inputs: one with prior inputs concatenated with the past key values and one with just the past key values. 

The test usually passes cause magnitude of the difference is usually likely to be small. Notably, when the vocab size is increased this pass rate goes up (and vice versa) since the increased representational capacity can help the model make more accurate decisions about which experts to use for each input. For example, increasing the vocab size in the config from its current 99 to 999 increases the pass rate from ~80% to ~95%.

I think this flakiness is inherent in the sparse layers, but if I understand right the point of the test is to check the decoder uses the past properly, so I edited the test to use dense layers and moved to the rtol down to 1e-3 to be in line with the other models’ version of this check. Wrote a loop to test this on a 1000 passes and they all passed.


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Comments:
_The documentation is not available anymore as the PR was closed or merged._

cc @ydshieh 

Happy to!