[Core] Pluggable sleep-mode backend abstraction (RFC #34303) by matteso1 · Pull Request #44074 · vllm-project/vllm

matteso1 · 2026-05-30T18:52:53Z

Purpose

This is PR 1 of the two-PR plan discussed in RFC #34303: a thin backend abstraction in front of the sleep/wake-up GPU path, with the existing cumem mechanism wrapped as the default backend. It is a no-op for every existing user and unblocks the cuda_checkpoint backend (Phase 1/2, #37921/#37925) and out-of-tree backends to land as siblings without touching the public API.

This implements the shape @elizabetht asked the thread to converge on, modeled on the precedent I cited there: KVConnectorFactory (vllm/distributed/kv_transfer/kv_connector/factory.py).

What changes

File	Change
`vllm/device_allocator/sleep_mode_backend.py` (new)	`SleepModeBackend` ABC, `CuMemBackend` default, `SleepModeBackendFactory`
`vllm/config/model.py`	`+ sleep_mode_backend: str = "cumem"` (auto-exposed as `--sleep-mode-backend`)
`vllm/v1/worker/gpu_worker.py`	`sleep()` / `wake_up()` dispatch through the factory
`tests/test_sleep_mode_backend.py` (new)	CPU-only registry/factory + capability-flag tests

+306 / −8. The only runtime delta in the worker is that the inline CuMemAllocator.get_instance().sleep(...) / .wake_up(...) calls now live inside CuMemBackend and are reached through the factory. Same calls, same arguments → identical behavior.

The abstraction

class SleepModeBackend(ABC):
    @abstractmethod
    def suspend(self, level: int = 1) -> None: ...
    @abstractmethod
    def resume(self, tags: list[str] | None = None) -> None: ...
    def state(self) -> Literal["RUNNING", "SUSPENDED", "RESUMING"]: ...

    # Capability introspection - @classmethod so the executor, /health, and
    # AUTO selection can query a backend without instantiating it.
    @classmethod
    def is_supported(cls) -> bool: ...
    @classmethod
    def preserves_nccl(cls) -> bool: ...
    @classmethod
    def preserves_compiled_artifacts(cls) -> bool: ...
    @classmethod
    def preserves_graphs_with_nccl(cls) -> bool: ...
    @classmethod
    def supports_durable_storage(cls) -> bool: ...

SleepModeBackendFactory mirrors KVConnectorFactory exactly - a lazy _registry, register_backend(name, module_path, class_name), and a registration block at import time. Third-party backends register the same way from a vllm.general_plugins entry point, so they need no changes to vLLM core.

Why this collapses several RFC open questions

The open questions on the RFC are mostly "what does this mechanism preserve?" - which become per-backend capability flags rather than global branches:

Q1 / Q8 (NCCL reinit, CUDA graphs with NCCL): cumem.preserves_nccl() == True; cuda_checkpoint returns False and the executor rebuilds NCCL / re-captures affected graphs.
Q3 (torch.compile cache): preserves_compiled_artifacts() - cuda_checkpoint's biggest wake-time win, advertised on the surface; cumem returns False.
Q6 (backward compat): --enable-sleep-mode is untouched here; an auto resolver that probes is_supported() per registered backend is the natural follow-up.
/health while suspended (raised by @aerialhedgehog): the state() enum lets /health return 503 {"status": "suspended"} so a half-woken engine never takes a request.

What this PR deliberately does NOT do

Kept minimal so it reviews as a no-op:

No cuda_checkpoint backend - that's PR 2, porting Phase 1/2 ([Core] CUDA Checkpoint/Restore — Phase 1: C Extension, Python Wrapper, Worker Methods #37921/[Core] CUDA Checkpoint/Restore — Phase 2: Engine/Executor/API Integration #37925) onto this abstraction.
No --enable-sleep-mode → --sleep-mode rename / deprecation. sleep_mode_backend is additive.
No executor-level NCCL reinit. That lands with the first backend that needs it (cuda_checkpoint).

