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Accuracy + Performance Test

Start vLLM serve with the target model, run accuracy benchmarks (when FlagEval is available) and performance benchmarks (vllm bench serve) across multiple profiles.

Skill Components

perf-test/
├── SKILL.md                            # This file — execution flow
├── scripts/
│   ├── run_benchmark.py                # Run single benchmark profile (JSON output)
│   └── run_all_benchmarks.py           # Run all 5 profiles, collect + summarize (JSON)
└── references/
    └── benchmark-profiles.md           # Profile definitions, metrics, vllm bench usage

Reused from env-verify:

• env-verify/scripts/testservemode.py — can be used to verify server is healthy

before benchmarking (optional pre-check)

Prerequisites

• Running container with software stack installed
• model-verify completed — know which stack to use (full vs base)
• Model path, TP size, and recommended stack from model-verify

If invoked standalone, ask for container name, model path, TP size, and stack config. If invoked from /flagrelease, these are passed as context.

Execution Flow

Step 1: Start vLLM Server

Use the stack recommended by model-verify. Read references/benchmark-profiles.md for the vllm serve command pattern.

docker exec -d <CONTAINER> bash -c '
export USE_FLAGGEMS=<0|1>
export FLAGCX_PATH=<path_or_unset>
export VLLM_PLUGINS=<fl_or_unset>
vllm serve <MODEL_PATH> \
    --tensor-parallel-size <TP_SIZE> \
    --max-num-batched-tokens 4096 \
    --max-num-seqs 256 \
    --trust-remote-code \
    --port 8000 \
    <EXTRA_ARGS>
'

Wait for server ready (poll /health, timeout 300s):

docker exec <CONTAINER> bash -c '
for i in $(seq 1 150); do
    if curl -s http://localhost:8000/health 2>/dev/null | grep -qE "ok|200|\{\}"; then
        echo "SERVER_READY"; break
    fi
    sleep 2
done
'

If server doesn't start, report error and exit.

Step 2: Get Model Name from Server

docker exec <CONTAINER> bash -c '
curl -s http://localhost:8000/v1/models | python3 -c "
import json, sys; print(json.load(sys.stdin)[\"data\"][0][\"id\"])"
'

Part A: Accuracy Test (FlagEval) — PLACEHOLDER

STATUS: FlagEval test client not yet available.

When FlagEval becomes available, update this section with:

• Docker image URL or pip package name
• Supported benchmarks (MMLU, GSM8K, HumanEval, etc.)
• Required arguments and configuration
• Expected output format
• Pass/fail criteria (accuracy thresholds)

Current behavior: Report accuracy test as SKIPPED.

Part B: Performance Benchmarks

Step 3: Run All Benchmark Profiles

Copy scripts into the container and run:

docker cp <SKILL_DIR>/scripts/run_benchmark.py <CONTAINER>:/tmp/
docker cp <SKILL_DIR>/scripts/run_all_benchmarks.py <CONTAINER>:/tmp/

docker exec <CONTAINER> python3 /tmp/run_all_benchmarks.py \
    --model <MODEL_NAME> \
    --tokenizer <MODEL_PATH> \
    --port 8000 \
    --output-dir /data/results/perf

The script runs all 5 default profiles (see references/benchmark-profiles.md), saves per-profile JSON to /data/results/perf/, and outputs a combined JSON report with a summary table.

Important: One profile failure does NOT skip remaining profiles.

Step 4: Stop Server

docker exec <CONTAINER> bash -c 'pkill -f "vllm serve" || true'

Step 5: Produce Report

{
  "status": "PASS | PARTIAL | FAIL",
  "stage": "perf-test",
  "model": "<MODEL_PATH>",
  "tensor_parallel_size": 8,
  "flags": {"USE_FLAGGEMS": "1|0", "FLAGCX_PATH": "..."},
  "accuracy": {
    "status": "SKIPPED",
    "reason": "FlagEval test client not yet available"
  },
  "performance": {
    "status": "PASS | PARTIAL | FAIL",
    "profiles_passed": "5/5",
    "profiles": [ "...per-profile results..." ],
    "summary_table": "...markdown table..."
  }
}

Present the summary table to the user:

| Profile | Input | Output | Prompts | Req/s | Tok/s | TTFT(ms) | TPOT(ms) | P99(ms) | Status |
|---------|-------|--------|---------|-------|-------|----------|----------|---------|--------|
| ...     | ...   | ...    | ...     | ...   | ...   | ...      | ...      | ...     | ...    |

Status logic:

• PASS — all profiles completed
• PARTIAL — some passed, some failed
• FAIL — server didn't start or all profiles failed

Error Handling

Timeout Rules