[V1][FT] Decoupled fault-tolerance framework: hooks + registry + supervisor + pluggable recovery plans

[tzulingk]

Overview:

TL;DR. This PR introduces a fault-tolerance framework for V1 under a single new directory, vllm/v1/fault_tolerance/. It gives vLLM a clean way to (a) detect faults during inference, (b) publish them on a status bus, and (c) execute pluggable recovery actions via one HTTP endpoint — all without scattering FT logic across the engine, worker, and executor hot paths.

The problem. Today, when something goes wrong during inference, vLLM's options are limited:

• A RuntimeError in the busy loop takes down the engine.
• A stuck NCCL collective hangs the entire DP group with no clean recovery.
• A dead engine in a multi-engine deployment leaves the load-balancing client routing requests to it (until [EP] Fault tolerance: automatic elastic scale-down on DP engine death #38862 lands; even then, a paused engine still receives requests).

Four open PRs each tackle one slice — #34833 (reporting), #38534 (pause), #40468 (cleanup + retry), #38862 (elastic scale-down on death) — but they layer three new "sentinel" classes on top of the existing Executor.collective_rpc channel and reach into engine/core.py, worker/gpu_model_runner.py, worker/dp_utils.py, and executor/multiproc_executor.py. Reviewers (gemini-code-assist, jeffreywang-anyscale, SageMoore) flagged the structural duplication and the brittle EngineLoopPausedError.PREFIX in result exception-string matching. This PR delivers the same external behavior with a different shape.

What users / orchestrators see

                        +-------------------------+
                        |  /fault_tolerance/...   |   <-- one HTTP surface
                        |    GET  status          |
                        |    POST apply           |
                        +-----------+-------------+
                                    |
                                    v
   ZMQ PUB <-- [FaultStateBus] <-- [FaultSupervisor] --+
   (status      (#34833 wire             |             |
    events)      format)                 |             |
                                         |             |
   3 hooks in run_busy_loop:             |             v
   before_step / after_step /        engine-local   cluster
   on_step_error                     plans:         plans:
                                     pause          scale_down
                                     retry          abort
                                     abort_comm                  -- recovery library

End-to-end usage looks like this:

# CLI
vllm serve ... --enable-fault-tolerance [--fault-supervisor my_orchestrator]

# Subscribe
zmq.SUB connect tcp://...   # receives FaultInfo (engine_index, status, kind, ...)

# React (orchestrator decides what to do, vLLM does not)
curl -X POST .../fault_tolerance/apply \
     -d '{"instruction": "retry",      "params": {}}'
curl -X POST .../fault_tolerance/apply \
     -d '{"instruction": "abort",      "params": {"engine_index": 2}}'
curl -X POST .../fault_tolerance/apply \
     -d '{"instruction": "scale_down", "params": {"dead_engine_index": 2}}'

Or extend (lives outside vLLM, registered like GMS's register_model_loader):

@register_recovery_plan("my_action")
class MyPlan(EngineLocalRecoveryPlan):
    instruction = "my_action"
    kv_action   = KvAction.NONE
    def execute(self, executor, params): ...

@register_fault_supervisor("my_orchestrator")
class MySupervisor(FaultToleranceHooks):
    def __init__(self, vllm_config): ...

Concrete walkthrough — DP rank 2 hits an all-reduce timeout

#	What happens	Where
1	_run_ar raises RuntimeError; returns AllReduceResult(ok=False, fault_signal=FaultSignal("dp_allreduce_failed", e)). No global pause Event, no EngineLoopPausedError.	vllm/v1/worker/dp_utils.py
2	The signal is attached to the worker's output via the new ModelRunnerOutput.fault_signal field.	vllm/v1/outputs.py
3	Engine busy loop's after_step hook reads the signal. (Three unconditional hook calls; no if enable_fault_tolerance: checks scattered through hot-path files — when FT is off, hooks resolve to a NoOpFaultHooks singleton.)	vllm/v1/engine/core.py::run_busy_loop
4	DefaultFaultSupervisor.after_step enqueues a FaultInfo and marks state PAUSED. The hook never blocks the engine thread: it does _queue.put_nowait(...). A daemon thread inside the supervisor drains the bounded queue to the PUB socket.	vllm/v1/fault_tolerance/default_supervisor.py
5	Subscribers receive the bus event. DPRoutingPolicy removes engine 2 from eligible_engines() so the load-balancing client stops sending it new requests. The orchestrator (Dynamo, k8s controller, custom) also receives the event.	dp_routing_policy.py, core_client.py
6	Orchestrator decides — vLLM does not. POSTs /fault_tolerance/apply {"instruction":"retry"}.	external
7	Supervisor runs the retry plan: executor.collective_rpc("ft_pause") → preempt + logical free of KV blocks → executor.collective_rpc("ft_resume_after_retry"). The plan declares KvAction.PREEMPT_LOGICAL_FREE upfront, so the KV impact is part of the plan's contract — not buried in prose.	plans/retry.py, worker/gpu_worker.py
8	Supervisor flips to HEALTHY; bus emits the transition; router re-adds engine 2 to eligible_engines().

