[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

Run this in WSL2:
vllm serve "unsloth/Qwen3.5-4B"
--tokenizer "unsloth/Qwen3.5-4B"
--gpu-memory-utilization 0.88
--max-model-len 262144
--dtype float16
--kv-cache-dtype fp8_e4m3
--cpu-offload-gb 16

Observe vllm will crash due to the lack of pinned memory support (noted in log).

Test Result

pytest tests/cuda

============================================= test session starts ============================================== platform linux -- Python 3.12.3, pytest-9.0.3, pluggy-1.6.0 rootdir: /home/llm/github/vllm configfile: pyproject.toml plugins: anyio-4.13.0, hypothesis-6.152.4, rerunfailures-16.1, forked-1.6.0, cov-7.1.0, typeguard-4.5.1, schemathesis-4.17.0, mock-3.15.1, buildkite-test-collector-0.1.9, asyncio-1.3.0, 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 29 items Running 29 items in this shard

tests/cuda/test_cuda_compatibility_path.py ..................... [ 72%]
tests/cuda/test_cuda_context.py FFF. [ 86%]
tests/cuda/test_pin_memory.py .. [ 93%]
tests/cuda/test_platform_no_cuda_init.py .. [100%]

Serving models with --cpu-offload-gb in WSL2 now works as expected.

---

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[claude]

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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?

[thisisjimmyfb]

@DarkLight1337
Thank you for your feedback. Initial benchmark run showed that using pinned memory actually resulted in perf regression because async_tensor_h2d hardcodes a WSL check for using pinned tensors. Additionally the use of small pinned tensors by InputBatch also contributed to the perf regression due to WSL overhead. After fixing both issues I was able to see a perf improvement:

without pinned memory:
vllm bench throughput --model "unsloth/Qwen3.5-4B" --dataset-name "sonnet" --num-prompts 200 --max-num-seqs 16 --gpu-memory-utilization .88 --dtype float16 --kv-cache-dtype fp8_e4m3 --max-model-len 16384

Throughput: 4.91 requests/s, 5661.71 total tokens/s, 629.08 output tokens/s
Total num prompt tokens: 204800
Total num output tokens: 25600

with pinned memory:
vllm bench throughput --model "unsloth/Qwen3.5-4B" --dataset-name "sonnet" --num-prompts 200 --max-num-seqs 16 --gpu-memory-utilization .88 --dtype float16 --kv-cache-dtype fp8_e4m3 --max-model-len 16384

Throughput: 5.36 requests/s, 6174.69 total tokens/s, 686.08 output tokens/s
Total num prompt tokens: 204800
Total num output tokens: 25600

After rebasing to the tip I am seeing new regression, potentially because of the new changes that landed recently. I will investigate more and update here.