Sglang Prod Incident Triage

SGLang Serving Debug

Overview

Use this skill to turn a live serving problem into a debug path you can replay.

Use one loop:

• collect a baseline bundle
• save the failing request or crash dump
• replay on a clean target
• only then switch tools

Do not start with profiling.

This skill should work with more focused skills instead of re-implementing them:

• debug-cuda-crash when replay plus coredump points to a CUDA crash path
• debug-distributed-hang when the problem is clearly a TP/PP/DP/EP hang
• llm-torch-profiler-analysis when the issue is already narrowed to a compute-side path

Three examples are included:

• TTFT spike with low queue time
• replay-first CUDA crash flow
• request-shaped distributed hang flow

Output Contract

Return:

• problem class
• what was checked
• strongest signal so far
• current best guess
• what was ruled out
• next step
• production risk

When To Use It

• /health or /health_generate is unhealthy
• latency or throughput regressed under serving load
• queue size grows while health still looks green
• one request class times out or hangs
• the server crashes only after some requests
• outputs changed after a deploy, topology change, or weight switch
• one older commit is known-good and a newer commit is known-bad

Workflow

1. Collect a baseline bundle

If a live server is reachable, collect a read-only bundle before anything more intrusive:

python3 scripts/incident_artifact_tool.py collect-bundle \
  --base-url http://127.0.0.1:30000 \
  --outdir /tmp/incident_bundle

python3 scripts/incident_artifact_tool.py summarize-bundle \
  /tmp/incident_bundle

If the server is protected:

python3 scripts/incident_artifact_tool.py collect-bundle \
  --base-url http://127.0.0.1:30000 \
  --token "$SGLANG_BEARER_TOKEN" \
  --outdir /tmp/incident_bundle

The bundle script collects:

• /health
• /health_generate
• /model_info
• /server_info
• /v1/loads?include=all
• /v1/loads?include=core,queues,disagg,spec
• /metrics
• /hicache/storage-backend on a best-effort basis

Use the summary for a quick read on:

• health vs. active health state
• topology and runtime flags
• point-in-time queue and token usage
• TTFT / E2E / queue-time heuristics from Prometheus metrics

If the summary says the bundle was captured while the server was idle, recollect it during traffic or move quickly to dump plus replay.

If no live server is reachable, start from the best dump or log already available:

• crash dump
• request dump
• logs
• CUDA coredump
• OTel trace
• torch profile

2. Save the failing request

Read references/decision-tree.md only if the problem class is still unclear:

• server down or unhealthy
• latency or throughput regression
• wrong output or behavior regression
• intermittent timeout or hang

Then preserve the request payload that actually triggers the problem:

• crash path: use --crash-dump-folder
• non-crash path: enable request dump or save the exact trigger request

Do not jump straight from a live symptom to low-level debugging without first saving something you can replay.

3. Replay on a clean target

Read references/endpoints-and-signals.md when you need help reading the baseline bundle or the replay target.

Read references/replay-trace-profile.md when you need the replay, trace, profile, or bisect paths.

Standard order:

1. collect baseline bundle
2. capture request dump or crash dump
3. restart a clean debug target if needed
4. replay the same issue
5. collect replay-time logs and dumps

4. Only go deeper after replay

Replay

Use replay when:

• a crash dump exists
• a request dump exists
• the problem depends on request shape or workload mix

If a crash dump exists, summarize it first:

python3 scripts/incident_artifact_tool.py summarize-dump \
  --input-file /path/to/crash_dump.pkl

Then replay:

python3 /path/to/sglang/scripts/playground/replay_request_dump.py \
  --input-file /path/to/crash_dump.pkl \
  --host 127.0.0.1 \
  --port 30000 \
  --parallel 128

If safe_pickle_load blocks a locally captured trusted dump, use:

python3 scripts/replay_trusted_request_dump.py \
  --input-file /path/to/request_dump.pkl \
  --host 127.0.0.1 \
  --port 30000 \
  --parallel 1

If replay indicates a CUDA crash path, restart the same build with coredumps enabled before reproducing again:

SGLANG_CUDA_COREDUMP=1 \
SGLANG_CUDA_COREDUMP_DIR=/tmp/sglang_cuda_coredumps \
python -m sglang.launch_server \
  --model-path ... \
  --crash-dump-folder /tmp/sglang_crash_dump \
  ...

Then inspect the generated coredump:

cuda-gdb "$(which python3)" \
  -ex "target cudacore /tmp/sglang_cuda_coredumps/cuda_coredump_<host>.<pid>.<ts>"

For a replay-first crash example, read references/case-studies.md.