Testing

tests/test_sleep_mode_backend.py - CPU-only: cumem resolves to CuMemBackend, capability flags, unknown-backend error, duplicate-registration guard, third-party register+resolve, ABC non-instantiability, lifecycle state() transitions.
GPU sleep/wake parity is already covered by tests/basic_correctness/test_cumem.py - unchanged, and it exercises the cumem backend through the new dispatch path.

For maintainers

CODEOWNERS for the touched paths are vllm/v1/worker (@njhill, @WoosukKwon) and vllm/config. @elizabetht - this is the PR-1 shape from the thread; happy to adjust naming or location (e.g. if you'd prefer it under vllm/v1/worker/ rather than vllm/device_allocator/). If Phase 1/2 should land first and this becomes the follow-up abstraction, I'll rebase onto whatever merges - cuda_checkpoint stays the canonical built-in either way.

Refs #34303

github-actions · 2026-05-30T18:53:01Z

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mergify · 2026-06-07T03:01:54Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @matteso1.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

matteso1 · 2026-06-07T05:10:21Z

@njhill @WoosukKwon this is the SleepModeBackend abstraction from RFC #34303 — the cumem default is a behavioral no-op, it just routes the existing allocator calls through a pluggable backend. rebased onto main, mergeable; small surface (one new module + factory, a config field, the gpu_worker dispatch).

it's my first PR here so full CI is gated behind the ready label — could one of you add it so the tests run, and take a look when you have a window? happy to adjust.

@matteso1

…offload on top of vllm-project#44074 Stacks on @matteso1's pluggable SleepModeBackend abstraction (PR vllm-project#44074) to implement the tag-based selective offload that @AlanFokCo (Alibaba Cloud ACK team) described in RFC vllm-project#34303 comment 4689082496 - same-architecture model switch in ~1-2s by reusing CUDA graphs and rebuilding only the weight pages. The new backend is a thin subclass of CuMemBackend that exposes the existing CuMemAllocator.sleep(offload_tags=...) / .wake_up(tags=...) primitives as a first-class backend API. Resolution precedence is explicit-call-arg > construction-time override > level-based default; with no override the behavior is byte-identical to CuMemBackend (asserted by a dedicated test). Also adds a supports_selective_offload() classmethod on the base class so the executor and /health can introspect whether explicit per-tag lifecycles are available - parallel to the existing preserves_nccl / preserves_compiled_artifacts capability flags. Depends on vllm-project#44074. Filed in parallel for visibility, to give RFC vllm-project#34303 reviewers a concrete second backend to validate the abstraction against. Refs vllm-project#34303 vllm-project#44074

@matteso1

…offload on top of vllm-project#44074 Stacks on @matteso1's pluggable SleepModeBackend abstraction (PR vllm-project#44074) to implement the tag-based selective offload that @AlanFokCo (Alibaba Cloud ACK team) described in RFC vllm-project#34303 comment 4689082496 - same-architecture model switch in ~1-2s by reusing CUDA graphs and rebuilding only the weight pages. The new backend is a thin subclass of CuMemBackend that exposes the existing CuMemAllocator.sleep(offload_tags=...) / .wake_up(tags=...) primitives as a first-class backend API. Resolution precedence is explicit-call-arg > construction-time override > level-based default; with no override the behavior is byte-identical to CuMemBackend (asserted by a dedicated test). Also adds a supports_selective_offload() classmethod on the base class so the executor and /health can introspect whether explicit per-tag lifecycles are available - parallel to the existing preserves_nccl / preserves_compiled_artifacts capability flags. Depends on vllm-project#44074. Filed in parallel for visibility, to give RFC vllm-project#34303 reviewers a concrete second backend to validate the abstraction against. Refs vllm-project#34303 vllm-project#44074

