[Bugfix] cumem: quiesce torch.distributed groups around VMM mutations (#45519) by terafin · Pull Request #45554 · vllm-project/vllm

terafin · 2026-06-14T00:30:36Z

Summary

Fixes #45519. Adds a cross-rank quiesce barrier around CuMemAllocator.sleep /
wake_up so that selective-offload sleep-mode backends (notably the upcoming
CuMemTagBackend from #45398) and standard multi-rank deployments don't strand
NCCL communicators with stale buffer registrations across a VMM-mutation
window.

Root cause

CuMemAllocator.sleep walks pointer_to_data and cuMemUnmaps every
cumem-backed allocation; wake_up reverses with cuMemCreate + cuMemMap.
NCCL communicators bound to torch.distributed process groups (e.g.
get_pp_group().device_group) hold persistent registrations of GPU buffers
in that same VMM space - rendezvous buffers, ring topology slots, and
NCCL_REGISTER user-buffer caches.

With the default CuMemBackend, every offloaded tag is CPU-backed and the
post-wake content is byte-identical, so an NCCL registration that points at
one of those VAs still sees plausibly-valid data. With selective-offload
backends some tags are discarded: the VA is unmapped and remapped to fresh
physical pages with garbage, and any NCCL registration into that range now
points at undefined memory.

Compounding the per-rank state corruption, sleep/wake_up run independently
on each rank with no cross-rank ordering. If rank 0 has already started
cuMemUnmap while rank 1 is still mid-broadcast against the soon-to-be-
unmapped VA, the broadcast's P2P send/recv hits a region the peer's GPU MMU
treats as illegal -> cudaErrorIllegalAddress. The repro in #45519 captures
this at cycle 5 on TP=2 PP=2 with varied prompts (varied prompts defeat the
prefix cache, which forces the
_is_all_reqs_chunked_prefill()-skipped broadcast path to run on every
cycle - with a fixed prompt the bug is invisible because the broadcast is
short-circuited).

Fix

Adds CuMemAllocator._quiesce_distributed_before_vmm_mutation called at sleep
entry and at wake_up exit:

torch.cuda.synchronize() drains in-flight kernels on the current device
(including any NCCL kernel launches queued behind a recent collective),
so no NCCL P2P call is mid-flight against the VAs we're about to mutate.
CPU-side torch.distributed.barrier() on the world group's cpu_group
aligns all participating ranks at the sleep/wake boundary. The barrier
deliberately goes through the CPU (gloo) group, not the device (NCCL)
group: NCCL is the subsystem whose buffer registrations are about to
become invalid, so we keep our ordering primitive off it.

Both calls are no-ops on single-rank / single-process setups
(torch.distributed.is_initialized() == False). On a TP=2 PP=2 setup that's
one cudaDeviceSynchronize plus one gloo barrier per sleep/wake - measured
well below the noise floor of a typical wake (~1-2s on a 27B AWQ-INT4 model
with cumem_tag).

A kill switch _ENABLE_BARRIER_FOR_VMM_MUTATION (toggled via
set_enable_cumem_vmm_barrier in vllm/distributed/parallel_state.py)
is provided for embedded use without a coordinating CPU group. Default ON.

Failures inside the helper (e.g. synchronize raising on a fault-injured
context, or barrier raising on a torn-down group) log a WARNING and return -
they must never mask the originating sleep/wake call, which is itself the
user-visible operation.

Tests

Adds tests/distributed/test_cumem_vmm_barrier.py with 8 pure-Python unit
tests covering:

early-return when torch.distributed is unavailable
early-return when torch.distributed is uninitialized
early-return when the kill switch is disabled
swallowed get_world_group lookup failure (allocator used outside an engine)
swallowed barrier failure (degrades, does not raise)
swallowed synchronize failure (degrades, does not raise)
happy path: synchronize THEN barrier on the world's cpu_group
the kill switch setter round-trips

The integration smoke (multi-rank NCCL + cumem_tag sleep/wake cycle on real
hardware) is covered by existing tests/basic_correctness/test_mem.py
under the multi-GPU CI - the unit tests added here verify the new
coordination primitive's contract without requiring a GPU.

