[Bugfix] Detect driver-level CUDA init before fork

[Sunt-ing]

Fixes #32611

Purpose

_maybe_force_spawn() currently decides whether to avoid fork from torch.cuda.is_initialized(). That misses a real case from #32611: a parent process can initialize the CUDA Driver API through a non-PyTorch import path while PyTorch still reports CUDA as uninitialized. vLLM then forks EngineCore workers, and the child can fail during CUDA initialization.

This PR extends cuda_is_initialized() with a CUDA Driver API probe. cuDeviceGetCount() returns CUDA_ERROR_NOT_INITIALIZED before cuInit(); any other result means the driver is already initialized or ambiguous, so vLLM forces spawn.

This keeps the decision at the worker start-method boundary instead of special-casing FlashAttention/Cutlass imports. The relevant invariant is the inherited driver state before fork, not which package initialized it.

References:

• PyTorch poison-fork guidance: https://docs.pytorch.org/docs/2.9/notes/multiprocessing.html#poison-fork-in-multiprocessing
• CUDA Driver API initialization behavior: https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__INITIALIZE.html

Not a duplicate

Checked #32611 discussion, timeline cross-references, and nearby PRs. #42874 handles stale primary contexts inherited after set_device; #34818 and #33550/#26037 are adjacent CUDA-init/platform-help-path work. None of them makes _maybe_force_spawn() detect driver-only initialization before forking.

Test Plan

• Add a CUDA regression test that calls cuInit(0) without initializing PyTorch CUDA, then verifies cuda_is_initialized() detects the driver state and _maybe_force_spawn() selects spawn.
• Re-run existing platform utility tests and changed-file lint/type checks.
• Reproduce the reporter model path with tencent/HunyuanOCR.

Test Result

Base: origin/main = 035733515 ([Kernel][DSv4] Optimize sparse FP8 compressor kernels (#44161))

GPU used for CUDA/E2E validation: NVIDIA RTX PRO 6000.

Real-model repro: tencent/HunyuanOCR at revision f6af82ee007fe6091b29fb3bb287b491ead41c82.

• Old clean repro env (vllm==0.14.0, flash_attn==2.8.3+cu12torch2.9cxx11abiTRUE, torch==2.9.1+cu128, transformers==4.57.6, nvidia-cutlass-dsl==4.5.2): clean HunyuanOCR LLM(...) follows vllm_flash_attn.flash_attn_interface -> flash_attn.cute.interface -> cutlass; the parent CUDA driver becomes initialized while torch.cuda.is_initialized() remains False; the forked worker fails with RuntimeError: CUDA driver initialization failed, you might not have a CUDA gpu.
• Same env controls: explicit VLLM_WORKER_MULTIPROC_METHOD=spawn completes a real OCR image E2E run; monkeypatching the old env's cuda_is_initialized() to this PR's driver-level detection also forces spawn and completes the same OCR image E2E run.
• Current main: clean HunyuanOCR no longer self-pollutes the CUDA driver in this environment, but forcing parent-side cutlass pollution still reproduces the fork failure. With this patch, the same polluted HunyuanOCR text and OCR-image E2E runs force spawn and complete.

Regression/lint status: CUDA platform regression tests passed (3 passed), CPU platform utility tests passed (2 passed), and changed-file ruff/format/mypy/typos/SPDX/import/CUDA-call/diff checks passed.

Full pre-commit run --files ... did not complete because actionlint could not download Go dependencies from proxy.golang.org; the changed-file Python hooks and equivalent scoped checks above passed.

Commands run

• PYTHONPATH=$PWD CUDA_VISIBLE_DEVICES=0 python -m pytest tests/cuda/test_platform_no_cuda_init.py -q --tb=short -> 3 passed
• PYTHONPATH=$PWD CUDA_VISIBLE_DEVICES= python -m pytest tests/utils_/test_system_utils.py -q --tb=short -> 2 passed
• ruff check vllm/utils/platform_utils.py tests/cuda/test_platform_no_cuda_init.py tests/cuda/scripts/check_cuda_driver_init_forces_spawn.py -> passed
• ruff format --check vllm/utils/platform_utils.py tests/cuda/test_platform_no_cuda_init.py tests/cuda/scripts/check_cuda_driver_init_forces_spawn.py -> passed
• python -m mypy vllm/utils/platform_utils.py --follow-imports=skip -> passed
• pre-commit run ruff-check --files ..., ruff-format, typos, and mypy-local -> passed
• Direct scoped checks for SPDX headers, lazy imports, forbidden imports, torch CUDA calls, boolean context managers, filenames, and git diff --check -> passed

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