@matteso1

…offload on top of vllm-project#44074 Stacks on @matteso1's pluggable SleepModeBackend abstraction (PR vllm-project#44074) to implement the tag-based selective offload that @AlanFokCo (Alibaba Cloud ACK team) described in RFC vllm-project#34303 comment 4689082496 - same-architecture model switch in ~1-2s by reusing CUDA graphs and rebuilding only the weight pages. The new backend is a thin subclass of CuMemBackend that exposes the existing CuMemAllocator.sleep(offload_tags=...) / .wake_up(tags=...) primitives as a first-class backend API. Resolution precedence is explicit-call-arg > construction-time override > level-based default; with no override the behavior is byte-identical to CuMemBackend (asserted by a dedicated test). Also adds a supports_selective_offload() classmethod on the base class so the executor and /health can introspect whether explicit per-tag lifecycles are available - parallel to the existing preserves_nccl / preserves_compiled_artifacts capability flags. Depends on vllm-project#44074. Filed in parallel for visibility, to give RFC vllm-project#34303 reviewers a concrete second backend to validate the abstraction against. Refs vllm-project#34303 vllm-project#44074

@matteso1

…offload on top of vllm-project#44074 Stacks on @matteso1's pluggable SleepModeBackend abstraction (PR vllm-project#44074) to implement the tag-based selective offload that @AlanFokCo (Alibaba Cloud ACK team) described in RFC vllm-project#34303 comment 4689082496 - same-architecture model switch in ~1-2s by reusing CUDA graphs and rebuilding only the weight pages. The new backend is a thin subclass of CuMemBackend that exposes the existing CuMemAllocator.sleep(offload_tags=...) / .wake_up(tags=...) primitives as a first-class backend API. Resolution precedence is explicit-call-arg > construction-time override > level-based default; with no override the behavior is byte-identical to CuMemBackend (asserted by a dedicated test). Also adds a supports_selective_offload() classmethod on the base class so the executor and /health can introspect whether explicit per-tag lifecycles are available - parallel to the existing preserves_nccl / preserves_compiled_artifacts capability flags. Depends on vllm-project#44074. Filed in parallel for visibility, to give RFC vllm-project#34303 reviewers a concrete second backend to validate the abstraction against. Refs vllm-project#34303 vllm-project#44074

@matteso1

…offload on top of vllm-project#44074 Stacks on @matteso1's pluggable SleepModeBackend abstraction (PR vllm-project#44074) to implement the tag-based selective offload that @AlanFokCo (Alibaba Cloud ACK team) described in RFC vllm-project#34303 comment 4689082496 - same-architecture model switch in ~1-2s by reusing CUDA graphs and rebuilding only the weight pages. The new backend is a thin subclass of CuMemBackend that exposes the existing CuMemAllocator.sleep(offload_tags=...) / .wake_up(tags=...) primitives as a first-class backend API. Resolution precedence is explicit-call-arg > construction-time override > level-based default; with no override the behavior is byte-identical to CuMemBackend (asserted by a dedicated test). Also adds a supports_selective_offload() classmethod on the base class so the executor and /health can introspect whether explicit per-tag lifecycles are available - parallel to the existing preserves_nccl / preserves_compiled_artifacts capability flags. Depends on vllm-project#44074. Filed in parallel for visibility, to give RFC vllm-project#34303 reviewers a concrete second backend to validate the abstraction against. Refs vllm-project#34303 vllm-project#44074

@matteso1

…offload on top of vllm-project#44074 Stacks on @matteso1's pluggable SleepModeBackend abstraction (PR vllm-project#44074) to implement the tag-based selective offload that @AlanFokCo (Alibaba Cloud ACK team) described in RFC vllm-project#34303 comment 4689082496 - same-architecture model switch in ~1-2s by reusing CUDA graphs and rebuilding only the weight pages. The new backend is a thin subclass of CuMemBackend that exposes the existing CuMemAllocator.sleep(offload_tags=...) / .wake_up(tags=...) primitives as a first-class backend API. Resolution precedence is explicit-call-arg > construction-time override > level-based default; with no override the behavior is byte-identical to CuMemBackend (asserted by a dedicated test). Also adds a supports_selective_offload() classmethod on the base class so the executor and /health can introspect whether explicit per-tag lifecycles are available - parallel to the existing preserves_nccl / preserves_compiled_artifacts capability flags. Depends on vllm-project#44074. Filed in parallel for visibility, to give RFC vllm-project#34303 reviewers a concrete second backend to validate the abstraction against. Refs vllm-project#34303 vllm-project#44074