Mechanism vs policy. vLLM ships mechanism only: detect, publish, expose /fault_tolerance/apply, execute the action correctly. Policy (when to pause vs. retry vs. scale-down vs. migrate) lives outside vLLM, in an orchestrator like Dynamo. Concretely: PR #38862's auto scale-down on engine death is no longer auto in this redesign — death is published as DEAD on the bus, and the orchestrator decides whether the right response is scale_down, abort (fail in-flight only), or wait-and-retry. This is an explicit design choice — a deployment without an orchestrator will see faults reported but not auto-recovered. (Preserving today's "auto on death" UX is a Dynamo-side configuration, not a vLLM feature.)

Net-new functionality vs. the four existing PRs

1. Health-aware DP routing — DPRoutingPolicy excludes PAUSED / RECOVERING / UNHEALTHY engines from routing eligibility, not just DEAD ([EP] Fault tolerance: automatic elastic scale-down on DP engine death #38862 covers DEAD only). Stops requests piling up on a paused rank.
2. Per-plan KvAction declaration — every recovery plan declares its KV-cache effect (NONE, PREEMPT_LOGICAL_FREE, LOSE_DEAD_ENGINE_BLOCKS, ADD_NEW_ENGINE_BLOCKS). Today this contract is implicit / described in prose.
3. Typed FaultSignal at IPC boundaries — replaces EngineLoopPausedError.PREFIX in result (string-prefix match in multiproc_executor.py).
4. Unified instruction surface — pause, retry, abort, abort_communicator, and scale_down all flow through /fault_tolerance/apply; engine-local and cluster scopes routed automatically.
5. abort plan (cluster-scope, no scale-down) — fail in-flight requests on a target engine without scaling down. Lets an orchestrator say "fail any in-flight on the dead rank, but don't yet scale down" while diagnosing, then follow up with scale_down if recovery is warranted.
6. Plugin extension surface — @register_fault_supervisor / @register_recovery_plan decorators (mirrors GMS's register_model_loader). Orchestrators ship their FT layer as a plugin and iterate independently of vLLM releases.

AI assistance was used to draft this change. The submitter has reviewed every changed line and is responsible for defending the change end-to-end.

Details:

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[gemini-code-assist]

Code Review

This pull request introduces a comprehensive fault-tolerance framework for vLLM, featuring a supervisor-based architecture, HTTP recovery endpoints, and non-blocking communicator abort mechanisms to handle engine failures in EP and DP configurations. Key improvements include logic for fault-triggered scale-down and expert reassignment. Feedback highlights a critical issue where catching BaseException in the supervisor could prevent clean shutdowns from system signals. Additionally, a call to a missing update_expert_map method in the FusedMoE layer was identified, which would cause crashes during recovery.

[gemini-code-assist]

Catching BaseException and returning Disposition.PAUSE_LOOP will prevent the engine from exiting on critical signals like SystemExit or KeyboardInterrupt if they occur during a step. These exceptions should be propagated immediately to allow for clean shutdown and responsive signal handling.

Suggested change

	def on_step_error(self, engine: Any, exc: BaseException) -> Disposition:
	# Typed FT exceptions carry a structured signal; fall back to
	# FaultInfo.from_exception for everything else.
	if isinstance(exc, FaultToleranceError) and exc.signal is not None:
	def on_step_error(self, engine: Any, exc: BaseException) -> Disposition:
	if not isinstance(exc, Exception):
	return Disposition.PROPAGATE
	# Typed FT exceptions carry a structured signal; fall back to
	# FaultInfo.from_exception for everything else.
	if isinstance(exc, FaultToleranceError) and exc.signal is not None:

[gemini-code-assist]

The code calls module.update_expert_map(), but this method is not implemented in the FusedMoE layer in this PR (or in the current vLLM codebase). Since the comment explicitly states that this is required to avoid out-of-bounds access during indexing with new expert IDs after an EP size change, the missing implementation will lead to crashes during recovery when MoE is used.

