[Bugfix][Quantization] Guard the MXFP8 FlashInfer path on FlashInfer availability

[LH-and-FPGA]

Purpose

Two places select or enter the FlashInfer MXFP8 path on device capability alone,
without checking that FlashInfer is actually importable.

1. Kernel selection — vllm/model_executor/kernels/linear/mxfp8/flashinfer.py

class FlashInferCutlassMxfp8LinearKernel(Mxfp8LinearKernel):
    @classmethod
    def is_supported(cls, compute_capability=None):
        if current_platform.has_device_capability(100):
            return True, None
        return False, "requires >=sm_100 (Blackwell)"

can_implement() is an unconditional return True, None, and the class sits at
index 1 of _POSSIBLE_MXFP8_KERNELS[CUDA]:

[FlashInferCutedsl, FlashInferCutlass, Marlin, B12x, Emulation, Humming]

So on any Blackwell GPU it is selected ahead of MarlinMxfp8LinearKernel and
B12xMxfp8LinearKernel — both of which would have run — and then fails at the
first forward. The sibling class 70 lines below in the same file does check:

class FlashInferCutedslMxfp8LinearKernel(Mxfp8LinearKernel):
    @classmethod
    def is_supported(cls, compute_capability=None):
        if not (current_platform.is_cuda()
                and current_platform.is_device_capability_family(100)):
            return False, "requires sm_100/sm_103 (Blackwell)"
        if not has_flashinfer_cutedsl():
            return False, "requires FlashInfer CuTe-DSL module"
        return True, None

Reached from ModelOptMxFp8LinearMethod (modelopt.py:1762),
CompressedTensorsW8A8Mxfp8 (compressed_tensors_w8a8_mxfp8.py:38),
Mxfp8OnlineLinearMethod (online/mxfp8.py:45) and the INC scheme
(inc/schemes/inc_mxfp8_linear.py:21).

2. Activation quantization — layers/quantization/utils/mxfp8_utils.py

if current_platform.has_device_capability(100):
    from flashinfer import mxfp8_quantize as flashinfer_mxfp8_quantize
    ...
# ROCm Triton path
...
return _mxfp8_e4m3_quantize_torch(x, is_sf_swizzled_layout)

The function ends with a pure-torch implementation, but on Blackwell without
FlashInfer the first branch raises ModuleNotFoundError before it can be
reached — so that fallback is currently dead code on the hardware where it is
most likely to be wanted.

Scope, stated plainly

flashinfer-python is pinned in requirements/cuda.txt, so a standard install
has it and will not hit this. What the patch fixes is the case where it is
absent — source builds that exclude it, or environments where the import fails —
and, in the second site, it restores a fallback the code already ships but can
never execute. This is a robustness fix, not a fix for the default path.

Fix

Add the availability checks, mirroring FlashInferCutedslMxfp8LinearKernel in
the same file.

The device gate deliberately stays >= sm_100. I checked whether it should
be narrowed to is_device_capability_family(100) like the sibling, and it
should not: FlashInfer 0.6.16.post3 builds an mxfp8_gemm_cutlass_sm120 variant,
and I verified the full path — JIT compile, mxfp8_e4m3_quantize, and
apply_weights — works on an RTX PRO 4000 Blackwell (sm_120). Narrowing the
check would drop a working backend to Marlin. The is_cuda() term is added for
symmetry with the sibling; it is redundant given the PlatformEnum.CUDA lookup
table, and harmless.

Not a duplicate

Checked on 2026-08-17 against main @ 017e9f444:

• is:pr is:open mxfp8 flashinfer → 28 results; none modifies
  FlashInferCutlassMxfp8LinearKernel or mxfp8_utils.py.
• [Kernel] Add FlashInfer TRTLLM MXFP8 linear backend #52204 ("[Kernel] Add FlashInfer TRTLLM MXFP8 linear backend") is the
  closest: it touches the same file. I pulled its diff — it adds a new
  FlashInferTrtllmMxfp8LinearKernel class, leaves FlashInferCutlassMxfp8LinearKernel
  unchanged, and does not touch mxfp8_utils.py. Worth noting that the new
  kernel it introduces does include if not has_flashinfer(): return False, "requires FlashInfer" — the guard this PR adds to the existing class. [Kernel] Add adaptive layouts to TRTLLM MXFP8 linear backend #52275
  is stacked on it and equally disjoint.
• git log -S 'has_device_capability(100)' -- .../mxfp8/flashinfer.py returns
  only the commit that introduced the line.

Test Plan and Results

Hardware: NVIDIA RTX PRO 4000 Blackwell, compute capability 12.0.
torch 2.13.0+cu130, triton 3.7.1, flashinfer 0.6.16.post3.

Reproduction before the fix

On sm_120 with FlashInfer made unimportable in-process:

Cutlass.is_supported()       = (True, None)          <- should be False
init_mxfp8_linear_kernel()  -> FlashInferCutlassMxfp8LinearKernel
apply_weights()             -> ModuleNotFoundError: No module named 'flashinfer'

New unit tests

tests/kernels/quantization/test_mxfp8_kernel_selection.py — CPU-only, matching
the existing test_mxfp4_kernel_selection.py / test_mxfp6_kernel_selection.py
convention. There was no MXFP8 equivalent in that directory.

Four tests: the availability contract, the >= sm_100 gate (so a later change
cannot silently narrow it), the selector falling through to a runnable kernel,
and the quantizer reaching its torch fallback.

$ pytest tests/kernels/quantization/test_mxfp8_kernel_selection.py -q

	before	after
result	4 failed	4 passed

Before the fix, test_quantizer_falls_back_to_torch_without_flashinfer fails
inside flashinfer/quantization/kernels/mxfp8_quantize.py — it entered
FlashInfer despite the patched-out availability, which is exactly the dead
fallback described above.

No regression on the path that works

With FlashInfer present on sm_120, after the fix:

has_flashinfer(): True
is_supported(): (True, None)
selected: FlashInferCutlassMxfp8LinearKernel
apply_weights OK: (32, 1024) torch.bfloat16

Same kernel, same result — the change only removes selections that could not
have run.

Lint

$ ruff check   <the three files>     ->  All checks passed!
$ ruff format --diff <the three files> ->  3 files already formatted

Accuracy

No evaluation is included. When FlashInfer is present the selected kernel and
its output are unchanged (shown above); when it is absent the previous behaviour
was a crash, so there is no prior output to compare against. Happy to run gsm8k
if a reviewer would like it.

AI assistance

This change was assisted with AI assistance (Claude). I reviewed every changed
line, ran every command reported above on the hardware listed, and can 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.

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[github-actions]

✅ Triggered Buildkite CI #84285 for commit 652c19b831d6.