@matteso1

…offload on top of vllm-project#44074 Stacks on @matteso1's pluggable SleepModeBackend abstraction (PR vllm-project#44074) to implement the tag-based selective offload that @AlanFokCo (Alibaba Cloud ACK team) described in RFC vllm-project#34303 comment 4689082496 - same-architecture model switch in ~1-2s by reusing CUDA graphs and rebuilding only the weight pages. The new backend is a thin subclass of CuMemBackend that exposes the existing CuMemAllocator.sleep(offload_tags=...) / .wake_up(tags=...) primitives as a first-class backend API. Resolution precedence is explicit-call-arg > construction-time override > level-based default; with no override the behavior is byte-identical to CuMemBackend (asserted by a dedicated test). Also adds a supports_selective_offload() classmethod on the base class so the executor and /health can introspect whether explicit per-tag lifecycles are available - parallel to the existing preserves_nccl / preserves_compiled_artifacts capability flags. Depends on vllm-project#44074. Filed in parallel for visibility, to give RFC vllm-project#34303 reviewers a concrete second backend to validate the abstraction against. Refs vllm-project#34303 vllm-project#44074

@matteso1

…offload on top of vllm-project#44074 Stacks on @matteso1's pluggable SleepModeBackend abstraction (PR vllm-project#44074) to implement the tag-based selective offload that @AlanFokCo (Alibaba Cloud ACK team) described in RFC vllm-project#34303 comment 4689082496 - same-architecture model switch in ~1-2s by reusing CUDA graphs and rebuilding only the weight pages. The new backend is a thin subclass of CuMemBackend that exposes the existing CuMemAllocator.sleep(offload_tags=...) / .wake_up(tags=...) primitives as a first-class backend API. Resolution precedence is explicit-call-arg > construction-time override > level-based default; with no override the behavior is byte-identical to CuMemBackend (asserted by a dedicated test). Also adds a supports_selective_offload() classmethod on the base class so the executor and /health can introspect whether explicit per-tag lifecycles are available - parallel to the existing preserves_nccl / preserves_compiled_artifacts capability flags. Depends on vllm-project#44074. Filed in parallel for visibility, to give RFC vllm-project#34303 reviewers a concrete second backend to validate the abstraction against. Refs vllm-project#34303 vllm-project#44074

@matteso1

…oject#34303) Implements the tag-selective sleep-mode backend on top of @matteso1's SleepModeBackend abstraction (vllm-project#44074). CuMemTagBackend carries per-allocation tags (weights, kv_cache, compiled_kernels) so that swap-group peers in the same process can selectively suspend/resume - unlocking the in-process multi-model swap path described in RFC vllm-project#34303. Addresses @matteso1's design feedback: - Adds sleep_mode_backend_options dict to ModelConfig so backend-specific options (e.g. suspend_tags) are reachable from CLI/config, not just constructor-injection. SleepModeBackendFactory.create_backend() **-unpacks the dict into the concrete backend's __init__; validation lives in the backend, not in ModelConfig, so adding a new backend with new options doesn't require a config-side change. - Refactors test_explicit_tags_override_defaults to assert on the public effective_suspend_tags() / suspend_tags surface rather than the prior private _explicit_suspend_tags attribute. The attribute itself was promoted to the public API for the same reason. - Adds SleepModeBackendFactory.unregister() helper for plugin-author test cleanup, replacing the prior need to mutate the private _registry dict directly. Idempotent on missing names. Refs vllm-project#34303 vllm-project#44074 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Signed-off-by: terafin <terafin@users.noreply.github.com>

