fix(allocator): use tracked allocation device instead of current context in async free accounting (#310) - #311
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[APPROVALNOTIFIER] This PR is NOT APPROVED This pull-request has been approved by: AyushSrivastava1818 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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ChangesAsynchronous free accounting
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to This is a localized correction to async-free accounting with a focused regression test; no actionable merge-blocking risk remains after normal checks and review. Suggested labels: Suggested reviewers: Poem
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Full details: Title checkExplanation The title clearly and specifically summarizes the main allocator fix: using the tracked allocation device instead of the current context during asynchronous free accounting. It is concise enough for history scanning and includes the relevant issue number.
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In `@src/allocator/allocator.c`:
- Line 251: Update the rm_gpu_device_memory_usage call to include spaces after
each comma, preserving its existing arguments and behavior.
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src/allocator/allocator.ctest/CMakeLists.txttest/test_alloc_async_free_wrong_device.c
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…k_async remove_chunk_async() used cuCtxGetDevice() -- the calling thread's currently-bound context's device -- to pick which device's tracked usage counter to decrement, instead of val->entry->dev, the device the chunk was actually allocated on and accounted against (mirroring the already-correct pattern in the synchronous remove_chunk() a few lines above). cuMemFreeAsync(dptr, hStream) takes an explicit stream, not an implicit "current device": nothing requires the caller's current context to match the stream's/allocation's device (multi-GPU worker pools that round-robin cuCtxSetCurrent, async cleanup running under a different device's context, etc.). When they differ, the bug corrupts accounting for two devices at once: the real allocation's device is never decremented (its tracked usage stays permanently inflated even though the memory was freed), and the wrong device is decremented for memory it never held (its unsigned counter underflows to a huge bogus value). Fixes Project-HAMi#310. Adds test_alloc_async_free_wrong_device, which allocates on device 0, switches the current context to device 1, frees via the real hooked cuMemFreeAsync using device 0's stream, and asserts via get_gpu_memory_usage() that device 0's usage returns to baseline and device 1's is untouched. Needs >= 2 GPUs; skips (CTest SKIP_RETURN_CODE) rather than failing when only one is visible. Signed-off-by: AyushSrivastava1818 <ayush.sri0705@gmail.com>
- allocator.c: add the missing space after each comma in the new rm_gpu_device_memory_usage() call (whitespace/comma), the one cpplint warning CodeRabbit flagged that was actually part of this PR's diff. Verified against a pre/post cpplint diff: this was the only new-line violation; the surrounding untouched lines carry pre-existing violations out of this PR's scope. - test_alloc_async_free_wrong_device.c: removed comments that only restated what the adjacent printf/fprintf text, or the file's own header (which already lists the same 5-step reproduction sequence), already said. Kept the ones explaining non-obvious behavior (why cuCtxCreate requires re-selecting device 0's context, why the first allocation's delta must equal ALLOC_BYTES exactly, "best-effort teardown" matching the rest of the test suite's convention). Signed-off-by: AyushSrivastava1818 <ayush.sri0705@gmail.com>
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@archlitchi have a look Sir...if theres no issue I would resolve the conflicts then get it merged |
| for (val = a_list->head; val != NULL; val = val->next) { | ||
| if (val->entry->address == dptr) { | ||
| t_size=val->entry->length; | ||
| CUdevice t_dev = val->entry->dev; /* capture before LIST_REMOVE frees entry */ |
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#290 already rewrites remove_chunk_async the same way. did you two settle which one keeps it?
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not discussed yet will do
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@AyushSrivastava1818 yoo hi, #290 has had this since ee875ef I'd say it stays there. your 2-device test is worth landing on top though, once rebased. works?
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@Eshiv-Pandey Yes you can go on i will do the required changes once yours gets merged!
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Fixes
Closes #310
Background
In
src/allocator/allocator.c,remove_chunk_async()(used by thecuMemFreeAsynchook viafree_raw_async()) determines which device's usage counter to decrement by callingcuCtxGetDevice(&dev)— the device of the calling thread's currently-bound context — instead of usingval->entry->dev, the device the chunk was actually allocated on and accounted against.cuMemFreeAsync(dptr, hStream)takes an explicit stream, not an implicit "current device," and CUDA does not require the calling thread's current context to match the device the stream or memory belongs to. In any multi-GPU process (worker threads round-robiningcuCtxSetCurrent, pipelines that allocate on one device and free from a code path where another device happens to be current), this mismatch is fully reachable — nothing in HAMi-core's hook chain pins or validates the device before the call reachesremove_chunk_async.rm_gpu_device_memory_usage()decrementsused[dev]in the per-process, per-device shared-memory usage table thatget_gpu_memory_usage()/oom_check()read to enforce the vGPU memory limit — the core mechanism this library exists to provide. A device mismatch here corrupts accounting for two devices at once: the real allocation device is never decremented (usage stays permanently inflated, eventually causing spurious OOM rejections), while the wrong device is decremented for memory it never held (usage under-counts real consumption, letting a process exceed its actual limit undetected).The sibling synchronous function,
remove_chunk(), already does this correctly viat_dev = val->entry->dev.remove_chunk_asyncdiverged from that pattern.Full details and impact analysis are in #310.
Changes
src/allocator/allocator.c: capturet_dev = val->entry->devbeforeLIST_REMOVEfrees the entry, and pass that torm_gpu_device_memory_usageinstead ofcuCtxGetDevice(). Removed the now-deadCUdevice dev; cuCtxGetDevice(&dev);lines. 3-line net diff.test/test_alloc_async_free_wrong_device.c: new regression test. Allocates on device 0, switches current context to device 1, frees via device 0's stream through the real hookedcuMemFreeAsync— the exact mismatch — then readsget_gpu_memory_usage()(linkingmultiprocess_memory_limit.cdirectly into the test binary, same pattern astest_postinit_owner_death) to assert device 0's usage returns to baseline, device 1's stays untouched, and a subsequent clean allocation on device 1 isn't affected by any lingering corruption. Skips gracefully via CTest'sSKIP_RETURN_CODEon environments with fewer than 2 GPUs.test/CMakeLists.txt: registered the new test target, reusing the plainLD_PRELOAD=libvgpu.sosetup — no fault-injection shim needed since this test exercises a genuine two-device code path rather than forcing a driver call to fail.Verification
cpplint --linelength=120 src/allocator/allocator.c: no new warnings (diffed precisely against the pre-fix file; same 5 pre-existing warnings, unrelated, shifted by line number only — the new/changed lines are clean).python3 hack/check_cuda_hook_consistency.py: PASS.add_gpu_device_memory_usage/rm_gpu_device_memory_usageto confirm the assertions are deterministic and not dependent on driver-specific quirks.cuda.havailable (gcc -fsyntax-onlyfails immediately on the missing header). This PR has not been built or executed against a real CUDA driver or real multi-GPU hardware. Requesting CI/maintainer verification before merge, same as fix(allocator): free GPU allocation and tracking entry on async post-allocation driver failure (#306) #307.AI disclosure
This issue, fix, and PR description were produced with substantial AI assistance (Claude): the bug was found via AI-driven source review of
allocator.c, the fix and the new regression test were written by an AI coding agent, and this PR body was drafted by AI. All of it has been reviewed and is being submitted by me as the human author of record — I take responsibility for its correctness. Per the Verification section above, this fix is unvalidated against real CUDA hardware due to the sandboxed environment's lack of a CUDA toolchain/GPU; I'd appreciate maintainer/CI scrutiny on the multi-GPU test behavior in particular before merge.Summary by CodeRabbit
Bug Fixes
Tests