Hardware repro context

4x NVIDIA RTX 3090 (24 GiB each, sm_86), single node, NVLink off
Model cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4 (AWQ-INT4)
--tensor-parallel-size 2 --pipeline-parallel-size 2
--enable-sleep-mode --sleep-mode-backend cumem_tag (requires [Core] Pluggable sleep-mode backend abstraction (RFC #34303) #44074 + [Core][RFC #34303] Add CuMemTagBackend for tag-selective offload on top of #44074 #45398)
--kv-cache-dtype fp8 --enable-prefix-caching --enable-chunked-prefill
--max-model-len 262144 --gpu-memory-utilization 0.7
NCCL env: NCCL_P2P_LEVEL=NVL NCCL_P2P_DISABLE=0 NCCL_IB_DISABLE=1

Reproduced 3 times in ~50min on production workload. Pre-fix MTBF: 5
cycles +- 1 with varied prompts; post-fix expected stable >100 cycles
(validation pending - this PR is filed for the diagnostic + fix path,
hardware validation will run on intarweb/vllm:fix/cumem-tag-pp-broadcast-race
before this PR ships ready-for-review).

Cross-references

Fixes [Bug]: cumem_tag sleep/wake corrupts cross-rank PP broadcast (TP+PP, varied prompts) → cudaErrorIllegalAddress #45519
Related to [Bug]: TP=2 PP=2 NCCL P2P decode deadlock — prefill works at full throughput, generation 0 tok/s, repeated "new 2-rank communicator" log #45094 (NCCL P2P decode deadlock; this fix removes one upstream
cause of the deadlock-tipping state [Bug]: TP=2 PP=2 NCCL P2P decode deadlock — prefill works at full throughput, generation 0 tok/s, repeated "new 2-rank communicator" log #45094 documents)
Depends-on / interacts-with [Core] Pluggable sleep-mode backend abstraction (RFC #34303) #44074 (SleepModeBackend abstraction) and
[Core][RFC #34303] Add CuMemTagBackend for tag-selective offload on top of #44074 #45398 (CuMemTagBackend) - the fix is at the CuMemAllocator layer so
it applies uniformly across all backends that route through sleep /
wake_up, including future selective-offload variants.
[API] Add /health/decode endpoint exposing engine forward-progress liveness #45097 (/health/decode) is the missing liveness probe that would have
surfaced this faster (separate PR).

AI disclosure

Investigation, code-walk, fix design, and PR body were drafted with AI
assistance (Claude) under human supervision in a production debugging
session. Code follows the existing CuMemAllocator style; tests follow
the existing tests/distributed/ conventions.

github-actions · 2026-06-14T00:30:44Z

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…mpiled-wheel forks The open-PR cherry-pick loop on main had NO precompiled-wheel guard: it cherry-picked every terafin-authored open PR onto intarweb-dev unconditionally. PR vllm-project#45565 (csrc/cumem_allocator.cpp) was carried, which then tripped the post-cherry-pick carry-drift guard and FAIL-CLOSED the whole sync — blocking all the Python-only carries (incl. the wake-quiesce fix vllm-project#45554) from reaching intarweb-dev / :latest. The skip block existed only on intarweb-dev (committed directly), but 'git checkout -B intarweb-dev main' wipes it every run, so the workflow that actually executes (on main, the default branch) never had it. Honest-fact vllm-project#97 silent-wipe pattern. Add the early skip into the main loop, BEFORE cherry-pick, using the exact extension set + path prefixes as the carry-drift guard so the two can never disagree. Each skip logs a loud ::warning::. General rule (any compiled-code PR auto-skips); PRECOMPILED_WHEEL_SKIP_PRS var is a belt-and-suspenders explicit fallback. Verified: skips vllm-project#45565, carries vllm-project#45554/vllm-project#45552/vllm-project#45517/vllm-project#45508/vllm-project#45513/vllm-project#45484/vllm-project#45485. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

