fix(device-plugin): auto-set CUDA_DISABLE_CONTROL for whole-GPU allocations - #2664
fix(device-plugin): auto-set CUDA_DISABLE_CONTROL for whole-GPU allocations#2664Rickydama3 wants to merge 6 commits into
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…ations Fixes Project-HAMi#2641. When a pod is assigned a whole GPU (no memory or core limits), libvgpu.so's interception is unnecessary and causes deadlocks in vLLM's pynccl. This commit automatically sets CUDA_DISABLE_CONTROL=true and skips mounting ld.so.preload when devreq indicates no limits, preventing the hang. Signed-off-by: Ricky Dama <rickydama2006@gmail.com>
This addresses the bot's feedback by ensuring that if a user explicitly sets CUDA_DISABLE_CONTROL=false on a whole-GPU allocation, we respect it and do not overwrite it to true. Signed-off-by: Ricky Dama <rickydama2006@gmail.com>
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[APPROVALNOTIFIER] This PR is NOT APPROVED This pull-request has been approved by: Rickydama3 The full list of commands accepted by this bot can be found here. DetailsNeeds approval from an approver in each of these files:Approvers can indicate their approval by writing |
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Included review availability: Your plan provides up to 8 included reviews per hour; 7 remain after this review. 📝 WalkthroughWalkthroughThe NVIDIA device plugin detects whole-GPU allocations and defaults ChangesWhole-GPU CUDA control
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to The change auto-enables CUDA control bypass for whole-GPU allocations while preserving an explicitly configured user value; no actionable merge-blocking risk remains after normal checks and review. Sequence Diagram(s)sequenceDiagram
participant AllocationRequest
participant NVIDIADevicePlugin
participant ContainerSpec
AllocationRequest->>NVIDIADevicePlugin: submit device allocation and environment
NVIDIADevicePlugin->>NVIDIADevicePlugin: classify whole-GPU usage
NVIDIADevicePlugin->>ContainerSpec: set or preserve CUDA_DISABLE_CONTROL
NVIDIADevicePlugin->>ContainerSpec: add or omit /etc/ld.so.preload
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Inline comments:
In `@pkg/device-plugin/nvidiadevice/nvinternal/plugin/server.go`:
- Around line 823-834: Update the device-allocation loop around isWholeGPU to
evaluate each dev.Usedcores, marking isWholeGPU false whenever any device has a
nonzero SM limit while preserving the response environment value. Add a
multi-device test covering zero limits on the first device and a nonzero
Usedcores value on a later device.
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pkg/device-plugin/nvidiadevice/nvinternal/plugin/alloc_refactor_test.gopkg/device-plugin/nvidiadevice/nvinternal/plugin/server.go
Signed-off-by: Ricky Dama <rickydama2006@gmail.com>
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Signed-off-by: Ricky Dama <rickydama2006@gmail.com>
Signed-off-by: Ricky Dama <rickydama2006@gmail.com>
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| for i, dev := range devreq { | ||
| limitKey := fmt.Sprintf("CUDA_DEVICE_MEMORY_LIMIT_%v", i) | ||
| response.Envs[limitKey] = fmt.Sprintf("%vm", dev.Usedmem) | ||
| if dev.Usedmem != 0 || dev.Usedcores != 0 { |
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usedmem 0 and usedcores 0 means no limits, not a whole gpu. the scheduler can still place other pods on this card, only coresreq 100 is exclusive. and a real exclusive request with gpucores 100 fails this check and keeps the preload. why not key on coresreq == 100?
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I misunderstood how the scheduler handled 0 and didn't realize it meant unbounded limits instead of exclusive access. I just pushed a fix to check dev.Usedcores == 100 instead so it actually catches true whole-GPU requests. thanks for your insight
| } | ||
| } | ||
| if !found { | ||
| if isWholeGPU && !hasControlSetting { |
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with control off the pod stops writing the vgpu cache, so vgpumonitor loses metrics for it. this silently changes observability for every count-only pod, not just the vllm case.
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Since auto-setting this breaks vgpumonitor metrics for all exclusive pods, should we just drop this logic entirely and advise vLLM users to manually set CUDA_DISABLE_CONTROL="true" instead?
Signed-off-by: Ricky Dama <rickydama2006@gmail.com>
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What type of PR is this?
/kind bug
What this PR does / why we need it:
HAMi allows multiple pods to share a single GPU by enforcing memory limits. It enforces these limits by pushing a file called
ld.so.preloadthat intercepts GPU commands. However, when a user requests a whole GPU, HAMi was still pushing that interception file. That interception was causing a major issue: it was making vLLM (a popular AI model runner) freeze up in a deadlock when it tried to initialize its networking (NCCL).My PR changes the code in
server.goto detect when a pod is asking for a whole GPU. Because a whole GPU doesn't need memory limits, we can safely bypass the interception. To do this, our code automatically injects an environment variable calledCUDA_DISABLE_CONTROL=trueinto the pod. When this variable is set to true, HAMi skips theld.so.preloadinterception, which allows vLLM to start up perfectly without freezing or stuck.We had to be careful: what if a user explicitly wants the interception and manually sets
CUDA_DISABLE_CONTROL=falsein their pod configuration? Our code includes a check for this. It looks at the pod's environment variables first, and if the user explicitly set a value, we never overwrite it.To prove our code works, we added two new unit tests in
alloc_refactor_test.go. One test proves that we auto-set the variable when a whole GPU is requested, and the second test proves that we preserve the user's manual choice if they explicitly set it to false.Which issue(s) this PR fixes:
Fixes #2641
Special notes for your reviewer:
Hardware Validation:
CUDA_DISABLE_CONTROL=trueset, the deployment is live and verified: pod 1/1 Running, health checks passing, serving inference."AI Assistance Notice:
I used an chatgpt to help me understand the vLLM deadlock and write the
CUDA_DISABLE_CONTROLinjection logic, but I fully understand the code produced and have explicitly verified it via the new unit tests.Does this PR introduce a user-facing change?:
Summary by CodeRabbit
New Features
Bug Fixes