Revert "[Bugfix][MoE] Tune FlashInfer experts to scheduler token limit" (#52989)

[vllm-agent]

Reverts #52989 ("[Bugfix][MoE] Tune FlashInfer experts to scheduler token limit", merged as bfb6c1349).

Why

Nightly build #84887 (bfb6c1349, the merge commit of #52989) turned :nvidia: (B200) LM Eval PCP red for the first time. That job was green in the previous four nightlies (84473, 84555, 84687, 84753).

evals/gsm8k/test_gsm8k_correctness.py::test_gsm8k_correctness[GLM-5.2-NVFP4-TP2-PCP2-EP] now dies with Server exited unexpectedly.; every one of the four workers raises:

MemoryError: CUDA out of memory. Tried to allocate 12.22 GiB.
GPU 0 has a total capacity of 178.35 GiB of which 9.68 GiB is free.
  File ".../fused_moe/experts/flashinfer_cutlass_moe.py", line 368, in apply
  File ".../flashinfer/fused_moe/core.py", line 636, in cutlass
  File "<unknown>", line 0, in FusedMoeRunner::getWorkspaceInfo(...)

Line 368 is the flashinfer_cutlass_fused_moe(...) call this PR modified. The job runs with max_num_batched_tokens=32768, so fi_moe_largest_bucket() returns max(32768 * dp_size, 8192) and the FlashInfer CUTLASS autotune bucket jumps 8192 -> 32768, quadrupling the workspace getWorkspaceInfo reserves.

fi_moe_largest_bucket's own docstring warns that "overestimation may be dangerous, increasing tuning-cost and memory use" and that PCP is not modelled by the estimate — which is exactly the configuration that broke. The sibling GLM-5.2-NVFP4-TP1-PCP4-EP config, which has a smaller per-rank footprint, still passes (0.9325).

Related: open issue #51071 tracks earlier OOMs in this job.

Note for reviewers

This is a draft. The underlying autotune-coverage problem #52989 set out to fix is real; a narrower fix that caps the tuned bucket (or accounts for PCP/EP memory headroom) would be preferable to this revert. Reverting is proposed only to get the B200 PCP eval green while that fix is prepared. The revert applied cleanly with no conflicts and is an exact inverse of the original diff (6 files, 1 insertion, 12 deletions).

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[jeejeelee]

cc @mgoin

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[mgoin]

Let's just revert for now to avoid causing OOMs for users

[github-actions]

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