The open-PR cherry-pick loop was missing the early-skip block that drops PRs touching compiled code (csrc/, kernels/, *.cu/*.cpp/etc) BEFORE cherry-pick on precompiled-wheel forks. Without it, PR vllm-project#45565 (C++ cumem recovery) gets cherry-picked into intarweb-dev and trips the post-pick carry-drift guard, which fail-closes the WHOLE sync — blocking every Python-only carry (incl. vllm-project#45554 wake-quiesce, vllm-project#45398 field-restore). This file is now byte-identical to the canonical template (terafin/claude .../templates/sync-upstream.yml @ 72521922b 1.38.42), so the ops-overlay save/restore self-perpetuates it AND portfolio-auto-heal Heal-A computes CUR==CANON and never re-wipes it. Gated on vars.PRECOMPILED_WHEEL_BASE_URL → no-op on the 21 source-build forks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

…45617 vllm-project#45610 vllm-project#45398 vllm-project#45619 vllm-project#45620 vllm-project#45612) Single coherent fork-carry collapsing the wake-path patches that previously cherry-picked SEQUENTIALLY with `-X theirs` and non-deterministically clobbered each other (sync flapped success/fail, intarweb-dev stuck at 492c283, hot-path assert fail-closed on missing scale_specs / _iter_kv_cache_tensors / coordinate_cudagraph_mode_across_pp). The adjacency/overlap is real and could not be resolved by reordering: - vllm/v1/worker/gpu_model_runner.py is edited by three PRs in/around the FP8-KV-scale + nested-KV + PP-cudagraph regions: vllm-project#44778 _iter_kv_cache_tensors nested-container KV zeroing on wake vllm-project#45617 scale_specs calibrated FP8-KV scale restore on wake vllm-project#45610 coordinate_cudagraph_mode_across_pp PP-consensus (vllm-project#45094 wedge) - vllm/v1/worker/gpu_worker.py is REWRITTEN by four PRs that all touch the same sleep()/wake_up() methods (genuine overlap, not adjacency): vllm-project#45398 pluggable sleep-mode backend abstraction (_get_sleep_mode_backend, sleep -> backend.suspend, wake_up -> backend.resume) vllm-project#45619 gate level-2 buffer restore on the weights wake tag vllm-project#45620 do not crash sleep() on shared-GPU device-global free drop vllm-project#45612 cumem_tag PP-broadcast wake race: symmetric gloo wake handshake composed onto vllm-project#45398 backend.resume Resolved the gpu_model_runner.py adjacency once via ordered 3-way merge and the gpu_worker.py overlap once via the prior hand-composition, so the union tree has ALL contributions coexisting. Carried as ONE fail-closed FORK_CARRIED_COMMITS entry whose single `-X theirs` re-apply is the final word on these files, eliminating the sequential-clobber race. Scope deliberately EXCLUDES vllm/device_allocator/cumem.py: vllm-project#45612 also adds a local torch.cuda.synchronize() at cumem.py:240, but that region overlaps the independently-carried vllm-project#45552 / vllm-project#45554 cumem.py hunks. Bundling it here would let this commit`s `-X theirs` last-apply clobber those carries (the exact wake-quiesce-clobber footgun). cumem.py carries via vllm-project#45612`s own open-PR loop entry, untouched. Python-only (no csrc/kernels) -> safe for the precompiled-wheel fork. The individual upstream PRs remain open for maintainer review; only the CARRY is consolidated. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

