[Bugfix][Kimi K3] Enable deferred MoE finalization before weight loading

[zyongye]

Summary

• Enable K3 deferred MoE finalization from the selected monolithic expert class during runner construction.
• Keep the existing hidden-size and parallel-topology guards.
• Log when deferred top-k finalization is active and add a regression test for the pre-weight-loading lifecycle.

Root cause

#53152 checked quant_method.moe_kernel in LatentMoERunner.__init__. Mxfp4MoEMethod initializes that field to None and only creates the kernel later in process_weights_after_loading, so the deferred path was always disabled during runner construction even when the supported FlashInfer TRTLLM MXFP4 expert class had already been selected.

The tail operator must know whether it consumes an UnfinalizedMoEOutput during construction so its CuTeDSL warmup units are registered with the correct top-k shape. This change therefore uses the already-selected expert class at that lifecycle point.

Duplicate-work check

• This is a follow-up bug fix to merged PR [K3 Perf] Fuse MXFP4 top-k finalization into latent-tail, ~5% E2E latency reduction #53152 and belongs to [Feature]: Kimi K3 Performance Optimization #50587.
• Searched open PRs for 53152, 50587, K3 latent MoE deferred finalize, and latent moe tail fusion.
• Open PR [Kimi K3 Refactor] Add UnfinalizedMoEOutput proto following up for #53152 #53310 also follows [K3 Perf] Fuse MXFP4 top-k finalization into latent-tail, ~5% E2E latency reduction #53152, but it changes UnfinalizedMoEOutput typing in other runners and does not touch this initialization-order bug.
• No open PR addresses this fix.

Tests

.venv/bin/python -m pytest \
  tests/models/kimi_k3/test_latent_moe_tail.py::test_deferred_finalize_enabled_before_moe_kernel_setup \
  tests/models/kimi_k3/test_latent_moe_tail.py::test_latent_moe_tail_deferred_finalize_matches_finalized \
  -q

Result: 2 passed on 8x NVIDIA B300. The second test compares deferred and finalized outputs across TP8.

.venv/bin/pre-commit run ruff-check --files \
  vllm/models/kimi_k3/nvidia/latent_moe_runner.py \
  tests/models/kimi_k3/test_latent_moe_tail.py
.venv/bin/pre-commit run ruff-format --files \
  vllm/models/kimi_k3/nvidia/latent_moe_runner.py \
  tests/models/kimi_k3/test_latent_moe_tail.py

Result: both passed.

Serving validation: Kimi-K3 TP8 selected the FlashInfer TRTLLM MXFP4/MXFP8 backend, reported deferred top-k finalization enabled, and completed 40/40 requests with no failures.

AI assistance

OpenAI Codex was used to diagnose the initialization-order issue, draft the code and test, run validation, and prepare this PR. The human submitter must review every changed line and understand and defend the change end-to-end.

[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.

[zyongye]

/ci run

[github-actions]

✅ Triggered Buildkite CI #85122 for commit bbbf7405d192.