@matteso1

…oject#34303) Implements the tag-selective sleep-mode backend on top of @matteso1's SleepModeBackend abstraction (vllm-project#44074). CuMemTagBackend carries per-allocation tags (weights, kv_cache, compiled_kernels) so that swap-group peers in the same process can selectively suspend/resume - unlocking the in-process multi-model swap path described in RFC vllm-project#34303. Addresses @matteso1's design feedback: - Adds sleep_mode_backend_options dict to ModelConfig so backend-specific options (e.g. suspend_tags) are reachable from CLI/config, not just constructor-injection. SleepModeBackendFactory.create_backend() **-unpacks the dict into the concrete backend's __init__; validation lives in the backend, not in ModelConfig, so adding a new backend with new options doesn't require a config-side change. - Refactors test_explicit_tags_override_defaults to assert on the public effective_suspend_tags() / suspend_tags surface rather than the prior private _explicit_suspend_tags attribute. The attribute itself was promoted to the public API for the same reason. - Adds SleepModeBackendFactory.unregister() helper for plugin-author test cleanup, replacing the prior need to mutate the private _registry dict directly. Idempotent on missing names. - Normalizes suspend_tags=[...] (list) to a tuple in __init__ so list inputs from CLI/JSON round-trip into the same shape effective_suspend_tags() returns. The None sentinel is preserved. Round 2 fixes (4 audit findings on the matteso1-approved revision): 1. Per-call tags= override now reaches the allocator. The backend exposed suspend(level, tags=) but the worker dispatch layer ignored the kwarg, so per-call overrides were unreachable from outside the backend. GPUWorker.sleep, Executor.sleep, and the abstract SleepModeBackend.suspend signature all accept tags= and thread it through to the backend. Backends that don't support selective offload accept-and-ignore the kwarg. 2. Selective-suspend wake no longer corrupts live KV cache. CuMemTagBackend now records which tags it offloaded on suspend, exposes them via suspended_tags(), and refuses resume() calls for non-suspended tags. GPUWorker.wake_up consults the backend's suspended_tags() before running post_kv_cache_wake_up, so a selective suspend(weights only) followed by wake_up(tags=None) cannot re-init a still-live KV cache. 3. L2 weight wake fails fast with RuntimeError instead of silently returning garbage pages. Level-2 suspend discards allocator pages; reloading weights is the worker's responsibility (load_model / reload_weights), not the allocator's wake_up. Until that wiring exists in this dispatch path, this backend refuses to mark L2-suspended weights RUNNING - which would otherwise present as the AWQ smoke pattern (HTTP 200, garbage tokens). 4. Unit tests cover all of the above: per-call tags propagation through the backend, list-to-tuple suspend_tags normalization (matteso1's nit plumbed through both the ctor and the factory), the kv_cache-from- weights-only-suspend rejection path, the L2 weight-wake RuntimeError, and partial-wake bookkeeping cleanup. Refs vllm-project#34303 vllm-project#44074 Signed-off-by: Justin Wood <justin.m.wood@me.com> Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

