Skip cudaHostRegister for oversized CPU offload mmap regions

[ateska]

Purpose

Fix a startup crash when CPU RAM KV cache offloading is enabled with a large --kv-offloading-size (e.g. 196 GiB) in a container with a low default RLIMIT_MEMLOCK.

pin_mmap_region() calls cudaHostRegister() on the entire shared /dev/shm mmap used for the CPU offload buffer. When the mmap is larger than the process memlock limit, cudaHostRegister() returns cudaErrorInvalidValue. That failure leaves the CUDA context in a bad state, so later CUDA operations fail with misleading errors such as CUDA error: invalid argument, usually during model warmup. This makes it look like a warmup/kernel bug when it is actually a memory-locking limit.

The change adds _max_pinnable_mmap_bytes(), which uses the hard RLIMIT_MEMLOCK when it is finite, and falls back to half of physical RAM when the limit is unlimited. pin_mmap_region() now skips cudaHostRegister() when the offload region exceeds the bound and logs an informative message explaining that transfers will use unpinned DMA and how to raise the limit.

Fixes startup crashes with large CPU RAM KV cache offloading.

Test Plan

1. Unit test for the new helper:

pytest tests/v1/kv_offload/cpu/test_gpu_worker.py::test_max_pinnable_mmap_bytes -v

2. Integration-style startup test on a 4x GPU node with the DeepSeek-V4-Flash-0731 launch configuration and CPU KV offload enabled:

• Run vLLM with --kv-offloading-size 196 and default container memlock (8 MiB).
• Verify the server starts and /v1/models responds.
• Verify the log contains the new info message: Skipping mmap host registration: region size ... GiB exceeds the pinnable memory bound ... GiB.

3. Confirm the offload mmap is created in /dev/shm:

ls -lh /dev/shm/vllm_offload_*.mmap

Test Result

• pytest tests/v1/kv_offload/cpu/test_gpu_worker.py::test_max_pinnable_mmap_bytes -v passes.
• On server with 4x RTX PRO 6000 Blackwell, 256 GiB RAM with --kv-offloading-size 196 and default Docker memlock, the previous code crashed during DeepSeek-V4 mHC warmup with:
  torch.AcceleratorError: CUDA error: invalid argument

After the patch, startup completes successfully and the server responds:

curl -s http://localhost:8000/v1/models
# {"object":"list","data":[{"id":"deepseek-ai/DeepSeek-V4-Flash-0731", ... }]}

The log shows the expected skip message and the 196 GiB mmap file is present:

-rw------- 1 root root 196G ... /dev/shm/vllm_offload_<engine_id>.mmap

Startup is slightly slower because transfers use unpinned DMA, but the server is stable and serving traffic.

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