[BugFix][MoE] Warm up FlashInfer b12x NVFP4 MoE to avoid mid-serving JIT stall (#47458)

[anmolxlight]

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Purpose

Fixes #47458 — a ~17.5s engine-wide, stop-the-world stall on the FlashInfer
flashinfer_b12x NVFP4 MoE backend (SM120/SM121) when the request mix
transitions from large/long-context prefills to a burst of small requests.
Every in-flight request (including ones mid-decode) freezes simultaneously,
and it recurs under sustained mixed-shape load.

Root cause

Per the diagnosis in the issue and the follow-up narrowing in the comment
thread, the stall is CPU-bound CuteDSL (MLIR) JIT compilation, not a slow
GPU kernel:

• The flashinfer_b12x fused MoE is a CuteDSL kernel that lazily JIT-compiles
  a specialization per input-shape bucket.
• Its small-token ("micro") path keys the compiled kernel on both
  num_tokens and the internal shared workspace's max_rows.
• A large prefill grows that shared workspace. Because max_rows is part of
  the cache key, growing it invalidates the previously-compiled small-batch
  kernels. The next small request then eats a multi-second, engine-blocking
  recompile — the stop-the-world stall. Under mixed load it recurs each time a
  large shape re-grows the workspace after small kernels were compiled.

(The original "GDN CuteDSL prefill" theory doesn't hold on SM120 — that path is
platform-gated off there — the _cutlass_ir frames come from the b12x MoE
path, which is the CuteDSL consumer active in this config.)

Fix

Add a startup warmup, flashinfer_b12x_moe_warmup, wired into
kernel_warmup(). When (and only when) the b12x MoE backend is actually in use
on SM120, it:

1. runs a dummy forward at the max token count first, growing the
   shared workspace to its serving-time high-water mark, then
2. runs dummy forwards at the small micro-path token sizes, so those
   kernels JIT-compile against the final workspace and stay cache-valid for
   the whole session.

Ordering is the crux: the large shape must establish max_rows before the
small-shape kernels compile, otherwise the first real large prefill would
invalidate them again. It's placed after the large-shape warmup sweep and
before CUDA-graph capture / the JIT monitor is armed. Each rank warms
independently (the stall is per-rank; PP ranks pay it sequentially).

It is a no-op for every non-b12x deployment — cheap platform + module-walk
gates return immediately.

Reuse-of-real-path note: the warmup drives model_runner._dummy_run(...)
(the same mechanism the existing FlashInfer-attention warmup already uses in
kernel_warmup), so the kernel is exercised through the genuine forward path
with correct FP4 inputs/scales rather than hand-constructed tensors.

Test plan

• ruff check + ruff format --check pass on all changed files.
• Added CPU-only unit tests
  (tests/model_executor/warmup/test_flashinfer_b12x_moe_warmup.py) covering
  the token-size selection (descending, max-first, dedup, filtering, empty
  budget) and the b12x-layer detection walk (positive + negative).
• Requires human validation on SM120 hardware (I do not have access to
  an SM120 GPU): run the issue's repro (6 large distinct prefills →
  small-request burst → sustained mixed load) with --moe-backend=flashinfer_b12x
  and jit_monitor_mode=error, and confirm no b12x CuteDSL compile fires
  during serving and the stall is gone. Please also run the GPU-gated
  tests/kernels/moe/test_flashinfer_b12x_moe.py.

Duplicate-work check

gh issue view 47458 --comments, gh pr list --search "47458 in:body", and
searches for flashinfer b12x moe / b12x warmup turned up no open PR adding
a b12x MoE warmup or otherwise addressing this JIT stall. The existing open
b12x PRs (#47392 activation plumbing, #47577 auto-select, #43687 cutlass
prefill threshold, etc.) are orthogonal.

Note on AI assistance

This change was written with AI assistance (Claude). The root-cause reasoning
follows the issue + comment thread; the code follows the existing
vllm/model_executor/warmup/* conventions (deepseek_v4_mhc_warmup,
deep_gemm_warmup, the FlashInfer-attention _dummy_run warmup). Per
AGENTS.md, the SM120 runtime validation and full test run must be completed
by the human submitter before merge.

[claude]

Claude Code Review

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[chatgpt-codex-connector]

Warm every eager micro-token shape

When CUDA graphs are disabled (for example --enforce-eager / cudagraph_mode=NONE) there are no cudagraph_capture_sizes, so this list is the only coverage for small b12x MoE shapes. The comment says each distinct num_tokens in the micro region compiles its own specialization, but counts such as 3, 5, 6, 7, 9, etc. are not warmed after the max-token workspace growth; the first real small request with one of those token counts can still trigger the same mid-serving JIT stall this warmup is intended to eliminate. Consider warming the actual micro range for eager/no-graph configurations, or otherwise deriving the exact runtime bucket sizes rather than only powers of two.

Useful? React with 👍 / 👎.

[waynehacking8]

Ran the mechanism on an RTX PRO 6000 (flashinfer wheel 0.6.12 - moe_dispatch.py is byte-identical to the 0.6.13 vllm pins) with a synthetic MoE (E=32, topk=4, bf16-in) driving B12xMoEWrapper.run the way FlashInferB12xExperts does. Three datapoints that may help tune the warm set:

1. The ordering claim holds: after running m=64 then m=8 once (19.0s / 15.7s first-hit compiles), repeating both hits the cache at <1ms.

2. One nuance vs the PR description: a large prefill does not invalidate the micro kernels. m=8, m=4096, m=8 leaves the third call at 0.7ms, because routed_rows > 640 takes the dynamic backend, and backend is part of the workspace cache key in _get_cached_workspace - the dynamic workspace is a separate entry. The invalidation behind [Bug] ~17.5s engine-wide stall on large-to-small request shape transitions (NVFP4/b12x, GDN CuteDSL prefill kernel) #47458 is growth within the static region: m=8, m=64, m=8 recompiles the third call (15.2s), since both share the static workspace and 64 replaces it.

3. That leaves a residual hole in the warm set: a serving token count in the static region above the largest warmed static size grows the static workspace and re-invalidates everything you warmed. After warming 64 and 8, an unwarmed m=100 (routed_rows 400, still under the 640 cutover) forced m=8 to recompile again (15.1s). Note the max_num_batched_tokens run goes down the dynamic path, so it does not set the static workspace's high-water mark. Adding the static-region ceiling - floor(640/topk) tokens, cutover from moe_dispatch - to the warm set would put the static workspace at its true max, so unwarmed in-between sizes only pay their own one-time compile instead of cascading.

FWIW flashinfer#3836 (pending upstream) buckets the workspace allocation, which makes growth events O(log) but keeps max_rows in the micro key, so the warmup and the ceiling-size point stay relevant either way.

[anmolxlight]

Great analysis — point #3 is spot on.

The fix adds _STATIC_REGION_ROUTED_ROWS = 640 to the warmup module and includes min(max_tokens, 640 // experts_per_token) in the warmup token set. The module's top_k is read from the b12x MoE layer itself, so the ceiling is computed correctly per-model.

With this change:

• The static workspace reaches its true high-water mark during warmup (instead of max_num_batched_tokens routing through the dynamic path and leaving the static workspace undersized).
• Unwarmed in-between sizes still pay a one-time self-compile, but no longer cascade-invalidate the micro-path kernels that were already compiled.
• Ordering is preserved: descending sort means the ceiling runs first (growing the static workspace), then the micro-path sizes compile against the final workspace.

Thanks for the careful data!