[Bug Fix] Allow pinned memory for WSL2

[thisisjimmyfb]

Purpose

Cuda graph restricts CPU and GPU tensor copy during graph capture to only pinned tensors. Therefore WSL's current restriction for pinned memory is preventing the use of --cpu-offload-gb in conjunction with cuda graph.

This change enables the use of pinned memory for WSL2, and warnings have been added to inform of the restrictions with pinned memory in WSL2.

This PR improves WSL2 support and potentially addresses issue #37883

Test Plan

Use the new test to verify no perf regression is introduced as a result of enabling pinned memory:

uv run --no-sync benchmarks/benchmark_pin_memory.py 

Test Result

uv run --no-sync python benchmarks/benchmark_pin_memory.py -v

============================================= test session starts ============================================== platform linux -- Python 3.12.3, pytest-9.0.3, pluggy-1.6.0 -- /home/llm/github/vllm/.venv/bin/python3 cachedir: .pytest_cache hypothesis profile 'default' rootdir: /home/llm/github/vllm configfile: pyproject.toml plugins: anyio-4.13.0, hypothesis-6.155.0, forked-1.6.0, cov-7.1.0, asyncio-1.4.0, rerunfailures-16.3, mock-3.15.1, buildkite-test-collector-0.1.9, schemathesis-4.20.2, typeguard-4.5.2, shard-0.1.2, timeout-2.4.0 asyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 4 items Running 4 items in this shard: benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_throughput[v1], benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_throughput[v2], benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_latency[v1], benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_latency[v2]

benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_throughput[v1] SKIPPED (pin_memory not av...) [ 25%]
benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_throughput[v2] PASSED [ 50%]
benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_latency[v1] SKIPPED (pin_memory not avail...) [ 75%]
benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_latency[v2] PASSED [100%]

=============================================== warnings summary ===============================================
benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_throughput[v1]
:488: DeprecationWarning: builtin type SwigPyPacked has no module attribute

benchmarks/benchmark_pin_memory.py::TestPinnedMemory::test_throughput[v1]
:488: DeprecationWarning: builtin type SwigPyObject has no module attribute

benchmarks/benchmark_pin_memory.py: 14 warnings
/home/llm/github/vllm/.venv/lib/python3.12/site-packages/torch/jit/_script.py:365: DeprecationWarning: torch.jit.script_method is deprecated. Please switch to torch.compile or torch.export.
warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================ 2 passed, 2 skipped, 16 warnings in 134.91s (0:02:14) =============================
sys:1: DeprecationWarning: builtin type swigvarlink has no module attribute

Serving models with --cpu-offload-gb in WSL2 such as the following now works as expected:

VLLM_WSL2_ENABLE_PIN_MEMORY=1 vllm serve unsloth/Qwen3.6-35B-A3B-NVFP4 
--tokenizer Qwen/Qwen3.6-35B-A3B 
--gpu-memory-utilization 0.35 
--max-model-len 8192 
--dtype float16 
--kv-cache-dtype fp8_e4m3 
--cpu-offload-gb 20 

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
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[claude]

Claude Code Review

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[gemini-code-assist]

Code Review

This pull request modifies the is_pin_memory_available function to perform a runtime probe for pinned memory support in WSL, allowing it to be enabled on modern drivers. A review comment identifies a critical risk where the probe might initialize the CUDA context in the master process, breaking multi-GPU functionality, and suggests caching the result to improve efficiency.

[DarkLight1337]

Have you done any performance benchmarks to check if pin_memory actually helps on WSL2?

[thisisjimmyfb]

@DarkLight1337 The intent to use pinned memory for WSL2 is out of necessity due to cuda graph restricting CPU and GPU tensor copy during graph capture to only pinned tensor. Therefore without this fix, we can't use --cpu-offload-gb with cuda graph.

I did some benchmark with and without cuda graph on my system (rtx 5080 16Gb vram + 64Gb system ram):

with cuda graph:
vllm bench latency --model "unsloth/Qwen3.5-4B" --max-num-seqs 1 --gpu-memory-utilization .88 --dtype float16 --kv-cache-dtype fp8_e4m3 --max-model-len 131072 --batch-size 8 --input-len 512 --output-len 128 --output-json cuda_graph.json

Avg latency: 11.348387527733575 seconds
10% percentile latency: 11.328151453500322 seconds
25% percentile latency: 11.335275465499763 seconds
50% percentile latency: 11.340350735501488 seconds
75% percentile latency: 11.347257409501253 seconds
90% percentile latency: 11.37050327579891 seconds
99% percentile latency: 11.436521534489184 seconds

no cuda graph:
vllm bench latency --model "unsloth/Qwen3.5-4B" --max-num-seqs 1 --gpu-memory-utilization .88 --dtype float16 --kv-cache-dtype fp8_e4m3 --max-model-len 131072 --batch-size 8 --input-len 512 --output-len 128 --enforce-eager --output-json cuda_graph.json

Avg latency: 22.25984884779973 seconds
10% percentile latency: 19.57793887630105 seconds
25% percentile latency: 21.659131986750253 seconds
50% percentile latency: 22.54327095000008 seconds
75% percentile latency: 23.37842245400043 seconds
90% percentile latency: 23.915846188899014 seconds
99% percentile latency: 25.148832654359357 seconds

[DarkLight1337]

In the case where it isn't necessary, does it cause any performance regression?

[mergify]

Hi @thisisjimmyfb, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

Tip

Is mypy failing?

mypy is run differently in CI. If the failure is related to this check, please use the following command to run it locally:

# For mypy (substitute "3.10" with the failing version if needed)
pre-commit run --hook-stage manual mypy-3.10

[thisisjimmyfb]

@DarkLight1337

Since checking flags in is_pin_memory requires a much bigger PR than necessary, I have changed this PR to use environment variables instead. I have change is_pin_memory_available to depend on VLLM_WSL2_ENABLE_PIN_MEMORY=1. This PR now touches a much smaller surface area now.

Thanks,
Jimmy

[DarkLight1337]

Sorry I forgot about this, LGTM now

[rs38]

"motivation for this PR is to fix --cpu-offload-gb for v1 model runner and v2 model runner since uvabuffer requires pinned memory."
so is
#43381
fixed?

[DarkLight1337]

I guess so. Feel free to try it