@matteso1

…oject#34303) Implements the tag-selective sleep-mode backend on top of @matteso1's SleepModeBackend abstraction (vllm-project#44074). CuMemTagBackend carries per-allocation tags (weights, kv_cache, compiled_kernels) so that swap-group peers in the same process can selectively suspend/resume - unlocking the in-process multi-model swap path described in RFC vllm-project#34303. Addresses @matteso1's design feedback: - Adds sleep_mode_backend_options dict to ModelConfig so backend-specific options (e.g. suspend_tags) are reachable from CLI/config, not just constructor-injection. SleepModeBackendFactory.create_backend() **-unpacks the dict into the concrete backend's __init__; validation lives in the backend, not in ModelConfig, so adding a new backend with new options doesn't require a config-side change. - Refactors test_explicit_tags_override_defaults to assert on the public effective_suspend_tags() / suspend_tags surface rather than the prior private _explicit_suspend_tags attribute. The attribute itself was promoted to the public API for the same reason. - Adds SleepModeBackendFactory.unregister() helper for plugin-author test cleanup, replacing the prior need to mutate the private _registry dict directly. Idempotent on missing names. - Normalizes suspend_tags=[...] (list) to a tuple in __init__ so list inputs from CLI/JSON round-trip into the same shape effective_suspend_tags() returns. The None sentinel is preserved. Round-1 audit fixes (4 findings on the matteso1-approved revision): 1. Per-call tags= override now reaches the allocator. The backend exposed suspend(level, tags=) but the worker dispatch layer ignored the kwarg, so per-call overrides were unreachable from outside the backend. GPUWorker.sleep, Executor.sleep, and the abstract SleepModeBackend.suspend signature all accept tags= and thread it through to the backend. Backends that don't support selective offload accept-and-ignore the kwarg. 2. Selective-suspend wake no longer corrupts live KV cache. CuMemTagBackend now records which tags it offloaded on suspend, exposes them via suspended_tags(), and refuses resume() calls for non-suspended tags. GPUWorker.wake_up consults the backend's suspended_tags() before running post_kv_cache_wake_up, so a selective suspend(weights only) followed by wake_up(tags=None) cannot re-init a still-live KV cache. 3. L2 weight wake fails fast with RuntimeError instead of silently returning garbage pages. Level-2 suspend discards allocator pages; reloading weights is the worker's responsibility (load_model / reload_weights), not the allocator's wake_up. Until that wiring exists in this dispatch path, this backend refuses to mark L2-suspended weights RUNNING - which would otherwise present as the AWQ smoke pattern (HTTP 200, garbage tokens). 4. Unit tests cover all of the above: per-call tags propagation through the backend, list-to-tuple suspend_tags normalization (matteso1's nit plumbed through both the ctor and the factory), the kv_cache-from- weights-only-suspend rejection path, the L2 weight-wake RuntimeError, and partial-wake bookkeeping cleanup. Round-2 audit fixes (2 HIGH findings on the round-1 revision): 5. resume() now passes the clamped wake set to the allocator. The round-1 fix added a clamped requested_set for validation but still handed the original tags (potentially None) to allocator.wake_up(). On a backend whose pool may also hold pages suspended by another caller in the same process - or by a prior selective suspend this resume should not have touched - an unclamped wake_up(None) would widen the wake beyond the recorded suspended set, silently zeroing live GPU state. The allocator call now sees the same effective_wake_tags tuple the validation gate just approved, derived from self._suspended_tags ordered to match suspend-time iteration. Tests pin the allocator-call shape (tuple, not list, derived from the clamped set) for both single-tag and multi-tag suspends. 6. Suspend bookkeeping is now post-allocator atomic. The round-1 code wrote self._state = SUSPENDED and self._suspended_tags = ... BEFORE calling allocator.sleep(), so an allocator-side OOM left the backend claiming SUSPENDED while GPU pages stayed live - and the executor's subsequent resume() would try to wake pages that were never sleep-prepared, masking the real failure with a confusing wake-up error. Allocator failure now leaves the backend RUNNING with no suspended set, matching actual GPU state. Symmetric for resume: if allocator.wake_up() raises mid-resume, the backend reverts to SUSPENDED rather than wedging in RESUMING. Validation-time errors (spurious tag, L2 weight wake) also no longer prematurely advance to RESUMING - the gate fires before any state mutation. Five new tests cover suspend-time atomicity, resume-time revert, and the validation-leaves-state-unchanged contract. Refs vllm-project#34303 vllm-project#44074 Signed-off-by: Justin Wood <justin.m.wood@me.com> Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.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>

AlanFokCo · 2026-06-23T03:08:36Z

@youkaichao @njhill — this PR has been rebased and mergeable for 3+ weeks and is blocking #45398 (CuMemTagBackend) and several downstream sleep-mode improvements.

We're from the Alibaba Cloud ACK team. We've been running multi-model hot-swap in production using the same CuMemAllocator primitives, and the backend abstraction here matches our deployment patterns — pluggable backends are exactly what's needed to support different sleep strategies (tag-selective offload, CUDA C/R, etc.) without coupling them to the allocator core.