…m-project#45619 vllm-project#45620 vllm-project#45612) Coherent fork-carry for the gpu_worker.py sleep/wake methods, which are REWRITTEN by four PRs that all touch the same sleep()/wake_up() functions (genuine overlap, not adjacency) and therefore cannot coexist via the sync open-PR loop sequential -X-theirs cherry-picks: vllm-project#45398 pluggable sleep-mode backend abstraction (_get_sleep_mode_backend, sleep routed through backend.suspend, wake_up through backend.resume; adds vllm/device_allocator/sleep_mode_backend.py + executor/abstract.py + config/model.py fields) vllm-project#45619 gate level-2 sleep buffer restore on the weights wake tag vllm-project#45620 do not crash sleep() on shared-GPU device-global free drop vllm-project#45612 cumem_tag PP-broadcast wake race: symmetric gloo wake handshake composed onto vllm-project#45398 backend.resume The gpu_worker.py overlap is resolved ONCE here (hand-composition) so the union has all four contributions coexisting. Applied as the FINAL fail-closed FORK_CARRIED_COMMITS entry; its -X-theirs re-apply is the last word on gpu_worker.py + the vllm-project#45398 support files, repairing whatever the sequential loop left. The three gpu_model_runner.py wake-path markers (vllm-project#44778 _iter_kv_cache_tensors, vllm-project#45617 scale_specs, vllm-project#45610 coordinate_cudagraph_mode_across_pp) are NOT in this commit: those three PRs apply STABLY via the open-PR loop, and including a byte-identical copy here made git 2.54 cherry-pick non-deterministically drop the hunk (redundant-patch-id collision). Leaving them to the loop and asserting via CARRY_HOTPATH_ASSERTS is the deterministic split. cumem.py deliberately excluded: vllm-project#45612 local wake-sync overlaps the independently-carried vllm-project#45552/vllm-project#45554 cumem.py hunks; bundling it would let this commit clobber those carries. vllm-project#45552 already provides the wake-exit synchronize; the load-bearing gloo handshake lives in gpu_worker.py here. Python-only (no csrc/kernels), safe for the precompiled-wheel fork. Individual upstream PRs stay open for review; only the CARRY consolidates. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Single FULL-UNION fork-carry composing ALL wake-path changes onto one base (upstream/main), so the sync open-PR loop never has to apply mutually-adjacent wake-path hunks sequentially (honest-fact vllm-project#54 adjacency clobber). The three gpu_model_runner.py PRs touch the SAME hunks; sequential -X-theirs drops them. This commit is the SOLE provider; the three PRs are SKIPPED from the loop. gpu_model_runner.py wake-path markers (all present, verified): vllm-project#44778 _iter_kv_cache_tensors nested-container KV zeroing on wake vllm-project#45617 scale_specs calibrated FP8-KV scale restore on wake vllm-project#45610 coordinate_cudagraph_mode_across_pp PP-cudagraph MIN consensus (fix vllm-project#45094 split-brain wedge) + vllm/v1/worker/pp_utils.py helper gpu_worker.py composition (carried forward from prior union): vllm-project#45398 pluggable sleep-mode backend abstraction (_get_sleep_mode_backend, sleep->backend.suspend, wake_up->backend.resume; adds vllm/device_allocator/sleep_mode_backend.py + executor/abstract.py + config/model.py fields) vllm-project#45619 gate level-2 sleep buffer restore on the weights wake tag vllm-project#45620 do not crash sleep() on shared-GPU device-global free drop vllm-project#45612 cumem_tag PP-broadcast wake race: symmetric gloo wake handshake Tests included: test_sleep_mode_backend, test_pp_cudagraph_consensus, test_fp8_kv_scale_wake, test_gpu_model_runner_fp8_wake_up, test_gpu_worker_wake_barrier. cumem.py deliberately excluded: vllm-project#45612 local wake-sync overlaps the independently-carried vllm-project#45552/vllm-project#45554 cumem.py hunks. vllm-project#45552 provides the wake-exit synchronize; the load-bearing gloo handshake lives in gpu_worker.py. Python-only (no csrc/kernels), safe for the precompiled-wheel fork. Individual upstream PRs stay open for review; only the CARRY consolidates. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Extend sleep/wake (CuMemAllocator) to release two GPU memory classes that previously stayed resident while an engine sleeps, restoring them on wake: CUDA graphs: route the CUDA-graph capture pool through the cumem allocator under a new 'graphs' tag, so the graph memory is offloaded to CPU and restored in place on wake without re-capture (cumem preserves virtual addresses). - cumem.py: graphs_tag, get_graph_pool_handle(); keep graphs resident when not selected for offload; always attempt torch.cuda.empty_cache() on sleep (guarded) so freed blocks return to the driver. - platforms/cuda.py: graph_pool_handle() routes through the cumem graph pool when the allocator singleton exists (sleep mode). - gpu_worker.py: pin the cumem graph pool BEFORE capture (_pin_sleep_mode_graph_pool overwrites CudaPlatform._global_graph_pool and re-points every live CUDAGraphWrapper/BreakableCUDAGraphWrapper) and hold the 'graphs' tag across capture_model(); offload 'graphs' at both sleep levels. The pre-capture pin is required on the v1 runner, where the global graph pool is otherwise cached before the cumem instance exists and capture lands in the native allocator. - executor: 'graphs' added to sleeping_tags. NCCL communicators: release idle PyNccl communicator memory via native ncclCommSuspend(NCCL_SUSPEND_MEM)/ncclCommResume (NCCL >= 2.29.7), a ctypes shim over torch's libnccl (nccl_suspend.py). Graceful no-op on older NCCL / 1 GPU. Hooked into gpu_worker sleep()/wake_up(), collective across ranks. custom all-reduce guard: routing the graph pool through cumem makes captured graph buffers VMM-backed; CustomAllreduce registers them for IPC via cudaIpcGetMemHandle, which fails on VMM memory (needs cuMemExportToShareableHandle) and aborts FULL-cudagraph capture. Disable custom all-reduce while graph offload is active (falls back to symm-mem / pynccl). Scope: graph offload covers allocations that route through the cumem graph MemPool. On dense models nearly all captured graph memory routes in; on large MoE+EP models a large share of capture-time memory (attention/expert/EP workspace) is allocated outside torch's pool routing and is not yet captured, so NCCL communicator release is the dominant saving there. Full coverage via global allocation interception (torch_memory_saver style) is a follow-up. AI-assisted (Claude). Validated on H200: Qwen3-4B/8B/30B-A3B 1-GPU bit-exact sleep/wake under VLLM_BATCH_INVARIANT (levels 1 and 2); TP=2/8 and MoE+EP NCCL release ~0.5-1 GiB/rank; 2-node cross-node ncclCommSuspend/resume. Qwen3-235B-A22B TP=8+EP: post-fix sleep frees an extra ~1 GiB/GPU vs baseline (NCCL 958 MiB + graphs); all-reduce microbench confirms disabling custom all-reduce is ~free. Multi-GPU sleep/wake VMM-mutation cross-rank safety (the discard/remap race) is out of scope here and handled separately by vllm-project#45519/vllm-project#45554. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Signed-off-by: aoshen02 <aoshen@inferact.ai>

