[BugFix] Fix cumem_tag PP-broadcast wake race with symmetric gloo wake handshake (#45519) by terafin · Pull Request #45612 · vllm-project/vllm

terafin · 2026-06-14T23:03:25Z

Purpose

Fixes the cumem_tag PP-broadcast wake race tracked in #45519.

With --sleep-mode-backend=cumem_tag on a TP>1 / PP>1 deployment, wake_up is dispatched to each worker independently via collective_rpc, and each worker re-maps its own VMM-backed regions at its own pace. The very next decode step issues a cross-rank torch.distributed.broadcast on pp.device_group (_pp_receive_prev_sampled_token_ids_to_input_batch). If a faster rank reaches that collective before a slower rank has finished re-mapping the regions backing the broadcast buffers, the collective issues against memory the peer's MMU still treats as invalid → CUDA_ERROR_ILLEGAL_ADDRESS. After that the NCCL communicator is permanently corrupt: the engine deadlocks while /health keeps returning 200.

The fix

Gate Worker.wake_up on a cross-rank wake-success handshake performed after the local allocator wake and before returning to the caller, so no rank can reach a device-group collective until every rank has finished its local wake.

The handshake is an all-reduce of a per-rank success flag (ReduceOp.MIN), deliberately not a bare barrier:

A bare barrier placed after the local wake would strand peers forever if one rank's allocator.wake_up() raised before reaching the barrier — re-introducing the exact full-fleet hang this fix is meant to eliminate.
The all-reduce instead lets every rank learn that a peer failed and raise symmetrically — loud, no hang, and no rank silently proceeding into a device-group collective against a peer whose wake never completed.

The handshake runs on get_world_group().cpu_group (gloo) by design, so the synchronization itself never touches the not-yet-resynced NCCL device_group.

A local torch.cuda.synchronize() is also added at the end of CuMemAllocator.wake_up, so a rank drains its own re-map work before reporting success into the cross-rank all-reduce. This is a purely local guarantee; all cross-rank ordering lives in Worker.wake_up.

Why this rather than the C++ retry in #45565

This is the correct, fully-Python fix at the synchronization layer. #45565 takes a C++ allocator-retry approach that addresses a different symptom (and, being compiled code, can't be applied without rebuilding the wheel). The Python handshake here closes the race at its source — the absence of a cross-rank ordering point between independent per-rank wakes and the first device-group collective.

Tests (GPU-free; fail-pre / pass-post)

tests/v1/worker/test_gpu_worker_wake_barrier.py — handshake fires on multi-rank cumem configs (TP/PP combinations); ordered after the local wake and before return; skipped on single-rank and cumem-disabled configs; routed through cpu_group (gloo) only and never a device-group collective; and the adversarial regressions — a failed local wake still participates in the handshake (peers don't hang), and a failed peer makes an otherwise-healthy rank raise symmetrically.
tests/device_allocator/test_cumem_wake_synchronize.py — wake_up calls torch.cuda.synchronize() after the remap/restore work, ordered last.

CI should run those two files to confirm 15/15 pass. They are pure-Python (mocked allocator + distributed groups), so they run without a GPU.

github-actions · 2026-06-14T23:03:33Z

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Under async scheduling (max_concurrent_batches > 1), EngineCore.step_with_batch_queue submits execute_model(non_block=True) and returns without awaiting the returned future when the batch queue is not yet full (vllm/v1/engine/core.py). A subsequent /sleep reaches CuMemAllocator.sleep, which loops over allocations calling unmap_and_release -> cuMemUnmap, tearing down the KV/weight virtual address space while the still-running forward kernel is writing to it. This races VA teardown against in-flight device work and surfaces as an Xid-31 write-to-unmapped-VA fault that kills EngineCore. The synchronous step() path is safe because future.result() drains the work before any sleep can run, but the async batch-queue path is exposed. The existing torch.cuda.synchronize() in _python_free_callback only covers the PyTorch GC-driven free path, not this explicit sleep() unmap loop -- that asymmetry is the gap. Add a device-wide torch.cuda.synchronize() at the top of sleep(), before the unmap loop, to drain all in-flight kernels before any cuMemUnmap. The cost is negligible relative to the seconds-scale sleep/offload work. This is the sleep-side analog of the wake-side drain (vllm-project#45612); both defend the same cuMemUnmap/cuMemMap-vs-in-flight-kernel race that manifests as the steady-state Xid-31 MMU faults seen under sleep/wake with async scheduling. Cross-ref vllm-project#45519. Adds a CPU-runnable regression test that monkeypatches torch.cuda.synchronize and unmap_and_release to assert the synchronize happens before any unmap in sleep(); it fails on the pre-fix code and passes with this change. The full GPU async-drain repro (long forward under max_concurrent_batches=2, /sleep mid-step, assert no Xid-31) is the integration variant. Signed-off-by: terafin <terafin@users.noreply.github.com> Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