Would appreciate if a maintainer could give this a review pass or add the ready label for CI. Happy to help verify any allocator interaction concerns — we have extensive test data across A10 and H20 clusters.

mergify · 2026-06-29T22:10:00Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @matteso1.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

matteso1 · 2026-06-29T22:42:17Z

Thanks @AlanFokCo - good to hear the ACK team is running the same CuMemAllocator hot-swap pattern in production; that deployment shape is exactly what the pluggable backend is meant to keep decoupled from the allocator core.

just rebased onto main (head 9f9b4dcb). the only conflict was in gpu_worker.py sleep(), where main's tag-selective offload_tags and the ROCm sync/retry loop landed after this branch was cut. Resolved so the cumem path is byte-identical to current main: CuMemBackend.suspend() issues the same get_mem_allocator_instance().sleep(offload_tags=...) call and the worker keeps main's synchronize() + retry loop. Diff is back to +308/−4 (two new files, the sleep_mode_backend field, the sleep/wake dispatch). Mergeable again.

@youkaichao @njhill - could one of you add the ready label and give it a review pass? happy to address anything. As noted above this is blocking #45398 (CuMemTagBackend) and the cuda_checkpoint backends. Thanks!

AlanFokCo · 2026-06-30T02:14:38Z

Thanks for the quick rebase @matteso1 — the cumem path staying byte-identical to main confirms this is a clean refactor with no behavioral change for existing users.

We're eager to see this land. On our side, we've submitted #46438 (CuMemAllocator.discard() for tag-selective GPU memory release) as the first step toward multi-model switching. Once this backend abstraction merges, we can integrate the discard/selective-sleep primitives as proper backend methods rather than patching the allocator directly — which is a much cleaner path for the codebase.

@youkaichao @njhill this has been open for some weeks and is blocking several downstream efforts. Would really appreciate it if you could tag it ready and give it a review when you get a chance — looking forward to getting this merged and building on top of it. Happy to help with any concerns on the allocator interaction side.

galletas1712 · 2026-07-01T00:32:45Z

I am from NVIDIA's Dynamo team and we are also eager to see this land, as we have features that would benefit from a custom suspend() and resume() backend as well. Though I am not sure if this extra API surface is necessary:

    def preserves_nccl(cls) -> bool: ...
    @classmethod
    def preserves_compiled_artifacts(cls) -> bool: ...
    @classmethod
    def preserves_graphs_with_nccl(cls) -> bool: ...
    @classmethod
    def supports_durable_storage(cls) -> bool: ...

NCCL is not the only comms backend in vLLM, so I would be hesitant to introduce anything NCCL-specific in what is supposed to be a generic API.

aoshen02 · 2026-07-01T00:38:16Z

I am from NVIDIA's Dynamo team and we are also eager to see this land, as we have features that would benefit from a custom suspend() and resume() backend as well. Though I am not sure if this extra API surface is necessary:
    def preserves_nccl(cls) -> bool: ...
    @classmethod
    def preserves_compiled_artifacts(cls) -> bool: ...
    @classmethod
    def preserves_graphs_with_nccl(cls) -> bool: ...
    @classmethod
    def supports_durable_storage(cls) -> bool: ...
NCCL is not the only comms backend in vLLM, so I would be hesitant to introduce anything NCCL-specific in what is supposed to be a generic API.

Hi thanks, I will push these several pr forward if make sense, and the nccl offload pr is ready #46234

simon-mo · 2026-07-01T01:00:07Z

can you please move this to an appropriate directory or change existing files instead?

thanks for the review and the approval. moved the tests to tests/v1/worker/ and updated the plugin-registration path to match, plus annotated the _sleep_mode_backend field to clear the mypy error. rebased onto main, CI's re-running now.