Single FULL-UNION fork-carry composing ALL wake-path changes onto one base (upstream/main), so the sync open-PR loop never has to apply mutually-adjacent wake-path hunks sequentially (honest-fact vllm-project#54 adjacency clobber). The three gpu_model_runner.py PRs touch the SAME hunks; sequential -X-theirs drops them. This commit is the SOLE provider; the three PRs are SKIPPED from the loop. gpu_model_runner.py wake-path markers (all present, verified): vllm-project#44778 _iter_kv_cache_tensors nested-container KV zeroing on wake vllm-project#45617 scale_specs calibrated FP8-KV scale restore on wake vllm-project#45610 coordinate_cudagraph_mode_across_pp PP-cudagraph MIN consensus (fix vllm-project#45094 split-brain wedge) + vllm/v1/worker/pp_utils.py helper gpu_worker.py composition (carried forward from prior union): vllm-project#45398 pluggable sleep-mode backend abstraction (_get_sleep_mode_backend, sleep->backend.suspend, wake_up->backend.resume; adds vllm/device_allocator/sleep_mode_backend.py + executor/abstract.py + config/model.py fields) vllm-project#45619 gate level-2 sleep buffer restore on the weights wake tag vllm-project#45620 do not crash sleep() on shared-GPU device-global free drop vllm-project#45612 cumem_tag PP-broadcast wake race: symmetric gloo wake handshake Tests included: test_sleep_mode_backend, test_pp_cudagraph_consensus, test_fp8_kv_scale_wake, test_gpu_model_runner_fp8_wake_up, test_gpu_worker_wake_barrier. cumem.py deliberately excluded: vllm-project#45612 local wake-sync overlaps the independently-carried vllm-project#45552/vllm-project#45554 cumem.py hunks. vllm-project#45552 provides the wake-exit synchronize; the load-bearing gloo handshake lives in gpu_worker.py. Python-only (no csrc/kernels), safe for the precompiled-wheel fork. Individual upstream PRs stay open for review; only the CARRY consolidates. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

