[yavarb]

Related: #52540, #34948

Purpose

Root-cause fix-out for the SM120 NVFP4 serving crashes in #52540 (and at least some reports in #34948): a shared GEMM workspace that changes device address after CUDA graphs have captured launches against it turns every subsequent replay into a use-after-free.

The defect that kills the #52540 configuration lives in FlashInfer: every cuDNN GEMM execute path in flashinfer/gemm/gemm_base.py grows its shared 32 MiB workspace with tensor.resize_() when an execution plan asks for more. resize_() frees the old storage and moves the buffer; launches already enqueued, and CUDA graphs captured earlier, keep dereferencing the old address, which the caching allocator immediately reuses for unrelated tensors. Fix filed upstream as flashinfer-ai/flashinfer#4553. Instrumented evidence from the failing machine (full matrix in #52540):

Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  89%|████████▉ | 8/9
[F5WS 21:32:46] RESIZE old_ptr=0x7efadc000000 old=33554432 new=33554688
[F5WS 21:32:46] RESIZE done new_ptr=0x7efa8d200000

One cuDNN FP8 plan wanted 256 bytes more than the 32 MiB default, mid-capture; the eight piecewise graphs already captured kept the stale pointer, and the server died minutes into serving — a GPU coredump caught cudnn_generated_fort_native_* faulting on a "pointer" that was two int32s of unrelated tensor data (0x27000013FF).

This PR does two things on the vLLM side:

1. Pre-grow FlashInfer's shared FP8 GEMM workspace during kernel warmup, before any capture (presize_flashinfer_gemm_workspaces in vllm/utils/flashinfer.py, called from kernel_warmup). With the address fixed at its high-water mark before the first capture, the in-place grow in current FlashInfer releases never fires. This is a guard, not the root fix; it is cheap (one 64 MiB buffer per device on the FlashInfer FP8 path), and it protects every vLLM+FlashInfer user until a FlashInfer release carries bugfix: never resize_() the shared cuDNN GEMM workspace (use-after-free under CUDA graphs) flashinfer-ai/flashinfer#4553.

2. Fix the same defect class in vLLM's own tree: SM100Workspace.ensure_size in vllm/v1/attention/backends/mla/cutlass_mla.py grew its shared workspace with resize_() too. Growth now allocates a fresh buffer and retires the old one instead of freeing it, so graphs captured against an older generation keep a live, correct address (their capture-time size was sufficient for the shapes baked into them).

Diagnosis trail (short form; full evidence in #52540)

• Same model FP8-quantized end-to-end: stable 35 h. NVFP4-mixed build: dies/wedges in ~16.5 min under 24-way load, graphs ON. Established by falsification: not the NVFP4 GEMM kernels (swapping/removing them changes nothing), not KV shape, not MTP/prefix-cache.
• GPU coredump-on-exception on the failing config names the faulting kernel: a cuDNN FP8 GEMM (matMul_pointwise_pointwise) reading garbage pointer arguments — the FP8 projections feeding the GDN/attention layers, not the NVFP4 GEMMs themselves.
• Instrumenting FlashInfer's eight resize_ sites shows the workspace moving during vLLM's piecewise capture on this build; the FP8-only build happens to hit its workspace high-water mark before capture, which is why it never crashed. The quantization scheme was never the mechanism — it only determined when the first oversized cuDNN plan arrived.
• Instrumenting under CUDA_LAUNCH_BLOCKING=1 attributed the hang mode to the same lane: CUDA error 702 in seconds, raised through execute_cudnn_gemm_fp8_graph → execute_plan_at_index. cuDNN split-K plans keep completion semaphores inside the workspace (the oversized request is exactly 32 MiB + 256 B); non-zeroed workspace memory makes the kernel spin forever. Hence the second commit of bugfix: never resize_() the shared cuDNN GEMM workspace (use-after-free under CUDA graphs) flashinfer-ai/flashinfer#4553 and zero_init=True in this PR's pre-size guard.
• With both workspace fixes applied, a third failure remained (~13.5 min): a GPU coredump caught a cuDNN sm120 GEMM kernel dereferencing pointer value 0x1 — pointer slots holding what look like GEMM dims/strides, consistent with cuDNN device-side parameter staging being rewritten while captured launches replay. Excluding the cuDNN runner from the FlashInfer fp8/bf16 GEMM autotune lanes ran the same soak clean for the full 55 minutes (3.3× baseline MTTF, 2,708 completions, 0 errors) at ~22% higher throughput (cublas/cutlass fp8 beat the tuned cuDNN config on this workload).
• FlashInfer main already gates cuDNN out of bmm_fp8 auto-candidates on SM12x when override_shape is unavailable (citing "async CUDA fault escapes"); flashinfer 0.6.14 — what current vLLM images ship — predates that gate.
• Operator workaround available today with no patch: VLLM_DISABLED_KERNELS=FlashInferFP8ScaledMMLinearKernel, which falls back to CutlassFP8ScaledMMLinearKernel. Validated on the failing configuration (30-minute soak, graphs ON, clean; goldens byte-identical) — numbers in [Bug]: ModelOpt NVFP4 on SM120 dies or wedges under sustained load with CUDA graphs (not the NVFP4 GEMM backend) #52540.
• 50 temperature-0 classification goldens are byte-identical across every fixed configuration.

A question for maintainers beyond this PR: whether vLLM should avoid FlashInferFP8ScaledMMLinearKernel (or FlashInfer should avoid autotuning Cudnn*GemmRunners) wherever the lane will be captured into CUDA graphs while the same cuDNN handle keeps serving eager executes, until the parameter-staging interaction is resolved upstream.

Test Plan

• presize_flashinfer_gemm_workspaces: exercised on 2× RTX PRO 6000 Blackwell (SM120) with the [Bug]: ModelOpt NVFP4 on SM120 dies or wedges under sustained load with CUDA graphs (not the NVFP4 GEMM backend) #52540 reproducer; no behavioural change beyond the pre-allocation.
• cutlass_mla.py: mechanical change, growth path preserved; covered by existing CUTLASS MLA tests on SM100.

Test Result

See validation numbers above; goldens byte-identical.

🤖 Generated with Claude Code

https://claude.ai/code/session_01WbnhJtzC1GpmDBiLQiboiD

[claude]

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[LucasWilkinson]

nit: could we use the workspace manager here instead? in workspace.py?

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @yavarb.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork