smithery.ai

vllm-async-loading-debug

Debug vLLM async KV loading and PegaFlow connector behavior. Use when investigating async KV loads, WAITING_FOR_REMOTE_KVS states, load/save intents, connector metadata flow, prefetch behavior, or preemption interactions in vLLM scheduler/worker code.

First seen Apr 4, 2026

Installation

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Package contents

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  • skill md SKILL.md 1,456 B
  • docs SUMMARY.md 283 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Vllm Async Loading Debug

Overview

Trace the async KV loading path between vLLM scheduler and worker connectors, including prefetch and preemption edges.

Workflow

1) Confirm async-load symptom

  • Record request IDs and statuses: WAITING, RUNNING, WAITINGFORREMOTE_KVS, PREEMPTED.
  • Check async-load logs and whether prefetch is in progress.

2) Follow scheduler-side decisions

  • python/pegaflow/connector/scheduler.py: prefetch query + LoadIntent creation.
  • .project-plans/scheduler.py (private): loadkvasync gating and WAITINGFORREMOTE_KVS transitions.

3) Follow worker-side lifecycle

  • python/pegaflow/connector/worker.py: startloadkv() calls engine_client.load() and tracks PyLoadState.
  • getfinished() polls isready() and emits finished_recving.

4) Check preemption and retries

  • .project-plans/scheduler.py (private): preemptrequest() and resetprefixcache() behavior.
  • invalidblockids handling can trigger recompute or failure based on kvloadfailure_policy.

Reference Files

  • Read references/async-loading.md for full call flow and log markers.