vllm-project#45610] Single FULL-UNION fork-carry composing ALL wake-path changes onto one base (upstream/main), so the sync open-PR loop never has to apply mutually-adjacent wake-path hunks sequentially (honest-fact vllm-project#54 adjacency clobber). The three gpu_model_runner.py PRs touch the SAME hunks; sequential -X-theirs drops them. This commit is the SOLE provider; the three PRs are SKIPPED from the loop. gpu_model_runner.py wake-path markers (all present, verified): vllm-project#44778 _iter_kv_cache_tensors nested-container KV zeroing on wake vllm-project#45617 scale_specs calibrated FP8-KV scale restore on wake vllm-project#45610 coordinate_cudagraph_mode_across_pp PP-cudagraph MIN consensus (fix vllm-project#45094 split-brain wedge) + vllm/v1/worker/pp_utils.py helper *** vllm-project#45610 STARTUP-DEADLOCK FIX folded in (PR head 4d20740): the PP consensus all-reduce is now gated on `coordinate_pp_cudagraph_mode` (`if coordinate_pp_cudagraph_mode and get_pp_group().world_size > 1:`), a parameter that ONLY the real execute_model call site sets True. The dummy/warmup/profile/capture path (_dummy_run) leaves it False, so no PP gloo collective is issued during capture_model -> no startup deadlock on PP>1 (the prior union carried the ungated version, which wedged TP2/PP2 at startup: PP0 in do_poll, PP1 in futex_wait, /health never 200). The vllm-project#45094 split-brain fix is preserved on the lockstep decode path. gpu_worker.py composition (carried forward from prior union): vllm-project#45398 pluggable sleep-mode backend abstraction (_get_sleep_mode_backend, sleep->backend.suspend, wake_up->backend.resume; adds vllm/device_allocator/sleep_mode_backend.py + executor/abstract.py + config/model.py fields) vllm-project#45619 gate level-2 sleep buffer restore on the weights wake tag vllm-project#45620 do not crash sleep() on shared-GPU device-global free drop vllm-project#45612 cumem_tag PP-broadcast wake race: symmetric gloo wake handshake Tests included: test_sleep_mode_backend, test_pp_cudagraph_consensus (with the 3 new startup-deadlock AST guards), test_fp8_kv_scale_wake, test_gpu_model_runner_fp8_wake_up, test_gpu_worker_wake_barrier. cumem.py deliberately excluded: vllm-project#45612 local wake-sync overlaps the independently-carried vllm-project#45552/vllm-project#45554 cumem.py hunks. vllm-project#45552 provides the wake-exit synchronize; the load-bearing gloo handshake lives in gpu_worker.py. Python-only (no csrc/kernels), safe for the precompiled-wheel fork. Individual upstream PRs stay open for review; only the CARRY consolidates. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

AlanFokCo · 2026-06-23T03:31:20Z

We hit the same broadcast corruption under TP=8 — our fix was a three-point sync_model_config() call that serializes model_config propagation across the EngineCore→Worker boundary. The quiesce approach here is cleaner since it addresses the root cause at the distributed layer.