…llm-project#45398 backend.resume Fork-only adaptation. On intarweb-dev both vllm-project#45398 (SleepModeBackend / CuMemTagBackend, RFC vllm-project#34303) and the vllm-project#45519 PP-broadcast wake-race fix are composed. vllm-project#45398 rewired Worker.wake_up to dispatch the local wake through the pluggable backend (backend.resume(tags)) instead of the raw allocator.wake_up(tags), and added a suspended_tags() snapshot that gates post_kv_cache_wake_up so a selective (weights-only) wake does not zero a still-live KV cache. vllm-project#45612 (filed upstream against main, where the backend abstraction does NOT exist) correctly wraps the raw allocator.wake_up. But upstream/main has no backend.resume, so when vllm-project#45612 is cherry-picked onto the composed intarweb-dev with -X theirs, the 3-way merge silently drops the handshake hunk entirely (the surrounding context diverged too far) -- the symmetric cross-rank wake handshake never lands on the fleet image. This commit re-expresses the vllm-project#45612 handshake against the composed tree so it gates backend.resume(tags) (the live local-wake op post-vllm-project#45398) rather than the raw allocator. It preserves the suspended_before_resume snapshot and the KV-cache gate, keeps ONE wake mechanism on both single- and multi-rank paths, and fires the handshake on EVERY wake regardless of backend. Why it is additive over the existing CuMemAllocator._quiesce_distributed_before_vmm_mutation() barrier (already on intarweb-dev): that helper issues a *bare* gloo barrier() at wake_up exit. If one rank's local wake raises before reaching that barrier, surviving ranks block on it forever -- a single-rank wake failure becomes a full-fleet hang. This handshake all-reduces a per-rank success flag (ReduceOp.MIN) with the local wake wrapped in try/except, so a failed wake on any rank makes every rank raise loud + symmetric instead of deadlocking or silently proceeding into a device-group collective against a peer whose wake never completed. Tests (tests/v1/worker/test_gpu_worker_wake_barrier.py, GPU-free, all surfaces mocked): 17 cases incl. composition-regression tests asserting the handshake gates backend.resume (not the raw allocator) and composes with the suspended_tags() KV gate. FAIL on the pre-composition tree (no handshake), PASS post-fix. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Signed-off-by: terafin <terafin@users.noreply.github.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>

Under async scheduling (max_concurrent_batches > 1), EngineCore.step_with_batch_queue submits execute_model(non_block=True) and returns without awaiting the returned future when the batch queue is not yet full (vllm/v1/engine/core.py). A subsequent /sleep reaches CuMemAllocator.sleep, which loops over allocations calling unmap_and_release -> cuMemUnmap, tearing down the KV/weight virtual address space while the still-running forward kernel is writing to it. This races VA teardown against in-flight device work and surfaces as an Xid-31 write-to-unmapped-VA fault that kills EngineCore. The synchronous step() path is safe because future.result() drains the work before any sleep can run, but the async batch-queue path is exposed. The existing torch.cuda.synchronize() in _python_free_callback only covers the PyTorch GC-driven free path, not this explicit sleep() unmap loop -- that asymmetry is the gap. Add a device-wide torch.cuda.synchronize() at the top of sleep(), before the unmap loop, to drain all in-flight kernels before any cuMemUnmap. The cost is negligible relative to the seconds-scale sleep/offload work. This is the sleep-side analog of the wake-side drain (vllm-project#45612); both defend the same cuMemUnmap/cuMemMap-vs-in-flight-kernel race that manifests as the steady-state Xid-31 MMU faults seen under sleep/wake with async scheduling. Cross-ref vllm-project#45519. Adds a CPU-runnable regression test that monkeypatches torch.cuda.synchronize and unmap_and_release to assert the synchronize happens before any unmap in sleep(); it fails on the pre-fix code and passes with this change. The full GPU async-drain repro (long forward under max_concurrent_batches=2, /sleep mid-step, assert no Xid-31) is the integration variant. Signed-off-by: terafin <terafin@users.noreply.github.com> 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>

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>

Under async scheduling (max_concurrent_batches > 1), EngineCore.step_with_batch_queue submits execute_model(non_block=True) and returns without awaiting the returned future when the batch queue is not yet full (vllm/v1/engine/core.py). A subsequent /sleep reaches CuMemAllocator.sleep, which loops over allocations calling unmap_and_release -> cuMemUnmap, tearing down the KV/weight virtual address space while the still-running forward kernel is writing to it. This races VA teardown against in-flight device work and surfaces as an Xid-31 write-to-unmapped-VA fault that kills EngineCore. The synchronous step() path is safe because future.result() drains the work before any sleep can run, but the async batch-queue path is exposed. The existing torch.cuda.synchronize() in _python_free_callback only covers the PyTorch GC-driven free path, not this explicit sleep() unmap loop -- that asymmetry is the gap. Add a device-wide torch.cuda.synchronize() at the top of sleep(), before the unmap loop, to drain all in-flight kernels before any cuMemUnmap. The cost is negligible relative to the seconds-scale sleep/offload work. This is the sleep-side analog of the wake-side drain (vllm-project#45612); both defend the same cuMemUnmap/cuMemMap-vs-in-flight-kernel race that manifests as the steady-state Xid-31 MMU faults seen under sleep/wake with async scheduling. Cross-ref vllm-project#45519. Adds a CPU-runnable regression test that monkeypatches torch.cuda.synchronize and unmap_and_release to assert the synchronize happens before any unmap in sleep(); it fails on the pre-fix code and passes with this change. The full GPU async-drain repro (long forward under max_concurrent_batches=2, /sleep mid-step, assert no Xid-31) is the integration variant. Signed-off-by: terafin <terafin@users.noreply.github.com> 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>