[fangyuchu]

Thanks for the proposal — I think using collective_rpc to handle actions other than pause, and surfacing pause signals via the model runner output, are both cleaner and approaches.

Let me first summarize my understanding of the key differences compared to the previous framework — please correct me if anything is off:

1. A FaultSupervisor is introduced in the engine core (somewhat similar to the previous engine-core sentinel). Meanwhile, ClientSentinel and WorkerSentinel are removed, and their responsibilities are partially moved into the client and worker implementations.
2. For fault reporting, the aggregation previously handled by ClientSentinel is removed. Instead, engines report faults directly via the supervisor’s bus.
3. A plugin-style extension point is introduced, potentially allowing independent evolution of fault-tolerance logic in the future.

That said, I have a few architectural concerns (very possible I’m misunderstanding parts of the design):

---

1. Coupling fault-tolerance logic into client/worker

The current FaultSupervisor seems to play a role similar to the previous engine sentinel (i.e., managing engine-level state). However, logic that previously lived in ClientSentinel / WorkerSentinel is now pushed into the client and worker code paths.

This raises a couple of concerns:

• In the client, this may introduce issues for multi-API (multi-client) scenarios, where ensuring consistent state across clients becomes harder.
• From a maintainability perspective, fault-tolerance logic was previously more centralized within sentinel components. Now it appears more scattered across inference paths.

---

2. Scope of the plugin mechanism

I like the direction of introducing a plugin-based fault-tolerance mechanism. However, it seems that the current extensibility is limited to the engine-core level via FaultSupervisor.

---

3. Code complexity / simplification

From a code simplification perspective, it doesn’t yet feel like this change reduces complexity. The overall code size is still quite large (~3.5k LOC), and some of the complexity seems to have shifted rather than reduced.

[fangyuchu]

Seems like each EngineCore creates its own DefaultFaultSupervisor, which initializes a FaultStateBus. With the current setup, it looks like multiple EngineCores may end up sharing the same pub endpoint/socket, which can cause conflicts.

More importantly, this seems like an architectural shift from the previous centralized model (client_sentinel collecting and publishing) to per-engine external publishing. The serving layer would now need to subscribe to every engine’s pub channel (including the engine manager)?

[fangyuchu]

Since now it will not collect execution results from the worker, is it safe to assume that abort will always success?

[fangyuchu]

It seems the introduction of register_fault_supervisor is intended to support more customizable fault-tolerance control logic in the future.

However, from the current design, FaultSupervisor appears to exist only within the engine core. Does this imply that the plugin/replacement capability is limited to the engine-core level?

[fangyuchu]

For retry, both dp_group in the engine core and in the workers likely need to be rebuilt. These DP groups are based on Gloo, and after a failure they may already have hit a timeout and become unusable. This situation can be more common under async scheduling (e.g., failure happens in the previous batch, and the next batch observes Gloo timeout).

Given this, it is necessary to recreate these process groups using a new port.

[fangyuchu]

With the current approach, the fault-tolerance logic and state appears to be coupled within the client. In a multi-API (i.e., multi-client) scenario, this makes it difficult to guarantee that state updates are consistently propagated and refreshed across all clients. My understanding is that the current elastic EP implementation has a similar limitation, effectively only supporting api-server-count=1.

This was one of the reasons we previously chose to factor out the ClientSentinel into a standalone component with its own communication endpoint, accessible by all clients. This allows fault state and transitions to be managed in a single place (the ClientSentinel) and remain consistent across all clients.

[fangyuchu]

I’m not entirely sure about the intended design here. My understanding was that the goal is for multiple engines to share a single FaultSupervisor, and potentially support pluggable supervisor implementations in the future?

However, with the current approach, it seems each engine ends up having its own FaultSupervisor instance.

[fangyuchu]

It seems that _last_step_output might not have been assigned. More importantly, for some steps, the engine executes execute_dummy_batch, which does not produce any output. As a result, pause signals that occur during inference within execute_dummy_batch cannot be propagated back through the model runner output. This may lead to situations where such pauses are silently missed.

[mergify]

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

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