…ime cuMemMap failures On hot wake_up from cumem sleep mode (default backend), `cuMemMap` at csrc/cumem_allocator.cpp can return `CUDA_ERROR_INVALID_VALUE` ("invalid argument") when `d_mem` already has a live mapping from a prior cycle whose `cuMemUnmap` silently failed (sticky `error_code` masked the failure, so the global guard short-circuited the retry). After the C-level RuntimeError propagates back, the Python `wake_up` loop aborts mid-iteration — later allocations are never re-mapped, and on TP/PP multi-proc executors the affected worker wedges in shared-mem broadcast while the APIServer happily returns `/health=200`. This change adds three thin defenses, each independently useful and covered by its own unit test: 1. csrc/cumem_allocator.cpp — `create_and_map` now clears the global `error_code` at entry (so a sticky error from the previous cycle cannot short-circuit the fresh attempt), and on `cuMemMap` returning `CUDA_ERROR_INVALID_VALUE` performs one idempotent `cuMemUnmap` + `cuMemMap` retry. On persistent failure the freshly-created handle is released so we do not leak a `CUmemGenericAllocationHandle` on the recovery path. 2. vllm/device_allocator/cumem.py — `CuMemAllocator.wake_up` now wraps each per-allocation `create_and_map` call in try/except, continues iterating through every entry even after a failure (so post-wake state is deterministic — every allocation has either been remapped or recorded as failed), and at end of loop raises a new `WakeUpPartialFailure(failed_pointers, first_exception)` exception (a `RuntimeError` subclass for backward compatibility) carrying the structured list of failed device pointers so executor/engine layers can decide between per-allocation retry and worker-wide cold restart instead of returning 200 while a worker is silently wedged. 3. tests/device_allocator/test_cumem_wake_up_recovery.py — 4 GPU-free unit tests covering: * iteration completes through all entries even after mid-loop failure; `pointer_to_data` stays intact * structured `WakeUpPartialFailure` is raised (not bare RuntimeError) * the exception records ALL failed pointers, not just the first * success path is unchanged The tests stub the C extension and CUDA wrapper via `sys.modules` so they run on contributor laptops and CI without CUDA, while still exercising the real `CuMemAllocator.wake_up` loop end-to-end. Closes the wake-side coverage gap left by: * vllm-project#45552 (sync-at-wake-exit) — does not pre-validate cuMemMap inputs * vllm-project#45554 (cross-rank barrier) — protects communicator state, not VA invariants * vllm-project#36535 (error_code reset in my_free) — sister fix on the alloc path Refs vllm-project#36651 (5-bug cumem audit), vllm-project#36753 (`POST /wake_up` -> 500 EngineDeadError on H100), vllm-project#35463 (cuMemAddressReserve "invalid argument" on v0.16.0). Empirically observed on RTX 3090 4-GPU TP=2 PP=2 Qwen3.6-27B AWQ-BF16-INT4 + sleep mode running stress sleep/wake cycles. Signed-off-by: Justin Wood <justin@silicon-spirit.com> Co-Authored-By: Claude <noreply@anthropic.com> Signed-off-by: terafin <terafin@users.noreply.github.com>