mergify · 2026-07-01T01:10:03Z

Hi @matteso1, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

mergify · 2026-07-01T01:55:08Z

Hi @matteso1, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

Introduce a thin backend abstraction in front of the sleep/wake-up GPU path so alternative mechanisms (CUDA checkpoint, CRIU, durable snapshot) proposed in RFC vllm-project#34303 can be selected by name without changing the public API. - New vllm/device_allocator/sleep_mode_backend.py: - SleepModeBackend ABC (suspend/resume + capability classmethods + RUNNING/SUSPENDED/RESUMING state). - CuMemBackend default - wraps CuMemAllocator 1:1. - SleepModeBackendFactory mirroring KVConnectorFactory (lazy registry, plugin-registerable via vllm.general_plugins). - ModelConfig.sleep_mode_backend: str = "cumem" (new field, default preserves current behavior; auto-exposed as --sleep-mode-backend). - GPUWorker.sleep()/wake_up() dispatch through the factory. The cumem backend issues the identical allocator calls, so behavior is unchanged for every existing user. - CPU-only unit tests for the registry/factory contract and capability flags (GPU suspend/resume stays covered by test_cumem.py). Refs vllm-project#34303 Signed-off-by: Nils Matteson <nils@thaw.sh>

matteso1 · 2026-07-01T04:57:28Z

I am from NVIDIA's Dynamo team and we are also eager to see this land, as we have features that would benefit from a custom suspend() and resume() backend as well. Though I am not sure if this extra API surface is necessary:
    def preserves_nccl(cls) -> bool: ...
    @classmethod
    def preserves_compiled_artifacts(cls) -> bool: ...
    @classmethod
    def preserves_graphs_with_nccl(cls) -> bool: ...
    @classmethod
    def supports_durable_storage(cls) -> bool: ...
NCCL is not the only comms backend in vLLM, so I would be hesitant to introduce anything NCCL-specific in what is supposed to be a generic API.

hey ! thanks @galletas1712, cool to have the Dynamo team on this. The capability flags are for the auto selection in RFC #34303.. the executor needs to know whether a resume requires reiniting comms / recompiling / recapturing graphs without instantiating the backend. agreed they shouldn't be NCCL-specific, though; I'll rename to preserves_communicators / preserves_graphs_with_communicators. Since it's already approved I can fold that in now or as a quick follow-up either works. Curious what Dynamo needs from suspend()/resume() so the interface covers it :)

mergify Bot added the v1 label May 30, 2026

matteso1 force-pushed the rfc-34303-sleep-mode-backend branch from 85d83e4 to f16a263 Compare May 30, 2026 18:55

matteso1 marked this pull request as ready for review May 30, 2026 18:58

matteso1 requested review from ProExpertProg, WoosukKwon, hmellor, houseroad, mgoin, njhill, robertgshaw2-redhat, tlrmchlsmth, yewentao256 and youkaichao as code owners May 30, 2026 18:58

matteso1 mentioned this pull request May 30, 2026

[RFC]: CUDA Checkpoint/Restore for Near-Zero Cold Starts #34303

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mergify Bot added the needs-rebase label Jun 7, 2026

matteso1 force-pushed the rfc-34303-sleep-mode-backend branch from f16a263 to f399c8d Compare June 7, 2026 04:56

mergify Bot removed the needs-rebase label Jun 7, 2026

matteso1 mentioned this pull request Jun 12, 2026

P1: KV path's V1-inproc requirement is architectural; document it and define the upstream-interface plan thaw-ai/thaw#80

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terafin mentioned this pull request Jun 12, 2026

[Core][RFC #34303] Add CuMemTagBackend for tag-selective offload on top of #44074 #45398

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This was referenced Jun 13, 2026

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

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

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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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This was referenced Jun 30, 2026

[Core] Add CuMemAllocator.discard() for tag-selective GPU memory release #46438

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[Core] Release NCCL communicator memory in sleep mode #46234

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simon-mo approved these changes Jul 1, 2026

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simon-mo mentioned this pull request Jul 1, 2026

Address PR 44074 sleep backend review comments simon-mo/vllm#6

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[Core] Make sleep-mode backend capability flags communicator-agnostic #47243

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[Core] Consume SleepModeBackend capability flags in the worker suspend/resume path #47500

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