Under async scheduling (max_concurrent_batches > 1), EngineCore.step_with_batch_queue submits execute_model(non_block=True) and returns without awaiting the returned future when the batch queue is not yet full (vllm/v1/engine/core.py). A subsequent /sleep reaches CuMemAllocator.sleep, which loops over allocations calling unmap_and_release -> cuMemUnmap, tearing down the KV/weight virtual address space while the still-running forward kernel is writing to it. This races VA teardown against in-flight device work and surfaces as an Xid-31 write-to-unmapped-VA fault that kills EngineCore. The synchronous step() path is safe because future.result() drains the work before any sleep can run, but the async batch-queue path is exposed. The existing torch.cuda.synchronize() in _python_free_callback only covers the PyTorch GC-driven free path, not this explicit sleep() unmap loop -- that asymmetry is the gap. Add a device-wide torch.cuda.synchronize() at the top of sleep(), before the unmap loop, to drain all in-flight kernels before any cuMemUnmap. The cost is negligible relative to the seconds-scale sleep/offload work. This is the sleep-side analog of the wake-side drain (vllm-project#45612); both defend the same cuMemUnmap/cuMemMap-vs-in-flight-kernel race that manifests as the steady-state Xid-31 MMU faults seen under sleep/wake with async scheduling. Cross-ref vllm-project#45519. Adds a CPU-runnable regression test that monkeypatches torch.cuda.synchronize and unmap_and_release to assert the synchronize happens before any unmap in sleep(); it fails on the pre-fix code and passes with this change. The full GPU async-drain repro (long forward under max_concurrent_batches=2, /sleep mid-step, assert no Xid-31) is the integration variant. Signed-off-by: terafin <terafin@users.noreply.github.com> Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

…lm-project#45610 HOIST f24612b] 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 wake-path PRs touch the SAME hunks; sequential -X-theirs drops them. This commit is the SOLE provider; the PRs (vllm-project#45610 vllm-project#45617 vllm-project#44778 vllm-project#45611 vllm-project#45565) are SKIPPED from the loop via PRECOMPILED_WHEEL_SKIP_PRS. *** vllm-project#45610 DECODE-WEDGE FIX RE-FOLDED: now carries the PP-consensus HOIST (PR vllm-project#45610 head f24612b), REPLACING the earlier pre-hoist gate variant (4d20740). The cudagraph-mode consensus all-reduce is hoisted out of the model runner's execute_model and into Worker.execute_model, issued BEFORE the inter-stage irecv_tensor_dict (gpu_worker.py). This eliminates the structural deadlock cycle that wedged TP2/PP2 at decode step 0 (PP0 ranks parked in coordinate_cudagraph_mode_ across_pp -> all_reduce while PP1 ranks parked in irecv gloo waitRecv). The model runner no longer self-issues the consensus all_reduce; it consumes the agreed value via the new pp_synced_cudagraph_mode kwarg. Applies to V1 (gpu_model_runner.py), V2 (gpu/model_runner.py), DP (gpu/dp_utils.py), and Ray (executor/ray_utils.py) paths, plus pp_utils.py helper. gpu_model_runner.py wake-path markers (HOTPATH_FILE_RESTORE / CARRY_HOTPATH_ASSERTS): 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 gpu_worker.py composition (carried forward from prior union; disjoint region from the hoist's execute_model edits, no adjacency conflict): 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 AUDIT-D composition assertions (verified, not just grepped): - wake_up dispatches backend.resume EXACTLY ONCE (needs_wake_sync if/else are mutually-exclusive branches); NO raw allocator.wake_up Block B double-wake. - the hoist consensus block precedes irecv_tensor_dict in Worker.execute_model. - runner does NOT self-issue the consensus all_reduce (hoist-correct). Tests: test_sleep_mode_backend, test_pp_cudagraph_consensus (V1 hoist), test_pp_cudagraph_consensus_v2 (V2 hoist), test_fp8_kv_scale_wake, test_gpu_model_runner_fp8_wake_up, test_gpu_worker_wake_barrier. cumem.py deliberately excluded (carried by separate FORK_CARRIED entries vllm-project#45615/vllm-project#45552); the load-bearing gloo wake handshake lives in gpu_worker.py. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>