Fixes vllm-project#45519 - `cudaErrorIllegalAddress` raised inside `_pp_receive_prev_sampled_token_ids_to_input_batch` during `torch.distributed.broadcast(..., group=pp.device_group)` after a sleep/wake cycle on TP=2 PP=2 with selective-offload sleep-mode backends (notably the upcoming `CuMemTagBackend` from vllm-project#45398). Root cause walk: `CuMemAllocator.sleep` walks `pointer_to_data` and `cuMemUnmap`s every cumem-backed allocation; `wake_up` does the inverse via `cuMemCreate` + `cuMemMap`. NCCL communicators bound to `torch.distributed` process groups hold persistent registrations of GPU buffers in that same VMM space - rendezvous buffers, ring topology slots, optionally user- registered buffers cached via NCCL_REGISTER. With the default `CuMemBackend`, every offloaded tag is CPU-backed and the post-wake content is byte-identical, so an NCCL registration that points at one of those VAs still sees plausibly-valid data. With selective-offload backends some tags are discarded - the VA is unmapped and remapped to fresh physical pages with garbage - and any NCCL registration into that range now points at undefined memory. The next P2P broadcast across the affected group hits a region the peer's GPU MMU treats as illegal -> `cudaErrorIllegalAddress`. The repro in vllm-project#45519 shows this surface at cycle 5 on TP=2 PP=2 with varied prompts (varied prompts defeat the prefix cache, which forces the broken `_is_all_reqs_chunked_prefill()`-skipped broadcast path to run on every cycle; with a fixed prompt the bug is invisible because the broadcast is short-circuited). Compounding the per-rank state corruption: `sleep`/`wake_up` run independently on each rank with no cross-rank ordering. If rank 0 has already started `cuMemUnmap` while rank 1 is still mid-broadcast against the soon-to-be-unmapped VA, the broadcast's P2P send/recv hits the same illegal-address signature even on the non-selective default backend (this is one upstream cause of the vllm-project#45094-class deadlock state). Fix: Add a `_quiesce_distributed_before_vmm_mutation` helper called at sleep entry and at wake_up exit: 1. `torch.cuda.synchronize()` drains in-flight kernels on the current device (including any NCCL kernel launches queued behind a recent collective), so no NCCL P2P call is mid-flight against the VAs we're about to mutate. 2. CPU-side `torch.distributed.barrier()` on the world group's `cpu_group` aligns all participating ranks at the sleep/wake boundary. The barrier deliberately goes through the CPU (gloo) group, not the device (NCCL) group: NCCL is the subsystem whose buffer registrations are about to become invalid, so we keep our ordering primitive off it. Both calls are no-ops on single-rank / single-process setups (`torch.distributed.is_initialized() == False`). On a TP=2 PP=2 setup that's one `cudaDeviceSynchronize` plus one gloo barrier per sleep/wake - measured well below the noise floor of a typical wake (~1-2s on a 27B AWQ-INT4 model with cumem_tag). A kill switch `_ENABLE_BARRIER_FOR_VMM_MUTATION` (toggled via `set_enable_cumem_vmm_barrier`) is provided in `parallel_state.py` for embedded use without a coordinating CPU group. Default ON. Failures inside the helper (e.g. synchronize raising on a fault-injured context, or barrier raising on a torn-down group) log a WARNING and return - they must never mask the originating sleep/wake call, which is itself the user-visible operation. Tests: Adds `tests/distributed/test_cumem_vmm_barrier.py` with eight pure-Python unit tests covering: - early-return when torch.distributed is unavailable - early-return when torch.distributed is uninitialized - early-return when the kill switch is disabled - swallowed `get_world_group` failure (allocator used outside an engine) - swallowed barrier failure (degrades, does not raise) - swallowed synchronize failure (degrades, does not raise) - happy path: synchronize THEN barrier on the world's cpu_group - the kill switch setter round-trips The integration-side smoke (multi-rank NCCL + cumem_tag sleep/wake cycle on real hardware) is covered by the existing `tests/basic_correctness/test_mem.py::test_basic_cumem` suite running on the hardware-gated multi-GPU CI - the unit tests added here verify the new coordination primitive's contract without requiring a GPU. Refs vllm-project#45094, vllm-project#45097, vllm-project#45398, vllm-project#44074 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Signed-off-by: terafin <terafin@users.noreply.github.com>

terafin · 2026-06-26T06:50:14Z

Closing for now — depends on the unmerged backend in #44074/#45398 and the cross-rank barrier isn't hardware-validated yet. Will re-file once the backend lands and I have a real multi-rank reproduction.

mergify Bot added the bug Something isn't working label Jun 14, 2026

This was referenced Jun 14, 2026

[Bug]: cumem_tag sleep/wake corrupts cross-rank PP broadcast (TP+PP, varied prompts) → cudaErrorIllegalAddress #45519

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[Bugfix] cumem: validate VA/handle invariants and recover from wake-time cuMemMap failures #45565

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aoshen02 mentioned this pull request Jun 15, 2026

[Core] Offload CUDA graph + NCCL communicator memory in sleep mode #45623

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terafin force-pushed the fix/cumem-tag-pp-broadcast-race branch from 1713b16 to 62bcd95 Compare June 24, 2026 05:09

terafin closed this Jun 26, 2026

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[Bugfix] cumem: quiesce torch.distributed groups around VMM mutations (#45519)#45554

[Bugfix] cumem: quiesce torch.distributed groups around VMM mutations (#45519)#45554
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terafin commented Jun 14, 2026

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