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.

OTel trace

Use tracing when:

• request-stage timing is unclear
• router vs. worker attribution is unclear
• PD prefill/decode transfer may be implicated

If tracing was enabled at startup, you can change the level without restart:

curl "http://127.0.0.1:30000/set_trace_level?level=1"
curl "http://127.0.0.1:30000/set_trace_level?level=2"

Torch profile

Use profiling when:

• the issue is already narrowed to compute-side ownership
• replay already reproduces the problem
• metrics and loads do not explain the regression

At that point, switch to llm-torch-profiler-analysis. Do not duplicate its profiling workflow here.

For a low-noise latency example, read references/case-studies.md.

Distributed hang

If this looks like a collective stall, save the failing request, replay it on a clean target, collect the replay-time bundle and stacks, then switch to debug-distributed-hang.

For an example of that flow, read references/case-studies.md.

Regression between two commits

If one commit is known-good and another is known-bad, build a deterministic harness before doing deeper manual debugging:

1. choose a stable reproducer: request replay, benchmark command, or correctness check
2. make the harness return 0 on good behavior and non-zero on bad behavior
3. run git bisect start <bad> <good>
4. run git bisect run <harness>
5. return here only after a candidate commit is isolated

Prefer replay-backed bisect when the regression depends on request shape or long-running serving state.

6. Switch tools when the boundary is clear

Switch tools once the fault class is clear:

• llm-torch-profiler-analysis for kernel and overlap attribution
• debug-distributed-hang for collective or rank-divergence hangs
• debug-cuda-crash for CUDA crash reproduction and kernel API logging

Do not switch tools before collecting the first bundle unless the user already has decisive logs or dumps.

References

Load only what the current step needs:

• references/decision-tree.md
  • problem classes, tool switch points, return shape
• references/endpoints-and-signals.md
  • endpoint behavior, auth notes, field reading
• references/replay-trace-profile.md
  • request dump, crash dump, replay, trace, profiler step, bisect
• references/case-studies.md
  • compact examples for replay-first CUDA crash, latency, and distributed-hang triage

Scripts

• scripts/incident_artifact_tool.py
  • collect a read-only live bundle
  • summarize a collected bundle into a compact debug note
  • summarize a trusted request dump or crash dump before replay
• scripts/replay_trusted_request_dump.py
  • replay a trusted request dump when safe_pickle_load blocks stock replay

If a live bundle was collected, include its path.

If replay, trace, or profiling was chosen, say why bundle plus dump were not enough.